development and validation of a solid phase microextraction method for simultaneous

DEVELOPMENT AND VALIDATION OF A SOLID PHASE

MICROEXTRACTION METHOD FOR SIMULTANEOUS

DETERMINATION OF PESTICIDE RESIDUES IN FRUITS

AND VEGETABLES BY GAS CHROMATOGRAPHY

CHAI MEE KIN

FACULTY OF SCIENCE

UNIVERSITY OF MALAYA

KUALA LUMPUR

2008

DEVELOPMENT AND VALIDATION OF A SOLID PHASE

MICROEXTRACTION METHOD FOR SIMULTANEOUS

DETERMINATION OF PESTICIDE RESIDUES IN FRUITS

AND VEGETABLES BY GAS CHROMATOGRAPHY

CHAI MEE KIN

THESIS SUBMITTED IN FULFILMENT

OF THE REQUIREMENTS

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

FACULTY OF SCIENCE

UNIVERSITY OF MALAYA

KUALA LUMPUR

2008

ii

ABSTRACT

Modern trends in analytical chemistry are towards the simplification and

miniaturization of sample preparation, as well as the minimization of organic solvent

usage. In view of this, several novel micro-extraction techniques have been developed

in order to reduce the analysis step, increase the sample throughput and to improve the

quality and the sensitivity of analytical methods. One of the emerging techniques is

solid-phase microextraction (SPME). A headspace solid phase microextraction (HS-

SPME) method has been developed for the determination of eight pesticides in fruits

and vegetables by using gas chromatography with an electron capture detector (ECD)

followed by gas chromatography – mass spectrometry (GC-MS) confirmation. Factors

such as fiber coating, extraction and desorption parameters, stirring rate, ionic strength,

pH, the fiber depth in the injector, the effect of dilution, the effects of organic solvents

and washing by different solutions were studied and optimized. The optimized HS-

SPME conditions were obtained using 100 µm polydimethylsiloxane (PDMS) fiber,

10% NaCl, 2% (vol/weight) of methanol/acetone (1:1) with optimum dilution, HS

extraction at 60 oC for 30 min; with 800 rpm without any pH adjustment. Desorption

was done at 240 oC for 10 min.

Good linearity, detection limits, precision and sensitivity were obtained with this

method for all the investigated pesticides. The regression coefficients in the linearity

were better than 0.9950 in all cases with the relative standard deviation (RSD) value

less than 7%. The detection limits ranged from 0.01 µg/L to 1.0 µg/L, with repeatability

ranging from 0.3% to 3.7% and intermediate precision from 0.8% to 2.5%. The

iii

optimized procedures resulted in more than 80% recovery for all the investigated fruit

and vegetable samples with RSD values below 5%. The developed HS-SPME with the

internal quality control method applied to the analysis of ten real local samples. All the

pesticide residues detected were lower than the MRLs.

As a comparison, a solid phase extraction (SPE) and headspace single drop

microextraction (HS-SDME) were applied to quantify all the investigated pesticides.

HS-SPME and SPE showed the better results than HS-SDME in terms of detection

limits, precision and recovery. However, HS-SPME possessed the advantages of speed

and reduced solvent usage than that of the SPE method.

A gas chromatography (GC) method has been developed to analyze simultaneously

separate nine different pesticide formulations using the internal standard method. A

mixture of pure standard solution spiked with 1-chloro-4-fluoro benzene as the internal

standard was injected into the GC-ECD and a six point calibration curve that

demonstrated a linear range was established for each target compound. Samples of each

formulation, mixed with internal standard were analyzed five times to obtain

coefficients of variation which are less than 1%. Three concentration levels of each

formulation were determined and the results were within the specification with the

accuracies obtained were within 98.1% to 101.9%. This method involves a quick

analysis and without any sample pre-treatment process. This measurement method can

be very useful for determining pesticide formulations in routine analysis.

iv

ABSTRAK

Trend moden dalam bidang kimia analisis adalah menuju ke arah simplifikasi dan

miniaturkan kaedah dalam langkah penyediaan sampel, serta meminimumkan

penggunaan pelarut organik. Dengan ini, beberapa teknik pengekstrakan mikro baru

untuk mengurangkan langkah analisis, menambah daya pemprosesan dan meningkat

kualiti serta kepekaan kaedah analisis telah dibangunkan. Salah satu teknik terkini

ialah pengekstrakan mikro fasa pepejal (SPME). Teknik pengekstrakan mikro fasa

pepejal dengan ruang wap (HS-SPME) telah dibangunkan untuk menentukan lapan

pesticid dalam buah-buahan dan sayur-sayuran menggunakan kromatografi gas dengan

pengesan tangkapan electron (GC-ECD), diikuti dengan penggunaan kromatografi gas

spektrometri jisim (GC-MS) untuk pengesahan. Faktor-faktor seperti jenis penyalut

serabut, parameter pengekstrakan dan penyahserapan, kadar mengacau, kekuatan ion,

pH, kedalaman serabut dalam penyuntik GC, kesan pencairan, kesan pelarut organik

dan pencucian dengan pelbagai jenis larutan telah dikaji dan dioptimumkan. Keadaan

HS-SPME yang optimum diperoleh dengan menggunakan jenis serabut 100 µm poli

dimetilsiloksana (PDMS), 10% NaCl, 2% (isipadu/berat) methanol/aseton (1:1) dengan

pencairan optimum, pengekstrakan HS pada suhu 60 oC selama 30 min dengan 800 rpm

tanpa ada pelarasan pH. Penyahserapan pada suhu 240 oC selama 10 min telah

dijalankan.

Kelinearan, had pengesanan, ketepatan dan kepekaan yang baik dengan menggunakan

kaedah ini terhadap semua pesticid yang dikaji telah diperoleh. Semua pemalar linear

korelasi adalah lebih baik daripada 0.9950 dengan sisihan piawai relatif (RSD) kurang

daripada 7%. Had pengesanan adalah dalam julat 0.01 µg/L hingga 1.0 µg/L dengan

keboleh ulangan daripada 0.3% hingga 3.7% dan ketepatan pertengahan adalah dalam

v

julat 0.8% hingga 2.5%. Prosidur optimum ini menghasilkan perolehan kembali lebih

daripada 80% bagi semua buah-buahan dan sayur-sayuran yang dikaji dengan nilai

RSD kurang daripada 5%. Kaedah HS-SPME yang terbangun dengan kawalan kualiti

dalaman diaplikasikan untuk menganalisis sepuluh sampel tempatan sebenar. Semua

sisa pesticid yang dikesan adalah lebih rendah daripada had sisa maksimum pesticid

yang dibenarkan (MRLs).

Sebagai perbandingan, pengekstrakan fasa pepejal (SPE) dan pengekstrakan mikro titik

tunggal dengan ruang wap (HS-SDME) telah digunakan untuk penentuan kuantiti

semua pesticid yang dikaji. HS-SPME dan SPE menunjukkan keputusan yang lebih

baik dalam had pengesanan, ketepatan dan perolehan kembali berbanding dengan HS-

SDME. Walaupun demikian, HS-SPME mempunyai kelebihan dalam kelajuan dan

kekurangan penggunaan larutan berbanding dengan kaedah SPE.

Kaedah kromatografi gas (GC) untuk menganalisis formulasi pesticid berasingan secara

serentak dengan menggunakan kaedah piawaian dalaman telah dibangunkan. Satu

campuran tulen larutan piawai yang dipakukan dengan 1-kloro-4-fluoro benzena

sebagai piawaian dalaman disuntikkan ke dalam GC-ECD dan satu keluk kalibrasi

enam titik yang menunjukkan julat linear bagi setiap sebatian telah diperoleh. Setiap

sampel formulasi yang dicampur dengan piawaian dalaman dianalisiskan sebanyak

lima kali untuk memperoleh pekali variasi yang kurang daripada 1%. Tiga kepekatan

bagi setiap formulasi ditentukan dan semula keputusan yang diperoleh adalah dalam

spesifikasi dengan julat kejituan 98.1% hingga 101.9%. Kaedah ini melibatkan analisis

yang pantas tanpa sebarang proses pra-perlakuan sampel. Keadah pengukuran ini amat

berguna untuk menentukan formulasi pesticid dalam analisis rutin.

vi

ACKNOWLEDGEMENTS

First, I would like to express my sincere gratitude to my supervisor, Prof. Dr. Tan Guan

Huat for his supervision, guidance and patience throughout the course of this study

during these few years.

I would like to thank the Univesiti Tenaga Nasional for supporting me in my study and

also the Universiti Malaya for providing the opportunity and facilities to undertake this

research work. I also wish to thank the Malaysia Toray Science Foundation for the

award of a research grant to undertake this study.

I offer my sincere thanks to Associate Prof. Dr. Richard Wong for the invaluable

discussion and encouragement throughout my research. I would also like to extend my

thanks to my friends, Asha Kumari, Ooi Mei Lee and Chan Chun Fong for their

continuous encouragement, advice and invaluable discussion.

To my family, I am grateful to my mother and parents-in-law for all their love and

understanding. Lastly, with deepest love and appreciation, I would like to thank my

beloved husband, Chew Eng Keat who has always given his constant support,

understanding and encouragement throughout my study. Finally, my children, Chew

Zhe Ru and Chew Zhe Hui who have sacrificed time so that I can complete this study, I

dedicate this to them.

vii

LIST OF CONTENTS

CHAPTER 1 INTRODUCTION

1.1 General 1

1.1.1 Pesticide Use 1

1.1.2 World Pesticide Consumption 3

1.2 Pesticides in Malaysia 5

1.2.1 Major Crops in Malaysia 5

1.2.2 Pesticides Consumption in Malaysia 6

1.2.3 Pesticide Poisoning Cases in Malaysia 8

1.3 Pesticide Residues 11

1.3.1 Pesticide Residues in Food 11

1.3.2 Standards for Pesticide Residues 13

1.3.3 Pesticide Regulations in Malaysia 15

1.4 Pesticides – Physical and Chemical Properties 16

1.4.1 Water Solubility 19

1.4.2 Vapor Pressure and Henry‟s Law Constant 19

1.4.3 Octanol-Water Partition Coefficient (Kow) 20

1.4.4 Adsorption 21

1.4.5 Toxicity of Pesticides 22

1.4.6 Pesticides‟ Mode of Action 23

viii

1.5 Organochlorine Pesticides 24

1.5.1 Chemical Structures and Properties 24

1.5.2 Toxicological Effects 26

1.6 Organophosphorus Pesticides 27



1.7 Carbamates 30



1.8 Pesticides Selected for Present Study 32

1.8.1 Acephate 33

1.8.2 Chlorpyrifos 34

1.8.3 Diazinon 35

1.8.4 Dimethoate 36

1.8.5 Malathion 37

1.8.6 Profenofos 38

1.8.7 Quinalphos 39

1.8.8 Chlorothalonil 40

1.8.9 α-Endosulfan and β-Endosulfan 41

1.8.10 Carbaryl 42

1.9 Scope and Objective of Study 43

ix

CHAPTER 2 REVIEW OF GAS CHROMATOGRAPHY FOR THE

ANALYSIS OF PESTICIDE RESIDUES IN FRUITS

AND VEGETABLES AND PESTICIDE FORMULATIONS

2.1 Trace Analysis of Pesticides by Gas Chromatography 44

2.2 Gas Chromatography (GC) 49

2.2.1 Carrier Gas or Mobile Phase 50

2.2.2 Sample Injection Port 52

2.2.3 GC Columns 54

2.2.4 Stationary Phases in GC 56

2.2.5 Column Oven in GC 60

2.2.6 GC Detectors 62

2.3 Gas Chromatography - Electron Capture Detector (GC-ECD) 64

2.4 Gas Chromatography – Mass Spectrometry (GC-MS) 68

2.5 Fast Gas Chromatography 70

2.6 Fast Gas Chromatography-Mass Spectrometry (Fast GC-MS) 76

2.6.1 Microbore GC-MS 77

2.6.2 Fast Temperature Programming GC-MS 78

2.6.3 Low-pressure GC-MS (LP-GC-MS) 78

2.6.4 Supersonic Molecular Beam GC-MS (GC-SMB-MS) 80

2.6.5 Pressure-tunable GC x GC-MS 81

2.7 Analysis of Pesticide Formulations 82

2.7.1 Chromatographic Determination of Pesticide Formulations 82

2.7.2 FTIR Determination of Pesticide Formulations 84

2.7.3 FT-Raman Determination of Pesticide Formulations 85

x

2.7.4 Near Infrared (NIR) Determination of Pesticide Formulations 87

2.7.5 Spectrophotometric Determination of Pesticide Formulations 88

CHAPTER 3 REVIEW OF PESTICIDE RESIDUE ANALYSIS IN

FRUITS AND VEGETABLES

3.1 Pesticide Residues and Legislation 92

3.2 Analytical Techniques for Pesticide Residues in Fruits and Vegetables 94

3.2.1 Sample Preparation 94

3.2.2 Extraction 95

3.2.3 Sample Cleanup 96

3.3 Sample Extraction Techniques 99

3.3.1 Solid Sample Extraction Techniques 100

3.3.1.1 Supercritical Fluid Extraction (SFE) 101

3.3.1.2 Pressurized Fluid Extraction (PFE) 105

3.3.1.3 Microwave-assisted Extraction (MAE) 108

3.3.1.4 Matrix Solid-phase Dispersion (MSPD) 111

3.3.2 Liquid Sample Extraction Techniques 116

3.3.2.1 Liquid-liquid Extraction (LLE) 117

3.3.2.2 Gel Permeation Chromatography (GPC) 120

3.3.2.3 Enzyme-linked ImmunoSorbent Assay (ELISA) 122

3.3.2.4 Solid-phase Extraction (SPE) 127

3.4 Solid-phase Microextraction (SPME) 136

3.4.1 Basic Extraction Theory 138

3.4.2 Extraction Modes 141

xi

3.4.3 SPME Optimization 145

3.4.3.1 Fiber Type 146

3.4.3.2 Extraction Time and Temperature 151

3.4.3.3 Ionic Strength 152

3.4.3.4 pH 153

3.4.3.5 Agitation 153

3.4.3.6 Sample Volume 155

3.4.3.7 Desorption Time and Temperature 156

3.5 Alternative Techniques 161

3.5.1 Single-drop Microextraction (SDME) 161

3.5.2 Liquid-phase Microextraction (LPME) 167

3.5.3 Stir-bar Sorptive Extractions (SBSE) 170

CHAPTER 4 EXPERIMENTAL

4.1 Materials 174

4.1.1 Chemicals and Reagents 174

4.1.2 Standards 174

4.1.3 Glassware 175

4.1.4 Apparatus 175

4.1.5 Materials for Solid-phase Microextraction (SPME), Solid-phase 176

Extraction (SPE) and Single-drop Miroextraction (SDME)

4.2 Instrumentation 176

4.2.1 Gas Chromatography – Electron Capture Detector (GC-ECD) 176

4.2.2 Gas Chromatography – Mass Spectrometry (GC-MS) 177

xii

4.3 Pesticide Residue Analysis 178

4.3.1 Standard Stock Solutions 178

4.3.2 Samples 179

4.3.3 Sample Preparation 180

4.3.3.1 Solid-phase Microextraction (SPME) 180

4.3.3.2 Solid-phase Extraction (SPE) 183

4.3.3.3 Single-drop Microextraction (SDME) 186

4.3.3.4 Pesticide Formulations 187

4.4 Validation of Quantitative Chromatography Method 187

4.4.1 Calibration Curve (Linearity) 187

4.4.2 Precision and Accuracy 188

4.4.3 Selectivity / Specificity 188

4.4.4 Limits of Detection (LOD) and Limits of Quantification (LOQ) 189

4.4.5 Recovery 190

4.5 Pesticide Formulations 190

CHAPTER 5 RESULTS AND DISCUSSION

5.1 Optimization of Chromatographic Conditions 192

5.1.1 Gas Chromatography – Electron Capture Detector (GC-ECD) 192

5.1.1.1 Injection Port Temperature 192

5.1.1.2 Detector Temperature 194

5.1.1.3 Column Flow Rate 195

5.1.1.4 Equilibrium Time 196

xiii

5.1.2 Gas Chromatography – Mass Spectrometry (GC-MS) 197

5.1.2.1 Injection Port Temperature 198

5.1.2.2 Interface Temperature 199

5.1.2.3 Column Flow Rate 201

5.1.2.4 Purge-off Time 202

5.1.3 Gas Chromatographic Separation 204

5.2 Multiresidue Analysis of Pesticide Residues in Fruits and Vegetables 208

5.2.1 Solid-phase Microextraction (SPME) 208

5.2.1.1 Direct Immersion (DI) – SPME versus Headspace 209

(HS) – SPME

5.2.1.2 Selection of SPME coating 211

5.2.1.3 Effect of Extraction Time 214

5.2.1.4 Effect of Extraction Temperature 217

5.2.1.5 Effect of Stirring Rate 221

5.2.1.6 Effect of Ionic Strength 222

5.2.1.7 Effect of pH 226

5.2.1.8 Effect of Desorption Temperature 228

5.2.1.9 Effect of Desorption Time 230

5.2.1.10 Effect of Fiber Depth in the Injector 232

5.2.1.11 Fiber Coating Lifetime 233

5.2.1.12 Effect of Dilution on Sample Extraction 234

5.2.1.13 Effect of the Organic Solvent on Sample Extraction 238

5.2.1.14 Effect of Washing on Pesticide Residues by Different 242

Solutions

xiv

5.2.2 Validation of Quantitative Chromatography Method 245

5.2.2.1 Calibration Curve (Linearity) 245

5.2.2.2 Precision 248

5.2.2.2 (a) Repeatability 248

5.2.2.2 (b) Intermediate Precision 249

5.2.2.3 Selectivity / Specificity 252

5.2.2.4 Limits of Detection (LOD) and Limits of 254

Quantification (LOQ)

5.2.2.5 Recovery 254

5.2.2.6 Confirmation of Pesticide Residue Determination 258

by GC-MS

5.2.2.7 Application of HS-SPME on Real Samples 261

5.3 Comparison of HS-SPME, SPE and HS-SDME for the 263

Determination of Pesticide Residues in Fruits and Vegetables

5.3.1 SPE Method 263

5.3.2 HS-SDME Method 263

5.3.2.1 Effects of Solvent Types and Drop Volume 264

5.3.2.2 Effects of Extraction Time and Temperature 266

5.3.2.3 Effect of Stirring Rate 269

5.3.2.4 Effect of Ionic Strength 270

5.3.3 Analytical Performance of the HS-SPME, SPE and 271

HS-SDME Methods

xv

5.4 Pesticide Formulations 276

5.4.1 Specificity 276

5.4.2 Linearity of Response and Range 276

5.4.3 Repeatability of Injections 277

5.4.4 Precision of the Method 278

5.4.5 Accuracy of the Method and Sample Analysis 279

CHAPTER 6 CONCLUSION 282

Suggestions for Future Work 286

REFERENCES 287

LIST OF PUBLICATIONS AND PRESENTATIONS 310

xvi

LIST OF FIGURES

Figure 1.1: World Pesticide Consumption in 2005 4

Figure 1.2: Pesticide Consumption in Malaysia (1990 -2005) 7

Figure 1.3: Pesticide Consumption in Malaysia (tonnes) for Various 8

Pesticide Categories for year 1990 and 2005

Figure 2.1: The Design of a Modern Gas Chromatograph 51

Figure 2.2: (a) A Split Injection System 53

Figure 2.2: (b) A Septum Injection System 53

Figure 2.3: Wave form of Electron Capture Detector Pulses 65

Figure 2.4: Electron Capture Detector 66

Figure 2.5: The Basic, Simplified Equation that Controls Retention 71

Time (tR) in GC

Figure 3.1: Schematic Diagram of a SFE System 102

Figure 3.2: Schematic Diagram of a PFE System 106

Figure 3.3: Schematic Diagram of a Focused MAE Setup 110

Figure 3.4: MSPD Extraction Procedures 112

Figure 3.5: Schematic Diagram of a GPC system 121

Figure 3.6: ELISA Operation Procedures 123

Figure 3.7: SPE Operation Procedures 128

Figure 3.8: Disposable SPE Sorbent Containers 130

Figure 3.9: Commercial SPME Device Made by Supelco 137

Figure 3.10: Extraction Process by HS-SPME and DI-SPME, and 144

Desorption Systems for GC and HPLC Analyses

Figure 3.11: Structure of Polydimethylsiloxane (PDMS) 149

xvii

Figure 3.12: Structure of PDMS-Carboxen Coating 150

Figure 3.13: GC liners. The Right Liner is Suitable for SPME Desorption 157

Figure 3.14: Schematic Diagram of a SDME Setup 161

Figure 3.15: Schematic Diagram of a LPME Setup 168

Figure 3.16: Schematic Diagram of a SBSE Setup 171

Figure 4.1: Flow Chart of Multiresidue Analysis of Pesticides using 182

the SPME Method

Figure 4.2: Flow Chart of Multiresidue Analysis of Pesticides using 185

the SPE Method

Figure 5.1: Effect on Peak Area at Various Injector Port Temperatures 193

(GC-ECD)

Figure 5.2: Effect on Peak Area at Various Detector Temperatures (GC-ECD) 194

Figure 5.3: Effect on Peak Area at Various Column Flow Rates (GC-ECD) 195

Figure 5.4: Effect on Peak Area at Various Equilibration Times (GC-ECD) 196

Figure 5.5: Effect on Peak Area at Various Injection Port Temperatures 199

(GC-MS)

Figure 5.6: Effect on Peak Area at Various Interface Temperatures (GC-MS) 200

Figure 5.7: Effect on Peak Area at Various Column Flow Rates (GC-MS) 201

Figure 5.8: Effect on Peak Area at Various Purge-off Times (GC-MS) 203

Figure 5.9: Chromatogram of the Standard Mixture of 11 Pesticides Solution 204

and the Internal Standard under Optimum Conditions (GC-ECD)

Figure 5.10: Total Ion Chromatogram of the Standard Mixture of 11 Pesticides 206

Solutions and the Internal Standard under Optimum Conditions

in Full Scan Mode (GC-MS)

Figure 5.11: Comparison of the Pesticides Extracted by DI-SPME and 210

HS-SPME from the Spiked Vegetables

Figure 5.12: Comparison of the Adsorption Efficiencies of Five Different 212

SPME Fibers.

Figure 5.13: Effect of Extraction Time on Peak Area using a 100 µm 215

PDMS Fiber

xviii

Figure 5.14: Effect of Extraction Time on Peak Area using a 85 µm 215

PA Fiber

Figure 5.15: Effect of Extraction Temperature on Peak Area using 219

a 100 µm PDMS Fiber

Figure 5.16: Effect of Extraction Temperature on Peak Area using 219

a 85 µm PA Fiber

Figure 5.17: Effect of Stirring Speed on Peak Area using a 100 µm 222

PDMS Fiber

Figure 5.18: Effect of Various Types of Salt (10%, w/v) on Peak Area 223

using a 100 µm PDMS Fiber

Figure 5.19: Effect of NaCl (%) on Peak Area using a 100 µm PDMS Fiber 224

Figure 5.20: Effect of NaCl (%) on Peak Area using a 85 µm PA Fiber 224

Figure 5.21: Effect of pH on Peak Area using a 100 µm PDMS Fiber 227

Figure 5.22: Effect of Desorption Temperature on Peak Area using 229


Figure 5.23: Effect of Desorption Temperature on Peak Area using 230

a 85 µm PA Fiber

Figure 5.24: Effect of Desorption Time on Peak Area using a 100 µm 231

PDMS Fiber

Figure 5.25: Effect of Fiber Depth in the Injector Port on Peak Area 232

using a 100 µm PDMS Fiber

Figure 5.26: Effect of Number of Extractions on Peak Area using 234


Figure 5.27: Effect of Dilution on the Extraction of Pesticides from Cucumber 236

Figure 5.28: Effect of Dilution on the Extraction of Diazinon from Various 237

Fruits and Vegetables

Figure 5.29: Comparison of the Recovery (%) of Malathion and β-Endosulfan 238

with Dilution Factor of 5 on Strawberry

Figure 5.30: Effect of Organic Solvents Addition on Extraction Efficiency 240

in Guava Samples

Figure 5.31: Selectivity Chromatograms (a) Spiked Cucumber Sample 253

(b) Blank Cucumber Sample

xix

Figure 5.32: Effect of Solvent Types on Peak Area in HS-SDME 265

Figure 5.33: Effect of Solvent Drop Volume on Peak Area in HS-SDME 266

Figure 5.34: Effect of Extraction Time on Peak Area in HS-SDME 267

Figure 5.35: Effect of Extraction Temperature on Peak Area in HS-SDME 268

Figure 5.36: Effect of Stirring Rate on Peak Area in HS-SDME 269

Figure 5.37: Effect of NaCl (%) on Peak Area in HS-SDME 270

xx

LIST OF TABLES

Table 1.1: World Pesticide Consumption (x 100,000 metric tonnes) 3

from 1990 to 2005

Table 1.2: Planted Areas of Selected Crops ( x 1000 ha) 5

Table 1.3: Acreage, Production and Export of Vegetables and 6

Fruits (2000-2004) in Malaysia

Table 1.4: The Main Types of Compounds Used as Pesticides 19

Table 1.5: Scale of Rating for Volatility and Henry‟s Law Constant 20

Table 1.6: The WHO Hazard Classification of Pesticides 23

Table 1.7: Structural Classification of Organochlorine 25

Table 1.8: Physical and Chemical Properties of Acephate 33

Table 1.9: Physical and Chemical Properties of Chlorpyrifos 34

Table 1.10: Physical and Chemical Properties of Diazinon 35

Table 1.11: Physical and Chemical Properties of Dimethoate 36

Table 1.12: Physical and Chemical Properties of Malathion 37

Table 1.13: Physical and Chemical Properties of Profenofos 38

Table 1.14: Physical and Chemical Properties of Quinalphos 39

Table 1.15: Physical and Chemical properties of Chlorothalonil 40

Table 1.16: Physical and Chemical Properties of α-Endosulfan 41

and β-Endosulfan

Table 1.17: Physical and Chemical Properties of Carbaryl 42

Table 2.1: Gas Chromatography Detectors Used for Pesticide 63

Residue Analysis

Table 2.2: Recent Studies on Pesticide Determinations using FTIR 90

Spectrometry

Table 3.1: Materials Used for the Preparative Chromatography of 97

Pesticides in Food

xxi

Table 3.2: SPE Methods for the Analysis of Pesticides in Fruits and 134

Vegetables

Table 3.3: Summary of Commercially Available SPME Fibers 148

Table 3.4: Agitation Methods in SPME 154

Table 3.5: SPME Methods for the Analysis of Pesticides in Fruits 158

and Vegetables

Table 3.6: SDME Methods for the Analysis of Pesticides in 166

Environment Matrix

Table 4.1: The Generic Pesticides Used in the Pesticide Formulation 180

Experiments

Table 5.1: Optimum Parameters and the Temperature Programming 197

Conditions for the GC-ECD

Table 5.2: Optimum Parameters and the Temperature Programming 203

Conditions for GC-MS

Table 5.3: Monitoring Parameters, Linearity Ranges, Regression 205

Coefficients (r2), and LOD for GC-ECD

Table 5.4: Monitoring Parameters, Selected Ions, Linearity Ranges, 207

Regression Coefficients (r2) and LOD for GC-MS

under SIM acquisition

Table 5.5: Physicochemical Properties of the Investigated Pesticides 208

Table 5.6: Buffer Solutions from pH 4 to pH 10 226

Table 5.7: Boiling Point, Vapor Pressure and Polarity of the Tested Solvents 239

Table 5.8: Comparison of Average Recovery (%) of the Fruit and Vegetable 242

Samples between Condition 1 (without dilution or organic solvent

added) and Condition 2 (optimum dilution and 2 % (vol/weight)

of methanol/acetone (1:1) added)

Table 5.9: The Effect of Washing on Pesticide Residues in Cucumber 244

by Different Solutions

Table 5.10: Calibration Curve for Three Different Conditions 246

Table 5.11: Comparison of the linearity, r2 and RSD (%) Values of the 247

Investigated Pesticides in Distilled Water and in the

Cucumber Sample

xxii

Table 5.12: Repeatability of the Optimized HS-SPME Method in the Spiked 250

Cucumber and Strawberry Samples at Three Concentration Levels

Table 5.13: Intermediate Precision of the Optimized HS-SPME Method 251

in the Spiked Cucumber and Strawberry Samples at Three

Concentration Levels

Table 5.14: Limits of Detection (LOD), Limits of Quantification (LOQ) and 255

Maximum Residue Levels from Codex Alimentarius of the

Investigated Pesticide using the Optimized HS-SPME Method

Table 5.15: Spiked Concentration Levels and Relative Recoveries over 257

Fortified Fruits and Vegetables using GC-ECD

Table 5.16: GC-MS Retention Time, Linear Range, r2 Value, LOD, LOQ 258

and MRLs from Codex Alimentarius in Fruits and Vegetables

Table 5.17: Spiked Concentration Levels and Relative Recoveries over 260

Fortified Fruits and Vegetables using GC-MS

Table 5.18: Pesticide Level Detected in Investigated Fruits and Vegetables 262

Table 5.19: Maximum Residue Levels (MRL) from Codex Alimentarius 262

Table 5.20: The Chemical Characteristics of Three Extraction Solvents 264

Table 5.21: Monitoring Parameters, Linearity Ranges, Regression 274

Coefficients, and Mean RSD (%) for HS-SPME, SPE and

HS-SDME

Table 5.22: Monitoring Parameters: Limits of Detections, and Mean 274

Recovery (%) for HS-SPME, SPE and HS-SDME

Table 5.23: Statistical Parameters of Calibration and Repeatability of 277

Pesticide Formulation

Table 5.24: Results of Nine Pesticide Formulations Determination at 280

Three Concentration Levels

xxiii

LIST OF ABBREVIATIONS

AChE - Acetylcholinesterase

ADI - Acceptable daily intake

AOAC - Associations of Analytical Communities

ASE - Accelerated solvent extraction

BHC - Benzene hexachloride

CGC - Capillary gas chromatography

CIPAC - Collaborative International Pesticide Analytical Council

CODEX - Codex Alimentarius Commission

CW - Carbowax

DCM - Dichloromethane

DDD - Dichloro diphenyl dichloroethane

DDT - Dichloro diphenyl trichloroethane

DEA - Diethylaminopropyl

DI - Direct immersion

DOA - Department of Agriculture

DVB - Divinylbenzene

EC - European Commission

ELISA - Enzyme-linked immunosorbent assay

EPA - Environmental Protection Agency

EU - European Union

FAO - Food and Agriculture Organization

FIA - Flow injection analysis

FTD - Flame thermionic detection

xxiv

FTIR - Fourier transform infrared

GAP - Good agricultural practice

GCB - Graphitized carbon black

GC-ECD - Gas chromatography-electron capture detection

GC-FID - Gas chromatography-flame ionization detection

GC-FPD - Gas chromatography-flame photometric detection

GC-NPD - Gas chromatography-nitrogen phosphorus detection

GC-MS - Gas chromatography-mass spectrometry

GC-SMB-MS - Supersonic molecular beam GC-MS

GDP - Gross domestic product

GPC - Gel permeation chromatography

HCB - Hexachlorobenzene

HCH - Hexachlorohexane

HECD - Hall electrolytic conductivity

HF-LPME - Hollow fiber – liquid phase microextraction

HPLC - High performance liquid chromatography

HS - Headspace

IA - Immunoassays

ICH - International Conference on Harmonization

IPM - Integrated Pest Management

ITD - Ion trap detector

IUPAC - The International Union of Pure and Applied Chemistry

Kd - Partition coefficient

Koc - adsorption coefficient

Kow - Octanol-water partition coefficient

xxv

LC-MS - Liquid chromatography-mass spectrometry

LD50 - Lethal dose needed to kill 50% of the test animals

LLE - Liquid-liquid extraction

LOD - Limits of detection

LOQ - Limits of quantification

LP-GC-MS - Low-pressure gas chromatography-mass spectrometry

LPME - Liquid-phase microextraction

MAE - Mirowave-assisted extraction

MARDI - Malaysian Agricultural Research and Development Institute

MASE - Membrane assisted solvent extraction

MRL - Maximum Residue Level

MRM - Multiresidue method

MSPD - Matrix solid-phase dipersion

NIR - Near infrared

NP - Mormal-phase

OC - Organochlorine

ODS - Octadecylsiloxane

OP - Organophosphate

PA - Polyacrylate

PAH - Polycylic aromatic hydrocarbon

PCB - Polychlorinated biphenyl

PCDD - Polychlorinated dibenzodioxin

PCDF - Polychlorinated dibenzofuran

PDMS - Polydimethylsiloxane

PDV - Polydivinylbenzene

xxvi

PFE - Pressurized fluid extraction

POP - Persistent organic pollutant

PSA - Primary secondary amine

RF - Response Factor

RP - Reversed-phase

RSD - Relative standard deviation

Rt - Retention time

SAX - Strong anion-exchange sorbents

SBSE - Stir-bar sorptive extraction

SDE - Steam distillation extraction

SDME - Single-drop microextraction

SFC - Supercritical fluid chromatography

SFE - Supercritical fluid extraction

SIM - Selected ion monitoring

S/N - Signal-to-noise

SPC - Solid phase cleanup

SPE - Solid-phase extraction

SPME - Solid-phase microextraction

SRM - Single residue method

SWE - Subcritical water extraction

TEPP - Tetraethyl pyrophosphate

TLC - Thin layer chromatography

TOF - Time-of-flight

WHO - World Health Organization

WP - Wettable powder

1

CHAPTER 1

INTRODUCTION

1.1 General

1.1.1 Pesticide Use

The Food and Agriculture Organization (FAO) defines a pesticide as “any substance or

mixture of substances intended for preventing, destroying, attracting, repelling, or

controlling any pest including unwanted species of plants or animals during the

production, storage, transport, distribution, and processing of food, agricultural

commodities, or animal feeds or which may be administered to animals for the control

of ectoparasites (International Code, 2005).

The introduction of pesticides in agriculture has helped to increase productivity and has

thus contributed to steadily rising food production since the Second World War

(Barbash, 2006). The use of fungicides and insecticides has led to increased yields in

arable farming, and the use of herbicides has reduced the need for manual labor. In

addition, pesticides make it possible to avoid losses during storage of the products.

Pesticides thus have many applications that affect the production and consumption of

the means of production in a number of ways. Besides, the controlled use of pesticides

has contributed to our health through control of certain vector-borne disease such as

malaria.

2

Pesticides contribute tremendously to the economy of the developing countries,

especially those in tropical regions seeking to enter the global economy by providing

off-season fresh fruits and vegetables to countries in more temperate climates. These

developing nations are becoming important “breadbaskets” to the world, being capable

of growing two or even three crops each year (Ecobichon, 2001). However, these goals

cannot be achieved without the increased use of pesticide, principally insecticides,

herbicides and fungicides, which are not used as extensively in traditional agricultural

practices.

Most developing nations are undergoing a transition from an agrarian economy to an

industrialized society, with migration of the skilled agricultural workforce to urban

centers in search of increasing economic prosperity. In Malaysia, between the years

1990 and 2005, the number of people involved in agriculture declined from 26.0% to

about 14.6% (Global Market Info. Database, 2006). Such demographic shifts in the

workforce introduce several major problems: (a) division of the workforce, the less

educationally advantaged remaining on the farms. (b) increased domestic food

production by fewer individuals; (c) production of additional food to support the

urbanized workforce, frequently requiring changes to alternative agricultural methods,

e.g. greenhouses, mechanized rather than labor-intensive practices; (d) attraction of

growing non-traditional export product as a means of increasing farm income and

earning valuable foreign currency for the country (Ecobichon, 2001). These problems

cannot be addressed without the increased use of pesticides and fertilizers, introducing

predictable product and environmental contamination accompanied by real and

potential adverse health effects in the agricultural workforce and their families, as well

as to local and global consumers.

3

1.1.2 World Pesticide Consumption

Table 1.1 shows the world pesticide consumption from year 1990 to 2005 (Global

Market Info. Database, 2006). The world wide consumption of pesticides in 2005 is

about 1.6 million metric tonnes per year, of which 24% is consumption in the Latin

America alone, 23% in North America, 21% in Western Europe and 32% in the rest of

the world. Pesticide use in Africa and Middle East is the lowest overall of all the

continents because of poverty, instability, unreliable climate and because of different

soil conditions small-holder agricultural practices are prevented from modernizing in

much of the region (Figure 1.1).

Table 1.1: World Pesticide Consumption (x 100,000 metric tonnes) from 1990 to 2005

Year 1990 1995 2000 2002 2004 2005

Latin America 1.23 1.68 3.00 3.45 3.88 4.08

North America 3.60 3.79 3.72 3.69 3.66 3.65

Western Europe 4.23 4.08 3.58 3.41 3.35 3.31

Austraiasia 1.23 1.40 1.25 1.53 1.86 2.02

Asia Pacific 1.68 1.63 1.69 1.46 1.47 1.48

Eastern Europe 1.60 1.39 0.92 0.82 0.77 0.74

Africa and Middle East 0.81 0.68 0.65 0.69 0.72 0.74

World (total) 14.37 14.64 14.80 15.05 15.71 16.02

(Global Market Info. Database, 2006)

4

Figure 1.1: World Pesticide Consumption in 2005


Generally, the pesticide market is affected by changes and trends in agriculture,

climatic variables (e.g. rainfall, temperature) and government policies. At present, the

adoption of more sophisticated farming techniques in developing nations has

encouraged the use of chemical pest control agents. Western Europe‟s agricultural

reforms have hindered demand by restricting the area planted. Both regions, however,

face mounting regulatory and environmental pressures to improve the safety of

pesticides and to limit the production and export of potentially dangerous compounds.

Latin America,

24%

Eastern

Europe, 5%

North

America, 23%

Western

Europe, 21%

Africa and

Middle East,

5%

Austraiasia,

13%

Asia Pacific,

9%

5

1.2 Pesticides in Malaysia

1.2.1 Major Crops in Malaysia

Agriculture is an important sector in the Malaysian economy. In 2004, it accounted for

9.5% of the Gross Domestic Product (GDP), 12.81% of total export earnings and

employed 15% of the total workforce (Global Market Info. Database, 2006). The oil

palm sub-sector remains the backbone of the agricultural sector with export earnings of

RM30 billion in 2004. Table 1.2 shows the major crops and their planted acreage in

Malaysia from 2000 to 2004.

Table 1.2: Planted Areas of Selected Crops (x 1000 ha)

Crops / year 2000 2001 2002 2003 2004

Oil Palm

Rubber

Paddy

Fruits

Vegetables

Coconut

Cocoa

Tea

Pepper

3431

1660

699

288

40

159

76

3.52

13

3633

1564

674

277

43

151

58

3.46

14

3714

1545

679

283

42

139

42

3.48

12

3593

1570

672

282

44

150

59

3.49

13

3647

1560

675

281

46

147

53

3.48

13

(Regional Stakeholder Con., 2005)

Besides oil palm, the cultivation of fruits and vegetables has been given much

prominence in the recent years. Table 1.3 shows the acreage, production and exports of

vegetables and fruits in Malaysia from year 2000 to 2004. The export of vegetables has

6

increased from RM278 million to RM465 million, amounting to 167% increase in

value. However, the export of fruits did not have a corresponding increase.

The agriculture sector is expected to register a higher growth rate and contribute more

to the country‟s development. This sector is being re-structured and re-organized to

increase its productivity in order to transform it into the third engine of growth for the

Malaysian economy. There will be a shift from small-scale mono-cropping and low

technology farming to large scale, integrated farming and employing high technology

to increase farm production.

Table 1.3: Acreage, Production and Export of Vegetables and Fruits (2000-2004)

in Malaysia

Vegetables Fruits

2000 2001 2002 2003 2004 2000 2001 2002 2003 2004

Area („000 ha)

40 43 42 44 46 288 277 283 282 281

Production

(tonne) 404 1378 1442 1509 1662 993 1378 1442 1509 1662

Average Yield

(Ton/ha) 10.1 32.8 34.3 34.2 36.1 3.4 5.0 5.1 5.4 5.9

Export

(RM million) 278 312 358 391 465 512 497 523 513 467

(Regional Stakeholder Con., 2005)

1.2.2 Pesticides Consumption in Malaysia

Following the same trend in developing countries, plantations in Malaysia have

developed tremendously with the help of pesticides to protect against insects, moulds,

viruses and other pests which reduce yield and quality. Agricultural chemicals

7

including pesticides have made significant contributions to the efficiency and overall

productivity of cultivated land from agriculture.

Between 1990 and 2005, pesticides consumption in Malaysia increased considerably

(Figure 1.2). In the year 1990, 8489 metric tonnes of pesticides were consumed in

Malaysia (Figure 1.3). Among the pesticides, herbicides use was about 5859 metric

tonnes, accounting for 69.02% of the total; next was fungicides (23.51%) and

insecticides (16.89%). However, the contribution pattern of different classes of

pesticides in 2005 was as follows: herbicides (74.66%), insecticide (15.99%) and

fungicides (9.34%). The total was 13048.14 metric tonnes accounting for an increase of

53.7% compared to year 1990 (Global Market Info. Database, 2006).

Figure 1.2: Pesticide Consumption in Malaysia (1990 -2005)


The increasing use of herbicides and its corresponding decreasing use of fungicides

showed that there is a shift from labor intensive to mechanized agricultural practices

employing fewer people.

0

2000

4000

6000

8000

10000

12000

14000

1988 1993 1998 2003 2008

me

tric

to

nn

es

Year

Fungicide

Herbicide

Insecticide

Total

8

Figure 1.3: Pesticide Consumption in Malaysia (tonnes) for Various Pesticide

Categories for year 1990 and 2005 (Global Market Info. Database, 2006)

1.2.4 Pesticide Poisoning Cases in Malaysia

Exposure to pesticides, through environmental contamination or occupational use can

occur to the general population. This exposure to the residues of pesticides, including

its physical and biological degradation products in air, water and food can pose a health

hazard for humans.

Occupational exposure occurring at all stages of pesticide formulation, manufacture

and application involves exposure to complex mixtures of different types of chemicals,

active ingredients and by-products present in technical formulations such as impurities,

solvent and other compounds produced during the storage procedure. Moreover,

although inert ingredients have no pesticidal activity, they may be biologically active

and could sometimes be the most toxic component of a pesticide formulation

(Bolognesi, 2003).

1996

5859

1434

8489

1219

9742

2087

13048

0

2000

4000

6000

8000

10000

12000

14000

Fungicide Herbicide Insecticide Total

me

tric

to

nn

es

1990

2005

9

The ingestion of pesticides is the most common method of self inflicted poisoning in

the developing world. Three million cases of pesticide poisoning, nearly 220,000 fatal,

occur world-wide every year (Bolognesi, 2003). Many less dramatic cases of food

poisoning are unreported. There may be long term health risks from small quantities of

pesticide residues in food such as DDT in human breast milk and residues of

endocrine–disrupting pesticides (Bolognesi, 2003). Certain classes of pesticides such as

organophosphates have a common mode of action and their effect may be cumulative.

The prevalence of toxic products applied by untrained users in many developing

countries gives rise to concern for consumer safety in those countries and in the

produce for the export market.

In Malaysia in 1997 and 1998 paraquat accounted for a greater proportion (19%) of

occupational poisonings than the organophosphates (16%) (Sirajuddin et al., 2001).

Earlier it was reported that among 225 (249) pesticides identified in poisonings in

Malaysia in 1987 and 1988, paraquat was the causal agent in 62% (71%) of the total,

while organophosphates were identified in 17% (14%) of the cases (Tenagenita, 1992).

Based on a 1990 report that covers a 10 year period (1979-1988), pesticides accounted

for 40.3% of the total cases of poisoning in Malaysia (Regional Stakeholder Con.,

2005). It has been estimated that about 73% of poisonings involving paraquat are

suicide attempts compared with 14% due to accidents and 1% to occupational

exposure. This survey also showed that only 4531 vegetable farmers in the Cameron

Highlands suffered from poisoning by pesticides which represents 14.5% of the total

poisoning cases in Malaysia. Hospital admissions revealed that 32.1% of pesticide

poisoning cases were accidental and 67.9% were suicide case (Regional Stakeholder

10

Con., 2005). Another study showed that the total amount of pesticides measured in the

general population in Malaysia, is 14 times higher than that measured in the US

(Regional Stakeholder Con., 2005). Other studies have shown that pesticides can cause

lowered sperm counts, decreased ovulation, inability to conceive and possible birth

defects (Regional Stakeholder Con., 2005).

On August 27, 2002, the Malaysian government instituted an immediate ban on

paraquat, announcing that all new or re-registered application would be cleared, and

that previously registered products, such as Syngenta‟s Gramoxone, would be phased

out. Malaysia is the first Asian country to ban this controversial herbicide and the

Malaysian government justified its decision by pointing out that other cost efficient and

less dangerous alternatives are readily available (Environ. News Serv., 2002).

There have been some studies on occupational poisoning cases among farmers and

industrial workers in Malaysia, but there is no cause for alarm because there is

legislation to minimize the occurrence of poisoning at work (Tenagenita, 2002). The

main Act that safeguards worker safety and health is the Occupational Safety and

Health Act 1974 and the rules and regulations prescribed under the Act thereafter, and

to a lesser extent, the Pesticide (Highly Toxic Pesticides) Regulation, 1996 which

controls only the use of certain highly toxic pesticides such as methamidophos and

monocrotophos only (Tenagenita, 2002).

11

Occupational exposures associated with these poisoning cases could be identified with:

(a) careless handling during preparation and application; (b) lack of personal protective

equipment or failure to use it due to heat-related discomfort; (c) laxity of safekeeping of

the chemicals ; (d) careless disposal of empty pesticide containers; (e) consumption of

food and beverage while working; (f) lack of personal hygiene; (g) deficiencies in

safety training; and (h) weakness in occupational health legislation and regulations

(Ecobichon, 2001). Bystander poisonings can be attributed to drifting spray, residues in

homes, improper storage of pesticides in homes, contamination of soil in areas where

mixing and loading occurs, improper use of empty containers for the storage of water,

vegetable oils or food.

1.3 Pesticide Residues

1.3.1 Pesticide Residues in Food

Pesticides are used widely throughout the world to control insects, diseases and weeds

in food crops grown for human consumption. Food safety depends on strict standards to

prevent undesirable residues and provide consumers with safe products. The Codex

Alimentarius Commission (CODEX) is the international body of government

representatives establishing food safety standards, with a remit to: “guide and promote

the elaboration and establishment of definitions and requirements for foods, to assist in

their harmonization and, in doing so, to facilitate international trade. The Food and

Agriculture Organization (FAO) and World Health Organization (WHO) have been

evaluating the safety of residues in foods since 1962 and establishing Maximum

Residue Levels (MRLs) to help ensure that pesticides are not overused and that any

residue found in food is safe for human consumption. Over 2500 MRLs are currently

12

approved covering 195 active ingredients (PAN, 1998). Standards do not exist for all

crops, or for all pesticides, as some are not used on food and not all pesticides leave

residues, they may only be used to clear weeds before planting. The permitted residue

levels are usually very low, being generally measured in parts per million. These

residues can arise from: (a) the use on a crop of legally allowed pesticides according to

good agricultural practice; (b) overuse of a pesticide, or its use near to harvest, of a

legally permitted pesticide; (c) illegal use of a pesticide that is not approved for that

crop, and (d) incorrect use of a pesticide after harvest, to reduce pest infestation in

storage or in transit (PAN, 1998).

Many factors can contribute to high pesticide residues in food samples found in

developing countries. Users are generally untrained, have poor literacy and are not

aware of the toxicity of the products they use. Instructions are complex, compounded

by labels which are often in the wrong language. Containers may have labels missing or

damaged. Overuse of pesticides can lead to insect resistance, which encourages farmers

to misuse the products. Pesticides appropriate for one crop may be misused on others,

or pesticides for public health purposes to combat malaria or locusts may be misused

on crops.

Governments and regulators in developing countries lack of the resources to conduct

surveillance of health and safety practices in pesticide application and to monitor the

incidence of residues in food. Newer pesticides are often too expensive for farmers in

developing countries and cheaper pesticides are often older and more hazardous (PAN,

1998).

13

1.3.2 Standards for Pesticide Residues

Pesticide use is controlled by national legislation, generally a system of registering each

pesticide formulation for a specific use and crop. Approvals are based on evaluation of

efficacy, user and consumer safety and environmental impact. Most industrialized

countries have also established laws setting the MRLs that are permitted in food, and

which apply to food produced domestically and imported.

The residue limits are set relying on a number of related concepts:

(a) Maximum Residue Levels (MRLs) – the maximum concentration of pesticide

residue resulting from the use of a pesticide according to Good Agricultural

Practice (GAP) that is legally permitted in or on a food commodity and

expressed in mg/kg (ppm) (Yeoh, 2000).

(b) Acceptable Daily Intake (ADI) – the amount of chemical that can be consumed

(in mg/kg bodyweight) per day for an individual‟s entire lifetime, on the basis

of all known facts at the time of evaluation of the chemical by the Joint

FAO/WHO committee on Pesticide Residues (Yeoh, 2000).

(c) Good Agricultural Practice (GAP) – The officially recommended or authorized

usage of pesticides under practical conditions at any stage of production,

storage, transport, distribution and processing of food, agricultural

commodities, and animal feed taking into consideration the variations in the

requirements within and between regions, which takes into account the

minimum quantities necessary to achieve adequate control, applied in a manner

so as to leave a residue which is measurable and which is toxicologically

acceptable (Yeoh, 2000).

14

In Malaysia, most of the pesticides in fruits and vegetables which have been found to

be above the MRLs can be divided into 3 main groups (Yeoh, 2000).

(a) Pesticides that are banned or not approved for use on vegetables. These

pesticides are either those that have been registered for use on vegetables but

after review by the Pesticides Board because of residue or toxicological

problems, their use on vegetables have been banned, e.g. methamidophos and

monocrotophos or those whose registration have been rejected by the Pesticides

Board as being too toxic, e.g. methyl parathion.

(b) Pesticides approved for vegetables, but the MRLs are very low. Some of the

organophosphorus pesticides with very low MRLs despite the high application

rate are profenofos, quinalphos, phenthoate, prothiophos and triazophos. The

use of triazophos on vegetables has been voluntarily withdrawn by the parent

company because its residue level always exceed MRL value.

(c) Dithiocarbamates such as mancozeb, maneb, propineb, zineb, ziram, ferbam and

metiram. These are protectant fungicides which are recommended to be sprayed

before any sign of disease is visible but their applications have been misused.

To overcome the problem of excessive pesticide residues found in vegetables would

require the involvement and commitment of various agencies such as the government,

research organizations, the industry, the non-government organizations and the farmers

to co-operate in efforts to reduce these pesticide levels in food to fulfill the aspirations

of the public for pesticide-safe food. Strengthening extension activities to create

awareness among the farmers, intensifying research activities to reduce the amount of

pesticides used, enhancing enforcement activities, monetary incentives for non-

15

pesticide farming and creating public awareness that healthy looking vegetables are not

necessarily pesticide-safe are some of the efforts that can be carried out.

1.3.3 Pesticide Regulations in Malaysia

The Pesticides Board is the pesticide-regulating authority in Malaysia and the Pesticide

Control Division of the Department of Agriculture is the secretariat to the Pesticides

Board. The principle legislation regulating pesticides in Malaysia is the Pesticides Act

1974 and the Rules/Regulations implemented under it.

The Pesticides (Registration Rules) 1976 control the import, manufacture and sale of

pesticides through a registration scheme. The scheme involves a comprehensive

evaluation of technical data on the pesticide relating to, among other things, its

formulation, toxicology including ecotoxicology, efficacy, residue, environmental fate,

packaging and labeling. The decision to register the pesticide is finally made after

making a risk-benefit analysis based on the above data and many other data available to

the Board. A pesticide has to be effective for the intended use while at the same time

does not pose unacceptable risk to human or animal health and the environment, before

it can be approved for registration.

The Pesticide (Importation for Educational and Research Purposes) Rules 1981 allow

for the importation of limited quantities of unregistered pesticides into the country for

the purpose of research or education. The Pesticides (Labeling) Regulation 1984

prescribe the manner for the labeling of registered pesticides. It essentially provides the

user or applicator with sufficient advice on the contents of the pesticide container,

16

including its ingredients and concentration, toxicity classification, recommended uses

as well as the precautions to be taken while preparing or using the pesticide; and

includes advice on steps to be taken in case of poisoning while awaiting medical

assistance. The Pesticides (Licensing for Sale and Storage for Sale) Rules 1988 provide

for the control of premises selling and/or storing pesticides, where all premises

involved in these activities have to be licensed to do so. The objective of the Rules is to

ensure that only registered pesticides are stored, displayed and sold, and that they are

stored and handled properly so as to minimize hazards to the public as well as to the

surrounding environment.

The Food Act 1983 and Food Regulations 1985 control, among other things, the use of

dyes and pigments in food and also incidental constituents like metal contaminants and

pesticide residues. Enforcement of the Food Act 1983 is done by the Ministry of

Health, Malaysia (Yeoh, 2000).

1.4 Pesticides – Physical and Chemical Properties

The United States Environmental Protection Agency (U.S. EPA) defines a “pesticide”

as any substance or mixture of substances intended for preventing, destroying,

repelling, or mitigating any pest. Pests can be insects, mice and other animals,

unwanted plants (weeds), fungi, or microorganisms like bacteria and viruses. Pesticides

may also be described as any physical, chemical or biological agent that will kill an

undesirable plant or animal pest. The term “pesticide” is a generic name for a variety of

agents that are usually more specifically classified on the basis of the pattern of use and

the organism killed.

17

The widespread use and disposal of pesticides by farmers, institutions and the general

public provide many possible sources of pesticides in environment. Pesticides may

possess different fates and behaviour when they are released into the environment and

three primary modes of degradation are (a) biological – breakdown by micro-

organisms. (b) chemical – breakdown by chemical reactions, such as hydrolysis and

redox reactions. (c) photochemical – breakdown by ultraviolet or visible light. Some

pesticides may be resistant to degradation and persist in the environment for a certain

period of time (Extension Toxico. Network, 1993).

Once a pesticide has been introduced into the environment, its chemical and physical

properties determine its fate: where it goes and how long it persists. Each pesticide has

its own unique set of properties. Pesticides that break down quickly do not offer much

opportunity for exposure. The degradation rate of a pesticide depends on the pesticide‟s

chemistry, as well as environmental factors, such as temperature, rainfall, and soil pH.

Pesticides are designed to be effective for a finite period to control pests and then

breakdown to non-toxic substances.

A pesticide‟s mobility depends on its water solubility, solubility in fat, adsorption to

soil, and its tendency to vaporize. A pesticide that is adsorbed to or taken up into a

plant is less likely to become a vapor, be washed off onto the soil, or be transferred to

the skin if the plant is touched. Pesticides that strongly adsorb to soil are not very

mobile in water that infiltrates toward groundwater, or water that runs off into surface

water, such as a pond, lake or stream. Pesticides strongly adsorbed to soil may still

enter the surface water if there is soil erosion. Pesticides strongly adsorbed onto soil do

not volatilize easily.

18

All pesticides are potentially toxic to some degree – none are considered harmless and

some are even classified as probable human carcinogens, neutrotoxics and endocrine

system disruptors. The toxicity level of a pesticide depends on the lethal dose (LD) of

the chemical, the length of exposure, and the route of entry or absorption by the body.

There are many different pesticides in use today with very different modes of action

and levels of toxicity. To protect the public, WHO has developed a hazard

classification system which is used to label all pesticide containers to warn users of the

acute hazards associated with each product. This hazard system is based on the LD50

for the pesticide in rats under either oral or dermal exposure conditions (Network for

Sustainable Agri., 2005).

Pesticide degradation in soil generally results in a reduction in toxicity; however, some

pesticides have breakdown products (metabolites) that are more toxic than the parent

compound (USDA, 1998). Pesticides mainly comprise insecticides, herbicides,

fungicides and some of the main types of compounds currently in use are given in

Table 1.4.

19

Table 1.4: The Main Types of Compounds Used as Pesticides

Insecticides Herbicides Fungicides

Organochlorine

Organophosphorus

Carbamates

Inorganic compounds

Biopesticides

Synthetic pyrethroids.

Phenoxyacetic acids

Toluidines

Triazines

Phenylureas

Bipyridyls

Glycines

Phenoxypropionates

Translocated carbamates

Hydroxyarylnitriles

Inorganic and heavy metal

compounds

Dithiocarbamates

Pthalimides

Antibiotics

Benzimidazoles

Pyrimidines

(Alloway and Ayres, 1997)

1.4.1 Water Solubility

The water solubility of the pesticides are presented as milligrams of solute per liter of

water (mg/L); or as parts per million (ppm), even for very soluble compounds. Water

solubility is important in determining the course of a pesticide through the

environment. A pesticide which is very water-soluble is more easily carried off with

rainwater, as run-off or through the soil as groundwater contaminant and will travel far

if it is persistent in its original form or will transform to less harmful breakdown

products or more toxic by-products.

1.4.2 Vapor Pressure and Henry’s Law Constant

Vapor pressure is a measure of the tendency of a pesticide to volatilize, a phase change

that can affect estimations of exposure. Generally, the lower the vapor pressure, the

lower the volatilization tendency of the chemical. The unit of measure is in mm Hg. To

convert to mPa, 1 mPa (millipascal)=7.5 x 10-6

mm Hg.

20

Table 1.5: Scale of Rating for Volatility and Henry‟s Law Constant

Volatility rating Vapor pressure at 20 to 30 oC

(mm Hg)

Henry‟s Law Constant

(atm m3/mol)

Non-volatile

Slightly volatile

Volatile

Highly volatile

< 1 x 10-7

1 x 10-7

to 1 x 10-4

1 x 10-4

to 1 x 10-2

>1 x 10-2

< 3.0 x 10-7

3 x 10-7

to 1 x 10-5

1 x 10-5

to 1 x 10-3

> 1 x 10-3

(Jenkins and Thomson, 1999)

Henry‟s Law Constant describes the tendency of a pesticide to volatilize from water or

moist soil. Its value is estimated through the vapor pressure, water solubility and

molecular weight of a pesticide (Jenkins and Thomson, 1999). A high value of Henry‟s

Law indicates that the pesticide has a high potential to volatize from moist soils; a low

value predicts a higher leaching potential of the pesticide.

1.4.3 Octanol-Water Partition Coefficient (Kow)

The octanol-water partition coefficient indicates how a chemical is distributed at

equilibrium between organic (octanol: is a relative non-polar solvent, representing fats)

and aqueous (water: is a polar solvent) phases. This coefficient is primarily used in

predicting the environmental fate of organic chemicals such as pesticides. The higher

the coefficient, the greater the propensity for the chemical to be partitioned into organic

phases. This means that the chemical will tend to adhere to organic matter in the soil,

but it may also indicate a tendency to accumulate in fats, although this behavior

depends on other biological factors in the body (Jenkins and Thomson, 1999).

Kow = Coctanol / Cwater, where, C = molar concentration

pKow = - log10 Kow

21

1.4.4 Adsorption

Adsorption is the accumulation of atoms, molecules, or ions at the surface of a solid or

liquid as the result of physical or chemical forces. It differs from absorption, in that an

adsorbed substance remains at the surface while an absorbed substance spreads

throughout the absorbing material. There are two types of adsorption, chemical

adsorption, or chemisorption, characterized by the formation of chemical bonds with

the substrate, and physical adsorption or physisorption, which results from the van der

Waals force.

Adsorption here refers to the attraction between a chemical and soil particles.

Compounds that are strongly adsorbed onto soil are not likely to leach, regardless of

their solubility. Compounds that are weakly absorbed, on the other hand, will leach in

varying degrees depending on their solubility.

The strength of sorption is a function of the chemical properties of the pesticide, the

soil type, and the amount of soil organic matter present. The adsorption partition

coefficient, Kd can be calculated by mixing soil, pesticide with water and then

measuring the concentration of pesticide in solution after equilibrium is reached. The

adsorption coefficient is the ratio of pesticide concentration in the adsorbed phase to

that in solution (Trautmann et al., 1990):

Where, Cad : Concentration of adsorbed chemical

Cs : Concentration of dissolved chemical

Kd = Cad

Cs

http://www.bookrags.com/Adsorption

22

The major drawback of using partition coefficient (Kd) to predict leaching of pesticides

is that it is highly dependent on soil characteristics. Organic matter is the most

important soil constituent determining pesticide retention. It is therefore useful to adjust

the Kd value by the percent of organic carbon in the soil. This yields another adsorption

coefficient (Koc) which is relatively independent of soil type (Trautmann et al., 1990):

The larger the adsorption coefficient, the more strongly the pesticide is held to soil

organic matter and the less likely it will leach.

1.4.5 Toxicity of Pesticides

The accepted method of recording the relative toxicity of a pesticide is to give the

median lethal dose (LD50) value, which is the chemical dose needed to kill 50% of a

group of test animals of one species under specific conditions. The mortality counts are

usually taken after 24 and 48 hours of exposure to the pesticide concerned (Yeoh,

2000). Table 1.6 shows the WHO hazard classification of pesticides. Because a dose or

dosage indicates the quantity of a pesticide applied per individual or per unit area,

volume or weight, the median lethal dose is expressed as:

LD50 value = weight (mg) of active ingredient per kg of the body weight of the test

animal (mg/kg).

Adsorption coefficient (Koc) = Partition Coefficient (Kd)

Percentage of organic carbon in soil

23

Table 1.6: The WHO Hazard Classification of Pesticides

Class Colour Band

on Label

LD50 for the rat (mg/kg body weight)

Oral Dermal

Solids liquids Solids Liquids

I a

I b

II

III

Extremely

hazardous

Highly

hazardous

Moderately

hazardous

Slightly

hazardous

Black

Red

Yellow

Blue

≤ 5

5-50

50-500

>500

≤ 20

20-200

200-2000

>2000

≤ 10

10-100

100-1000

>1000

≤ 40

40-400

400-4000

>4000

1.4.6 Pesticides’ Mode of Action

The mode of action refers to the mechanism by which a pesticide kills or interacts with

the target organism.

(a) Contact pesticides kill the target organism by weakening or disrupting the

cellular membranes; death can be very rapid (USDA, 1998).

(b) Systemic pesticides must be absorbed or ingested by the target organism to

disrupt its physiological or metabolic processes; generally they are slow acting

(USDA, 1998).

24

1.5 Organochlorine Pesticides

Organochlorine (OC) pesticides are insecticides composed primarily of carbon,

hydrogen, and chlorine. OC pesticides have a long history of widespread use around the

world. These compounds are typically very persistent in the environment, and are

known for accumulating in sediments, plants and animals. The most notorious

organochlorine is the insecticide DDT (dichloro diphenyl trichloroethane). Promoted as

a “cure all” insecticide in the 1940s, DDT was widely used in agricultural production

around the world for many years, it was also the chemical of choice for mosquito

control; until the 1960s, trucks sprayed DDT in neighbourhoods across the U.S. DDT

was also the primary weapon in the global war against malaria during that period, and

continues to be used for malaria control in a handful of countries. DDT was banned in

many countries in the 1970s in response to public concern and mounting scientific

evidence linking DDT with damage to wildlife. Since then, agricultural uses of DDT

have been prohibited worldwide. Other commonly known OCs that have been banned

in the U.S include aldrin, dieldrin, toxaphene, chlordane and heptachlor. Others that

remain in use include lindane, chlorothalonil, endosulfan, dicofol, methoxychlor and

pentachlorophenol (Krieger et al., 2001).

1.5.1 Chemical Structures and Properties

An organochlorine (OC) pesticide is an organic compound containing at least one

covalently bonded chlorine atom. Their wide structural variety and divergent chemical

properties lead to a broad range of uses. There are three major classes of

organochlorine pesticides. Table 1.7 shows the structural classification of OC

pesticides. OC pesticides are organic compounds with chlorine (Cl) atoms attached to

25

the ring structures. The Cl atoms prevent the organic compounds from being rapidly

degraded in the environment, resulting in their persistence and are therefore active for

long periods of time after application.

Table 1.7: Structural Classification of Organochlorine

Classes / Structures Examples

(a) Dichlorodiphenylethanes

DDT, DDD

Dicofol

Perthane

Methoxychlor

Methlochlor

(b) Cyclodienes

Aldrin, Dieldrin

Heptachlor

Chlordane

Endosulfan

(c) Chlorinated Benzenes Cyclohexanes HCB, HCH

Lindane

( -BHC)

(Krieger et al., 2001)

CH

C

Cl Cl

Cl

Cl

Cl

Cl

C(CCl)2

Cl

(Cl)6

Cl

Cl

Cl

Cl

Cl

Cl

26

1.5.2 Toxicological Effects

OCs contribute to many acute and chronic illnesses. Symptoms of acute poisoning can

include tremors, headache, dermal irritation, respiratory problem, dizziness, nausea,

and seizure. OCs are also associated with many chronic diseases. Studies have found a

correlation between OC exposure and various types of cancer, neurological damage

(several organochlorines are known neurotoxins), Parkinson‟s disease, birth defects,

respiratory illness, and abnormal immune system function (Reigart and Robert, 1999).

Many OCs are known or suspected hormone disruptors, and recent studies show that

extremely low levels of exposure in the womb can cause irreversible damage to the

reproductive and immune system of the developing fetus (Reigart and Robert, 1999).

As mentioned previously, the Cl atoms on the organic moieties in the OC pesticides

make these compounds very stable in the environment. This persistence can be

advantageous for the control of pests such as termites around buildings. The lack of

biodegradation and the high lipid solubility of these OC pesticides, however, has led to

problems with the accumulation of these compounds in animal tissues. In fish, for

example, the concentration of chlordane, are much higher in fish tissues than they are in

the water in which the fish are living via the “bioconcentration” process (Network for

Sustainable Agri., 2005). Because OC compounds are not metabolized and excreted by

the fish, they “biomagnify” up the food chain, which means that the larger, older fish

have higher body concentration of OC pesticides than the smaller fish. These smaller

fish have higher concentration than their food sources, the zooplankton. Birds which eat

fish have been shown to have very high concentrations of OC pesticides such as DDT

in their tissues. Thus, these persistent chlorinated compounds can cause adverse health

effects in organisms that are higher up in the food chain, such as birds.

27

Once the ecological impact of these pesticides was recognized, they were banned from

use in many countries, including the United States. They are still used in some

developing countries, though, because of their effectiveness in controlling diseases and

for increasing food production. They also are safer for humans to handle than the newer

insecticides that were developed to take their place, the organophosphate and carbamate

insecticides.

1.6 Organophosphorus Pesticides

Organophosphorus (OP) pesticides were first recognized in 1854, but their general

toxicity was not established until the 1930s. Tetraethyl pyrophosphate (TEPP) was the

first OP insecticide, which was developed in Germany during World War Two as a by-

product of nerve gas development (Minton and Murray, 1988). OPs are all derived

from phosphoric acid. They are generally among the most acutely toxic of all pesticides

to vertebrate animals. They are also unstable and therefore break down relatively

quickly in the environment (PAN, 2003). By the late 1970s, the use of OPs began to

over-take the OCs which included DDT. While OCs were relatively safe to use, their

problem was its persistence in the environment and detection in the human food chain.

OPs on the other hand are more acutely toxic, but, do not persist in the environment

beyond a few months. So with the replacement of OCs by OPs, it could lead to safer

food for the consumer but at the expense of the pesticide operator.

28


OP compounds can be considered as derivatives of inorganic phosphorus compounds in

which one or more of the hydrogen atoms have been replaced by organic groups. With

a few exception (RO group is substituted by primary amine group in some compounds,

such as fenamiphos and isofenphos), the OPs can be described by the same general

structural formula (Krieger et al., 2001):

In this formula R may be the methyl or the ethyl group and with all combinations of

oxygen and sulfur atoms attached to the phosphorus as indicated. The moiety Z exhibits

a great diversity from the aliphatic to aromatic and heterocyclic structures with

additional substituents. The OPs can be classified into the following four main groups:

RO O(S) – Z

P

RO O(S)

Where, R = CH3C2H5

Z = aliphatic, aromatic

or heterocyclic structure

RO O – Z

P

RO O

PHOSPHATES

RO S – Z

P

RO O

PHOSPHOROTHIOLATES

RO O – Z

P

RO S

PHOSPHOROTHIONATES

RO S – Z

P

RO S

PHOSPHORODITHIOATES

29

The OP compounds have diverse physical properties, due to their different structures

and chemical composition (atoms of S, O, N, Cl, and Br). The molecular weights of the

pesticide compounds range from 141 to 466. The pesticide vapor pressures span six

orders of magnitudes, from < 0.001 to 1600 mPa (at 20 oC). The water solubility of the

pesticides also varies widely from one compound to another: from 0.14 mg/L for the

least soluble, to 4 x 106 mg/L for the most soluble (Krieger et al., 2001).


OPs are generally acutely toxic. However the active ingredients within the group

possess varying degrees of toxicity. Minton and Murray (1988) have divided OPs into

three groups. The first most and toxic group, e.g. chlorfenvinphos, has a LD50 in the

range of 1-30 mg/kg, the LD50 range for the second group, e.g. dichlorvos, is 30-50

mg/kg, and the least toxic group, e. g. malathion has a range of 60-1300 mg/kg.

OPs work by inhibiting important enzymes of the nervous system which play a vital

role in the transmission of nerve impulses. When exposured to OPs, the inhibitase

enzyme is unable to function and a build-up of acetycholine occurs, which causes

interference with the nerve impulse transmission at nerve endings (Krieger et al., 2001).

In humans, poisoning symptoms include: excessive sweating, salivation and

lachrimation, nausea, vomiting, diarrhea, abdominal cramp, general weakness,

headache, poor concentration and tremors. In serious cases, respiratory failure and

death can occur (Reigart and Robert, 1999).

30

OPs kill insects by interfering with the nervous system function. Normally, impulses

are transmitted chemically from the end of one nerve cell to the beginning of another;

one of the chemical transmitters used in animal nervous systems is called acetylcholine.

After transmitting the nerve impulse, acetylcholine is destroyed by an enzyme called

acetylcholinesterase (AChE) in order to clear the way for another transmission. The

OPs attach to AChE and prevent it from destroying acetylcholine, causing

overstimulation of the nerves (Reigart and Robert, 1999).

1.7 Carbamates

Carbamates were originally extracted from the calabar bean, which grows naturally in

West Africa (Alloway and Ayres, 1997). The extracts of this bean contain

physostigmine, a methylcarbamate ester. Carbamates which are derivatives of carbamic

acid are non-persistent in the environment which is similar to the OP pesticides.

Aliphatic esters of carbamic acid were synthesized in the early 1930s and while

exhibiting herbicidal and fungicidal activities, were not insecticidal. Research on the

carbamates was not carried out until 1950 when there was a search for insecticides

having anticholinesterase activity with more selectivity and less mammalian toxicity

than some of the organophosphorus ester which are in use (Krieger et al., 2001).

Carbaryl is perhaps the best known and most widely applied carbamate pesticide.

Carbamates are among the most popular pesticides for home use, both indoors and on

gardens and lawns.

31

There are three distinguishable classes of carbamates (Yeoh, 2000):

(a) Methyl carbamates with a phenyl-ring structure. (e.g. carbaryl)

(b) Methyl carbamates and dimethyl carbamates with a heterocyclic structure. (e.g.

carbofuran)

(c) Methyl carbamate of oximes having a chain structure. (e.g. aldicarb)


Carbamate esters used as insecticides have this common structure (Krieger et al., 2001):

R O C(O) N(CH3) R‟

Where R is an alcohol, oxime, or phenol and R‟ is a hydrogen or a methyl group. The

nature of the substituent groups alters both the physicochemical properties of the

insecticide and the biological activity. Most of these insecticides dissolve readily in

organic solvents but are only slightly soluble in water, thereby conferring varying

degrees of lipid solubility. This lipophilicity enhances the insecticidal potency, the

agents readily penetrating insect cuticles and tissues, but it also presents problems of

oral and dermal absorption in other animal species, and enhances storage in tissues. A

wide range of melting points (50 oC to 150

oC) is found for these agents, determined

largely by the size of the substituent group. Vapor pressures range from less than 5 x

10-6

to 5 x 10-2

mm Hg. While high melting points and low vapor pressures enhance the

environmental stability of the compound, decomposition can be markedly enhanced by

increasing temperatures, a 10 oC temperature rise will raise hydrolysis rate by two to

three fold (Krieger et al., 2001).

32


Like the organophosphorus pesticides, the carbamates elicit toxicity by inhibiting

nervous tissue acetylcholinesterase (AChE). However, it is a transient, reversible

inhibition, since there is a relatively rapid reactivation of the enzyme. Poisoning by

carbamates and organic phosphorus compounds coupled with its severity depend not

only on the degree of reduction of acetylcholinesterase activity in the nervous system

but also on the rate of inhibition and the type of inhibitory action. The most striking

differences between the clinical effects of the two groups are the much more rapid and

spontaneous recovery from poisoning by carbamates and the relatively wide separation

between the smallest dosage of any carbamate that will cause mild illness. Both these

differences have their pharmacological basis in the relatively rapid and spontaneous

reactivation of acetylcholinesterase inhibited by a carbamate. Another difference is

based on the ratio between the dosages producing the first signs of illness resulting in

death is constant for organic phosphorus compounds but for carbamates, it depends on

the rate of infusion (Krieger et al., 2001).

1.8 Pesticides Selected for Present Study

Seven organophosphorus compounds (acephate, chlorpyrifos, diazinon, dimethoate,

malathion, profenofos and quinalphos), three organochlorine compounds

(chlorothalonil, α-endosulfan and β-endosulfan), and one carbamate pesticide (carbaryl)

were selected for this study. From a survey carried out by the Residue Section,

Pesticides Control Division, Department of Agriculture, Report 2003 - Imported

Pesticide Amounts as an Active Ingredient in Malaysia at 1998 – 2001, it was found

that all the eleven selected pesticides are popular and widely used by local farmers in

fruit and vegetable cultivation (Yeoh, 2000).

33

1.8.1 Acephate

Table 1.8: Physical and Chemical Properties of Acephate

Common Name Acephate

Chemical Name O,S-dimethyl acetylphosphoramidothioate

Structural Formula

Empirical Formula C4H10NO3PS

Molecular Weight

(g/mol)

183.16

Density (g/cm3) 1.35

Melting Point (oC) 92-93

Water Solubility (mg/L) 7.0 x105

Vapor Pressure (mm Hg) 1.7 x 10-6

at 23-25 oC

Octanol/Water Partition

(Log10 Kow)

0.13 at 25 oC

Partition Coefficient -1.87

Adsorption Coefficient 0.48

Oral LD50 rat (mg/kg) 866-945 (WHO Class III)

MRL on fruit and

vegetable (ppm)

1.0

Stability Relatively stable to hydrolysis. At 40 oC, 50% hydrolysis

occurs in 60 hours at pH 9 and in 710 hours at pH 3.

Mode of Action Systemic insecticide with contact and stomach action.

Cholinesterase inhibitor.

(Downing, 2000; Kidd and James, 1991)

O H O

CH3S

P N C CH3

CH3O

34

1.8.2 Chlorpyrifos

Table 1.9: Physical and Chemical Properties of Chlorpyrifos

Common Name Chlorpyrifos

Chemical Name O,O-diethyl o-(3,5,6-trichloro-2-pyridyl) phosphorothioate

Structural Formula

Empirical Formula

C9H

11Cl

3NO

3PS

Molecular Weight

(g/mol)

350.6


Melting Point (oC) 42 – 43.5

Water Solubility (mg/L) 2 .00



(Log10 Kow)

4.70 at 20 oC

Partition Coefficient 13490

Adsorption Coefficient 6070

Oral LD50 rat (mg/kg) 135-163 (WHO Class II)

MRL on fruit/vege (ppm) 0.05 ppm on fruits and 0.5 ppm on vegetables

Stability

Stable in neutral and weakly acidic media. Hydrolyzed

by strong alkalis.

Mode of Action Non-systemic insecticide with contact, stomach

and respiratory action. Cholinesterase inhibitor.

(ETN, 1996a; Kidd and James, 1991)

NCl

Cl Cl

OP(OCH2CH3)2

S

35

1.8.3 Diazinon

Table 1.10: Physical and Chemical Properties of Diazinon

Common Name Diazinon

Chemical Name O,O-diethyl0-2-isopropyl-6-methyl(pyrimidine-4-yl)

phosphorothioate.

Structural Formula

Empirical Formula C12H21N2O3PS

Molecular Weight (g/mol)

304.36


Melting Point (oC) Decomposes at temperature higher than 125

oC

Water Solubility (mg/L) 40 at 25 oC



(Log10 Kow)

3.30




MRL on fruit/vege (ppm) 0.5

Stability Susceptible to oxidation about 100 oC. Stable in

neutral media, but slowly hydrolyzed in alkaline media,

and more rapidly in acidic media. Decomposes above 120 oC

Mode of Action Non-systemic insecticide with contact, stomach,


(ETN, 1996b; Kidd and James, 1991)

H

(CH3)2C

S

O P OC2H5

OC2H5

CH3

N

N

36

1.8.4 Dimethoate

Table 1.11: Physical and Chemical Properties of Dimethoate

Common Name Dimethoate

Chemical Name O,O-dimethyl-S-methylcarbamoylmethyl phosphorodithioate

Structural Formula

Empirical Formula C5H12NO3PS2

Molecular Weight

(g/mol)

229.28

Density (g/cm3) 1.277 at 65

oC

Melting Point (oC) 45 - 52.5

Water Solubility (mg/L) 3.9 x 104 at 21

oC


at 25 oC


(Log10 Kow)

0.704

Partition Coefficient 0.6990


Oral LD50 rat (mg/kg) 310


Stability Relatively stable in aqueous media at pH 2-7. Hydrolyzed

in alkaline solution (50% hydrolysis occurs in 12 days at

pH 9). Decomposes on heating, forming the O,S-dimethyl

analogue.

Mode of Action Systemic insecticide with contact and stomach

action. Cholinesterase inhibitor.

(ETN, 1996c; Kidd and James, 1991)

CH3O S O

P

CH3O S CH2 C N CH3

H

37

1.8.5 Malathion

Table 1.12: Physical and Chemical Properties of Malathion

Common Name Malathion

Chemical Name Diethyl (dimethoxy thiophosphorylthio) succinate

Structural Formula

Empirical Formula C10H19O6PS2

Molecular Weight

(g/mol)

330.36


Melting Point (oC) 2.85

Water Solubility (mg/L) 130


at 30 oC


(Log10 Kow)

2.75

Partition Coefficient 2.7482


Oral LD50 rat (mg/kg) 1375-2800 (WHO class III)


Stability Relatively stable in neutral, aqueous media. Decomposed

by acids and alkali.

Mode of Action Non-systemic insecticide with contact, stomach


(ETN, 1996d; Kidd and James, 1991)

S

H3CO P S

OCH3

O OC2H5

O OC2H5

38

1.8.6 Profenofos

Table 1.13: Physical and Chemical Properties of Profenofos

Common Name Profenofos

Chemical Name O-4-bromo-2-chlorophenyl-O-ethyl-s-propylphosphorothioate

Structural Formula

Empirical Formula C11H15BrClO3PS

Molecular Weight

(g/mol)

373.6


Melting Point (oC) 153 - 154

oC




(Log10 Kow)

4.74

Partition Coefficient Not available


Oral LD50 rat (mg/kg) 328 (WHO Class II)


Stability Stable under neutral and slightly acidic conditions.

Unstable under alkaline conditions.

Mode of Action Non-systemic insecticide with contact and stomach

action. Exhibits a translaminar effect.

Cholinesterase inhibitor.

(ETN, 1996e; Kidd and James, 1991)

O

O P S C3H7

OC2H5

Cl

Br

39

1.8.7 Quinalphos

Table 1.14: Physical and Chemical Properties of Quinalphos

Common Name Quinalphos

Chemical Name O,O-diethyl O-quinoxalin-2-yl phosphorothioate

Structural Formula

Empirical Formula C12H15N2O3PS

Molecular Weight

(g/mol)

298.3






(Log10 Kow)

4.44



Oral LD50 rat (mg/kg) 71 (WHO Class II)


Stability 50% decomposition occurs in 56 days at pH 5 in

40 days at pH 7 and in 30 days at pH 9

Mode of Action Insecticide with contact and stomach action. By penetrating

the plant tissues through translaminar action, exhibits a

systemic effects and cholinesterase inhibitor.

(ETN, 1996f; Kidd and James, 1991)

S

O – P – OC2H5

OC2H5

N

N

40

1.8.8 Chlorothalonil

Table 1.15: Physical and Chemical properties of Chlorothalonil

Common Name Chlorothalonil

Chemical Name 2,4,5,6 - Tetrachloroisophthalonitrile

Structural Formula

Empirical Formula C8Cl4N2


265.92

Density (g/cm3) 1.7


Water Solubility (mg/L) 0.6



(Log10 Kow)

3.05



Oral LD50 rat (mg/kg) > 10,000 (WHO Class IV)

MRL on fruit/vege (ppm)

5.0

Stability Stable to heat and UV light. Stable to acidic

and alkali aqueous solutions

Mode of Action Non-systemic foliar fungicide with protective action.

(ETN, 1996g; Kidd and James, 1991)

CN

Cl Cl

Cl CN

Cl

41

1.8.9 α-Endosulfan and β-Endosulfan

Table 1.16: Physical and Chemical Properties of α-Endosulfan and β-Endosulfan

Common Name Endosulfan

Chemical Name 6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-

methano-2,4,3-benzodioxathiepin 3-oxide

Structural Formula

Empirical Formula C9H6Cl6O3S

Molecular Weight

406.95 (g/mol)



Water Solubility (mg/L) 0.32 at 22 oC


– 5.96 x 10-7

at 25 oC


Log10 Kow=3.83




MRL on fruit/vege (ppm)

1.0

Stability Stable to sunlight. Slowly hydrolyzed in aqueous acids

and alkalis with the formation of the diol and SO2

Mode of Action Non-systemic insecticide with contact and stomach action.

(Pest. Manag. Info. Prog., 1993; Kidd and James, 1991)

α-endosulfan

H

H

H

H H

O

O O

S

H H

β-endosulfan

H H

H

H H O

O O

S

H H

42

1.8.10 Carbaryl

Table 1.17: Physical and Chemical Properties of Carbaryl

Common Name Carbaryl

Chemical Name 1-naphthalenylmethylcarbamate

Structural Formula

Empirical Formula C12H11NO2


201.23

Density (g/cm3) 1.23 at 20

oC

Melting Point (oC) 142

Water Solubility (mg/L) 40 at 30 oC


at 25 oC


(Log10 Kow)

1.85 at 25 oC





Stability Stable under neutral and weakly acidic

conditions. Hydrolyzed in alkaline media to

1-naphthol. Stable to light and heat.

Mode of Action Insecticide with contact and stomach action,

and slight systemic properties. Weak cholinesterase

inhibitor. Also acts as a plant growth regulator.

(ETN, 1996h; Kidd and James, 1991)

O

OCNHCH3

43

1.9 Scope and Objective of Study

The objectives of this study are:

(a) Development of a rapid, accurate and environment friendly method for the

simultaneous determination of multiclass pesticides in fruits and vegetables via

chromatographic techniques.

(b) The implementation and validation of the developed method on the analysis of

pesticide residues in fruits and vegetables.

(c) Comparison of HS-SPME, SPE and HS-SDME for the determination of

pesticide residues in fruits and vegetables.

(d) Multiclass determination on different active ingredients in commercial pesticide

formulations.

44

CHAPTER 2

REVIEW OF GAS CHROMATOGRAPHY FOR THE ANALYSIS

OF PESTICIDE RESIDUES IN FRUITS AND VEGETABLES AND

PESTICIDE FORMULATIONS

2.1 Trace Analysis of Pesticides by Gas Chromatography

The field of trace analysis including pesticide residue analysis has made tremendous

advances in terms of selectivity and detection limits. In the 1940s and early 1950s

(Herdman et al., 1988), gravimetric and bioassay techniques were the mainstays in

“trace” analysis, extending detection limits to the then frontier levels of about 1 ppm.

These were time-consuming methods, lacking in compound selectivity but broad-based

in terms of responding to whole classes of chemicals. In the 1950s and early 1960s

(Hoff and Zoonen, 1999), pesticide residue analysis was determined by colorimetric

methods, for example DDT was analysed in vegetables employing derivatization to

yield a blue color with subsequent colorimetric determination. Drawbacks of these

methods are the impossibility to analyze more than one pesticide simultaneously. A

first step towards multiresidue methods was based on thin layer chromatography

(TLC), which employed on-plate detection often based on biological activity such as

cholinesterase inhibition or fungi-spores (Hoff and Zoonen, 1999). The major source of

positive findings in fruits and vegetables originates from insecticides or fungicides.

Moreover most of the residues reported are compounds amenable to gas

chromatography (GC), thus emphasizing the role of GC to this field.

45

In the 1960s (Hoff and Zoonen, 1999), the real breakthrough of GC in pesticide

residue analysis was induced by the introduction of electron capture detection, enabling

simultaneous analysis of various chlorinated pesticides at detection levels a hundred

times lower than the available flame detectors. Early electron capture detectors

consisted of a titanium foil on which 3H was embedded. The upper temperature limit of

these foils was only 225 oC thus limiting the oven temperature during gas

chromatographic separation. Moreover cleaning the detector at elevated temperature is

impossible, leading to rapid adulteration. The more thermostable 63

Ni source gradually

replaced the 3H source type since operation temperature can be used up to 400

oC.

Electron capture detection (ECD) only solved part of the problem, halogenated

pesticides could be detected sensitively and selectively, but pesticides without halogens

such as organophosphorus insecticides still lacked a sensitive detector in GC. Several

multiresidue methods employing GC-ECD to determine pesticides in food and in the

environment have been evaluated (Rohrig and Meisch, 2000; Correia et al., 2001;

Barrionuevo and Lancas, 2002; Lopez-Blanco et al., 2002; Chen et al., 2002; Perez et

al., 2002; Tomkins and Barnard, 2002; Bouaid et al., 2003; Cai et al., 2003; Deger et

al., 2003; Used et al., 2003; Zuin et al., 2004; Dong et al., 2005a; Chang and Doong,

2006; Zhao et al., 2006)

The success of the ECD prompted the development and application of other selective

detection principles for non-halogenated pesticides. Nitrogen phosphorus detection

(NPD) (Fernandez et al., 2001; Pitarch et al., 2001; Berrada et al., 2004; Lopez-Blanco

et al., 2006; Rodriguez et al., 2006) was discovered by the observation that an alkali

salt in the lame of a flame ionization detection (FID) system enhances the ionization of

46

N and P compounds, which led to the first detector with low detection limits and good

selectivity over carbon compounds. Long-term stability however can be a problem in

routine analysis when the detector bead, which consists of a rubidium salt, deteriorates.

Flame photometric detection (FPD) (Simplicio and Boas, 1999; Burbank and Qian,

2005; Berijani et al., 2006) is based on element specific luminescence produced when

sulfur or phosphorus compounds are burnt in a hydrogen-rich flame. These emission

bands of S2 for sulfur and HPO for phosphorus-containing species can be detected at

394 nm and 526 nm, respectively. Although selectivity is excellent for the

determination of phosphorus and sulfur compounds, quenching can occur due to high

carbon levels and a non-linear detector response in the case of sulfur. Recent

developments in detector technology resulted in the introduction of a pulsed flame

photometric detector (P-FPD) which has shown an improved performance compared to

the conventional FPD regarding sensitivity, selectivity and multi-element capability

(Hoff and Zoonen, 1999).

The confirmation of non-compliant sample has always been important for residue

laboratories and GC-MS has always been seen as one of the most conclusive

techniques. The application of mass spectrometric (MS) detection in gas

chromatography for pesticide residue analysis initially was inhibited by the fact that

direct coupling of packed columns, most commonly used in the early days of pesticide

analysis, was incompatible with the vacuum in the ionization chamber of the mass

spectrometer, due to the high carrier gas flow used for packed columns. Developments

finally led to a complicated jet separation system in order to selectively remove the

47

small carrier gas molecules. Direct coupling of GC to MS became feasible with the

introduction of capillary columns. The first bench top GC-MS systems, based on

quadrupole mass analyzers were introduced in the early 1980s (Hoff and Zoonen,

1999), but for pesticide residue analysis, those early expensive instruments lacked

sensitivity and tuning these instruments was tedious, rendering them not fully

applicable for routine analysis. Since then, a great number of GC-MS applications in

food and environment have been reported using capillary GC with quadrupole mass

analyzer detection and electron ionization both in full scan and in selected ion

monitoring (SIM) mode (Sen et al., 1997; Jarvenpaa et al., 1998; Otera et al., 2002;

Zambonin et al., 2002; Beltran et al., 2003; Lambropoulou and Albanis, 2003; Lee et

al., 2003; Goncalves and Alpendurada, 2004; Sanusi et al., 2004; Song et al., 2004;

Verzera et al., 2004; Giuseppe et al., 2005; Gonzalez et al., 2005; Mazida et al., 2005;

Beltran et al., 2006; Chen and Huang, 2006; Flores et al., 2006a; Flores et al., 2006b;

Sauret et al., 2006).

The introduction of ion trap detectors (ITD) coupled to GC in the early 1990s (Hoff and

Zoonen, 1999) showed to be more applicable for routine application for the analysis of

food as well as water. An important feature of the ion-trap detector is that there is no

loss in sensitivity when going from full scan data acquisition to selected ion monitoring

data. This makes the detector to be useful in pesticide analysis.

48

Recently, a sophisticated technique using GC-MS-MS has been reported to enable

analysis of pesticides and their metabolites at trace levels in the presence of many

interfering impurities. A recent analysis of more than 100 pesticide residues in fruits

and vegetables has been reported by Ahmed (2001). An ion trap instrument utilizes the

same ion regions for all MS/MS processes. Each pesticide is run with its own unique

set of parameters, which fragment the compound, retaining only the precursor ion. The

ion is then fragmented to create a product spectrum. Schachterle et al. (1996) found that

the selectivity and sensitivity of MS/MS is such that 1 – 5 ppb levels can easily be

measured. The spectra observed are also interference-free, which allows the desired

results of unambiguous identification, making it easy to identify and confirm

compounds even with a relatively dirty food matrix.

GC is the most widely used technique in pesticide analysis. At present, more than 60%

of registered pesticides and/or their metabolites are amenable to GC (Santos and

Galceran, 2002). GC was one of the first chromatographic separation techniques to be

developed and has not lost its eminence today. The popularity of GC is based on a

favorable combination of very high selectivity and resolution, wide dynamic

concentration range, good accuracy and precision.

49

2.2 Gas Chromatography (GC)

The first chromatography to be described in the literature was that constructed by the

inventors of the technique, James and Martin, in 1952 (Raymond, 1995). Gas

chromatography is a chromatographic separation based on the difference in the

distribution of species between two immiscible phase in which the mobile phase is a

carrier gas moving through or passing over the stationary phase contained in a column.

A detector then monitors the composition of the gas stream as it emerges from the

column carrying separated components; the resulting signals provide the input for data

acquisition. Gas chromatography can be applied to the analysis of mixtures, which

contain compounds with boiling points from near zero to over 700 K, or which can be

heated sufficiently without decomposition to give a vapor pressure of a few mm Hg

(Bartle and Myers, 2002).

In a GC analysis, a known volume of an analyte is injected into the injection port using

a microsyringe. Although the carrier gas sweeps the analyte molecules through the

column, this motion is inhibited by the adsorption of the analyte molecules either onto

the column walls or onto packing materials in the column. The rate at which the

molecules progress along the column depends on the strength of adsorption, which in

turn depends on the type of molecule and on the stationary phase materials. Since each

type of molecule has a different rate of progression, the various components of the

analyte mixture are separated as they progress along the column and reach the end of

the column at different retention time. A detector is used to monitor the outlet stream

from the column; thus, the time at which each component reaches the outlet and the

amount of that component can be determined. Generally, substances are identified by

50

the order in which they elute from the column and by the retention time of the analyte

in the column.

The modern gas chromatograph is a fairly complex computer controlled instrument.

The samples are mechanically injected, the analytical results are automatically

calculated and the results printed out, together with the pertinent operating conditions

in a standard format. The layout of the modern gas chromatograph is shown as a block

diagram in Figure 2.1.

2.2.1 Carrier Gas or Mobile Phase

The first unit, the gas supply unit, provides all the necessary gas supplies which may

involve a number of different gases, depending on the type of detector that is chosen.

The carrier gas or mobile phase acts as a transport medium and must be chemically

inert. Commonly used gases include N2 and He, which are usually employed for packed

column, and argon, N2, He and CO2 which are usually used for capillary columns. The

purity of the carrier gas is also frequently determined by the detector, through the level

of sensitivity needed can also play a significant role. Typically, gases with purities

higher than 99.99% are used. For the detector postulated, a minimum of three different

carrier gases would be required which will also involve the use of three flow

controllers, three flow monitors and possibly a flow programmer. In addition, the gas

supply unit would be serviced by a microprocessor to monitor flow rates, adjust

individual gas flows and, if necessary, program the carrier gas flow rate.

51

Figure 2.1: The Design of a Modern Gas Chromatograph

Gas Supply Unit

Flow Controller

Flow Programmer

Microprocessor for Flow

Controller and

Programmer

Sample Unit

Injector

(Manual or Automatic)

Injector

Oven

Column Unit

Column

Column

Oven

Detector Unit

Detector

Detector

Oven

Injector and Injector

Oven Controller

Column Oven Controller

and Programmer

Detector Electronics and

Computer Data Acquisition

and Processing System

Detector Oven Controller

52

The carrier gas flow rate affects the analysis. The higher the flow rate, the faster the

analysis, but the lower the separation between analytes. Selecting the optimum flow

rate is therefore a compromise between the level of separation and the length of

analysis as selecting the column temperature. The carrier gas flow, which is precisely

controlled, allows great precision in the retention times.

Gases are usually supplied from gas cylinders that include a primary reducing valve

that can apply a pressure ranging from zero to about 4 bar to the respective flow

controllers on the chromatograph. The controllers provide a precisely controlled gas

flow to either the detector or the injection systems and subsequently the column. The

flow rate controllers can vary from instrument to instrument, but generally can provide

flow rates from zero to approximately 50 mL or 100 mL per minute.

2.2.2 Sample Injection Port

The second unit is the sampling unit. Injection of a sample into the gas stream at the

column head is carried out by means of a syringe and a hypodermic needle. At first, a

re-sealable rubber cap was employed, but this has been replaced as early as 1964

(Bartle and Myers, 2002) by a heat resistant elastomeric septum compressed in a metal

fitting, the procedure which has persisted until today. The temperature of the injector

port and detector are usually kept hotter than the temperature of the column to promote

rapid vaporization of the injected sample and to prevent sample condensation in the

detector.

53

A septum injection system (on column injection system) which is used for packed

columns cannot be used for capillary columns. Due to the very small sample size that

must be placed on narrow bore capillary columns, a split injection system is necessary,

as shown in Figure 2.2. (a) and 2.2. (b) (Raymond, 1998).

Figure 2.2: (a) A Split Injection System Figure 2.2: (b) A Septum Injection System

The basic difference between the two systems is that the capillary column now projects

into the glass liner of this split injection system and a portion of the carrier gas sweeps

past the column inlet to waste. As the sample passes the column opening, a small

fraction is split off and flows directly into the capillary column. The split ratio is

changed by regulating the portion of the carrier gas that flows to waste which is

achieved by an adjustable flow resistance in the waste flow line. This device is only

used for small diameter capillary column where the sample size is critical (Raymond,

1998).

Syringe

Silicone Septum

Oven wall or Oven Top

Split Gas Stream

to Waste

Carrier

Gas

Heated Glass Liner

Capillary Column

Carrier

Gas

Packed

Colum

n

54

Optimization of injection conditions is critical to proper GC analysis. In the analysis of

persistent organic pollutants (POPs) and organochlorine (OC) pesticides, problems

often occur with nonvolatile coextractives such as triglycerides and pigments that,

despite various isolation procedures, are still present in the final extracts. Most GC

applications for polychlorinated biphenyls (PCBs) and OC pesticides analysis have

employed split/splitless injection systems, although on-column injection has also been

used.

2.2.3 GC Columns

The column is regarded as the “heart” of the analytical gas chromatograph; the quality

of the separation achieved by the whole system can be as good as that of the column

only. Early GC was carried out on packed columns, typically 1-5 m long and 1-5 mm

i.d., and filled with particles each of which was coated with a liquid or elastomeric

stationary phase. Micro-packed columns are similar but have i.d. less than 1 mm. The

resolution of packed columns is limited by their length, itself restricted by the pressure

drop resulting from the resistance to gas flow. This restriction was removed by the

invention of the capillary column, which was suggested by Martin at a meeting in 1956,

but independently realized in 1957 by Golay, who laid out the theory of operation and

demonstrated its use in 1958 (Bartle and Myers, 2002). The length of capillary columns

range from about 10 m to 100 m and can have internal diameters from 0.1 mm to 0.5

mm. The stationary phase is coated on the internal wall of the column as a film ranging

from 0.2 µm to 1.0 µm thick (Raymond, 1998).

55

One of the great advantages of capillary GC is the separation power which finally

resulted in the introduction of commercially available fused-silica capillary columns as

a great step forward with regard to the peak capacity. By using high resolution capillary

columns, individual congeners could be determined, leading to the unambiguous

determination of single congeners. For pesticide analysis the benefits of capillary gas

chromatography can be found in the gain in sensitivity due to the reduction in peak

width.

The most important breakthrough in GC was the introduction of open tubular columns.

Since then, tremendous developments in column fabrication and instrument design

have made the open tubular column the standard for most analytical applications.

Capillary gas chromatography (CGC) is the most efficient method for the analysis of

volatile and semivolatile compounds. The prevalence of capillary columns in GC

measurements was demonstrated in a survey where over 90% of GC methods are now

designed for used with capillary columns (Eiceman et al., 2004). Although capillary

columns are capable of refined separations, limitations exist and can be seen in the

treatment of the methods of production and choice of materials. A new type of open

tubular columns was devised and then the introduction of fused-silica columns in 1979

by Dandeneau and Zerenner (Bartle and Myers, 2002) which are highly flexible,

durable and chemically inert. The choice of the appropriate column for a given

separation depends on the chemical nature of the analyte, the sample matrix and the

solvent, and especially on the nature of the molecular interactions between analyte and

stationary phase. A detailed discussion on the column technology and the chemistry

and technology for producing bonded-phase capillary columns was given by Zeeuw

56

and Luong (2002). The multistep method of column manufacture has intrinsic

inefficiencies, and the sol-gel method for column preparation may be the most dramatic

change in column technology in the past decade. Other activities or patterns of

development with column technology are discernible. They involve the exploration of

methods to stabilize coating or to characterize extra thick films of stationary phases,

with up to 18 µm thickness in 0.53 mm i.d. column, are available with common

polysiloxane phases. These columns permit the separations of small molecular weight,

highly volatile molecules at ambient temperature rather than cryogenic temperatures

(Eiceman et al., 2004).

Hinz (2006) has developed a removable column-switching system that allows the usage

of up to eight separating columns for a commercially available gas chromatography.

The use of this removable column-switching device will increase the efficiency. The

time for allowing the injector and detector to cool down, and for insertion of the

columns, conducting seal integrity tests, conditioning the column and running a test

chromatogram is no longer needed. The system can automatically test the suitability of

as many as eight separating columns to analyze unknown samples.

2.2.4 Stationary Phases in GC

The polarity of the stationary phase applied to a capillary column can be classified into

three types, namely non-polar stationary phase which normally consists of

methylpolysiloxane as its packing material, semi-polar stationary phase which normally

contains 50% phenylpolysiloxane and 50% methylpolysiloxane as its packing material,

and polar stationary phase which normally contains polyethylene glycol as its packing

57

material (Santos and Galceran, 2002). For example, for OC pesticides, non-polar

stationary phases (DB-1 and DB-5) are usually used. Semi-polar stationary phases

(OV-17 and OV-1701) are usually chosen for the separation of polar pesticides, such as

OP pesticides. Polar stationary phases, e.g. DB-Wax, are suitable for more polar

compounds, such as methamidophos.

Stationary phase development has slowed down in recent years, and the vast majority

of separations are done on fewer than a dozen stationary phases with bonded-phase

capillary columns. The synthesis or discovery of new phases and characterization of

retention or classification of retention mechanisms has not been a prominent feature in

GC studies for over a decade. One new development is the use of a resinous coating as

a chromatographic stationary phase with a significant improvement in the separation of

a hydrocarbon mixture. A few application specific stationary phases were developed for

the separation of the congeners of polychlorinated dibenzodioxin (PCDDs) and furans

(PCDFs) and polychlorinated biphenyls (PCBs) (Eiceman et al., 2004). Another four

application-specific open-tubular columns (Rtx-CLPesticides, Rtx-OPPesticides, Rtx-

Dioxin and Rtx-Dioxin2) have been developed by Kiridena et al. (2006). The Rtx-

CLPesticides and Rtx-OPPesticides columns are shown to belong to the category

containing poly (dimethylmethyltrifluoropropylsiloxane) stationary phase with Rtx-OP

Pesticide having a similar selectivity to a poly (dimethylmethyltrifluoropropylsiloxane)

stationary phase containing 20% methyltrifluoropropylsiloxane monomer (DB-200).

The Rtx-CLPesticides separation exhibits properties for a stationary phase containing

less than 20% methyltrifluoropropylsiloxane monomer. The Rtx-Dioxin and Rtx-

Dioxin2 columns are located in the category dominated by the poly (dimethyl

58

diphenylsiloxane) stationary phases containing less than 20% diphenylsiloxane

monomer. The ionic liquid stationary phase exhibits thermal stability up to 260 oC and

provides distinctive retention behavior compared to methylphenyl polysiloxane. The

retention is governed in part by the cation and in part by the anion providing additional

flexibility or variability in designing phases (Belaidi et al., 2003).

Siloxane polymers, the most widely used polymers in GC today, were subjected to

refinements with hopes of improved thermal stability. Addition of aryl substituents in

the backbone and side chains improved stability to 400 oC and higher (Eiceman et al.,

2004). Addition of a silphenylene unit to form tetramethyl-p-silphenylenedimethyl-

diphenylsiloxane, resulted in reduced column bleed and increased the maximum

allowable operating temperature (Mayer et al., 2003a). The phenylene group enhances

thermal stability, presumably through stiffening of the backbone. However, the elution

temperatures of analytes were increased by 15 to 30 oC against comparable

polysiloxanes. Mayer et al. (2003b) made another polysiloxane by addition of alkyl

groups to form an n-octylmethyl, diphenylpolysiloxane phase called SOP-50-Octyl.

This copolymer was a gum with 52% octylmethyl and 48% diphenylpolysiloxane and

had a random microstructure. Despite high phenyl content, the phase showed low

overall polarity and this was attributed to the influence of the octyl substituent.

Unfortunately, the octyl substituent also resulted in column bleed and a maximum

allowable operating temperature of only 280 oC. However, the octyl substituent

measurably affected elution temperatures of non-polar compounds.

59

The exploration of mechanisms of retention and the link between molecular structures

of analyte and liquid phase are central to fundamental advances in GC. The interactions

between solutes and phases are probed using enthalpy and entropy obtained from

chromatographic retention. Such a study was made using several poly (3,3,3-methyl-

trifluoropropyl siloxane) stationary phases with 44 solutes (Eiceman et al., 2004).

Particular attention was given to the non-polar interactions with the stationary phase

and the effect of the solute dipole moment on the polar interactions. The adsorption of

compounds and water on silica bonded with polyfluoroalkyl groups showed that the

selectivity of retention was comparable to a conventional stationary phase such as OV-

210 (Eiceman et al., 2004). Residual SiOH groups of silica contributed little to

adsorption, seemingly because they were effectively screened by the neighboring

attached organic groups.

The role of solvent density on retention with a conventional phase was explored by

Gonzalez and Perez (2003) using capillary columns coated with oligomeric

poly(oxyethylene) stationary phases and polar solutes. The results showed that solvent

density had little measurable affect on the enthalpy of solubility and that the observed

decrease in solubility with the increasing density was attributed to changes in entropy.

Other nonconventional stationary phases including liquid crystals have been employed,

and a number of low molar mass polymeric liquid crystals that contain the same

mesogenic groups were evaluated. Activity coefficients and interaction parameters

were used to determine the types and sources for thermodynamic interactions (Price et

al., 2002). New liquid crystals were studied in a basic manner. The liquid crystals

60

contained a benzoyloxy azobenzene mesogenic core substituted with heptyloxy,

dioxyethylene ether groups, or both. Both these studies were good examples of studies

on molecular recognition in separation (Ammar et al., 2003). Chen et al. (2005) has

designed nine representative dialkylsulfides as probes to assess the use of discotic

copper complex-containing siloxane polymer as a GC stationary phase. It was observed

that solutes with branched alkyl substituents were greatly attracted to the discotic

lamellar phase, those with electron-releasing substituents to lamellar crystalline phase

and those with disk-like substituents to discotic hexagonal phase. Four linear equations

were derived to describe the quantitative interations between the sulfide probes and the

mesophases. The acid-base interaction prevails in the lamellar crystalline phase and the

polarizability interaction in the discotic hexagonal phase. The dispersion interaction is

found in the phases with higher crystallinity.

2.2.5 Column Oven in GC

The column in a GC is contained in an oven, the temperature of which is precisely

controlled electronically. The rate at which a sample passes through the column is

directly proportional to the temperature of the column. The higher the column

temperature, the faster the sample moves through the column. However, the faster a

sample moves through the column, the less it interacts with the stationary phase, and

the less likely will the analytes be separated.

61

In general, the column temperature is selected as a compromise between the analysis

time and the level of separation. The isothermal method is one which holds the column

at the same temperature for the entire analysis. Most methods, however, use the

temperature programming technique where the column temperature is increased during

the analysis from the initial temperature to a final temperature following a programmed

rate of temperature change.

The temperature programmer (hardware and software) usually has a range of linear

gradients from 0.5 oC/min to about 20

oC/min. Some programmers include nonlinear

programs such as logarithmic and exponential, but most GC analyses can be effectively

accomplished using only linear programs. The program rate can be changed at any time

in the chromatographic development or intermittent isothermal periods can be inserted

where necessary in the program. The temperature programming limits are usually the

same as those of the oven (5 oC to 400

oC). All connections between the column and

the detector that pass through the column oven wall to the detector oven are supplied

with their own heaters so that no part of the conduit can fall below the column oven

temperature. A cold spot in the conduit will cause condensation which can result in

broad and distorted peaks. (Raymond, 1998).

A temperature program allows analytes that elute early in the analysis to separate

adequately, while shortening the time it takes for late-eluting analytes to pass through

the column.

62

2.2.6 GC Detectors

The fourth component comprises the detector which is also kept in an oven. There is a

wide range of detectors today, each having unique operating parameters and its own

performance characteristics. The detector, and the conduit connecting the column to the

detector, must be maintained at a temperature at least 15 ˚C above that of the maximum

temperature the oven will reach during analysis to ensure no sample condenses in the

conduits or detector, consequently, separate conduit heaters are necessary. Any

condensation introduces serious detector noise into the system and also reduces the

detector response thus affecting both the detector sensitivity, accuracy and precision of

the results. The detector oven is set at a user defined temperature and is operated

isothermally, controlled by its own detector-oven temperature controller. The output

from the detector is usually electronically modified and then acquired by the data

processing computer which processes the data and prints out an appropriate report.

There are many types of detectors which can be coupled with the GC. Different

detectors give different types of selectivity. For example, a non-selective detector

responds to all compounds except the carrier gas. A selective detector responds to a

range of compounds with a common physical or chemical property, whereas a specific

type of detector responds to a single compound. Table 2.1 summarizes all the gas

chromatography detectors used for pesticide residue analysis.

63

The extracts of many commodities include indigenous compounds that can interfere

with chromatography, so most modern methods employ selective detectors. An ideal

selective detector for residue analysis would respond only to the target pesticides, while

other coextracted compounds remain transparent. The most frequently used detectors

include ECD, NPD, FPD and MS. The MS detector has become the standard

confirmatory technique.

Table 2.1: Gas Chromatography Detectors Used for Pesticide Residue Analysis

Detector Selectivity Detectability

Flame Thermionic (FTD)

Electron Capture (ECD)

Flame Photometric (FPD)

Hall Electrolytic

Conductivity (HECD)

Nitrogen Phosphorus

(NPD)

Mass Spectrometry (MS)

Organic P, N

Electronegative containing groups

Organic P, S

Organic Cl, S, N

Organic P, N

Everything except carrier gas

1 x 10-12

g P

1 x 10-10

g N

1 x 10-13

g Cl

1 x 10-12

g P

2 x 10-12

g S

1 x 10-13

g Cl

5 x 10-13

g S

1 x 10-12

g N

< 0.2 x 10-12

g P

< 0.4 x 10-12

g N

1 x 10-11

g

(Herdman et al., 1988)

64

2.3 Gas Chromatography - Electron Capture Detector (GC-ECD)

The electron capture detector (ECD) of Lovelock and Lipsky was the first selective

detector to be developed for gas chromatography in 1957 (Raymond, 1998). The

electron capture detector consists of a low energy -ray source which is used to

produce electrons to be captured by appropriate compounds. Although tritium adsorbed

onto a silver foil has been used as the particle source, it is relatively unstable at high

temperatures, hence the 63

Ni source is the preferred choice.

The detector can be used in two modes, either with a constant potential applied across

the cell (the DC mode) or with a pulsed potential across the cell (the pulsed mode). In

the DC mode, hydrogen or nitrogen can be used as the carrier gas and a small potential

(usually only a few volts) is applied across the cell that is just sufficient to collect all

the electrons available and provide a small standing current. If an electron capturing

molecule containing an halogen atom which has only seven electrons in its outer shell

enters the cell, the electrons are captured by the molecule and the molecules become

charged. The mobility of the captured electrons is much smaller than the free electrons

and the electrode current falls dramatically. The DC mode of detection, however, has

some distinct disadvantages. The most serious objection is that the electron energy

varies with the applied potential. The electron capturing properties of a molecule vary

with the electron energy, so the specific response of the detector will depend on the

applied potential

65

Operating in the pulsed mode, a mixture of 10% methane in argon is employed which

changes the nature of the electron capturing environment. The electrons generated by

the radioactive source rapidly assume only thermal energy and, in the absence of a

collecting potential, exist at the source surface in an annular region about 2 mm deep at

room temperature and about 4 mm deep at 400 ˚C. A short period square wave pulse is

applied to the electrode collecting the electrons and producing a base current. The

standing current, using 10% methane in argon is about 10-8

amp with a noise level of

about 5 x 10-12

amp. The pulse wave form is shown in Figure 2.3.

Figure 2.3: Wave form of Electron Capture Detector Pulses (Raymond, 1998)

In the inactive period of the wave form, electrons having thermal energy only will

attach themselves readily to any electron capturing molecule present in the cell with the

consequent production of negatively charged ions. The negative ions quickly

recombine with the positive ions (produced simultaneously with the electrons by the

particles) and thus become unavailable for collection. Consequently the standing

current measured during the potential pulse will be reduced.

Time

66

The period of the pulsed potential is adjusted such that relatively few of the slow

negatively charged molecules (molecules having captured electrons and not neutralized

by collision with positive ions) have time to reach the anode, but the faster moving

electrons are all collected. During the "off period" the electrons re-establish equilibrium

with the gas. The three operating variables are the pulse duration, pulse frequency and

pulse amplitude. By appropriate adjustment of these parameters the current can be

made to reflect the relative mobilities of the different charged species in the cell and

thus exercise some discrimination between different electron capturing materials. A

diagram of an electron capture detector is shown in Figure 2.4.

Figure 2.4. Electron Capture Detector (Raymond, 1998)

There are a large number of different detector designs but the basic electron capture

detector consists of a small chamber, one or two mL in volume with metal ends

separated by a suitable insulator. The metal ends act as electrodes and conduits for the

carrier gas to enter and leave the cell. The cell contains the radioactive source, usually

electrically connected to the conduit through which the carrier gas enters and to the

negative side of the power supply. A gauze diffuser is connected to the exit of the cell

and to the positive side of the power supply. The electrode current is monitored by a

Radioactive

Source

N2 or H2

Flow Diffuser

Insulator

Insulator

67

suitable amplifier. The electron capture detector is extremely sensitive, is 10 – 1000

times more sensitive than an FID, but has a limited dynamic range and finds its greatest

application in analysis of halogenated compounds. The detection limit for electron

capture detectors is 5 femtograms per second (fg/s) and the detector commonly exhibits

a 10,000 fold linear range. This makes it possible to detect halogenated compounds

such as organochlorine pesticides even at levels of only one part per trillion (ppt).

Aybar et al. (2005) used GC-µECD for detecting pesticide residues in vegetables. This

µECD detector used herein is a modification of the classic ECD and enables good

detection of pesticides from different chemical families, for example pyrethroids,

organochlorine compounds, and some organophosphorus compounds. This kind of

detector is highly sensitive and normally easy to handle if very simple

recommendations are followed, for example using oxygen-free carrier and make-up

gases, working at a temperature that is higher that the highest oven temperature,

premature use and use of regular baking to prevent dirt deposits.

The use of an electron capture detector (ECD) in fast GC has also been evaluated by

Kristenson et al., (2003). The results showed that the ECD make-up flow rate is a key

parameter when coupling narrow-bore columns to an ECD. The make-up flow should

be sufficiently high to eliminate peak tailing caused by the large detection cell volume

(450 µL). In addition, if the make-up flow is very high (400 - 1100 mL/min), the ECD

will exhibit a mass-flow, rather than a concentration-flow sensitive response, when a

slow make-up flow is used. A new ECD with an internal volume of only 150 µL and a

data acquisition rate of 50 Hz has been developed. In an earlier GC x GC study it was

68

tested using a slotted heater, but not with a cryogenic modulator which generates much

narrower peaks (Kristenson et al., 2003).

2.4 Gas Chromatography – Mass Spectrometry (GC-MS)

GC-MS was first used in the late 1950s only 4-5 years after the introduction of GC by

James and Martin (Abian, 1999). GC-MS is a method that combines the features of gas

chromatography and mass spectrometry to identify different substances within a test

sample. Gas chromatography employs the difference in the chemical properties

between different molecules in a mixture to separate the molecules. The molecules take

different amounts of time (retention time) to come out of the gas chromatographic

column, and this allows the mass spectrometer downstream to evaluate the molecules

separately in order to identify them. The mass spectrometer does this by breaking each

molecule into ionized fragments and detecting these fragments using their mass to

charge ratio. Each molecule has a specific fragment spectrum which allows for its

detection.

These two components when used together allow a much finer degree of substance

identification than either unit used separately. It is possible to make an accurate

identification of a particular molecule by gas chromatography or mass spectrometry

alone. The mass spectrometry process normally requires a very pure sample while gas

chromatography can be complicated by different molecular types that both happen to

take about the same amount of time to travel through the unit (have the same retention

time). Sometimes two different molecules can also have a similar pattern of ionized

fragments in a mass spectrometer (mass spectrum). Combining the two processes

69

makes it extremely unlikely that two different molecules will behave in the same way

in both a gas chromatograph and a mass spectrometer. Hence, when a mass spectrum

appears at a characteristic retention time in a GC-MS analysis, it is usually taken as

evidence of the presence of that particular molecule in the sample.

The primary goal of any chemical analysis is to identify the unknown substance. This is

done by comparing the relative concentrations among the atomic masses in the

generated spectrum. Two kinds of analysis are possible, comparative and original.

Comparative analysis essentially compares the given spectrum to a spectrum library to

see if its characteristics are present for a particular compound in the library. Another

analysis measures the peaks in relation to one another, with the tallest peak receiving

100% of the value, and the others receiving proportionate value, with all values above

3% being accounted for. A full spectrum/full scan analysis considers all the peaks

within a spectrum. However, selected ion monitoring (SIM) which looks only at a few

characteristic peaks associated with a candidate substance can also be done. This is

done on the assumption that at a given retention time, a set of ions which is

characteristic of a certain compound can yield a fast and efficient analysis. When the

amount of information collected about the ions in a given gas chromatographic peak is

reduced, the sensitivity of the analysis goes up. Hence, SIM analysis allows for a

smaller quantity of a compound to be detected and measured, but the degree of

certainty about the identity of that compound is reduced.

70

Generally, the most important use of a mass spectrometer in chromatographic detection

is that it can provide unique information about the chemical composition of the analyte.

It can provide a second dimension of information to the chromatographic analysis.

Furthermore, mass spectrometers show high sensitivity for volatile compounds and

because they are mass flow sensitive, the detector response can be used for quantitative

purposes

2.5 Fast Gas Chromatography

The analysis time of a GC separation depends on the sample type, the number of

components to be analysed and the chosen experimental conditions. For very complex

samples containing several dozens of peaks, the minimum obtainable separation time

will be typically in the range of several minutes. For simple mixtures, separations in the

millisecond range can be achieved. The terms “fast GC”, “very fast GC”, and “ultra fast

GC” are commonly used to describe such separation.

Interest in the development and implementation of faster GC methods continues to

increase. There are a number of ways to take advantage of the improved speed of

analysis by faster GC. The first and the most obvious are in the increased laboratory

throughput resulting in reduced cost per analysis and the required time to get results.

One of the most important applications of fast GC is in situations, where the results of

the analysis are required in close proximity to where the answer is needed (e.g., process

control, on-site environmental and industrial hygiene application), hence the shorter

time required to get results is very advantageous (field-portable GC instruments).

Another advantage of fast GC is that a total system can be better described if more

71

analytical data are available. Many more replicate analyses are performed in the same

time that it would take to perform a single conventional GC analysis. This can also be

associated with better analytical precision if more replicates can be done (Matisova and

Domotorova, 2003).

Figure 2.5 gives the simplified basic equation that determines retention time (tR) of a

compound and lists the main factors that control the GC analysis. In the equation, L is

the column length (in cm), u is the average linear carrier gas velocity (cm/s), and k‟ is

the unitless retention (or capacity) factor.

Figure 2.5: The Basic, Simplified Equation that Controls Retention Time (tR) in GC

(Mastovska and Lehotay, 2003).

Higher than optimum carrier gas velocity u > uopt ... H > Hmin

Faster temperature programming k’

df Thinner film of the stationary phase Qs α df

L Shorter capillary column Rs α

Larger diameter capillary column

g (for fixed column length)

dc

Uopt … H = Hmin Higher diffusivity of the solute in the gas phase:

i) Hydrogen as a carrier gas

ii) Low-pressure GC

dc Smaller diameter capillary column (for fixed resolution) Qs α dc3

u

tR = (k’+1) L

u

72

As Figure 2.5 shows, there are so many practical ways to adjust the factors that

decrease time of the GC analysis. One simple approach is to reduce L, which reduces

the number of theoretical plates (N) leading to a decrease in resolution between two

adjacent peaks (Rs) following the general resolution Equation (2.1) below (Guillaume

et al., 1995), where α is the separation factor given by the ratio of the capacity factor

for the two solutes between which resolution is being calculated. Thus, nearly all fast

GC utilize shorter columns (e. g. ≤ 10 m) in combination with other approaches

(Mastovska and Lehotay, 2003).

(2.1)

Another way to reduce tR is to reduce k’, which can be adjusted by altering the column

temperature, selecting a different stationary phase using a wider column diameter (dc),

and/or reducing the stationary phase film thickness (df). The reduction of df also results

directly in a lower sample capacity (Qs). In contrast, a larger Qs (more sensitivity) can

be obtained by increasing dc, which also serves to extend the column lifetime. For

specialized applications, a sequential combination of different GC columns may

provide improved or equivalent selectivity of the separation in a shorter amount of

time. This concept is known as two-dimensional GC (2D-GC), or GC x GC,

comprehensive GC, modulated GC, or pressure tubable GC-GC (depending on its

application and user). Besides, rapid temperature programming is a more practical way

to achieve faster GC separation in most application (Mastovska and Lehotay, 2003).

Rs =

N

4

k‟

1+ k‟ ( ) α - 1

α ( )

73

The last variable in the equation given in Figure 2.5 is u, which is inversely

proportional to tR, must be increased to cause a decrease in the time of analysis. The

most direct way to increase u is to use higher carrier gas flow. In this case, the

separation efficiency is reduced by an amount which the theoretical plate height (H)

will exceed the minimum H (Hmin), which occurs at the optimum u (uopt). Another way

to speed up the GC analysis is to effectively increase the value of uopt. This can either

be accomplished by using a shorter, narrower capillary column (decrease L and dc) to

achieve the better separation efficiency in a shorter time or increasing the diffusion

coefficient of the solute in the gas phase by using H2 rather than He as a carrier gas and

/or decreasing the pressure in the column (low pressure GC). H2 is not a common

carrier gas due to its hazardous nature, instrumental design consideration and surface

effects. Furthermore, H2 is an inflammable gas, thus it is not generally desirable for use

unless necessary, especially since He can meet the carrier gas needs for most GC

applications (Mastovska and Lehotay, 2003).

High-speed GC and miniaturized GC share some characteristics and have in common

numerous relevant features. An important distinction is that high-speed GC and

miniaturized GC embodies two distinct and sometimes exclusive goals. While many

miniaturized instruments provide improvements in separation speed, high-speed

separations can be achieved without miniaturization. An effective method for

comparing the speed of various multidimensional techniques (e.g., GC x GC or GC-

MS) with each other or with single-dimensional techniques is presented by Dewulf et

al. (2002).

74

ThermoFinnigan introduced the Ultrafast Module GC as a modification to their line of

benchtop instruments (Bicchi et al., 2004). In this, direct heating is made to a small-

bore open tubular column. A comparable approach is used by RVM Scientific, Inc. for

retrofits to Agilent brand instruments. All these systems provide high-speed separations

with conventional gas chromatographs. Some of the retrofit solutions can be

compromised by incompatible injector and detector systems. Some older units are

equipped with detector systems capable collecting data at rates of less than 50 points/s,

which in practice limits the minimum peak widths to 300 ms or more.

In 1991, comprehensive two-dimensional gas chromatography (GC x GC), began to

attract attention for many analytical chemists (Adahchour et al., 2006). This method

uses a thermal modulator to the sample effluent from a conventional GC separation.

The thermal modulator is kept at a low temperature so that material eluting from the

primary column is focused in the modulator. The modulator, a short length of column,

is then rapidly temperature programmed to produce an ultrafast micro chromatogram of

the material collected during the accumulation period. Because all of the primary

column effluent passes through the thermal modulator, the result is a two-dimensional

chromatogram with one long dimension. One striking feature of the comprehensive 2D

approach is that families of compounds (e.g. homologous series) appear as distinct

bands in the two-dimensional plane. Among the fast GC techniques, this approach is

generating the most attention among researchers. However, fast and ultrafast

comprehensive 2D separations have been made using a sample loop and a high-speed

valve to perform the transfer from the primary to the secondary column (Bueno and

Seelay, 2004).

75

One of the obstacles in performing comprehensive two-dimensional gas

chromatographic separations is being able to predict the average linear velocities of the

carrier gas in the two columns, especially when they have different diameters. A flow

model which was designed by Harynuk and Gorecki (2005) can calculate the flow rates

in the columns and predicts the appropriate delay loop dimensions for a given set-up.

Additionally, the model determines the pressure ramp that needs to be used in order to

maintain a constant average linear velocity within the modulator loop throughout the

course of the separation.

Variations in multidimensional separations have occurred including the addition of a

third dimension as illustrated with a time-of-flight (TOF) MS detector to produce three

dimensions of primary elution time, secondary elution time, and a mass spectrum

(Dalluge et al., 2002; Welthagen et al., 2003). The reliable identification of pesticides

in spiked and non-spiked vegetable sample extracts by GC x GC-TOF MS has been

investigated (Dalluge et al., 2002). Further studies concerning the improved

separation/identification of pesticides in fruit products from matrix co-extracts have

also been reported (Zrostlikova et al., 2003). In this study, twenty pesticides with a

broad range of physico-chemical properties were analyzed in apples and peach samples.

It has been demonstrated that the application of comprehensive two-dimensional gas

chromatography brings distinct advantages such as enhanced separation of target

pesticides from matrix co-extracts as well as their improved detectability. The limits of

detection of the pesticides ranged from 0.2 to 30 pg, which was 1.5 – 50 fold better

than one-dimensional GC-TOF MS analysis under the same conditions.

76

Another significant development in the evolution of high-speed GC has been the

application of chemometric techniques to extract information from high-speed GC data

(Hope et al., 2003; Barriada et al., 2007). In high-speed GC analysis, sample

throughput is a key goal. However, some chemical information may be obscured as

partially overlapping peaks when sample throughput is maximized. Sometimes, perhaps

often, the obscured information can be recovered by mathematical techniques rather

than the traditional approach of increasing resolution and thereby slowing down the

analytical process.

2.6 Fast Gas Chromatography-Mass Spectrometry (Fast GC-MS)

In theory and practice, GC-MS has the ability to separate, detect, and identify a wide

range of volatile and semi-volatile chemicals at trace levels in complex sample

matrices. Fast GC-MS has the potential to be a powerful tool in routine analytical

laboratories by increasing sample throughput and improving laboratory efficiency.

There are five current approaches to fast GC-MS, all of which typically utilize short

capillary columns: (a) microbore GC-MS; (b) fast temperature programming GC-MS;

(c) low-pressure GC-MS; (d) supersonic molecular beam GC-MS and; (e) pressure-

tunable GC x GC-MS.

77

2.6.1 Microbore GC-MS

The advantage of the microbore method is that separation efficiency need not be

compromised for speed of analysis. This inherently means that the peak widths will be

narrower in microbore GC than in the approaches that sacrifice GC separation

efficiency. The narrower peaks will require the instrument performance tolerances to be

more rigid, which generally leads to greater costs and complexity and less ruggedness

and reliability. Thus, microbore methods necessitate that the instruments must be able

to accommodate higher inlet pressures, narrower injection band widths, smaller dead

volumes, faster MS spectral acquisition rates, and greater data processing power. TOF

is a detector of choice for microbore applications due to the faster spectral acquisition

rate to still achieve full scan information. In terms of sensitivity, proponents of

microbore methods maintain that the greater S/N ratio achieved by having sharper

analyte peaks will still give low LOD despite less sample being introduced into the

column (Mastovska and Lehotay, 2003).

78

2.6.2 Fast Temperature Programming GC-MS

Increasing the temperature programming rate is a simple way to increase the speed of

the GC separation without the need for special instrumentation. The study of Fialkov et

al. (2003) shows that faster temperature programming rates lead to higher compound

elution temperature, decreased separation efficiency, greater thermal breakdown of

thermally-labile analytes, and potentially longer oven cool-down times. However, it

should be noted that the initial oven temperature affects the cool-down time more than

the final temperature because it usually takes longer for an oven to cool from 100 to 50

oC than 300 to 100

oC. Commercial systems have recently become available in which a

fused silica capillary column is inserted into a resistively heated metal tube or enclosed

in thermal wrapping tape, achieving temperature programming rates up to 20 oC/s. A

practical drawback of this approach is the difficulty in accessing the column to perform

routine maintenance. When the same temperature programming rate is applied in the

oven-based GC, the resistive heating technique can provides two prominent

advantages: (a) very rapid cool-down rate which results in higher sample throughput;

and (b) very good tR repeatability (Mastovska et al., 2001).

2.6.3 Low-pressure GC-MS (LP-GC-MS)

In the 1980s, a series of theoretical studies discussing advantages of low pressures for

improving the speed of analysis was published (Mastovska and Lehotay, 2003). Low-

pressure gas chromatography (LP-GC) is a fast chromatography technique that involves

the use of a relatively short (10 m) large-diameter column connected with a restriction

capillary (0.1 – 0.25 mm of appropriate length) at the inlet end. In contrast to fast

microbore GC, the use of megabore columns in LP-GC provides increased sample

79

capacity (Qs) which exceeds the capacity of conventional GC-MS. Speed of analysis

and increased Qs are the two main advantages of LP-GC-MS, but other improved

features include (Mastovska et al., 2001a): (a) no alterations to existing instruments are

needed; (b) peak widths are only slightly less than in traditional GC methods, thus MS

spectral acquisition rate does not have to be much faster than that commonly used in

GC-MS; (c) peak heights are somewhat increased which can lead to higher S/N ratios

and lower detection limits; (d) reduced thermal degradation of thermally labile

compounds; and (e) improved peak shape of relatively polar analytes (reduced tailing).

LP-GC has already proved its applicability to pesticide residue analysis. LP-GC in

conjunction with ion trap tandem mass spectrometry (MS-MS) was evaluated and

optimized then successfully applied to the analysis of pesticides in vegetables (Arrebola

et al., 2003). LP-GC with single quadruple MS operated in selected ion monitoring

(SIM) mode was optimized and evaluated for the analysis of 20 pesticides in carrots

(Mastovska et al., 2001a) and later for 57 pesticides in several food matrices

(Mastovska et al., 2004). Walorczyk et al. (2006) determined 78 pesticide residues in

vegetables using LP-MS with a triple quadrupole mass spectrometer. Other examples

are the determination of priority pesticides in baby foods (Leandro et al., 2005).

80

2.6.4 Supersonic Molecular Beam GC-MS (GC-SMB-MS)

GC-SMB-MS is a very promising technique and instrument which can vastly extend

the acceptable flow-rate range because SMB-MS requires high gas flow-rate at the

SMB interface (e.g. 130 mL/min He). In GC-SMB-MS, a nozzle of 100 µm is placed

between the GC outlet (1 atm) and the MS (vacuum). As organic molecules pass

through the small opening, they form a supersonic molecular beam (SMB) and are

supercooled in the process. The low thermal energy creates unique mass spectral

properties that have many advantages over conventional GC-MS, which include: (a) the

selectivity of the MS detection in EI is increased because the enhancement of the

molecular ion occurs for most molecules at the low temperatures of SMB, thus losses

of selectivity in the GC separation can be compensated by increased selectivity in the

MS detection; (b) the use of very high gas flow rates increases the speed and also

enables the GC analysis of both thermally labile and low-volatility compounds, thereby

extending the scope of the GC-SMB-MS approach to many analytes currently done by

liquid chromatography (LC); (c) the SMB-MS approach allows more versatility in

selection of injection techniques and column dimensions for fast GC-MS; (d) reduced

column bleed and lower matrix interference, due to lower elution temperatures and

enhanced molecular ions; (e) better peak shapes are obtained because tailing effects in

the MS ion source are eliminated; and (f) no self-induced chemical ionization takes

place, thus the isotopomer pattern can be deduced accurately to give chemical formulas

associated with spectral peaks (assuming that the S/N ratios are sufficient). All these

features and others have been extensively described in a series of publications about

GC-SMB-MS (Kochman et al., 2002; Fialkov et al., 2006; Kochman et al., 2006).

81

2.6.5 Pressure-tunable GC x GC-MS

For complex mixtures, fast GC-MS analysis performed with short columns may

become difficult because of the reduced selectivity. A possible solution to this problem

is the use of two columns with different types of stationary phases combined in series

(GC x GC). Pressure-tunable (also known as stop-flow) GC x GC is a unique technique

in which column pressures are adjusted at the column junction. An increase in the

junction point pressure leads to a lower pressure drop in the first column (thus reduced

mobile phase gas flow rate, u and a slower rate of compound elution), and a greater

head pressure on the second column (thus increased u). This increases the influence of

the stationary phase effects of the first column and decreases the influence of the

second column. Therefore, pressure-tunable GC x GC can alter retention patterns,

which can be used to improve the quality of the separation with respect to the

utilization of time. Pressure-tunable GC x GC offers advantages in flexibility and

performance over conventional GC x GC. It can provide greater resolution than

conventional GC x GC in the first dimension, while maintaining a comparable

secondary separation in a similar amount of time and significantly reducing the analysis

time required for a conventional GC x GC separation that would allow adequate

sampling of early-eluting peaks. Alternatively, it allows the use of longer secondary

columns, resulting in more powerful secondary separations than those possible with

conventional GC x GC, without sacrificing the resolution in the first dimension.

Harynuk and Gorecki (2006) had compared the performance of comprehensive two-

dimensional gas chromatography in conventional GC x GC-MS and Pressure-tunable

GC x GC-MS. Pressure-tunable GC x GC-MS offers clear advantages in flexibility and

can provide greater resolution than conventional GC x GC. However, pressure-tunable

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GC x GC is not easily available commercially because of the many optimization

parameters in complicated separations would significantly add to time and effort

needed for method development in analysis (Mastovska and Lehotay, 2003).

2.7 Analysis of Pesticide Formulations

Different organizations, such as the Collaborative International Pesticide Analytical

Communities Council (CIPAC) and the Associations of Analytical Communities

International (AOAC Int.) have developed official methods for the determination of

pesticides in commercial formulations. Methods suggested by the CIPAC for quality

control of pesticide formulations are, in general, based on the use of gas

chromatography (GC) or high performance liquid chromatography (HPLC). However,

it is evident that agrochemical products are much simpler matrices than treated crops

and the level of concentration in formulations is several orders of magnitude higher

than that found in crops. Hence, there is an on going interest in the development of fast,

simple procedures for pesticide analysis at those higher concentration levels in samples

containing only a few compounds.

2.7.1 Chromatographic Determination of Pesticide Formulations

In practice, an analytical method presented for a collaborative trial through AOAC or

CIPAC is a method developed by a manufacturing company. Hence, these methods are

valid only for particular formulations prepared by specific manufacturers. These

methods are optimized for those specific products and conditions. Each

chromatographic method has its own stationary phase, internal standard and mobile

phase. Due to the great variety of active ingredients and formulations of pesticides to be

83

monitored, the need for new methods with higher sample output and lower costs of

analysis has become imperative.

Lin and Hee (1998) established a method for the direct determination of the inert

components, manufacturing by-products of the pesticide, and the active ingredient in

two malathion formulations using capillary gas chromatography-mass spectrometry

(GC-MS) with the internal standard technique. Karasali et al. (2005) developed and

validated of a capillary gas chromatography method with a Flame Ionization Detector

(FID) for the quantitative determination of alachlor in its commercially available

emulsifiable formulations. Two columns of different polarities were used: low polar

CP-Sil 8Cb and a medium polar DB-1701. The relative standard deviation of the peak

areas was 0.7% for both columns.

Karasali et al. (2006) developed a multi-pesticide method and enlisted a single

laboratory for the quality control of commercial pesticides containing alachlor,

chlorpyrifos methyl, fenthion and trifluralin as active ingredients by using capillary gas

chromatography system with flame ionization detection (FID) and programmable

temperature vaporizing split injector. The performance characteristics (specificity,

linearity, precision and repeatability) of the method fulfilled international acceptability

criteria.

84

Wang et al. (2003) developed an isocratic reversed-phase high-performance liquid

chromatography (RP-HPLC) method for the simultaneous determination of five active

ingredients, (S)-methoprene, N-octylbicycloheptene dicarboximide (mgk264),

piperonyl butoxide, sumithrin and permethrin in a new complex pesticide formulation.

The method development emphasizes the usefulness of including column selection and

mobile phase composition in optimizing a complex separation. By selecting an RP-C8

column in combination with a ternary mobile phase, the RP-HPLC separation can

reduce runtime, improve resolution, increase peak height, and eliminate the need for

gradient separation. All the five active ingredients in the formulation could be separated

and determined in less than 30 min.

2.7.2 Fourier Transform Infrared (FTIR) Determination of Pesticide

Formulations

The most commonly-used vibrational technique is IR spectroscopy in the mid-IR

region of the electromagnetic radiation. Table 2.2 provides an overview of pesticides

determination using FTIR spectrometry in both stopped-flow and continuous data-

acquisition modes published in recent years.

The most common practice in direct analysis of solids by IR spectrometry is the use of

disks prepared from the samples embedded in a KBr pellet. This technique avoids the

use of any kind of solvent and does not require the analyte to be soluble. However, it

creates problems for the determination of the band pass and generally requires the use

of an internal standard. FTIR spectrometry has been used for the direct determination of

a dithiocarbamate pesticide on solid samples (Armenta et al., 2005b), mancozeb, which

85

is insoluble in common organic and inorganic solvents, has been determined by

absorbance measurements in KBr pellets.

Stopped-flow transmission FTIR measurements based on the peak-height or area data

of an absorption band of the active ingredient, after extraction in an appropriate solvent,

provides a simple, fast methodology that has been successfully applied in the

determination of different pesticide families in commercially available formulations.

The main drawback of this technique is related to the properties of the solvent.

Chlorinated solvents such as CHCl3, CH2Cl2, and CCl4 are the most commonly used are

a problem because these halogenated hydrocarbons are ozone depleting substances.

Flow-injection analysis (FIA) coupled to FTIR spectrometry provides ease of

operation, real time detection, and is a low-maintenance analytical technique. Cassella

et al. (2001) developed a more environment-friendly procedure for the determination of

ziram, using the vapor phase FTIR spectrometric technique.

2.7.3 FT-Raman Determination of Pesticide Formulations

The main advantage that FT-Raman presents over FTIR spectrometry is the very weak

Raman spectra of glass, water and plastic packaging, which makes possible direct

analysis of samples inside a glass bottle or a plastic bag without opening the package

and thus minimizing the risk of contamination. Aqueous samples are readily analyzed

without the need to use organic solvents, such as dichloromethane or chloroform,

generally employed in mid-IR spectrometry.

86

Skoulika et al. (2000) employed an FT-Raman spectroscopy based on band intensity

and peak area measurements for the quantitative determination of diazinon in pesticide

formulations. Bands at 554, 604, 631, 1562 and 2971 cm-1

were used for calibration.

Spectra were acquired by averaging 100 scans at a resolution of 4 cm-1

. All calibration

curves were linear. The precision ranged between 0.1 - 7.8% RSD and the solvent used

was xylene.

Quintas et al. (2004a) developed a fast, environment-friendly method for the

determination of malathion in emulsifiable pesticide-concentrate formulation using

standard glass vials. The method was based on the measurement of peak-height values

at 1737 cm-1

and the corrected Raman shift using a baseline defined at 1900 cm-1

.

Samples were diluted with CHCl3 and the FT-Raman spectra collected in the back-

scattering mode at a nominal resolution of 4 cm-1

, accumulating 50 scans per spectrum

and using a laser power of 1250 mW. The procedure developed provided an LOD of

1.8% w/w in the original sample. This procedure reduced dramatically the generation of

chlorinated solvent wastes and also avoided operator contact with toxic solvents.

An FT-Raman methodology for the quantitative determination of mepiquat chloride in

agrochemical formulation has been published (Quintas et al., 2004c). The spectra were

collected from samples confined in standard chromatographic screw-cap glass vials, at

a nominal resolution of 4 cm-1

, accumulating 25 scans per spectrum and using a laser

power of 100 mW and using aqueous solutions of standards.

87

Cyromazine has been determined in commercial pesticide formulations directly on the

powdered solid products (Armenta et al., 2004) using standard chromatographic glass

vials as sample cells and measuring the Raman intensity between 633 and 623 cm-1

. An

external calibration curve was achieved with a solid cyromazine standard diluted with

sodium chloride at different concentration levels. Repeatability of 0.4% as RSD and an

LOD of 0.8% (w/w) were obtained.

2.7.4 Near Infrared (NIR) Determination of Pesticide Formulations

Near infrared (NIR) spectroscopy provides high features to be used in routine control

analyses because of its ability to provide fast and accurate results, no complex sample

pre-treatment required, the low cost of analysis and the capability to perform

simultaneous determination of several parameters in a same sample. However, the main

drawback is that the overlapping bands of the NIR spectra are influenced by a number

of chemical, physical and structural variables and the use of chemometrics to extract

relevant information is necessary.

Moros et al. (2005) developed a near infrared (NIR)-based methodology for diuran

determination in pesticide formulations. The method is based on the pesticide

extraction with acetonitrile and subsequent transmittance measurement determination

by using the peak area between 2021 and 2047 nm, corrected with a baseline

established at 2071 nm. The repeatability, as relative standard deviation of five

independent analysis was 0.03% and the limit of detection was 0.013 mg/g. The reagent

consumption was reduced to 1 mL of acetonitrile. The sample throughput obtained was

120 samples per hour which is 10 times higher than that obtained by LC (12 samples

per hour).

88

Armenta et al. (2007a) employed a near infrared-based methodology for pesticide

determination in commercially available formulations. This methodology is based on

the direct measurement of the diffuse reflectance spectra of solid samples and a

multivariate calibration model (partial least squares, PLS) to determine the active

principle concentration in commercial formulations. The PLS calibration set was

developed based on using spiked samples by mixing different amounts of pesticide

standards and powdered samples (buprofezin, diuran and daminozide). The root mean

square value of errors of prediction found was 1.1, 1.7 and 0.7% (w/w) for buprofezin,

diuran and daminozide determination, respectively. The developed PLS-NIR procedure

allows the determination of 120 samples per hour, which do not require any sample

pre-treatment and avoids waste generation.

2.7.5 Spectrophotometric Determination of Pesticide Formulations

The spectrophotometric technique is based on UV/Vis detection and different types of

chromogenic reagents that form colored complexes in order to achieve an appropriate

selectivity and sensitivity of the spectrophotometric measurements. This method is still

one of the most commonly used techniques for the determination of pesticides because

it is inexpensive and easy to use.

Kumar et al. (2007) developed a facile, selective and sensitive spectrophotometric

method for the determination of bendiocarb in its insecticidal formulation. The method

was based on alkaline hydrolysis of the bendiocarb pesticide and the resultant

hydrolysis product of bendiocarb was reacted with 2,6-dibromo-4-methylaniline to give

a yellow color product with λmax of 474 nm or coupling with 2,4,6-tribromoaniline to

89

from an orange red colored product which has a λmax of 465nm. The recoveries found

were within the range 97.1 - 99.1% with the RSD value ranged from 0.87% to 2.57%.

Subrahmanyam et al. (2007) employed a spectrophotometric method for the

determination of fenitrothion in its formulations with a newly synthesized reagent. The

method was based on the alkaline hydrolysis of fenitrothion and the resultant

hydrolyzed product of fenitrothion was coupled by diazotizing with 4,4-methylene bis-

(p-amino-2-carboxybenzanilide) in a basic medium to give a yellow colored product

having λmax at 482 nm. The formation of colored derivatives with the coupling reagent

is instantaneous and stable for 30 hours. The results obtained were reproducible with

low relative standard deviations ranged from 0.267% and the recoveries were closed to

the manufacturer‟s specifications.

90

Table 2.2: Recent Studies on Pesticide Determinations using FTIR Spectrometry

Pesticide Measurement

mode

Wave number

range (cm-1

)

Baseline

(cm-1

)

LOD Recovery

(%)

RSD

(%)

Sample

throughput (h-1

)

Solvent Waste

generation

References

Buprofezin

Stopped-flow

FTIR

FIA-FTIR

1466-1342

2052

20 µg/g

100.5

0.1

0.8

4

6

CHCl3

25 mL

3 mL

Armenta et

al., 2002

Chlorpyrifos

Stopped-flow

FTIR

1549

1650

0.4 µg/g

0.2

30

CHCl3

2.5 mL

Armenta et

al., 2005a

Cypermethrin

Stopped-flow

FTIR

1747-1737

2000

0.7 µg/g

0.7

30

CHCl3

2.5 mL

Armenta et

al., 2005a

Cypermethrin

TLC-FTIP

1749

1770-1720

90-97

CHCl3

Sharma et

al., 1997

Cyromazine

Stopped-flow

FTIR

1622

1900

12 µg/g

101 ± 1

0.2

60

CH3OH

4 mL

Armenta et

al., 2004

Deltamethrin

TLC-FTIR

1743

1770-1720

90-97

CHCl3

Sharma et

al., 1997

Fluometuron

Stopped-flow

FTIR

1342-1321

1352-1294

6.5 µg/g

99

1.6

CHCl3

7 mL

Quintas et

al., 2003a

91

Table 2.2: Recent Studies on Pesticide Determinations using FTIR Spectrometry (continued)

Pesticide Measurement

mode

Wave number

range (cm-1

)

Baseline

(cm-1

)

LOD Recovery

(%)

RSD

(%)

Sample

throughput (h-1

)

Solvent Waste

generation

References

Folpet

Stopped-flow

FTIR

FIA-FTIR

1798

1810

17 µg/g

17 µg/g

100 ± 1

1.1

2.0

60

CHCl3

CHCl3

2.7 mL

Quintas et

al., 2003b

Malathion FIA-FTIR 1027-1017 1087-993 12 µg/mL 0.4 CHCl3 2 mL Quintas et

al., 2004b

Mancozeb

KBr disks

1525

1289

1579-1269

1556-1430

1556-1430

1272

0.6

1.7

1.3

Armenta et

al., 2005b

Metalaxyl

Stopped-flow

FTIR

FIA-FTIR

1677-1667

1692-1628

16 µg/g

16 µg/g

100 ± 1

1.9

2.6

60

CHCl3

CHCl3

2.7 mL

Quintas et

al., 2003b

Ziram

Vapor phase-

FTIR

1600-1450

1600-1450

55 µg

103 ± 2

6

17

Cassella et

al., 2001

92

CHAPTER 3

REVIEW OF PESTICIDE RESIDUE ANALYSIS IN FRUITS AND

VEGETABLES

3.1 Pesticide Residues and Legislation

The use of pesticides provides unquestionable benefits in increasing agricultural

production. However, it has the drawback of pesticide residues which remain on fruits

and vegetables, constituting a potential risk to consumers. This necessitate on one hand,

the establishment of legal directives to control their levels through the Maximum

Residue Levels (MRLs), and on the other, a continuous look for pesticides which are

less persistent and toxic to humans. This has increased tremendously the number of

pesticides registered and recommended, and the analytical difficulties for their control

(Torres et al., 1996).

Analytical methods are needed to screen, quantify, and confirm the pesticide residues in

fruits and vegetables for both research and regulatory purpose. Multiresidue methods

(MRMs) and single residue methods (SRMs) generally consist of the same basic steps,

but MRMs are preferred to the latter for the analysis of pesticides, because MRMs

provide the capability of determining different pesticide residues in a single analysis

run. A review of the existing methods used to extract, isolate, and quantify pesticide

residues in fruits and vegetables by monitoring agencies, demonstrates that they are

based on classical MRMs, some developed over 30 years ago (Torres et al., 1996).

Among the more widely used MRMs are those of Mills (Herdman et al., 1988); Mills,

93

Onley, and Gaither (Herdman et al., 1988); Storherr (Herdman et al., 1988); Luke

(Herdman et al., 1988); and Krause (Herdman et al., 1988).

The method adopted by the Association of Official Analytical Chemists (AOAC) is the

internationally recognized procedure for MRM. It allows the determination of many

pesticide residues in fruits and vegetables, and involves an aqueous acetone extraction

but with laborious cleanup. Such methods, generally, involve an extraction step with a

water miscible solvent, followed by a cleanup step, with an organic solvent of limited

water capacity, to achieve the removal of interferences present in the sample extract

and solid phase cleanup with silica or florisil. Finally the analyte determination is

performed by gas chromatography (GC) or high-performance liquid chromatography

(HPLC) with selective detectors (Torres et al., 1996). However, these methods are still

in use despite their disadvantages, such as (a) their inefficiency as screening methods;

the methods are too complex, and they do not allow the generation of relevant data in a

short time to prevent contaminated foods from entering the marketplace, because these

procedures are very time-consuming and labour-intensive; (b) the amount of chemicals

and toxic solvents that are used: it is usually by a factor of 108 – 10

10 greater than that

of the pesticide residues to be determined; (c) the newly developed groups of pesticides

are more polar and thermally-labile and should be incorporated into the existing

MRMs.

94

The permissible levels of pesticide residues in food are controlled by the MRLs, which

are established by each country and may cause conflicts because the maximum residue

levels acceptable in a particular one country may be unacceptable in another. To

overcome this problem, there is a need to harmonize the different MRLs, adopted by

different countries and this has been addressed by two international organizations: the

European Union (EU) at European level and the Codex Alimentarius Commission of

the Food and Agriculture Organization (FAO) and the World Health Organization

(WHO) (Torres et al., 1996).

3.2 Analytical Techniques for Pesticide Residues in Fruits and Vegetables

Pesticides may occur in fruits and vegetables at trace concentration levels. Trace levels

are generally at concentrations of parts per million, that is, one microgram of pesticide

per gram (µg/g) of sample or less. Measuring such small amounts of pesticides in the

presence of enormous amounts of other substances that occur naturally in food is a

challenge because those substances may interfere with the measurement accuracy. A

variety of analytical methods are currently used to detect pesticide residues, and there

are certain basic steps in the application which include the following:

3.2.1 Sample Preparation

First, the fruit and vegetable samples are cut up and blended. Precautions are taken to

avoid the loss of volatile pesticide residues and to prevent contamination of the sample

with other pesticides or interfering compounds. Cutting and grinding followed by

blending and mixing are steps designed to produce a homogeneous composite sample

from which subsamples can be taken and to disrupt the gross structural components of

95

the food to facilitate extracting pesticides from the sample. Performing this step can be

time-consuming and labour-intensive.

3.2.2 Extraction

Extraction is performed with a solvent to remove the pesticide residue of interest from

other components of the sample. In most analytical laboratories, a solvent such as

acetone or acetonitrile is used to extract pesticides from the sample. The solvent is

blended with the food, and smaller amounts can be further homogenized using an

ultrasonicator. Salts, such as sodium chloride or sodium sulfate, can be added to absorb

water. Additional water can be added, if desired, so that the resulting aqueous solution

can be partitioned with a water–immiscible solvent in a subsequent cleanup step.

Extraction times vary from a few minutes to several hours, depending on the pesticide

to be analyzed and the sample type. Problems that occur during the extraction process

include incomplete recovery and emulsion formation. Incomplete recovery generally

can be remedied by selecting a more efficient solvent. Emulsions, the production of a

third phase or solvent layer, which will interfere with the partitioning process, can

usually be broken down by the addition of a salt to the sample / solvent combination.

Residual amounts of the extracting solvent or partitioning solvent should not be

allowed to reach the detector if it is an element-specific detector and if the solvent

contains that specific element. These problems can be solved by proper solvent

selection or by the removal of the interfering solvent during the cleanup process.

96

3.2.3 Sample Cleanup

Sample cleanup or the isolation of the analyte removes the constituents that interfere

with the analysis of the pesticide residue of interest. Cleanup is usually achieved by a

combination of partitioning and purification, and the latter is usually accomplished by

preparative chromatography. The degree of cleanup required is determined by the

efficiency with which the partitioning solvent can remove pesticides from the sample

extract.

The preparative chromatography typically used for purification can be classified as

follows: (a) adsorptive, or (b) gel permeation (or size exclusion) type. Adsorption

chromatography is based on the interaction between a chemical dissolved in a solvent

and an adsorptive surface. Particles of the chromatographic material are placed in large

glass columns (30 cm x 2 cm). The sample solution is deposited on the top of the

column and eluted with various types of organic solvents. Separation occurs when the

pesticide elutes in fractions different from the sample coextractives. Table 3.1

summarizes the materials that have been used with these two types of preparative

chromatographic modes, giving some of their distinguishing features.

97

Table 3.1: Materials Used for the Preparative Chromatography of Pesticides in Food

Materials Functions

Florisil

1. A diatomaceous earth adsorbent; retains some lipids

preferentially; particularly suited for cleanup of fatty foods.

2. Good for cleanup of non-polar pesticides, such as the

chlorinated hydrocarbons; produces very clean eluants,

removes most interferences when eluted with non-polar

solvents.

3. Difficult to use for fruits and vegetables when moderately polar

to polar pesticides are present.

4. Subject to variations from batch to batch

5. Sometimes oxidizes organophosphates with thio-ether linkages;

adsorbs some oxons irreversibly.

6. Most widely used material for sample cleanup.

Alumina 1. Basic alumina can be substituted for florisil for the cleanup of

fatty foods.

2. Does not vary from batch to batch as much as florisil.

3. Will decompose some organophosphates.

4. Not effective for separation of some plant materials from

pesticides.

Silica gel 1. Particularly useful for isolation of certain polar pesticides

without losses.

2. Not effective for separation of some plant coextractives from

certain pesticides.

3. Will separate some organochlorine pesticides from fatty

materials well enough to permit thin layer chromatography.

Carbon

Black

1. Unlike other absorbents, carbon has different elution

characteristics due to its lipophilic nature; absorbs

preferentially non-polar and high molecular weight pesticides.

2. Effecting for removal of chlorophyll well from vegetables.

3. Strongly affected by pretreatment.

4. Difficult to maintain constant flow rates in columns.

(Herdman et al., 1988)

98

Gel permeation (or size exclusion) chromatography (GPC) is a technique that separates

compounds from each other on the basis of differences in molecular size. Preparative

columns similar to those used in adsorption chromatography are used, and samples are

placed at the top of the column and then eluted with a solvent; larger molecules elute

before smaller ones in an ordered fashion. The ordering by size in gel permeation is a

result of small cavities in the particles placed in the column that retard the movement of

smaller molecules through the column. Such size separation does occur on adsorption

columns.

The advantages of gel permeation over adsorption chromatography are that no loss of

pesticide occurs on the column, either by irreversible adsorption or by chemical

reactions. A disadvantage is that a medium-pressure piston type pump is required to

deliver solvent to the column, making it necessary to have a sample injection valve.

The required equipment is more expensive than that used in adsorption chromatography

and an automated equipment is available.

The cleanup step is often a limitation in pesticide residue methods because it is

generally time-consuming and restricts the number of pesticides that are recovered in

some cases, as a result of losses in chromatography, partitioning, and other cleanup

steps.

99

3.3 Sample Extraction Techniques

Among the published methods for pesticide residue analysis, the sample size varies

from a few grams to greater than 100 grams and the volume of solvent for extraction

ranges from 40 mL to several hundred mL. In addition, the sample particle size can be

an important parameter for reproducible results as the extent to which the matrix is

broken up can influence the extraction rates. The analyte is desorbed from the matrix

and is dissolved in a solvent. Extraction of the analyte is therefore influenced by

solubility, penetration of the sample by the solvent and matrix effects. Solid samples

are usually prepared by grinding directly or after drying, followed by solid sample

extraction techniques (Section 3.3.1). Following the extraction procedure, the analytes

of interest are obtained in an organic or aqueous solution, which are then further

concentrated with additional cleanup. These extraction solutions can then be treated as

a liquid sample. Liquid sample can be handled directly by liquid sample extraction

techniques (Section 3.3.2).

Simplification of analytical procedure can reduce the analysis time and also the solvent

consumption at the same time. There are two types of practice for handling the sample

extracts. The first method relies on the removal of the analytes from the sample matrix

as thoroughly as possible by repeatedly extracting the samples and then washing the

remainder with large amounts of solvents (over 100 mL). All these extracts and the

wash are combined prior to subsequent treatment. This is the multiple extraction

technique. The other practice is to extract the sample with one large volume of solvent

and taking an aliquot of the extracts for subsequent treatment. This is the single

extraction technique.

100

To overcome the general drawbacks of the classical methods, significant development

has occurred in the extraction and determination of pesticide residue analysis in fruits

and vegetables. The main focus is on simplification, miniaturization, and improvement

of sample extraction and cleanup methods with universal microextraction procedures

such as supercritical fluid extraction (SFE), pressurized fluid extraction (PFE),

microwave-assisted extraction (MAE), matrix solid-phase dispersion (MSPD), solid-

phase extraction (SPE) or solid-phase cleanup (SPC) on cartridges to replace liquid-

liquid extraction (LLE), enzyme-linked immunosorbent assay (ELISA), solid-phase

microextraction (SPME), single-drop microextraction (SDME), liquid phase

microextraction (LPME) and stir bar sorptive extraction (SBSE).

3.3.1 Solid Sample Extraction Techniques

Sample pre-treatment is often required for solid samples, including sieving, grinding

and drying. Dispersion can be used to avoid the aggregation of sample particles and

ensure good solvent penetration. Drying is particularly important when using non-polar

solvents, as moisture can reduce the extraction efficiency and desiccants, such as

sodium sulphate, diatomaceous earth or cellulose can help overcome this problem. A

number of methods have been developed for extraction of samples that can be

examined or analyzed as powders or after absorption on a solid porous matrix.

101

3.3.1.1 Supercritical Fluid Extraction (SFE)

A general trend in the isolation of pesticide residues is to decrease the consumption of

expensive and toxic organic solvents and to increase the availability of a broad range of

analytes and matrices. A possible solution is to use supercritical fluid extraction (SFE).

SFE uses liquid such as compressed carbon dioxide (CO2) as an extracting phase that is

capable of removing less volatile compounds at ambient temperature. Supercritical

fluids possess both gas like mass transfer and liquid like solvating characteristics.

SFE utilizes commercially available equipment where the fluid is pumped, at a pressure

above its critical point (7.38 mPa & 31.1 oC), with the sample placed in an inert

extraction cell. The temperature of the cell is increased to overcome the critical point of

the fluid. After depressurization, analytes are collected in a small volume of organic

solvent or on a solid-phase filled cartridge (solid adsorbent trap). Extraction can be

performed in the static, dynamic or recirculating mode: in the static extraction mode,

the cell containing the sample is filled with the supercritical fluid, pressurized and

allowed to equilibrate; using the dynamic mode, the supercritical fluid is passed

through the extraction cell continuously; finally in the recirculating mode the same

fluid is repeatedly pumped through the sample and, after the required number of cycles,

it is pumped out to the collection system (Figure 3.1).

102

Figure 3.1: Schematic Diagram of a SFE System (Fidalgo-Used et al., 2007)

King et al. (1993) applied SFE with carbon dioxide for the selective isolation of

organochlorine, organophosphorus and organonitrogen pesticides from contaminated

cereals. The resulting extracts were cleaned-up by GPC and GC-FPD used for

quantitation. A determination method for 56 different pesticides was reported by

Lehotay and Garcia (1997). The sample was frozen and a drying agent consisting of

magnesium sulfate was mixed and homogenized with a small amount of dry ice. The

sample was extracted with supercritical CO2, trapped with C18 bonded silica, eluted

with acetone, and subsequently analyzed by GC ion-trap mass spectrometry.

Magnesium sulfate as a drying agent was mixed with the sample to get rid of water, and

gave a high recovery for methamidophos as well as for other pesticides.

Collection

Device

(Solvent Trap)

Extraction

Vessel

Oven

Pump

Syringe Pump or

Reciprocating

Pump

Carbon

Dioxide

Tank

103

Some highly polar pesticides such as the phosphorothioates and phosphoramidothioates

showed very low recoveries by the supercritical CO2 extraction method (e.g., acephate,

omethoate and vamidothion). Generally, a modifier is added to the supercritical CO2 to

improve the extraction yield. Stefani et al. (1997) worked on many extraction methods

using two steps, such as two subsequent extractions of the same sample without the

addition of a polar solvent to supercritical CO2. The two steps were similar except for

the volume of the trap solvent. Celite and anhydrous calcined sodium sulfate were

added as drying agents to the samples. The optimization of SFE on several

organochlorine and organophosphorus pesticides in samples with high water content

such as strawberry was performed. Lyophilization and addition of anhydrous sodium

sulfate were examined to solve the problem caused by the water content of vegetable

samples (Nerin et al., 1998). In addition, SFE has been adopted by the US EPA as a

reference method for extracting PAHs (Method 3561) and PCBs (Method 3562) from

solid environmental matrices. Ling et al. (1999) reported the extraction of several OC

pesticides from Chinese herbal medicines using SFE with CO2 at 25 MPa and 50 oC (5

min static extraction time and 20 min dynamic extraction time) using florisil as the

trapping sorbent. A similar procedure was used by Zuin et al. (2003) for the

determination of OC pesticides and OP pesticides in medicinal plants from Brazil. They

used a mild extraction conditions which was using pure CO2; 10 MPa and 40 oC, 5 min

static plus 10 min dynamic extraction time and C18 as the trapping adsorbent allowed

for direct analysis of the extract by GC-ECD/GC-FPD with no prior cleanup procedure.

104

In many ways carbon dioxide is an ideal solvent as it combines low viscosity,

inexpensive, non-inflammable, environment-friendly and high diffusion rate with a

high volatility. The salvation strength can be increased by increasing the pressure and

extractions can be carried out at relatively low temperatures. The high volatility means

that the sample is readily concentrated by simply reducing the pressure and allowing

the supercritical fluid to evaporate. Though carbon dioxide is non-polar, its polarity can

be adjusted with modifiers such as acetone and methanol

SFE works best for finely powdered solids with good permeability, such as soils and

dried plant materials and extraction of wet or liquid samples and solutions can be

difficult. Lipophilic compounds are frequently extracted along with the analytes of

interest, and one of the main applications for SFE in foods is the extraction of lipids

and the determination of fat content in raw and processed foods (Eller and King, 1998).

A review of the technique, including available instrumentation and several applications

is given by Smith (1999) and by Motohashi et al. (2000).

SFE efficiency is affected by a wide range of parameters such as the nature of the

supercritical fluid, temperature and pressure, extraction time, the shape of the extraction

cell, the sample particle size, the matrix type, the moisture content of the matrix and the

analyte collection system. Due to these numerous parameters affecting the extraction

efficiencies, SFE affords a high degree of selectivity and the extracts are relatively

quite clean. However, the presence of water and fat in food samples can require

extensive sample preparation and the development of more on-line cleanup procedures

for SFE should enable further applications for food analysis to be developed.

105

For example, sorbents, such as alumina, florisil and silica, can be placed in the

extraction cell, or used as a cleanup following extraction to increase selectivity.

Sorbents in the extraction cell can also be used for „inverse‟ SFE extraction, in which

interfering compounds are removed by a weak supercritical extraction fluid, leaving the

analyte trapped on the sorbent for subsequent extraction under stronger conditions

(King, 1998). Besides, the need to control so many operating parameters makes SFE

optimization tedious and difficult in practice. Other disadvantages of the SFE technique

include: limited sample size and high cost of the equipment.

3.3.1.2 Pressurized Fluid Extraction (PFE)

This technique, also named pressurized liquid extraction (PLE), is a solid-liquid

extraction process performed in closed vessels at relatively elevated temperature,

usually 80 to 200 oC, and elevated pressures, between 10 and 20 MPa. Therefore, PFE

is quite similar to SFE but CO2 is replaced by organic solvents to mitigate potential

polarity problems. Extraction is carried out under pressure to maintain the conventional

organic solvents in its liquid state, but extracting at temperature well above their

atmospheric boiling points. Therefore, the solvent is still below its critical condition

during PFE but has enhanced salvation power and low viscosities and hence allows

higher diffusion rates for analytes. In this way the extraction efficiency increases,

minimizing the amount of solvent needed and expediting the extraction process. The

time required for extraction is independent of the sample mass and the efficiency of

extraction is mainly dependent on the temperature. Figure 3.2 shows a schematic

diagram of a PFE system.

106

Figure 3.2: Schematic Diagram of a PFE System (Buldini et al., 2002)

Both static and flow through extraction systems can be used. In the static extraction

mode, the sample is loaded in an inert cell and pressurized with a solvent heated above

its boiling point for some time. The extract is then automatically removed and

transferred to a vial. In the flow through extraction mode, fresh solvent is continuously

introduced to the sample. This improves the extraction efficiency but, the extract is

subsequently diluted. The extract is pushed into the collection vial by a second aliquot

of solvent inserted into the extraction cell and this second aliquot is then collected into

the same vial by pushing it with an inert gas flow. The whole process takes

approximately 15-20 min.

Extraction

cell

Oven

Pump

Solvent

supply

Collection

vial

Inert gas

tank

Purge

valve

Static

valve

107

In PFE, the pressure is applied to maintain the solvent in its liquid state. This reduces

the number of parameters that need to be optimized to achieve efficient extractions

compared with SFE. The main parameters to consider now are temperature and time

and this reduces the time devoted to method development and optimization of the

extraction procedure. The method set up is generally straightforward because the same

solvent recommended in the official and routine Soxhlet methods can be used.

Therefore, PFE is an attractive technique because it is fast (e.g. extraction time

approximately 15 min per sample), uses less solvent volume (15-40 ml), no filtration is

required after extraction, the instrumentation allows extraction in unattended operation

and different sample sizes can be accommodated. The two main disadvantages of PFE

include limited selectivity because it usually requires further cleanup of the extract

obtained and higher initial cost than SFE and microwave-assisted extraction (MAE)

systems.

Tao et al. (2004) applied PFE for extracting DDT and its metabolites from wheat with

hexane/acetone (1:1, v/v) at 120 oC and a pressure of 101 MPa. Moreno et al. (2006)

investigated the extraction of 65 pesticides including OC pesticides from greasy

vegetable matrices such as avocado using PFE with ethyl acetate/cyclohexane (1:1, v/v)

at 120 oC and a pressure of 12 MPa. Adou et al. (2001) reported an analytical procedure

based on PFE before GC-ECD or GC-FPD for the determination of different pesticides

in fruits and vegetables. The recoveries were in the range of 70% for almost all the

compounds assayed.

108

When water is employed as the extraction solvent in PFE, different terminology is used

to highlight the fact that water is an environmental-friendly solvent. Thus, terms such

as pressurized hot water extraction, subcritical water extraction (SWE), superheated

water extraction and high temperature water extraction can be found in the literatures

(Ramos et al., 2002; Smith, 2003; Carabias-Martinez et al., 2005). Because the

polarity of water decreases markedly as the temperature is increased, superheated water

at 100 – 200 oC, under a relatively low pressure can act as a medium to non-polar

solvent (ethanol or acetone) and is an efficient extraction solvent for many analytes

(Ramos et al., 2002; Smith, 2003; Carabias-Martinez et al., 2005). A limitation in

extracting with hot water is the inability to recover compounds that are hydrophobic,

thermo labile, or easily hydrolyzed. Wenrich et al. (2001) also applied subcritical water

extraction to extract OC pesticides and chlorobenzenes from fruits and vegetables.

3.3.1.3 Microwave-assisted Extraction (MAE)

MAE uses microwave radiation (0.3 – 300 GHz) as the source of heating a solid-

solvent mixture sample. Due to the particular effects of microwaves on the matter

namely, dipole rotation and ionic conductance, heating with microwaves is

instantaneous and occurs in the bulk of the sample, leading to very fast extraction. Heat

generated in the sample by the microwave field requires the presence of a dielectric

compound. The greater the dielectric constant, the more thermal energy is released and

the more rapid of the heating for a given frequency. Consequently, the effect of

microwave energy is strongly dependent on the nature of both the solvent and the solid

matrix. Usually, the extraction solvent has a high dielectric constant, so that it strongly

absorbs the microwave energy. However, in some cases especially for thermo labile

109

compounds, the microwave may be absorbed only by the matrix, resulting in heating of

the sample and the release of the solutes into the cold solvent. Therefore, the nature of

the solvent is great importance in MAE: it should selectively and efficiently solubilize

the analytes in the sample but, at the same time, it should absorb the microwave

without leading to a strong heating to avoid eventual degradation of the analyte

compounds. Thus, it is common practice to use a binary mixture (e.g. hexane-acetone,

1:1) where only one of the solvent is absorbing the microwave energy. Other important

parameters affecting the extraction process are the applied power, the temperature and

the extraction time. Moreover, the water content of the sample needs to be carefully

controlled to avoid excessive heating, thus allowing reproducible results.

The application of microwave energy to the samples may be performed either in closed

vessels with pressure and temperature control (pressurized MAE) or in open vessels at

atmospheric pressure (focused MAE) (Figure 3.3). In focused MAE method, the

temperature is limited by the boiling point of the solvent at atmospheric pressure, but in

pressurized MAE the temperature may be elevated by applying an adequate pressure

(Dean. 2000).

The technique has proven to be better than soxhlet extraction by reducing the solvent

consumption and extraction time (Diagne et al., 2002; Barriada-Pereira et al., 2003;

Singh et al., 2007). Usually sample sizes range from 0.5 to 10 g and 10 ml of solvent is

sufficient for the extraction time from less than 1 to 10 min. The same laboratory

microwave unit previously described for sample digestion is used, so reducing costs;

the simultaneous extraction of many different samples is also possible without any

mutual interference.

110

Figure 3.3: Schematic Diagram of a Focused MAE Setup (Labbozzetta et al., 2005)

Cai et al. (2003) used MAE to extract OC pesticides from Chinese teas before solid-

phase microextraction followed by GC-ECD analysis. The recoveries of MAE were

compared with those of ultrasonic extraction and the results showed that MAE provided

better recoveries (efficiencies) and shorter extraction times than ultrasonic extraction.

The MAE procedure was applied to the determination of the 21 OC pesticides in tree

leaves namely, chestnut, hazel, oak and walnut tree by Barriada-Pereira et al. (2004)

and five species of plants namely, cytisus striatus, avena sativa, vicia sativa, solanum

nigra and chenopodium vulgare by Barriada-Pereira et al. (2005). Besides, Barriada-

Pereira et al. (2007) also carried out a comparative study between MAE and

pressurized liquid extraction (PLE) of 11 OC pesticides from vegetables using n-

hexane/acetone (5:5, v/v) as the solvent in MAE and with hexane/ethyl acetate (8:2,

v/v) in PLE. Both techniques showed similar recoveries but PLE extraction was more

Reflux system

Focused

microwaves

Wave guide

Water out

Water in

Extraction

vessel

Solvent

Sample

Magnetron

111

laborious and required higher solvent consumption and longer extraction times than

MAE.

MAE also limits contamination or absorption from the vessel, due to direct heating of

the sample. The main advantages of microwave pre-treatment are the low temperature

requirement, high extraction rate, complete automation and the possibility of

simultaneously extracting many different samples at the same time with little

interference. However, MAE has also several drawbacks such as the extract must be

filtered after extraction, polar solvents are needed, cleanup of extracts may be necessary

and the equipment is moderately expensive.

3.3.1.4 Matrix Solid-phase Dispersion (MSPD)

Since its introduction in 1989, matrix solid phase dispersion (MSPD) has been cited as

the extraction method employed in over 250 studies (Barker, 2007). It has proven to be

an efficient and somewhat generic technique for the isolation of a wide range of drugs,

pesticides, naturally occurring constituents and other compounds for a wide variety of

complex plant and animal samples. MSPD combines aspects of several analytical

techniques, performing sample disruption while dispersing the components of the

sample on and into a solid support, thereby generating a chromatographic material that

possesses a particular character for the extraction of compounds form the dispersed

sample.

112

In the MSPD process, a sample (liquid, semi-solid or solid) is placed in a glass or agate

mortar containing an appropriate bonded-phase or other solid support material such as

octadecylsiloxane (ODS) and derivatized silica (C18) or other suitable support materials

(Figure 3.4).

Figure 3.4: MSPD Extraction Procedures (Barker, 2007)

The solid support and sample are manually blended together using a glass or agate

pestle, a step that takes about 30 seconds. When blending is complete, the sample is

then packed into an empty column or on top of a solid-phase extraction (SPE) sorbent

without any further drying or cleanup prior to elution. The column is often an empty

syringe barrel or a cartridge with a stainless-steel or polypropylene frit, cellulose filter

Sample

Solid support

Blend with pestle Transfer

Blended sample

Frit and co-column

Compress with plunger Elute

Solvent

Sample for analysis

113

or a plug of silanized glass wool at the bottom. A second frit or plug is often placed on

top of the sample before compression with a syringe plunger. The main difference

between MSPD and SPE is that the sample is dispersed throughout the column and not

retained in only the first few millimeters. As regards elution, there are two possibilities:

(a) the target analytes are retained on the column and interfering compounds are eluted

in a washing step, followed by the target analytes being eluted by a different solvent; or

(b) the interfering matrix components are selectively retained on the column and the

target analytes directly eluted. Finally, additional cleanup is performed or the sample is

directly analyzed. Sometimes, the MSPD column is coupled on line with an SPE

column or, as in several recent applications; the SPE sorbent is packed in the bottom

part of the MSPD column to remove interfering matrix components (Kristenson et al.,

2006).

Several factors that have been examined for their effects in the MSPD extraction.

These include:

(a) the effects of average particle size diameter, where as expected, very small particle

sizes (3 - 10 m) would lead to extended solvent elution times and the need for

excessive pressures or vacuum to obtain an adequate flow. A blend of silicas

possessing a range of particle sizes (40 - 100 m) works quite well and such

materials also tend to be less expensive.

(b) the character of the bonded-phase. Depending on the polarity of the phase chosen,

various effects on the results may be observed. Applications requiring a lipophilic

bonded-phase employ C18 and C8 materials interchangeably.

114

(c) the use of underivatized silica or other solid support materials. Use of unmodified or

underivatized solids, such as sand to blend samples do not work in exactly the same

manner as originally described for the bonded-phase solid support, such as ODS.

Silica-based support materials (derivatized silica, silica gel, sand, florisil) are still

being used almost exclusively in MSPD. Blasco et al. (2004) have demonstrated the

use of an activated carbon fiber for the isolation of dithiocarbamates from fruits,

vegetables and cereals.

(d) the best proportion ratio of sample to solid support material. The most often applied

is 1 to 4, respectively, but it can vary from application to application. This ratio is

dependent on the method employed. Both smaller and greater ratios have been used

successfully.

(e) Chemical modification of the matrix or matrix solid support blend. Addition of

chelating agents such as acids and bases at the time of blending would affect the

distribution and elution of target analytes from the sample. The solution profile of

matrix components is likewise affected.

(f) The optimum choice of eluent and the sequence of their application to a column.

The elution solvent sequence is to isolate the analyte or further clean the column of

interfering substances with each solvent step. MSPD columns permit isolation of

analytes with different polarities or the entire chemical classes of compounds in a

single solvent, making MSPD amenable to multiresidue analysis on a single

sample. Several recent studies have reported the use of hot water as an eluting

solvent as well as the addition of pressure, which known as pressurized-liquid

extraction (PLE) or accelerated solvent extraction (ASE) (Bogialli et al., 2004).

Such applications demonstrate the potential to make extraction methods based on

MSPD free of hazardous solvents and even less expensive to perform.

115

(g) The elution volume. It has been observed that for an 8 ml elution of a 2 g MSPD

column blended with 0.5 g sample, the target analytes usually elute in the first 4 ml,

which is approximately one column volume. This will vary for each application and

should be examined to reduce the use of solvent and the unintended co-elution of

potential interferences.

(h) The effect of the sample matrix itself. All the components of the sample are

dispersed throughout the column, covering much of the bonded-phase solid support

surface, creating a new phase that can have dramatic effects on isolation in going

from one matrix to another (Barker, 2000a; Barker, 2000b).

Kristenson et al. (2001) developed a miniaturized automated MSPD method for

extracting pesticides from apples, pears and grapes. Only 25 mg of sample and 0.1 ml

ethyl acetate were used and the extracts were analyzed by GC-MS without any further

purification. In terms of recovery, C18, C8 and silica were compared for use as

dispersants. The best results were obtained by using C18. The LODs were 4 - 90 g/kg.

Bogialli et al. (2004) developed a simple, rapid and specific method for analyzing

seven widely used carbamate insecticides in fruits and vegetables. After matrix

deposition on crystobalite (sand), the analytes were extracted with water, heated to 50 -

100 o

C. At 50 oC, recoveries were between 76 to 99 %. A method based on MSPD and

GC was proposed for the determination of OC and pyrethroid insecticides in tea leaves

(Hu et al., 2005). After evaluating various extraction conditions, Hu et al. (2005) found

that the best compromise in terms of recovery and cleanup was the use of florisil as the

dispersant and hexane-dichloromethane (DCM) as the extractant. LODs of the method

ranged between 2 and 60 ng/g, which are lower than the MRLs set by the EU. Barker et

116

al. (2000b), Bogialli and Corcia (2007) detailed a number of applications of MSPD for

the analysis of residues and Kristenson et al. (2006) detailed recent advances in the

technique.

The main advantages of MSPD are: (a) it permits rapid sample turnover, enhancing

access to timely data on residue levels present in the sample; (b) it reduces the amount

of solvent used compared to the classical methods because it requires a small sample

size, and thus, in turn, decreases environmental contamination and improves worker

safety. Although useful for the analysis of trace contaminants in food, particularly as an

aid or an alternative to LLE or solid phase extraction, the MSPD technique is not easily

automated and could be time-consuming for a large number of samples. Although some

MSPD extracts are clean enough for direct instrumental analysis, a further cleanup step

is often required, particularly with fatty matrices.

3.3.2 Liquid Sample Extraction Techniques

The traditional method to obtain analytes from liquid samples has been either by

partitioning into an immiscible solvent, trapping the analyte onto a column or solid-

phase matrix of some sort, or as a last resort evaporation of the sample to dryness. The

most common method for an aqueous matrix is to use a separating funnel and extract

any organic compounds into a non-polar solvent. The process is slow, requires

considerable manpower and is hence costly. It generates a large volume of organic

wastes, which are environmentally unfriendly, and the disposal is becoming

increasingly difficult and costly. The repetitive manual operations often lead to errors

and could be a boring task for the operator, although crucial to obtaining reliable

117

results. So, there has been a considerable interest in the reduction of solvent usage and

in methods capable of automation.

3.3.2.1 Liquid-liquid Extraction (LLE)

Analytes in solutions or liquid samples can be extracted by direct partitioning with an

immiscible solvent. Liquid-liquid extraction (LLE) is based on the relative solubility of

an analyte in two immiscible phases and is governed by the equilibrium

distribution/partition coefficient. Extraction of an analyte is achieved by the differences

in solubilising power (polarity) of the two immiscible liquid phases.

LLE is traditionally one of the most common methods of extraction, particularly for

organic compounds from aqueous matrices. Typically a separating funnel is used and

the two immiscible phases are mixed by shaking and then allowed to separate. To avoid

emulsions, in some cases, a salt may be added and centrifugation can be used if

necessary. Alternatively an MSPD approach (as described in Section 3.3.1.4) can be

used to avoid emulsions. Both layers can be collected for further analysis. To ensure the

complete extraction of an analyte into the required phase, multiple extractions may be

necessary. Due to the limited selectivity, particularly for trace level analysis, there is a

need for cleanup or analyte enrichment and concentration steps prior to instrumental

analysis.

118

In the case of multiresidue methods, the extracting solvent has to be suitable for the

extraction of compounds within a wide polarity range from a variety of matrices

containing different amounts of water, fats, sugars and other substances. The usual way

for extracting pesticide residues from the sample is by thorough disintegration of the

matrix in a high speed homogenizer in the presence of the solvent or solvent mixture. In

this way, even the AOAC method, which is one of the most commonly instituted

methods, has been modified. The original methods which were extraction with

acetonitrile, followed by liquid-liquid partitioning with petroleum ether/dichloro

methane and a laborious florisil column cleanup, was modified in 1985 to include

acetone instead of acetonitrile (Torres et al., 1996).

Acetone extraction is usually preferred since it is suitable for both non-polar and polar

pesticides, as has been demonstrated in different comparative studies performed by GC

and HPLC. Acetone has low toxicity, is easy to purify, evaporate and filter and is

inexpensive. Fruit and vegetable extracts in acetone are usually cleaner than those

obtained with other solvents of similar polarity (Torres et al., 1996).

A rapid and efficient multiresidue extraction procedure using ethyl acetate and sodium

sulphate, followed by GPC on an SX-3 column, was first reported by Roos et al.

(1987). Recoveries better than 90% were obtained for OC and OP pesticides, fungicides

and chlorobiphenyls. The ethyl acetate and sodium sulphate extraction without further

cleanup was applied as a screening method for the analysis of eight OP pesticides with

different polarities in different types of vegetables using GC-FPD and GC-NPD. With

the use of specific detectors, interfering chromatographic peaks were reduced and the

119

analysis time and solvent usage were also reduced, resulting in cheaper analyses (Cai et

al., 1995).

Another proposed solution is the employment of a coagulation method. A multiresidue

method for 23 OP pesticides in fruits and vegetables, consisting of extraction with

acetone, cleanup by a coagulating solution of phosphoric acid and ammonium chloride

and re-extraction with benzene (Sasaki et al., 1987). This method is not suitable for the

determination of polar pesticides, such as mevinphos and phosphmidon, and water

insoluble pesticides, as crufomate and carbophenothion.

In another study, Barriada-Pereira et al. (2004) compared the use of cartridges filled

with four different sorbents: florisil, a tandem of florisil and alumina, silica, and carbon

black to cleanup plant leaves, extracts prior to OC pesticides determinations. Carbon

black was shown to be the sorbent, providing colorless eluates, cleaner chromatograms

and fewer interferences. Similarly florisil, silica and alumina cartridges as well as glass

columns filled with either florisil, silica or alumina were also compared for pine needle

extracts purification prior to final determination of PAHs and alumina disposable

cartridges were found to be the most efficient (Ratola et al., 2006).

The major drawbacks of LLE are: it is sub-optimal for oily crops, which require

additional sample cleanup; the low sample throughput due to the manual pre-

concentration steps; and the large amounts of organic solvents used, resulting in a large

volume of waste solvents. Although reduction of the volume of organic solvent to 1 ml

solvent per 1000 ml of sample has been attempted, the procedure resulted in

120

unfavorable phase ratios, which leads to low extraction efficiencies. Moreover, the

requirement for the extracting solvent to be completely immiscible with water sample is

difficult to achieve with the more polar solvents (Hoff and Zoonen, 1999).

3.3.2.2 Gel Permeation Chromatography (GPC)

The most universally applicable cleanup is GPC. Separation is generally performed by

using divinylbenzene-linked polystryrene gels, mostly Bio-Beads SX-3 (200 - 400

mesh, Bio-Rad, USA). It is suitable for OC, OP and nearly all other types of pesticides

and does not have adsorption losses. The GPC column, which consists of a porous

solid, such as glass or silica or a cross-linked gel containing pores of appropriate

dimensions to affect the separation desired. The liquid mobile phase is usually water or

a buffer for biological separation, and an organic solvent that is appropriate for the

sample and is compatible with the column packing for synthetic polymer

characterization. Solvent flow may be driven by gravity, or by a high pressure pump to

achieve the desired flow rate through the column. The sample to be separated is

introduced at the head of the column (Figure 3.5). As it progresses through the column,

small molecules can enter those pores larger than the molecule. Thus, the larger the

molecule, the smaller is the amount of pore volume available into which it can enter.

The sample emerges from the column in the inverse order of molecular size; that is, the

largest molecule emerges first followed by progressively smaller molecules. In order to

determine the amount of sample emerging, a concentration detector is located at the end

of the column. Additionally, detectors may be used to continuously determine the

molecular weight of species eluting from the column. The volume of solvent flow is

also monitored to provide a means of characterizing the molecular size of the eluting

species.

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Figure 3.5: Schematic Diagram of a GPC System (Tekel and Hatrik, 1996).

For the elution of pesticides, several solvent mixtures have been employed. The

mixture of cyclohexane/ethyl acetate (1:1, v/v) has been shown to be suitable for the

cleanup of pesticides and the metabolites (Tekel and Hatrik, 1996). The mixture of

cyclohexane/methylene chloride (1:1, v/v) is useful for the cleanup of more than 120

pesticides (Tekel and Hatrik, 1996). Under the conditions used for plant extracts, GPC

on Bio Beads SX-3 can be applied to the analysis of fats and oils, by effectively

removing lipids before the analysis of OC and the less polar OP pesticides. In addition,

an official EPA method (Method 3640A GPC Cleanup) has been approved for the

purification of organic extracts from solid environmental samples.

Solvent

supply

Heated oven

Sample loop

Six-port

valve

Sample

column

Reference

column

Sample inlet

Pump

Control valves

Detector

Differential

refractometer

Liquid flow

detector

Fraction

collector

122

The advantage of GPC is the prolonged lifetime of the GPC column. In general, it

could be used for several months without any deleterious effects on the retention

volumes or the cleanup capacity. A main disadvantage of the GPC system is that it is

difficult to completely remove all traces of the lipids. Therefore, further cleanup steps

are often necessary by resorting to the liquid adsorption chromatography column.

3.2.2.3 Enzyme-linked ImmunoSorbent Assay (ELISA)

The Enzyme-Linked ImmunoSorbent Assay (ELISA) is a biochemical technique used

mainly in immunology to detect the presence of an antibody or an antigen in a sample.

ELISA is a common example of an immunoassay using an enzyme tracer. A test tube

or sample well in a 96-well plastic micro liter plate is precoated with an antibody.

Then, a sample or control is added to each well. The enzyme conjugate is added and the

mixture is incubated at room temperature. The antibody binds to the immobilized

pesticide and also to the pesticide in the sample extract or the standards. The amount of

antibody which binds to the immobilized pesticide depends on the amount of pesticide

presents in the extractor standard.

The extract is then washed away, and the amount of antibody bound to the immobilized

pesticide can be measured using the enzyme tracer. A tracer enzyme can be attached to

the antibody or may attach by adding a second antibody (that binds to the first)

conjugated with the enzyme. If the latter is done, then any unbound secondary antibody

is washed away. Upon the addition of a solution of colorless substrate, the enzyme will

transform it to a colored product (Figure 3.6)

http://en.wikipedia.org/wiki/Biochemistry

http://en.wikipedia.org/wiki/Immunology

http://en.wikipedia.org/wiki/Antibody

http://en.wikipedia.org/wiki/Antigen

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1.

2.

3.

4.

5.

6.

Figure 3.6: ELISA Operation Procedures

The amount of antibodies bound to the immobilized pesticide is shown by the intensity

of the color: the greater the intensity, the less pesticide is in the sample. The intensity of

the color can be measured through the use of a micro spectrophotometer, which may be

linked to a computer with the data-analyzing software. This measurement is then

compared against a standard curve, derived from the standard, to give the amount of

pesticide in the sample.

In general, the development of an ELISA method involves three phases. (a) Reagent

preparation phase, consisting of the purification and modification of specific antibodies

or analytes to be utilized in the final assay format. In this step, plate coating parameter

and antibody concentrations will be assessed in order to attain the desired sensitivity.

Well

Antibodies

Conjugate

Sample

Wash

Unbound

sample and

conjugate

Bound sample

and conjugate Substrate

More

analyte

Less

analyte

124

(b) Assay optimization phase, consisting of the development of a functional standard

curve as well as selection of the proper conjugate and sample diluents. These diluents

will be prepared which is compatible with the sample matrix composition. (c) Assay

validation phase, consisting of defining and optimizing the essential assay parameters,

including sensitivity, recovery, linearity and precision. Further fine tuning of the assay

will be done during this phase to accommodate matrix effects which may compromise

any of the above mentioned assay parameters (Nunes et al., 1998).

The utilization and application of an analytical method depends on the absence of

interferences derived from reagents and the matrix. The interference problem must be

addressed by running appropriate blanks and controls. In this context, ELISA does not

differ from the other detection techniques. In 1987, Newsome and Collins (1987)

developed immunoassays (IA) for the determination of benomyl and thiabendazole in

three crops, but low sensitivities with limits of quantification (LOQ) of approximately

0.35 ppm for benomyl and 0.3 ppm for thiabendazole were obtained. No control was

carried out to improve the detection levels of the pesticide, and the low sensitivities

were attributed to the effects of the matrix. An important aspect in pesticide residue

analysis by immunoassays is sample preparation. Extraction of more polar compounds

is usually complicated. A competitive ELISA was developed by Bushway et al. (1990)

for the quantitation of methyl 2-benzimid-azolecarbamate in fruit juices. They

minimized the matrix effects by diluting the samples before the immune analysis. In

most ELISA investigations, the initial, more expensive and time-consuming

experimental part is the treatment before IA. The final extract must be diluted in order

to eliminate the solvent effect.

125

In general, ELISA methods for pesticide analysis in complex matrices are still

accompanied by sample pre-treatment in order to eliminate the interferences and to

minimize the cross-reactivities. But in some cases the method recoveries are lower

when compared to particular methods without previous sample treatment. If neither

cross-reactivities nor matrix interferences are observed, the application of the IA

directly to the non-treated sample is still preferable.

One of the major disadvantages of this technique is the need to initially develop the

antibody, which makes it not feasible for one-off analyses. The analyte-antibody

interation can also be affected by the sample matrix, leading to low extraction

recoveries. A review by Hennion and Pichon (2003), describes immuno-based

extraction sorbents and also the use of artificial antibodies. Most applications are for

biological or environmental samples, but food examples including determination of

pesticides (imazalil and phenylurea herbicides) in fruit juices have been investigated by

Watanabe et al. (2001).

ELISA is particularly suited for polar, water soluble pesticides and their degradation

products that are generally difficult to analyze using conventional analytical methods.

They can be significantly faster than some conventional methods. Comparisons of

quantitative immunoassay with conventional single residue methods using GC or

HPLC to analyze specific pesticides show that immunoassay can analyze four to five

times as many samples in a given time period (Tekel and Hatrik, 1996). The use of

automation and robotics could further increase the number of samples analyzed. The

principal steps of an ELISA that can be automated include coating of the wells or tubes

126

with the immobilized pesticide; addition of antibody, standards and samples; and

absorbance measurement. In addition, ELISA can be simpler to use than conventional

techniques, requiring less skilled personnel, and minimal instrumentation time and

comparatively inexpensive equipment.

Despite these advantages, the use of ELISA for monitoring pesticide residues in food

has been limited by a number of factors. ELISA may not be as sensitive for some

compounds as conventional methods, and they can have lower levels of reproducibility.

Because ELISA is compound-specific, they are not suitable for multiresidue analysis.

Therefore, while they may analyze more samples in a given time than multiresidue

methods, they can only detect fewer pesticides. Characteristics of the food or the

pesticide, in some cases, may also preclude the use of immunoassays. For food samples

and pesticides requiring considerable cleanup work, ELISA may be no faster than

conventional techniques. In addition, immunoassay may not work well in certain foods.

For some pesticides, which are very small molecules or having non rigid structures, it

may not be possible to develop antibodies. In addition, if the pesticide has little aqueous

solubility, it may not be possible to use an immunoassay.

127

3.3.2.4 Solid-phase Extraction (SPE)

Solid phase extraction (SPE) was developed in the mid-1970 as an alternative approach

to LLE for separation, purification, pre-concentration and solvent exchange of solutes

for solution (Thurman and Mills, 1998). SPE can be used directly as an extraction

technique for liquid matrices, or as a cleanup method for solvent extracts.

An SPE method always consists of three to four successive steps, as illustrated in

Figure 3.7. First, the solid sorbent should be conditioned using an appropriate solvent.

This step is crucial, as it enables the wetting of the packing material and the salvation

of the functional groups. In addition, it removes possible impurities initially contained

in the sorbent or the packaging. Also, this step removes the air present in the column

and fills the void volume with solvent. The nature of the conditioning solvent depends

on the type of the solid sorbent. Typically, for reversed phase sorbent, methanol is

frequently used, followed by water or an aqueous buffer whose pH and ionic strength

are similar to that of the sample. Precautionary steps are taken not to allow the solid

sorbent to dry between the conditioning and the sample treatment steps, otherwise the

analytes will not be efficiently retained giving rise to poor recoveries. If the sorbent

dries for more than several minutes, it must be reconditioned.

The second step is the percolation of the sample through the solid sorbent. Depending

on the system used, the volumes used can range from 1 mL to 1 L. The sample may be

applied to the column by gravity, pumping, aspirated by vacuum or by an automated

system. The sample flow rate through the sorbent should be low enough to enable

efficient retention of the analytes, and high enough to avoid excessive retention. During

128

this step, the analytes are concentrated on the sorbent. Even though the matrix

components may also be retained by the solid sorbent, some of them would pass

through, thus enabling some purification (matrix separation) of the sample.

Figure 3.7: SPE Operation Procedures

The third step (which is optional) may be the washing of the solid sorbent with an

appropriate solvent, having low elution strength, to eliminate matrix components that

have been retained by the solid sorbent, without displacing the analytes. A drying step

may also be advisable, especially for aqueous matrices, to remove traces of water from

the solid sorbent. This will eliminate the presence of water in the final extract, which, in

some cases, may hinder the subsequent concentration of the extract and the analysis.

The final step is the elution of the analytes of interest by an appropriate solvent, without

removing the retained matrix components. The solvent volume should be adjusted so

that quantitative recovery of the analytes is achieved with subsequent low dilution. In

Washing /

conditioning Loading Washing Elution

129

addition, the flow rate should be correctly adjusted to ensure efficient elution. It is often

recommended that the solvent volume be fractionated into two aliquots, and to allow

the solvent to soak the solid sorbent before the elution.

The SPE cartridge possesses two important features, standardization and hence greater

reproducibility, which includes a wide range of phases, from normal phase, reversed

phase to ion-exchange materials thus enabling aqueous solutions to be treated and

employing additional trapping mechanisms.

The selection of an appropriate SPE extraction sorbent depends on understanding the

mechanism(s) of interaction between the sorbent and the analyte of interest. That

understanding in turn depends on the knowledge of the hydrophobic, polar and

inorganic properties of both the solute and the sorbent. SPE procedures using different

sorbents such as C8- or C18- bonded silica phases, porous graphitic carbon, polymeric

resins, cation exchangers and reversed-phase supports have been used. Method

development in SPE is accomplished by investigating pH, ionic strength, polarity and

flow rate of the elution solvent and physico-chemical characteristics of the sorbent bed.

For matrices with a high water content, the use of SPE is increasing. The copolymer

styrene-divinylbenzene (DVB) is well known as a hydrophobic sorbent with retentions

equal or higher than on octadecyl-bonded silica (ODS). So only non-polar to

moderately polar analytes can be retained on this polymer. Sorbents for normal phase

are modified with cyano, diol, or amino groups. Non-polar to moderately polar analytes

are extracted from polar solutions onto non-polar silica sorbents (e.g. C18, C8, C2, C1,

130

CH, PH, and CN). Sorbents for reversed phase are modified with octadecyl, octyl,

cyclophenyl or phenyl groups (Tekel and Hatrik, 1996).

The sorbents come in different packaging: filled micro-columns, cartridge, syringe

barrels and discs. The disposable sorbent containers are illustrated in Figure 3.8.

Although the cartridges are for single use only and disposable, thus representing a

significant consumable cost, this has been shown to be much lower then the cost of

chemicals and the manpower needed for the corresponding traditional solvent

extraction methods. Other types of SPE have also been developed, including flat disks

with the stationary phase particles supported on a mesh, enabling very large volumes to

be rapidly extracted. Recent use of high flow rates through extraction cartridges has

been shown to give improved extraction but such “turbulent flow extractions” were

very similar to conventional extractions.

Figure 3.8: Disposable SPE Sorbent Containers

Syringe barrel Cartridge Disk

131

Many of the published methods for pesticide determination in fresh fruits and

vegetables use a combination of two or more commercially available SPE columns for

cleanup in the normal-phase (NP) mode. Weak anion-exchange sorbents such as

primary secondary amine (PSA), aminopropyl (NH2), or diethylaminopropyl (DEA)

modified silica are often used for cleanup of food samples together with strong anion-

exchange sorbents (SAX) (Sharif et al., 2006). Other SPE cleanup approaches include

the combination of GCB (graphitized carbon black) and PSA columns (Abhilash et al.,

2007). Besides, there are some application using reversed-phase (RP) SPE for pre-

concentration / cleanup of pesticide residues from fruit and vegetable samples. Using

RP-SPE non-polar to moderately polar analytes are extracted from polar solutions onto

non-polar sorbents, which include silica modified with octadecyl, octyl, cyclohexyl or

phenyl groups, modified or nonmodified poly(styrene-divinylbenzene) (PS-DVB) resin

and GCB (Niessner et al., 1999; Stajnbaher and Zupancic-Kralj, 2003). Niessner et al.

(1999) developed a multiresidue method to determine 28 multiclass pesticide residues

in various plant materials using SPE with PS-DVB sorbents and further cleaned-up

using florosil. In this study, recoveries obtained were generally in the range of 85% to

110% with relative standard deviation below 7%.

Before the SPE technique can be applied to a solid matrix such as fruits and vegetables,

a separate homogenization step and often filtration, sonication, centrifugation and

liquid-liquid cleanup are required. Stajnbaher and Zupancic-Kralj (2003) used solid-

phase extraction on a highly cross-linked polystryrene divinylbenzene column

(LiChrolut EN) for the simultaneous isolation of 90 pesticides of different physico-

chemical properties from fruits and vegetables and pre-concentration of the pesticides

132

from the water-diluted acetone extract. It only used small volumes of solvent per

sample (30 ml acetone and 14 ml ethyl acetate, 6 ml methanol). The majority of

pesticide recoveries for various fruits and vegetables were > 80% in the concentration

range from 0.01 to 0.50 mg/kg. Mussio el at. (2006) used the ready-to-use cartridges

filled with a macroporous diatomaceous material to extract in a single step insecticide

residues with dichloromethane from aqueous-acetone extracts of fruits and vegetables.

The eluate was evaporated, the residue redissolved with methanol and then analyzed.

Average recoveries were between 74.5% and 105% with the RSD values was less than

10%.

The use of fully automated on-line RP-LC/GC has also been reported and has

numerous advantages, especially when a large number of samples are to be analyzed.

The majority of the studies on the application of on-line SPE describe environmental

monitoring of aqueous samples with only a limited application for food analysis, e.g.,

mepiquat and chlormequat in pears, tomatoes, and wheat flour (Riediker et al., 2002),

and N-methylcarbamates and their metabolites in soil and food (Caballo-Lopez and

Castro, 2003).

133

One of the drawbacks of the SPE method is that the packing must be uniform to avoid

poor efficiency and although the pre-packed commercial cartridges are now considered

reliable, solid and oily components in a sample matrix may plug the SPE cartridge or

block pores in the sorbent causing it to become overloaded and also automated systems

can have difficulties with reproducibility for some sample types. The sample matrix can

also affect the ability of the sorbent to extract the analyte due to competition for

retention. Many traditional sorbents are limited in terms of selectivity and insufficient

retention of very polar compounds can pose a problem. The use of hydrophilic

materials for the improved extraction of the more polar compounds by SPE was

detailed by Fontanals et al. (2005). A comprehensive review, covering trends, method

development, coupled with liquid chromatography and different types of SPE sorbent

materials was published by Hennion (1999) and some examples of the use of SPE in

food analysis were given in a review by Buldini et al. (2002). SPE methods for the

analysis of pesticides in fruits and vegetables are listed in Table 3.2.

134

Table 3.2: SPE Methods for the Analysis of Pesticides in Fruits and Vegetables

Analytes Matrix SPE Extraction Conditions

Recoveries

(%)

Precisions

(%)

LOD Ref.

Cartridges

Conditioning solvent

Eluting solvent

Abamectin Apples, pears,

tomatoes

C18 5 mL

acetonitrile

5 mL acetonitrile 88-106 1.3-6.5 <1

µg/KG

(Diserens and

Henzelin,1999)

28 multiclass Apple, wheat,

flour, glass

PS-DVB &

Florisil

2-3 mL ethyl

acetate, MeOH & water

4 mL ethyl

acetate

85-110 <7 n.r (Niessner et al.,

1999)

Aldicarb and

metabolites

Potato, tomato,

orange

LC-CN

3 mL water

3 mL

dichloromethane: MeOH (98: 2)

68-89

6.8-18.4

0.5-1.3

ng/L

(Nunes et al.,

2000)

20 multiclass fruits RP-C18 3 mL water 3 mL MeOH 70-109 4-7 0.1-250 ng

(Colume et al., 2000)

Chlormequat

& mepiquat

Pear, tomato,

wheat flour

Bond-Elut SCX,

Isolute SCX & DVB SCX

2 mL MeOH &

2 mL water

2 mL acetonitrile

& 1 mL MeOH:H2O (1:1)

92 – 96 % 3.7 – 6 % < 3

µg/kg

(Riediker et al.,

2002)

21 OC vegetables LC- Florisil & Alumine

5 mL water 35 mL Hexane: ethyl acetate

(80:20, v/v)

75.5-132.7 1.3-15.5 n.r (Barriada-Pereira et al.,

2003)

5 herbicides potato C8 2 mL MeOH 1 mL acetonitrile 86-101 <10 6.0 –

50 ng/g

(Escuderos-

Morenas et al.,

2003)

135

Table 3.2: SPE Methods for the Analysis of Pesticides in Fruits and Vegetables (continued)

Analytes Matrix SPE Extraction Conditions Recoveries

(%)

Precisions

(%)

LOD Ref.

Cartridges Conditioning solvent

Eluting solvent

90 multiclass Fruits &

vegetables

PS-DVB 6 mL MeOH & 8

mL water

2 mL ethyl

acetate/acetone (90:10)

>80 <10 n.r (Stajnbaher and

Zupancic-Kralj, 2003)

50 multiclass juice C18 3 mL acetonitrile & 5 mL water

5 mL hexane: ethyl acetate (1:1)

> 91 <9 0.1-4.6 µg/L

(Albero et al., 2005)

6 OC & 3

pyrethroids

Grape, orange,

tomato, carrot, green mustard

SAX/PSA,

Florisil , C18

5 mL acetone:n-

hexane (3:7)

5 mL acetone:

n-hexane (3:7)

70-120 2-8 0.0003-

0.015 mg/kg

(Sharif et al.,

2006)

5 multiclass Grapes, Lettuces

C18, 10 mL MeOH & 10 mL water

10 mL dichloromethane

70-100 10-18 0.002-0.3µg/

mL

(Juan-Garcia et al., 2007)

23 multiclass Leafy vegetables

GCB/PSA 5 mL acetonitrile: tolunene (3:1)

20 mL acetonitrile: tolunene (3:1)

81-115 1-15 <0.010 mg/kg

Abhilash et al., 2007

n.r: not reported.

C18: octadecyl silica, C8: ortyl silica, PS-DVB : poly(styrene-divinylbenzene),

LC-CN: cyanopropyl silica, SCX: strong cation exchanger, SAX: Strong Anion Exchanger,

NH2 : aminopropyl, PSA: primary secondary amine, GCB: Graphitized carbon black

MEKC-DAD: micellar electrokinetic chromatography-diode array detection RM-MEKC: Reversed migration micellar electrokinetic chromatography

UPLC-MS/MS: ultra performance liquid chromatography coupled to tandem mass spectrometry

136

3.4 Solid-phase Microextraction (SPME)

Solid-phase microextraction (SPME), was developed by Pawliszyn and co-workers in

1990 in an attempt to redress the limitations of inherent in SPE and LLE (Kataoka et

al., 2000). It is a new sample preparation technique using a fused-silica fiber that is

coated on the outside with an appropriate stationary phase. The analyte in the sample is

directly extracted and concentrated onto the fiber coating. The method saves

preparation time, solvent usage and disposal costs, and can improve the detection limits

(Pawliszyn, 1997). It has been used routinely in combination with GC and HPLC, and

successfully applied to a wide variety of compounds, especially for the extraction of

volatile and semivolatile organic compounds from environmental, biological and food

samples (Eisert and Levsen, 1996; Pawliszyn, 1997; Prosen and Zupancic-Kralj, 1999).

The SPME apparatus is a very simple device (Figure 3.9). It looks like modified

syringe consisting of a fiber holder and a fiber assembly, the latter containing a 1-2 cm

long retractable SPME fiber. The SPME fiber itself is a thin fused silica optical fiber,

coated with a thin polymer film, conventionally used as a coating material in

chromatography. There are two typical SPME applications, sampling gases (headspace,

HS) or sampling solutions (direct immersion, DI). In either case the SPME needle is

inserted into the appropriate position (e.g. through a septum into the headspace), the

needle protecting the fiber is retracted and the fiber is exposed to the environment. The

polymer coating acts like a sponge, concentrating the analytes by the absorption/

adsorption process. Extraction is based on a similar principle to chromatography,

based on gas-liquid or liquid-liquid partitioning. After sampling, the fiber is retracted

into the metal needle (for mechanical protection), and the next step is the transfer of the

137

analyte from the fiber into the chromatography instrument. Gas chromatography (GC)

is one of the preferred used techniques. In this case, thermal desorption of the analyte

takes place in the hot GC injector. After inserting the needle into the injector, the fiber

is pushed outside the metal needle. The other common option is analysis by HPLC,

where the needle is placed into a modified Valco valve. The fiber is exposed and the

analytes are eluted by the mobile phase. Chromatography and detection takes place in a

conventional manner.

Figure 3.9: Commercial SPME Device Made by Supelco (Kataoka et al., 2000)

Plunger

Barrel

Plunger retaining screw

Z-slot

Hub viewing window

Tensioning spring

Adjustable needle

guide/depth gauge

Sealing septum

Fused-silica fiber

Fiber attachment tubing

Septum piercing needle

138

The main advantages of SPME extraction compared to solvent extraction are the

reduction in solvent use, the combination of sampling and extraction into one step and

the ability to examine smaller sample sizes. It can also have high sensitivity and can be

used for polar and non-polar analytes in a wide range of matrices by linking to both GC

and LC.

3.4.1. Basic Extraction Theory

The theory of SPME has been amply presented by Pawliszyn and his workers

(Pawliszyn, 1997). Solid phase microextraction is based on multiphase equilibrium

processes. In this discussion, only three phases are considered: the fiber coating, the gas

phase or headspace and a homogeneous matrix. During the sampling period, the

analytes migrate among the three phases until an equilibrium is achieved (this is an

ideal system without taking into account the inhomogeneity of the matrix or chemical

or physical characteristics of the analyte, such as instability or degradation).

The total mass of analyte present during extraction is therefore represented by the

following mass balance relationship (Pawliszyn, 1997):

CoVs = CcVc + ChVh + CsVs (3.1)

Where

Co is the initial concentration of analyte in the matrix;

Cc, Ch, Cs are the equilibrium or final concentrations of analyte in the coating,

headspace and sample.

Vc, Vh, Vs are the volumes of coating, headspace and sample respectively.

139

The coating/headspace distribution coefficient can be defined as:

Kch = Cc/Ch (3.2)

And the headspace/sample distribution coefficient can be defined as

Khs = Ch/Cs (3.3)

The mass of the analyte absorbed on or in the coating is given by:

n = CcVc (3.4)

which can be further expressed using Equations (3.1) to (3.4)

(3.5)

Since

Kcs = KchKhs (3.6)

Equation (3.5) can therefore be simplified as:

(3.7)

It is significant that Equation (3.7) states that the amount of analyte extracted is

independent of the location of the fiber in the system. It may be placed directly in the

sample matrix or the headspace as long as the volume of the fiber coating, headspace

and sample are kept constant.

The three terms in the denominator of Equation (3.7) represent the capacities of each of

the three phases for the analyte: fiber coating (KcsVc), headspace (KhsVh) and the

sample matrix (Vs). If there is no headspace in the system (such as liquid phase

sampling from a completely filled vial, the term KhsVh can be eliminated from Equation

(3.7):

n =

KchKhsVcCoVs

KchKhsVc + KhsVh + Vs

n =

KcsVcCoVs

KcsVc + KhsVh + Vs

140

(3.8)

in many cases, the fiber coating/sample matrix distribution constant (Kcs) is relatively

small with respect to the phase ratio of sample matrix to coating volume (Vc << Vs). In

such case, the capacity of the sample matrix is significantly larger than the capacity of

the fiber coating and Equation (3.8) becomes:

n = KcsVcCo (3.9)

meaning that it is not necessary to sample a well-defined volume of sample because the

amount of analyte extracted by the fiber coating is independent of the sample volume

(Vs) provided the conditions

KcsVc << Vs or Vc << Vs (3.10)

are fulfilled. This implies that the analyte concentration ratio between the sample and

fiber coating at equilibrium must compensate for the several orders of magnitude

difference in volume between the two phases. Therefore, SPME sampling can be easily

adapted to field applications and can be used for direct sampling of unknown sample

volumes. The amount of analyte extracted will correspond directly to its concentration

in the matrix, without being dependent on the sample volume. The above equations are

limited to liquid polymer coatings where the extraction is based on absorption and

strongly related to the extraction phase volume. The method of analysis for solid

sorbent coatings is also similar for low analyte concentrations, since the total surface

area available for adsorption is proportional to the coating volume assuming that the

sorbent has constant porosity. High concentrations of the competitive interference

n =

KcsVcCoVs

KcsVc + Vs

Cc

Cs

141

compound can displace the target analyte from the surface of the sorbent. The simplest

way to consider these high concentration effects is to replace the volume of the fiber

coating, Vc in the above equations as a measure of the total fiber surface area by a

fraction of the original coating volume corresponding to a free surface area available

for adsorption.

3.4.2. Extraction Modes

There are currently three SPME modes that require either fused-silica fibers or GC

capillary columns. Headspace (HS) and direct immersion (DI) SPME are the two fiber

extraction modes, while the in-tube SPME mode is applied in the LC or HPLC

instrument.

In the DI-SPME mode, the fiber is inserted into the sample medium and the analytes

are transported directly to the extraction phase. For aqueous matrices, more efficient

agitation techniques, such as fast sample flow, rapid fiber or vial movement, stirring or

sonication are required. These actions are undertaken to reduce the effect caused by the

“depletion zone” which occurs close to the fiber as a result of fluid shielding and slow

diffusion of analytes in the liquid media. DI-SPME is the most common mode for

pesticide analysis, and is conducted by directly inserting the fiber into the sample

matrix. A method for the determination of seven OP pesticides in fruits and fruit juice

samples was developed and validated by Simplicio and Boas (1999). Mean recoveries

were all above 75.9% and below 102.6% for juice and between 70% and 99% for the

fruit samples. Limits of detection of the method for fruits and fruit juice matrices were

below 2 g/kg for all pesticides. Beltran et al. (2003) has developed a DI-SPME

142

method for the determination of seven pyrethroid pesticides in tomatoes and

strawberries. Detection limits for tomato and strawberry samples were between 0.003

and 0.025 mg/kg with RSD values of less than 25%. Residues of metobromuron,

monolinuron and linuron herbicides and their aniline homologs in carrots, onions and

potatoes have been quantified with DI-SPME with the polyacrylate (PA) fiber. A juice

was obtained from samples, then diluted, added with sodium chloride and buffered.

Recoveries obtained were between 76 – 95% with RSD values of less than 10%

(Berrada et al., 2004).

In the headspace sampling mode, the analyte is transported through a layer of gas

before reaching the coating. This protects the fiber coating from damage by high

molecular weight substances and other non-volatile concomitants present in the liquid

sample matrix, such as humic materials or proteins. The amount of analyte extracted at

equilibrium using DI or HS sampling are identical as long as the sample and gaseous

headspace volumes are the same. This is a result of the equilibrium concentration being

independent of the fiber location in the sample/headspace system. If the above

condition is not satisfied, a significant sensitivity difference between the direct and

headspace technique exists only for very volatile analytes. The choice of sampling

mode has a significant impact on the extraction kinetics. When the fiber coating is in

the headspace, the analytes are removed from the headspace first, followed by indirect

extraction from the matrix. Therefore, volatile analytes are extracted faster than

semivolatile components since they are at a higher concentration in the headspace,

which contributes to faster mass transport rates through the headspace. The temperature

has a significant effect on the kinetics of the process by determining the vapor pressure

143

of the analytes. The equilibrium times for volatile components are shorter in the

headspace SPME mode than for direct extraction under similar agitation conditions.

This outcome occurs as a result of two factors: a substantial portion of the analyte is in

the headspace prior to extraction, and the diffusion coefficients in the gas phase are

about four orders of magnitude greater than in the liquid media. Navalon et al. (2002)

determined the fungicides, pyrimethanil and kresoxim-methyl in green groceries by

HS-SPME. The analysis yielded good reproducibility with the RSD values between

7.4% and 15%. Lambropoulou and Albanis (2003) extracted and quantified seven OP

pesticide residues in strawberries and cherries in the HS-SPME at an LOD < 13 g/kg.

HS-SPME has been used to quantify eight pesticides in wine and fruit juice (Zambonin

et al., 2004).

In-tube SPME using an open tubular capillary column as the SPME device was

developed to couple directing with HPLC or LC-MS. It is suitable for automation, and

can continuously perform extraction, desorption and injection using a standard

autosampler. With the in-tube SPME technique, organic compounds in aqueous

samples are directly extracted from the sample into the internally coated stationary

phase of a capillary column, and then desorbed by introducing a moving stream of

mobile phase or static desorption solvent when the analytes are more strongly absorbed

onto the capillary coating. The capillaries selected have coatings similar to those of

commercially available SPME fibers. The capillary column is placed between the

injector loop and the injection needle of the HPLC autosampler. While the injection

syringe repeatedly draws and ejects samples from the vial under computer control, the

analytes partition from the sample matrix into the stationary phase until equilibrium is

reached.

144

Figure 3.10: Extraction Process by HS-SPME and DI-SPME, and Desorption Systems

for GC and HPLC Analyses (Kataoka et al., 2000)

SPME

holder

SPME fiber

assembly

Sample

Hot plate

stirrer (a) pierce

sample

septum

(b) Expose

fiber /

extract

(c) Retract

fiber /

remove

(a) pierce

sample

septum

(b) Expose

fiber /

extract

(c) Retract

fiber /

remove

(A) Extraction step for HS-SPME

(B) Extraction step for DI-SPME

(C) Thermal desorption on GC injection port

(D) Solvent desorption using SPME interface

SPME

holder

SPME fiber

assembly

Sample

Hot plate

stirrer

Detector

GC

column

SPME fiber assembly

Desorption chamber

Six-port valve

Additional

solvent

Mobile phase

from pump To LC

column

Waste

145

Subsequently, the extracted analytes are directly desorbed from the capillary coating by

mobile phase flow or by aspirating a desorption solvent. The desorbed analytes are

transported to the HPLC column for separation, and then detected with the UV or mass

selective detection. The method was first developed for the identification of phenylurea

herbicides in water samples by Eisert and Pawliszyn in 1997 (Krutz et al., 2003), but

has been expanded to the identification of phenoxy acid and carbamate herbicides (Gou

et al., 2000) and OP pesticides in untreated environmental water samples (Chafer-

Pericas et al., 2007). Mitani et al. (2003) applied an automated on-line method for the

determination of the isoflavones, daidzein and genistein in soybean foods by using in-

tube SPME coupled to HPLC. The detection limits obtained were 0.4 – 0.5 ng/mL and

the recoveries were above 97%.

Another potential advantage of in-tube SPME is that it can be easily coupled to

miniaturized chromatographic systems thus enhancing the sensitivity. This has been

illustrated for triazines by Chafer-Pericas et al. (2006). The limits of detections

obtained for such pesticides were about 250 – 500 times lower than those achieved by

using on-fibre SPME combined with conventional LC.

3.4.3 SPME Optimization

Several factors influence the SPME efficiency and these are evaluated during method

development. Solid phase microextraction is optimized by adjusting parameters that

control analyte absorption and desorption. The primary parameters influencing analyte

absorption into the stationary phase are fiber type, extraction time and temperature,

ionic strength, pH, sample volume and agitation. For SPME-GC, the analyte desorption

is a function of time and temperature.

146

3.4.3.1 Fiber Type

The fiber can be used for extraction of gases, the headspace of the solid and liquid

matrices or for direct immersion into the liquid matrix. The fiber is coated with a thin

polymeric film, which concentrates the organic analytes during absorption or

adsorption from the sample matrix. There are two mechanisms, absorption or

adsorption according to the nature of the fiber. If the fiber is a liquid phase, the analyte

are extracted by absorption; if the fiber is a porous particle blend, the analytes are

extracted by adsorption. Absorption is a non-competitive process where analyte

dissolve into the bulk of the liquid, whereas adsorption is a competitive process where

analytes bind to the surface of the solid (Pawliszyn, 1999). In the adsorption case, there

are a limited number of sites where analytes can bind to. When all the sites are

occupied, the fiber is saturated. Therefore the linear range of the adsorption-type fibers

is smaller than the one for absorption-type fibers. In a competitive process, analytes of

higher affinity for the coating can displace analytes of lower affinity for the fiber.

The extraction principle is based on the general rules of different types of equilibrium

such as gas-liquid (HS) or liquid-liquid (DI), which uses the PDMS

(polydimethylsiloxane) fiber (Ulrich, 2000). For gas-solid (HS) sampling, the

carboxene fiber is used (Ulrich, 2000). The extraction kinetics is strongly influenced by

different factors such as geometry, sample size and fiber parameters. The time of

extraction is increased with increased fiber thickness and lower diffusion coefficients of

the analyte molecule in the sample. The time of extraction to reach equilibrium may be

decreased with the use of any agitation such as stirring and ultrasonication. For the

perfect agitation, the extraction time depends only on the geometry of the fiber and the

147

analyte diffusion coefficients in the fiber. The most important feature determining the

analytical performance of SPME is the type and thickness of the coating material.

Table 3.3 lists the most commonly available polymer coatings. Stationary phases are

immobilized by various coating method such as non-bonded, bonded, and cross-

linking. Non-bonded coatings have no cross-linking agents and are therefore the least

stable. Non-bonded coatings are stable with a water-miscible organic solvent which is

up to 20% organic content only, but slight swelling may occur when used with non-

polar solvents. Cross-linked coatings have cross-linking agents such as vinyl groups

which interact with each other to form a more stable film, however they are not bonded

to the fused silica core. Cross-linked coatings are stable in most water-miscible

solvents. Bonded coatings are the most stable because they not only have cross-linking

agents which interact with each other but they also are bonded to the fused silica core

with silanol bonds. Bonded coatings are compatible with the majority of organic

solvents except for some non-polar solvents such as hexane and dichloromethane

(Pawliszyn, 1997)

148

Table 3.3: Summary of Commercially Available SPME Fibers

Fiber

coating

Film

thickness

( m)

Polarity Coating

method

Analyte

PDMS

100

30

7

Non-polar

Non-polar

Non-polar

Non-bonded

Non-bonded

Bonded

Volatiles

Non-polar semivolatiles

Medium to non-polar emivolatiles

PDMS-

DVB

65

60

Bipolar

Bipolar

Cross-linked

Cross-linked

Polar volatiles

General purpose

PA 85 Polar Cross-linked Polar semivolatiles

PDMS-

Carboxen

75

Bipolar Cross-linked Gases and volatiles

Carbowax-

DVB

65

Polar

Cross-linked

Polar analytes (alcohols)

CW - Carbowax

DVB - Divinylbenzene

PA - Polyacrylate

PDMS - Polydimethylsiloxane

PDMS, PA and CW coatings extract samples via the absorption of analytes, which

dissolve and diffuse into the coating material. The PDMS coatings (Figure 3.11) are

non-polar and are the most commonly used due to its versatility and durability. The PA

coating is polar and is a crystalline solid phase at room temperature, but turns into a

liquid at the temperatures typically used for desorption in the GC injector. The

diffusion of the analytes in and out of the coating is slower, hence the equilibrium times

are longer and the desorption temperature needs to be higher. The PA coating is

relatively solvent resistant and thermally stable, but is susceptible to oxidation at

elevated temperatures. Oxidation taking place at elevated temperatures in presence of

149

oxygen and will turn the fiber to blown. The CW coating is polar and water soluble. Its

coating method must be cross-linking in order to reduce its water solubility properties

(Pawliszyn, 1999).

Figure 3.11: Structure of Polydimethylsiloxane (PDMS) (Pawliszyn, 1999)

The remaining types: CW-DVB, PDMS-carboxen and PDMS-DVB are mixed coatings

and extract via adsorption with the analytes staying on the surface as a monolayer of

the fiber (Vas and Vekey, 2004). Figure 3.12 shows the structure of PDMS-carboxen

coating. The porous particles blends have different pore sizes, and extract analytes

based on their sizes. They can be placed into three categories: micropores (<20 Å),

mesopores (20-500 Å), and macropores (>500 Å). The carboxen coating consists of

mostly micropores, the divinylbenzene consists mostly of mesopores, and the template

resin consists mostly of macropores Mixed phase coatings express complementary

properties compared to single phase films, enabling the adsorption of a broad range of

analytes with different chemical characteristics. The film thickness is considerable

enhanced when porous particles are suspended in a liquid coating. Porous coatings are

able to extract considerable more analytes than non porous ones, especially when the

analytes of interest are highly volatile (Scheppers Wercinski, 1999).

150

Figure 3.12: Structure of PDMS-Carboxen Coating (Ray and Robert, 2001)

Some phases have a different thickness such as PDMS fiber has three different

thickness. There are 7, 30 and 100 m and this affects both the equilibrium time and

sensitivity of the method. The use of a thicker fiber requires a longer extraction time

but the recoveries are generally higher. The time of extraction is independent of the

concentration of analyte in the sample and the relative number of molecules extracted is

also independent of the concentration of analyte (Ulrich, 2000). Usually the thinnest

acceptable film is suitable for extracting the compounds which have the large

distribution constant value (> 10000) and it is employed to reduce the extraction times.

Before using a new fiber or after long term storage for a used fiber, conditioning is

necessary, by applying the maximum desorption temperature for 0.5 – 4 hours prior to

GC applications. High-purity carrier gases are essential for conditioning, because

some extraction phases can be easily oxidized by trace levels of oxygen. The new fibers

can be conditioned before LC-MS applications by stirring them in methanol for about

10 – 30 minutes. Fibers can be reused up to 20 – 150 times or more depending on the

sample matrix (Vas and Vekey, 2004).

151

3.4.3.2 Extraction Time and Temperature

In the fiber SPME method, the amount of analyte extracted onto the fiber depends not

only on the polarity and thickness of the stationary phase, but also on the extraction

time and the concentration of analyte in the sample. An optimal approach to SPME

analysis is to allow the analyte to reach equilibrium between the sample and the fiber

coating. The equilibration time is defined as the time after which the amount of analyte

extracted remains constant and corresponds, within the limits of experimental error, to

the amount extracted after an infinite time. Care should be taken when determining the

equilibration time since, in some cases, a substantial reduction of the slope of the

response curve might be wrongly interpreted as the point at which equilibrium is

reached. Determination of the amount extracted at equilibrium allows calculation of the

distribution constants.

When equilibrium times are excessively long, shorter extraction times can still be used.

However, in such cases the extraction time and mass transfer conditions must be strictly

controlled to assure good precision. At equilibrium, small variations in the extraction

time do not affect the amount of analyte extracted by the fiber. On the other hand, in

the region of the extraction time-response curve, even small variations in the extraction

time may result in significant variations in the amount extracted. Extraction

temperature is very important, especially for the extraction of semivolatile compounds.

Temperature has a great influence on the vapor pressure of the analytes. Extraction

temperature is closely related to equilibrium time because an increase of temperature

results in an increase of Henry‟s Law constant and of the diffusion coefficient between

the headspace and sample. This will lead to a decrease of the equilibrium time and will

152

accelerate the analytical process considerably. High temperature facilitates also the

release of analytes from the sample matrix. An adverse effect of higher temperature is

the decrease of the amount of analyte extracted at equilibrium. This can be explained

by the decease of the distribution constant between the fiber coating and headspace due

to the exothermic nature of the absorption process when temperature is rising. Thus,

extraction temperature should be optimized to the highest possible level which provides

satisfactory sensitivity and extraction rate.

3.4.3.3 Ionic Strength

SPME methods can be optimized by altering the ionic strength of the matrix. Typically,

analyte solubility decreases as ionic strength increases. A decrease in analyte solubility

improves sensitivity by promoting analyte partitioning into the stationary phase. This

“salting out” effect is compound-specific. The addition of salts is preferred for HS-

SPME because the fiber coatings are prone to damage during agitation by DI-SPME.

The effects of salt addition to enhance the extracted amount of an analyte by SPME

have been studied in detail (Zambonin et al., 2002; Beltran et al., 2003; Cai et al.,

2003; Berrada et al., 2004; Zuin et al., 2004). Salting with the addition of sodium

chloride is well known to improve extraction of organics from aqueous solution.

Although salt addition usually increases the amount extracted, the opposite behavior is

also observed (Magdic and Boyd-Boland, 1996; Scheyer and Morville, 2006). A high

salt concentration in the sample matrix facilitates salt deposition on the fiber which

decreases extraction efficiency over time by DI-SPME (Jinno and Muramatsu, 1996;

Berrada et al., 2000; Yao et al., 2001). In general, the effects of salt addition increase

with the polarity of the compound.

153

3.4.3.4 pH

Matrix pH can be adjusted to optimize the SPME of acidic and basic pesticides.

Extraction efficiency for acidic pesticides increases as pH decreases. At low pH, the

acid-base equilibria of acidic pesticides is shifted towards the neutral form and analyte

partitioning into the stationary phase is enhanced. Conversely, basic pesticides shift

towards the ionized from as pH decreases and extraction efficiency decreases.

Generally, extraction is more effective if the compounds are kept undissociated, which

is similar to the LLE and SPE procedures. In DI-SPME, contact of the fiber with high

and low pH solution would increase damage to the coating.

3.4.3.5 Agitation

Extraction efficiency is associated with the analyte‟s equilibrium between the sample

matrix and the stationary phase. The analyte equilibrium time depends on the rate of

mass transfer of the analytes in the aqueous phase. So, agitation is required to facilitate

mass transport between the bulk of the aqueous sample and the fiber. Table 3.4

summarizes the properties of several agitation methods which have been tested with

SPME.

Magnetic stirring is the most commonly used method in SPME experiments since it is

available in the majority of analytical laboratories and can be conveniently used with

all three SPME sampling modes. Extraction is efficient when fast rotational speeds are

applied. Frequently, the rotation of the magnetic bar cannot be controlled to give a

constant speed, which could cause variation in agitation conditions during the

extraction and change the equilibrium times. The net effect could be poor measurement

154

precision. In addition, the base plate may heat up during stirrer operation, resulting

in variations of the distribution constant, which can also affect reproducibility of the

measurement. Intrusive stirring can improve the agitation further, but it requires a direct

connection between the stirrer and the motor, which is difficult to seal.

Table 3.4: Agitation Methods in SPME

Method Advantages

Disadvantage

Static (no agitation)

Simple, performs well for

gaseous phase

Limited to volatile analytes and

HS-SPME

Magnetic stirring Common equipment, good

performance.

Requires stirring bar in the vial.

Intrusive stirring Very good performance

Difficult to seal the sample

Vortex/moving vial Good performance, no need for

stirring bar in the vial

Stress on needle and fiber

Fiber movement Good performance, no need for

a stirring bar in the vial

Stress on needle and fiber,

limited to small volume.

Flow through Good agitation at rapid flows Potential for cross

contamination, requires

constant flows

sonication Very short extraction times

Noisy, heats up the sample.

(Pawliszyn, 1997)

The needle vibration technique uses an external motor and a cam to generate a shaking

motion of the fiber and the vial. In the vortex technique, on the other hand, the vial is

moved rapidly in a circular motion. Both techniques can provide good agitation,

resulting in equilibration times similar to those obtained by magnetic stirring. However,

155

for the needle vibration technique, good performance is generally limited to small vials

via the direct extraction mode. Flow through techniques are very useful in continuous

monitoring applications and also can be automated. However, some additional flow

metering devices may be required to ensure reproducible agitation.

The most efficient agitation method evaluated to date for SPME applications is the

direct probe sonication, which can provide very short extraction times, approaching the

theoretical limits calculated for perfectly agitated samples. This technique has

substantial drawbacks associated with the large amount of energy introduced into the

system, which heats up the sample and in some cases, can destroy the analyte.

3.4.3.6 Sample Volume

For a given detection system, the sensitivity achieved with SPME methodology is

dependent solely on the number of moles of analyte extracted from the sample, as

evident from Equation (3.8), (3.9) and (3.10). If the sample volume greatly exceeds

coating volume (Vs >> Vc), the amount of analyte extracted is independent of the

volume of sample and the distribution equilibrium is achieved. If the available sample

volume is not significantly greater than the coating volume, then it is necessary to

measure sample volume. Analyte losses via evaporation, adsorption or microbiological

activity must be minimized.

156

In HS-SPME, the volume and sample/gas contact area affects the kinetics of the

process, since the analytes need to be transported through the interface and the

headspace, in order to reach the fiber. The smaller the gas phase is with respect to the

sample, the more rapid is the transport of analytes from the sample matrix to the fiber

coating. In static SPME, the vial cross section will determine the mass transfer rate

between the sample and the headspace. The magnitude of the convection produced for

the same agitation technique is dependent on the shape of the vial. Long thin vials may

be difficult to stir uniformly compared to larger diameter vials. For example, a sample

contained in a standard 2 ml vial is difficult to agitate with magnetic stirring because of

the small diameter. Even though the small sample volumes can frequently provide

conditions close to optimum sensitivity, proper quantitation of heterogeneous samples

may require a larger volume of material to correctly represent the investigated system.

3.4.3.7 Desorption Time and Temperature

Efficient thermal desorption of an analyte in a GC injection port is dependent on the

analyte volatility, the thickness of the fiber coating, injection depth, injector

temperature and exposure time. For a regular liquid sample injection in a split/splitless

injector, the insert has to have a large volume (3-5 mm i.d.) because of the solvent

expansion. However, the rate of the linear flow around the SPME fiber obtained with

such a large volume is too low, and thus the mass transfer is slow. Since little or no

solvent is present in the case of SPME, a narrow bore (0.75 mm i.d.) unpacked

injection liner is required to ensure a high liner gas flow, to reduce desorption time and

prevent peak broadening. Injections are carried out in the splitless mode to ensure

complete transfer of analyte to increase sensitivity (Figure 3.13) (Pawliszyn, 1997).

157

Figure 3.13: GC liners. The Right Liner is suitable for SPME Desorption

The needle exposure depth should be adjusted to place the fiber in the center of the hot

injector zone. Generally, the optimal desorption temperature is approximately equal to

the boiling point of the least volatile analyte. To prevent peak broadening, the initial

GC column temperature should be kept low, or even cooled. Thus, pre-concentration of

analytes at the head of the column is achieved. The desorption time depends on the

injector temperature and the linear flow rate around the fiber. For non-polar, volatile

compounds, desorption is virtually complete in a few seconds, but the desorption

should be continued for another one or two minutes to ascertain that no carryover

occurs when a blank is inserted after a sample.

SPME methods for the analysis of pesticides in fruits and vegetables are listed in Table

3.5.

d = 3 mm

V = 7.1 x 10-7

m3

u = 0.24 cm s-1

d = 0.75 mm

V = 5.0 x 10-8

m3

u = 3.3 cm s-1

158

Table 3.5: SPME Methods for the Analysis of Pesticides in Fruits and Vegetables

Analytes Matrix Fiber

type

Mode SPME Extraction conditions Detection Recoveries

(%)

Precisions

(%)

LOD Ref.

7 OPs Pear fruits

and juice

100 µm

PDMS

DI 20 g samples was comminuted and

homogenized with 60 mL of water;

homogenate of 4 mL was further

diluted to 100 mL with water; 3 mL stirred sample extracted for 20 min

at room temperature; desorption at

250 oC for 2 min.

GC-FPD 50.5-102.6 1.4-13.0 0.3-

1.4

µg/L

(Simplicio

and Boas,

1999)

Dichlorvos Vegetables 100 µm

PDMS

HS Extraction over a slurry of 2 g of

vegetables and 20 mL of water; 20 mL stirred sample with 2 g NaCl

extracted for 10 min at 132 W

microwave power; pH 5; desorption at 220

oC for 3 min

GC-ECD 106.1 5.5-7.9 1

µg/L

(Chen et

al., 2002)

2 fungicides Grapes,

strawberries, Tomatoes

and ketchup

85 µm

PA

HS 6 g of diluted samples (dilution 1:2

in weight, buffer Brintton-Robinson 0.2 M, pH 7) with 2.16 g NaCl

extracted for 25 min at 100 oC;

desorption at 250 oC for 5 min.

GC-MS 91.2-107.2 7.4-15.0 1.8-

3.1 ng/g

(Navalon

et al., 2002)

4 triazoles strawberries 85 µm

PA

DI 50 g was homogenized and

centrifuged. 25 g mixed with 40 mL water and centrifuged. Then topped

with salt water (0.2 g/mL of NaCl)

to 100 mL. 5 mL stirred sample extracted for 45 min at 50

oC;

desorption at 250 oC for 5 min.

GC-MS n.r n.r 30-

100 ng/kg

(Zambonin

et al., 2002)

159

Table 3.5: SPME Methods for the Analysis of Pesticides in Fruits and Vegetables (continued)

Analytes Matrix Fiber

type

Mode SPME Extraction conditions Detection Recoveries

(%)

Precisions

(%)

LOD Ref.

7 PY Tomatoes,

strawberries

65 µm

PDMS/

DVB

DI Extraction over a slurry of 0.5 g

samples and 2.5 mL of water, 0.5 g

NaCl and 200 µL of hexane/

acetone (1:1, v/v) added in and shaken with ultrasonic bath for 30

min. 3 mL stirred sample extracted

for 30 min at 40 oC; desorption at

270 oC for 5 min

GC-MS n.r 7-25 0.003-

0.025

mg/kg

(Beltran et

al., 2003)

7 OPs Strawberries,

cheries

100 µm

PDMS

HS 5 mL diluted (30 or 50 %, v/v

water content) sample containing 15% (w/v) Na2SO4 extracted for 60

min at 75 oC; preheating period 10

min; desorption at 240 oC for 5

min

GC-MS 74-91 5-19 5.2-

12.7 µg/kg

(Lambropo

ulou and Albanis,

2003)

Ph Carrots, potatoes,

onions

85 µm PA

DI 5 mL juice from 50 g out of 2 kg sample was diluted with 25 mL

water. 2mL stirred sample with

14% NaCl in pH 4 or 11 extracted

for 60 min at 22 oC; desorption at

300 oC for 5 min

GC-NPD 76-95 3-8 n.r (Berrada et al., 2004)

8 OPs Orange, grape, and

lemon juice

85 µm PA

DI Fruit juice diluted with water (1:25). 5 mL stirred sample

extracted for 30 min at room

temperature; desorption at 250 oC

for 5 min

GC-MS 65-100 4-12 2-90 ng/mL

(Zambonin et al.,

2004)

160

Table 3.5: SPME Methods for the Analysis of Pesticides in Fruits and Vegetables (continued)

Analytes Matrix Fiber type Mode SPME Extraction conditions Detection Recoveries

(%)

Precisions

(%)

LOD Ref.

12 OCs Radish 60 µm

C[4]/OH-

TSO

HS 100 g of radish was comminuted and

homogenized with 100 mL;

homogenate of 25 g was further

diluted to 100 mL with water; 4 mL stirred sample with 1 g K2SO4

extracted for 30 min at 70 oC;

desorption at 270 oC for 2 min

GC-ECD 78.4-119.3 < 13.1 1.27-

174

ng/kg

(Dong et

al., 2005b)

8 OPs Apple

juice, apple,

tomato

Vinyl

crown ether

polar fiber

HS/DI HS-SPME: 15 mL of diluted apple

juice (1:30) with 5 g NaCl, extracted

for 45 min at 70 oC. DI-SPME: 15

mL homogenized apple (1:50) and

tomato (1:70) dilution with 5 g

NaCl, extracted for 60 min at 30 oC;

desorption at 270 oC for 5 min

GC-FPD 55.3-106.4 1.4-11.2 0.003-

0.09

ng/g

(Cai et al.,

2006)

5 OPs Fruit juice 85 µm PA HS Extraction over a slurry of 5 g of fruit and 5 mL of water; 3 mL stirred

sample with 0.8 g NaCl extracted for

20 min at 70 oC; preheating period

15 min; desorption at 230 oC for 4

min.

GC-NPD n.r 2.5 - 8 0.03-3 ng/mL

(Fytianos et al.,

2006)

HS: headspace, DI: direct immersion, PDMS: polydimethylsiloxane,

PA: polyacrylate, DVB: divinylbenzene, Ph : Phenylureas

Py: Pyrethroids OPs: organophosphorus OCs: organochlorines

n.r.: not reported

161

3.5 Alternative Techniques

3.5.1 Single-drop Microextraction (SDME)

Recently, alternative but SPME related concepts have been introduced for sample

extraction. The use of a single droplet for extraction purposes was first recommended in

the mid-1990s (Mester and Sturgeon, 2005). Figure 3.14 shows one possible

embodiment of SDME employing a microsyringe. The syringe needle is used to pierce

the septum of a closed container. When the tip of the needle is in the desired position

(in the aqueous phase or in the headspace) a hanging droplet of solvent is exposed to

the matrix by pressing the plunger of the syringe. After extraction is completed, the

droplet is withdrawn into the syringe barrel by lifting the plunger. The extracted

samples can then be submitted directly to GC analysis. Thus the system requires two

discrete parts: the first for extraction and the second for injection.

Figure 3.14: Schematic Diagram of a SDME Setup (Mester and Sturgeon, 2005)

Stirring plate

Stirring bar

Sample vial

Sample solution

Droplet

Needle

Syringe body

162

Generally, SDME have been applied in the extraction of various types of pesticide

residues from different water samples (Lopez-Blanco et al., 2003; Lambropoulou et al.,

2004; Xiao et al., 2005; Ahmadi et al., 2006; Zhao, E. et al., 2006a). However, only a

very limited number of studies have been performed on fruit and vegetable samples

because of their complex matrices (Deng et al., 2006; Zhao, E. et al., 2006b). The

acceptor solvents that are used frequently are non-polar, saturated hydrocarbons, like n-

hexane, isooctane, carbon tetrachloride, chlorobenzene, n-hexyl ether and cyclohexane

have been used. Toluene appears to be the most commonly used acceptor phase,

because it is high solubility for the target analytes, is immiscible in water and stable

enough over the extraction time. Based on this solvent as acceptor phase, several

methods were validated and applied to the determination of OP and OC pesticides in

liquid and solid samples (Lambropoulou et al., 2004; Xiao et al., 2005; Zhao, E. et al.,

2006b). Carbon tetrachloride has also been successfully applied to the extraction of OP

pesticides (Ahmadi et al., 2006); this solvent is, however, more prone to dissolve or

become dislodged when long extraction periods are used. Isooctane and n-hexane have

been also used for the determination of OP and OC pesticides (Zhao, L. and Lee, 2001;

Lopez-Blanco et al., 2003).

After selection of organic solvent as acceptor phase, the second step consists of

determining the best volumes of the donor and acceptor phases. In the SDME

procedure, solvent volumes lower than 3 µL are commonly used, due to the instability

of the microdrop at higher values as well as to the good compatibility with the GC

instruments.

163

SDME involves dynamic partitioning of the target compounds between the acceptor

phase and the sample solution, and the extraction efficiency depends on the mass

transfer of analyte from the aqueous phase to the organic solvent phase. Since the mass

transfer is a time-dependent process, a graph representing the relationship between peak

area and extraction time is typically reported. Generally, extraction yield increases over

relatively long exposure times. Since SDME is not an exhaustive extraction technique,

it is not always practical to match extraction time at extraction equilibrium, because the

potential for solvent loss due to dissolution increases with time. Therefore, extraction

times are rarely set at equilibrium but rather at a point where sensitivity and precision

are maximized over an acceptable experimental time. For pesticide analysis, extraction

times of 15-30 min are usually selected.

Agitation is a critical parameter in SDME procedures. The mass transfer of the target

compounds to the organic solvent can be enhanced by agitation of the sample solution,

thereby reducing the time required to attain thermodynamic equilibrium. However,

excessive agitation could make a dislodgement of the acceptor phase and difficulties in

analyte quantification, especially with prolonged exposure time.

164

The “salting out” effect was studied, and the results showed that high salt

concentrations in the aqueous samples usually decrease the diffusion of analytes toward

the organic phase thus impairing the extraction. This effect is more pronounced in the

case of SDME and thus most of the studies have been performed without or with a

small amount of salt addition (Zhao, L. and Lee, 2001; Xiao et al., 2005; Ahmadi et al.,

2006). Caution should be taken when high salt concentrations are used in the sample

matrix, since under these conditions, in combination with the agitation of the sample,

the formation of air bubbles was promoted, increasing the incidents of drop loss or

dislodgement of organic solvent.

Optimization of extraction temperature is generally more important in the headspace

mode. Zhao, L and Lee (2001) have studied the effect of temperature on the extraction

efficiency of SDME for eight OC pesticides in aqueous samples. By varying the

temperature between 23 oC and 55

oC, they observed that in that case it is preferable to

increase the temperature to 50 oC in order to improve the extraction efficiency. They

also investigated the effect of drop depletion, caused by a higher temperature,

observing that solvent evaporation and drop instability appeared at a temperature higher

than 50 oC.

Zhao, E. et al. (2006b) studied the feasibility of determining OP pesticides in fruit

juices by SDME. It was necessary to dilute the juice samples 25 times with distilled

water in order to reduce the matrix effects and achieve adequate quantification by the

use of an external standard. The precision of the method applied to spiked fruit juices,

was acceptable, with RSD values below 14%. The LODs were between 0.98 and 2.20

165

g/L at optimum conditions. SDME has emerged as a viable sample preparation

approach to obtain acceptable analytical data. It can and has been shown to be routinely

applicable to real samples. Due to its simplicity, ease of implementation, and

insignificant startup cost, SDME is accessible to virtually all laboratories. However, it

has some limitations, for example: (a) in its most basic, direct immersion mode it

requires careful and elaborate manual operation because of the problem of drop

dislodgment and instability; (b) the SDME is affected by the presence of humic acids or

suspended solids indicating that it has a limited advantage in complex matrices, in

which extra filtration of the sample is necessary; (c) notwithstanding the acceptable

analytical performance mentioned above, the sensitivity and the precision of SDME

methods can be improved. The main issue lies with the adverse consequences of

prolonged extraction time and fast stirring rate, since they may result in drop

dissolution and dislodgement; (d) SDME is not yet suitable as a routine online pre-

concentration procedure. Although some progress has been made to automate SDME,

cost considerations will mean that the approach will not be widely accessible (Xu et al.,

2007). Table 3.6 lists the SDME methods for the analysis of pesticides in environment

matrix.

166

Table 3.6: SDME Methods for the Analysis of Pesticides in Environment Matrix

Analytes Matrix SDME Extraction Conditions

Recoveries

(%)

Precisions

(%)

LOD Ref.

Mode Solvent,

volume (µL)

Ext time

(min)

Stirring

(rpm)

8 OCs Tap water &

reservoir water

DI n-hexane, 3 25 400 83.3-98.3 4.9-11.9 0.02-0.2 µg/L (Zhao, L. and

Lee, 2001)

Endosulfan Tap & surface

water

DI Isooctane,

1.5

20 800 n.r 1.7-5.5 0.01 µg/L (Lopez-Blanco

et al., 2003)

10 OPs Surface water DI Toluene, 1.5 20 800 57-102 7.9-25 0.010-0.073

µg/L

(Lambropoulo

u et al., 2004)

6 OPs Lake water &

fruit juice

DI Toluene, 1.5 20 600 77.7-113.6 1.7-10 0.21-0.56

ng/mL

(Xiao et al.,

2005)

13 OPs Farm water DI Carbon

tetrachloride,

0.9

40 1300 91-104 1.1-8.6 0.001-0.005

µg/L

(Ahmadi et al.,

2006)

5 herbicides Natural water DI Toluene, 1.6 15 400 80-102 3.9-11.7 0.0002-0.114

µg/L

(Zhao, E. et

al., 2006a)

7 OPs Orange juice DI Toluene, 1.6 15 400 76.2-108 4.6-14.1 0.98-2.2 µg/L (Zhao, E. et

al., 2006b)

DI: direct immersion, OPs: organophosphorus pesticides OCs: organochlorine pesticides

167

3.5.2 Liquid-phase Microextraction (LPME)

Hollow fiber – liquid phase microextraction (HF-LPME) is a technique for further

development of the SDME technique. In this HF-LPME, the micro-extract is contained

within the lumen of a porous hollow fiber, so the micro-extract is not in direct contact

with the sample solution. As a result, samples may be stirred or vibrated vigorously

without any loss of the micro-extract. Thus, HF-LPME is a more robust and reliable

alternative for SDME. In addition, the equipment needed is very simple and

inexpensive.

Figure 3.15 illustrates the basic principle of HF-LPME. The aqueous sample of interest

is filled into a small sample vial, and a piece of a hollow fiber of porous polypropylene

is placed within this sample. The volume of aqueous sample is typically in the range

0.1 – 4 ml, depending on the application, and the length of the hollow fiber is normally

1.5 – 10 cm. Prior to extraction, the hollow fiber is soaked in an organic solvent to

immobilize the solvent in the pores of the hollow fiber, and excess solvent is removed.

The solvent is immiscible with water to ensure that it remains within the pores during

extraction with no leakage to the aqueous sample. The organic solvent forms a thin

layer within the wall of the hollow fiber, which typically has a thickness of 200 µm.

The total volume of organic solvent immobilized is typically 15 – 20 l for ion stable

analytes and the pH of the sample is adjusted to a value where the analytes are non-

ionic to reduce their solubility within the aqueous sample and to improve their

extractability into the organic phase. Thus, the analytes are extracted from the aqueous

sample, through the organic phase in the pores of the hollow fiber, and further into an

acceptor solution inside the lumen of the hollow fiber.

168

Figure 3.15: Schematic Diagram of a LPME Setup

(Rasmussen and Pedersen-Bjergaard, 2004)

To speed up this process, extensive agitation or stirring of the sample is applied. Two

phase LPME may be applied to most analytes having a solubility in an organic solvent

which is immiscible with water. The acceptor solution in this mode is directly

compatible with GC. Alternatively, the acceptor solution may be another aqueous phase

providing a three phase system. In this case, the analytes are extracted from an aqueous

sample, through the thin film of organic solvent and into an aqueous acceptor solution.

Polypropylene has been selected because it is highly compatible with a broad range of

organic solvents. In addition, with a pore size of approximately 0.2 m, polypropylene

strongly immobilizes the organic solvents used in LPME. This strong immobilization is

Stirring plate

Stirring bar

Sample vial

Sample solution

Organic solvent

GC Syringe

Porous hollow

fiber

Porous hollow fiber

Sample solution

Acceptor solution

Analytes

Immobilized organic

solvent

169

important for ensuring that the organic phase does not leak during extraction, as that

could alter extraction performance and the characteristics of the system. This is

especially critical because extraction devices are often vigorously agitated to speed up

the extraction. The organic solvent used within the pores of the hollow fiber has to

satisfy several conditions (Rasmussen and Pedersen-Bjergaard, 2004): (a) it should be

immiscible with water to prevent leakage; (b) it should be strongly immobilized in the

pores of the hollow fiber to prevent leakage; and (c) it should provide an appropriate

extraction selectivity to give high extraction recoveries. Toluene is one of the most

commonly used acceptor phases, which ideally should provide good immobilization in

the hollow fiber pores having a high solubility for the target analyte, and is immiscible

with water and stable enough over the extraction period.

The disposable nature of the hollow fiber eliminates the possibility of carry over effects

and cross contamination, thus providing enhanced reproducibility. Furthermore, the

small pore size prevents large molecules and particles present in the donor solution

from entering the acceptor phase, providing effective matrix/analyte separation. A

drawback of this technique is a lack of precision, which may be attributed to its manual

operation from fiber preparation and conditioning to the handling of small extract

volumes.

Similar to SDME, most applications to date of LPME have been for water or

environmental samples, where the technique has yielded detection limits comparable to

traditional liquid-liquid extraction. Two phase LPME has been applied to extract

organochlorine pesticides from seawater and pond water, followed by GC-MS analysis,

170

with LODs down to the 0.01 g/L level (Basheer et al., 2002; Hou et al., 2003). Three

phase LPME has been reported for the determination of aromatic amines and

nitrophenols in tap water, surface water, and seawater, and in these cases, the

compounds were detected down to approximately 0.1 g/L (Zhu et al., 2001; Zhao, L.

et al., 2002). Three phase LPME was also utilized for the analysis of phenoxy

herbicides in bovine milk; using HPLC-UV with LODs of about 1 ng/mL (Zhu et al.,

2002). Currently this technique has only been applied to water samples via extraction

and further cleanup would be needed for food analysis.

3.5.3 Stir-bar Sorptive Extraction (SBSE)

Stir bar sorptive extraction (SBSE) was developed by Baltussen et al. (1999) to

overcome the limited extraction capacity of SPME fibers. A glass stirrer bar is coated

with a potentially thick bonded absorbent layer (polydimethylsiloxane – PDMS) to give

a large surface area of stationary phase, leading to a higher phase ratio and hence a

better recovery and sample capacity (Figure 3.16). The advantages of sorptive

extraction using PDMS include predictable enrichment, the absence of displacement

effects, inertness, and rapid thermal desorption at mild temperature. Stir bar sorptive

extraction of a liquid sample is performed by placing a suitable amount of sample in a

headspace vial. The stir bar is added and the sample is stirred, typically for 30 - 240

min. the extraction time is controlled kinetically, determined by sample volume, stirring

speed, and stir bar dimensions and must be optimized for a given application.

171

Figure 3.16: Schematic Diagram of a SBSE Setup

Normally, SBSE is applied to the extraction of aqueous samples containing low

concentrations of organic compounds. For samples containing high concentrations of

solvents, the solutions should be diluted before extraction. For the extraction of highly

non-polar solutes, an organic modifier is added to minimize wall adsorption. Thurs, the

optimization of the organic modifier concentration is necessary.

After extraction, the stir bar is removed, then placed on a clean tissue paper, rinsed with

distilled water to remove water droplets, and introduced in a thermal desorption unit.

This step will avoid the formation of non-volatile material during the thermal

desorption step. Rinsing would not cause any solute loss, because the sorbed solutes are

present inside the PDMS phase. After thermal desorption, the stir bars can be reused.

Typically, the lifetime of a single stir bar is approximately 20 to 50 extractions,

depending on the matrix (David and Sandra, 2007).

Cap

Glass vial

Sample solution

PDMS stir bar

172

Since SBSE using PDMS coating is similar to liquid-liquid extraction using a non-polar

solvent, the technique is mainly used for non-fatty matrices ( < 3% fat). The analysis of

pesticides in fruits and vegetables (Wennrich et al., 2001; Blasco et al., 2002; Sandra et

al., 2003; Juan-Garcia et al., 2004; Juan-Garcia et al., 2005; Zuin et al., 2006) has been

described. After homogenization, the fruit and vegetable samples are extracted using a

water miscible solvent. An aliquot of the extract is diluted with water and followed by

SBSE. Both LC-MS desorption and thermal desorption GC-MS have been used. In a

study on the detection of fungicide residues in grapes, good correlation was obtained in

comparison to SPE (Juan-Garcia et al., 2004). A comparison of steam distillation

extraction (SDE) and SBSE for the determination of volatile organic constituents of

grape juice (Caven-Quantrill and Buglass, 2006) showed that SBSE was more

sensitive, although the recoveries and reproducibility were not as efficient. Similar

conclusions were drawn by Zuin et al. (2006) who compared SBSE to membrane

assisted solvent extraction (MASE) for the determination of pesticide and

benzo[a]pyrene residues in Brazilian sugarcane juice. Generally faster analysis and

better recoveries were achieved using MASE, whereas greater sensitivity and

repeatability were obtained with SBSE. Blasco et al. (2002) investigated the use of

SBSE for the analysis of pesticide in oranges by LC-MS and concluded that, although

good sensitivity was obtained, but the technique has certain disadvantages such as very

poor or low recovery of polar pesticides. Demyttenaere et al. (2003) compared SBSE to

SPME for the analysis of alcoholic beverages and concluded that SBSE was more

sensitive with improved reproducibility and less artifact formation.

173

SBSE can be applied as a multiresidue method for a wide range of pesticides. However,

since the log Kow values of pesticides cover a very wide range, it is not possible to find

the optimum extraction conditions for all solutes. For the most non-polar pesticides, a

high content of organic modifier (acetonitrile, methanol) is recommended to reduce

wall adsorption and matrix effects. For polar pesticides, a high modifier content will

lead to lower recovery. Applications of SBSE in food analysis are increasing, but due to

the limitation of the PDMS phase, it is still currently limited to non-fatty food matrices

and non-polar or semi-polar analytes.

174

CHAPTER 4

EXPERIMENTAL

4.1 Materials

4.1.1 Chemicals and Reagents

All solvents used were HPLC grade. Acetic acid, acetone, acetonitrile, carbonic acid,

ethyl acetate, isooctane, methanol, n-hexane and toluene were purchased from Fisher

Scientific, Loughborough, U.K. Ammonium sulfate, anhydrous sodium sulfate, sodium

acetate, sodium chloride, sodium carbonate, sodium dihydrogen phosphate, sodium

hydrogen phosphate and sodium phosphate were purchased from J. T. Baker, New

Jersey, U.S.A. Ultra-pure distilled water and methanol were filtered through a 0.45 µm

membrane filter purchased from Millipore.

4.1.2 Standards

Eleven pesticides standards which are widely used by local farmers in fruit and

vegetable cultivation (Suzuki, 2003), namely, acephate, carbaryl, chlorpyrifos,

chlorothalonil, diazinon, dimethoate, malathion, profenofos, quinalphos, α-endosulfan

and β-endosulfan were more than 95% pure and purchased from AccuStandard Inc.

New Haven CT. U.S.A. The use of high purity reagents and solvents help to minimize

interference problems. 1-chloro-4-fluorobenzene (98.0%) was purchased from

AccuStandard and used as the internal standard in the pesticide formulation analysis

and multiresidue analysis of pesticides in fruits and vegetables via GC-ECD.

Tetracosane was purchased from AccuStandard and used as the internal standard in the

multiresidue analysis of pesticides in fruits and vegetables via GC-MS.

175

4.1.3 Glassware

All glassware were scrupulously cleaned to minimize interference problems. First, all

glassware was cleaned thoroughly using a detergent and a bottle brush and rinsed with

tap water. Then, the glassware was soaked overnight in a chromic acid bath which was

prepared by adding potassium dichromate (K2Cr2O7) to concentrated sulfuric acid

(H2SO4) until saturation was reached. After that, the glassware was rinsed with

abundant tap water and distilled water, and then dried in a drying oven at 105 oC. The

glassware was then capped with aluminium foil and stored in a cupboard to prevent any

accumulation of dust or other contaminants. The glassware was rinsed with acetone

prior to use.

4.1.4 Apparatus

(a) Food Processor – National MX 897 GM

(b) Rotary Vacuum Evaporator – Buchi Waterbath B – 480

(c) Weighing Instrument – Mettler Toledo AG245

(d) Ultrasonicator – Branson 3200

(e) Visiprep Solid Phase Extraction Vacuum Manifold – Supelco 12-port model

(f) Hot-plate Stirrer – Fisher SWT 960-030A

(g) Thermostatic Water Bath – Fisher FB 51691

176

4.1.5 Materials for Solid-phase Microextraction (SPME), Solid-phase Extraction

(SPE) and Single-drop Miroextraction (SDME)

The SPME device (manual syringe holder and fibers) were purchased from Supelco,

Bellefonte, PA, U.S.A. with a 7.5 cm of fiber attached to a 15 cm stainless steel needle

that is inserted inside the plunger of a Hamilton syringe (model, 7005) was used. Five

types of fibers: 7 µm PDMS (polydimethylsiloxane), 30 µm PDMS, 100 µm PDMS, 85

µm PA (polyacrylate), and 65 µm PDMS/DVB (divinylbenzene) were tested.

The SPE procedure was performed using a RP LC-18 (octadecyl - 10% C, endcapped)

sorbent with a surface area of 900 m2/g, particle size 80 – 160 µm and was purchased

from Supelco, Bellefonte, PA, U.S.A.

The SDME is performed by using a 10 µL Hamilton gas-tight microsyinge with a bevel

needle tip (length: 5.1 cm, I.D.: 0.013 cm, bevel 22o, model 1701), which was

purchased from Hamilton, Bonaduz, Switerland.

4.2 Instrumentation

4.2.1 Gas Chromatography – Electron Capture Detector (GC-ECD)

A Shimadzu GC 17A version 2.21 gas chromatograph with an electron capture detector

was used. A SGE BPX5, 30 m x 0.32 mm i.d. capillary column with a 0.25 m film

was used in combination with the following oven temperature program: initial

temperature 120 oC, then heated at 7

oC/min to a final temperature at 250

oC and held

for 4.5 min. The total run time was 23.07 min. A silanized narrow-bore injected liner

177

(0.75 mm ID) for the SPME injections was installed and the fiber was inserted into this

injector using the splitless mode. The injector temperature was set at 250 oC and the

detector temperature was set at 300 oC. Nitrogen gas (99.999%) was used as the carrier

gas with a gas flow at 24.4 cm/sec linear velocity and the pressure maintained at 94

kPa.

The possible parameters which can affect the performance of the GC-ECD are the

injection port temperature, detector temperature, column flow and equilibrium time.

Pesticide standard mixture solutions at concentrations of 0.5 – 50 µg/L were used to

optimize the performance of the GC-ECD.

4.2.2 Gas Chromatography – Mass Spectrometry (GC-MS)

GC-MS analysis was carried out using a Hewlett-Packard system 6890 gas

chromatograph coupled with a HP model 5972A quadrupole mass spectrometer. Data

acquisition and processing were provided by the Vectra VL 5/90 Series 3 computer

equipped with HPG 1030A Chemstation data system was used. The pesticides were

separated on a CB5-MS 5% phenyl-methylpolysiloxane 30 m x 0.25 mm i.d., 0.25 m

film capillary column. The splitless mode was used for the injection together with a

SPME silanized narrow-bore injected liner. Positive identification of compounds was

based on comparison of GC retention times and mass spectra of authentic compounds.

The column temperature was held at 80 oC for 2 min, then heated to 180

oC at a heating

rate of 30 oC/min, then heated to 200

oC at a heating rate of 1.5

oC/min. Finally

temperature was increased to 280 oC at a rate of 20

oC/min and held for 8 min. The total

run time was 30.66 min. Helium gas was used as the carrier gas with a flow rate 1.3

178

mL/min (linear velocity = 42 cm/sec). The injection port temperature and transfer line

temperature were maintained at 260 oC and 300

oC respectively. The ion source

temperature was set at 300 oC for the 70 eV electron impact modes. The dwell time was

adjusted so that the number of cycles per second was 1.5 throughout the

chromatographic run, providing a sufficient number of chromatographic points for all

compounds. The solvent delay time was set at 8 min. Selected ion monitoring (SIM)

mode was used in the quantitation. The most abundant and characteristic mass fragment

ion was chosen for quantification and two other ions for confirmation.

To improve the overall performance of a GC-MS for better sensitivity, numerous

parameters were optimized such as the injection port temperature, interface

temperature, column flow and purge off time. The pesticide standard mixture solutions

at concentrations of 1.5 – 15 mg/L were used to test the performance of the GC-MS

instrument by using the full scan mode (m/z 50 - 400 a.m.u)

4.3 Pesticide Residue Analysis

4.3.1 Standard Stock Solutions

All pesticides were dissolved in methanol at 1000 mg/L concentrations as the main

stock solution. Then, the mixed standard stock solution containing all the eleven

pesticides was prepared by pooling aliquots of the individual pure pesticide standard

solutions and then diluting with methanol. For GC-ECD analysis, a range of standard

mixture stock solutions containing 0.5 – 50 mg/L were prepared in methanol and stored

at 4 oC. Preparation of different concentration levels of stock solution is due to their

sensitivity to the ECD detector. Working standard solutions of a mixture of pesticides

179

were freshly prepared daily by volume dilution in distilled water. 1-chloro-4-

fluorobenzene (200 µg/L) was used as the internal standard to compensate for sample

and injection volume changes and was added to the vial prior to GC-ECD analysis. For

GC-MS analysis, stock solutions of each pesticide at different concentration level 0.25

– 1.75 g/L were prepared in methanol and stored at 4 oC. Tetracosane (C24H50, 2 mg/L)

was used as the internal standard. All these working standard solutions of a mixture of

pesticides were prepared for calibration and recovery tests.

4.3.2 Samples

In the multiclass and multiresidue analysis of pesticides in fruits and vegetables,

pesticide recovery studies were performed on three types of fruits namely strawberry

(fragaria ananassa), star fruit (averrhoa carambola) and guava (psidium guajava) and

three types of vegetables namely cucumber (cucumis sativus), tomato (lycopersicon

esculentum) and pakchoi (brassica parachinensis) which were obtained from a

pesticide-free farm in the Malaysian Agricultural Research and Development Institute

(MARDI). A known volume of each standard stock solution was added to the blank

control samples to obtain spiked control samples. Recoveries of pesticides were

determined by comparison of the ratio of the analyte against internal standard from the

spiked samples with that of the standard calibration solutions.

For pesticide formulations, the crude samples of pesticide were obtained from the

Department of Agriculture (DOA), Ministry of Agriculture, Malaysia, and a local

supplier, namely, Sin Theong Sdn. Bhd. Table 4.1 shows the list of generic pesticides

used in this study.

180

Table 4.1. The Generic Pesticides Used in the Pesticide Formulation Experiments.

No Brand Name of

Commercial

Formulation

Active

ingredient

Physical form Labelled

Value

(%)

Source

1

Ortin

Acephate

Soluble powder

73.0

DOA

2 Wesco 85 Carbaryl Soluble powder 85.0 Sin Theong

3 Lorsban Chlorpyrifos Emulsify concentrate 37.1 DOA

4 Chlorothalonil Soluble concentrate 12.30 DOA

5 WA Diazinon Diazinon Emulsify concentrate 55.0 Sin Theong

6 Rogor Dimethoate Emulsify concentrate 40.0 DOA

7 Wesco 84 Malathion Emulsify concentrate 84.0 Sin Theong

8 Selecron 500 EC Profenofos Emulsify concentrate 45.0 Sin Theong

9 Sandoz Quinalphos Emulsify concentrate 10.9 DOA

4.3.3 Sample Preparation

4.3.3.1 Solid-phase Microextraction (SPME)

For solid-phase microextraction, pesticide-free fruits and vegetables (100 g) were

weighed and finely chopped. A subsample of 30 g was accurately weighed and placed

in a 150 mL beaker. Aliquots of 0.3 mL (low), 1.8 mL (medium) and 6.0 mL (high) of

the stock solution at three concentration levels respectively were spiked into the

samples drop by drop to provide the spiked control samples. After being kept at room

temperature for 1 hour, the spiked sample was added with 30 g of distilled water,

blended and homogenized in a food processor. Then, the samples were placed in

separate vials.

181

1.0 g of the homogenized spiked sample was introduced into a 15 mL clear glass vial

and topped up with distilled water until 5.00 g. The sample was then added with the

internal standard and capped with a PTFE-faced silicone septum. The mixture was

shaken for 10 minutes in an ultrasonic bath. For direct immersion (DI) – SPME, the

fiber was directly immersed into a slurry sample. In the other technique, the fiber was

exposed to the headspace above the sample in the headspace (HS) – SPME mode.

Finally, thermal desorption of the analytes was achieved by inserting the sorbent fiber

into the GC injection port. Figure 4.1 shows the flow chart of the multiresidue analysis

of the pesticides using the SPME method.

Preliminary experiments were carried out to evaluate the SPME method by comparing

five coating materials with different polarities and thickness. Five different fibers: 7 µm

PDMS (polydimethylsiloxane), 30 µm PDMS, 100 µm PDMS, 85 µm PA

(polyacrylate), and 65 µm PDMS/DVB (divinylbenzene) were tested. Optimization of

the main parameters affecting the SPME analysis was investigated by employing

spiked aqueous solutions. These were the effects of extraction time and temperature,

the effect of stirring rate, the effect of ionic strength, the effect of pH, the effects of

desorption time and temperature, the effect of fiber depth in the injector, fiber coating

lifetime, the effects of dilution and organic solvent on sample extraction. In this study,

four organic solvents: acetone, acetonitrile, ethyl acetate and methanol were

investigated. In the optimization, pesticide-free fruit and vegetable samples were spiked

with the appropriate amount of the standard solutions.

182

Figure 4.1. Flow Chart of Multiresidue Analysis of Pesticides using the SPME Method

Fruit or vegetable

(100 g)

Chopped sample

(30 g)

- Weighed & chopped

Spiked sample

Homogenized spiked

sample

Slurry sample (5g)

DI- SPME

- Spiked with standard stock solution

- Kept at room temperature (1 hour)

- Added with 30 g distilled water

- blended

Homogenized spiked

sample (1.0 g)

- Weighed

- Topped with distilled water until 5.00 g

- Added with internal standard

- Ultrasonicated for 10 min

HS- SPME

GC analysis

183

For the effect of washing on pesticide residues by different solutions, pesticide-free

samples (100 g) were soaked in spiked tap water for 1 hour which was prepared by

dissolving 2 mL (0.5 – 50 mg/L) of standard mixture stock solutions in 2 L of tap

water. Then, the spiked samples were air dried overnight at room temperature. The dry

spiked samples were soaked for 10 min and 30 min in (i) an acidic reagent of 5% and

10% acetic acid solution; (ii) an alkaline reagent of 5% and 10% sodium carbonate

solution; (iii) a neutral reagent of 5% and 10% sodium chloride solution; and (iv) tap

water, respectively. The treated samples were air-dried overnight at room temperature,

and then analyzed by HS-SPME -GC-ECD.

4.3.3.2 Solid-phase Extraction (SPE)

In the solid-phase extraction (SPE) analysis, the samples (100 g) were finely chopped

and homogenized with a food processor. 10 g of the homogenized sample was placed

in a 250 mL conical flask. 100 µL of the standard mixture of the stock solution was

spiked into the sample. The sample was thoroughly mixed and the extraction solvent

(20 mL) was added. The sample was sonicated for 15 min in an ultrasonic water bath to

homogenize the sample solution. The supernatant liquid was filtered and concentrated

to 1 mL under a gentle stream of nitrogen.

RP LC18 supelclean SPE tubes (100 mg/mL) were used. First, anhydrous sodium sulfate

(1.0 mg) was loaded on the SPE tube prior to conditioning with 2 mL methanol,

followed by 2 mL distilled water. During the conditioning and sample loading step,

precautions were taken to prevent the sorbent from drying up. A Visiprep Vacuum

Manifold was used for the simultaneous extractions of 12 samples. Then, 0.5 mL of

184

sample solution was transferred to the reservoir which was partially filled with distilled

water. Sample loading was performed under vacuum using a flow rate of 5 mL/min.

After that, the sorbent was dried by vacuum aspiration under increased vacuum for 15

min. Three 2 mL of eluting solvent was used to elute the pesticides and the eluates were

collected in a 15 mL tube under gravity flow. Then, the eluate was evaporated to 1 mL

under a gentle stream of nitrogen and the solvent was changed to methanol by adding

two 2 mL portions of methanol and evaporating to a small volume after each addition.

The extract was transferred to a 5 mL GC vial and concentrated to 1 mL by a gentle

stream of nitrogen gas. 100 µL of the internal standard solution was added to the vial

and 2 µL was injected into the GC-ECD.

For the SPE procedure, the analysis was carried out using a mixture of acetone : ethyl

acetate : n-hexane (10:80:10, v/v/v) as the extraction solvent and 5% acetone in n-

hexane as the eluent on a LC18-silica SPE (100 mg/mL) cartridge with the flow rate of

5 mL/min.

185

Figure 4.2. Flow Chart of Multiresidue Analysis of Pesticides using the SPE Method

Homogenized sample

(10g)

Eluted sample

- Spiked with standard stock solution

Concentrated sample

(1.0 mL)

Extracted sample

Spiked sample

- Extracted with 20 mL extraction solvent

- Ultrasonicated for 15 min

- Filtered

- Concentrated with nitrogen gas

Concentrated sample

(0.5 mL)

- Measured with syringe

- Conditioning of SPE tubes with 2 mL

MeOH & 2 mL distilled water.

- Cleanup with SPE LC-18, 100 mg/mL

- Eluted with 3 x 2 mL eluting solvent

- Solvent changed to MeOH with 2 x 2 mL.

- Evaporate to 1 mL with nitrogen gas

- Spiked with 100 µL internal standard

GC analysis

186

4.3.3.3 Single-drop Microextraction (SDME)

The fruit and vegetable sample preparation for the SDME procedure is the same as that

for the SPME method. A 10 µL microsyringe with a bevel needle tip (Hamilton) was

used for introducing the microdrop to the sample. Before each extraction, the

microsyringe was washed at least 10 times with the solvent in order to eliminate the

bubbles in the barrel and the needle. The sample solution is agitated with a magnetic

stirrer by means of a 10 mm x 5 mm stir bar. A specific volume of organic solvent is

drawn into the microsyringe before the extraction. The mirosyringe was fixed with a

stand and clamp and then inserted through the septum of the sample vial (15 mL

capacity) and the tip of needle was located approximately 1 cm above the surface of the

stirred solution. The plunger is pushed down to expose the microdrop above the stirred

solution for a fixed period. After the extraction is completed, the drop was retracted

into the microsyringe and injected directly into the GC-ECD inlet for chromatographic

analysis. A fixed concentration of internal standard was prepared in the extracting

solvent. The analytical signal is taken as the peak area ratio of the analyte to the

internal standard. Optimization of the main parameters affecting the SDME was

evaluated by the selection of an appropriate extraction solvent, the drop volume, the

effects of extraction time and temperature, the effects of stirring rate and ionic strength.

187

4.3.3.4 Pesticide Formulations

In the pesticide formulation analysis, the sample solution was prepared by weighing

1.00 g of the sample in a 100 mL volumetric flask and then made up to the volume with

methanol. The solution was serially diluted to the concentration range of interest with

methanol and a known constant amount of the internal standard was added. Then, the

solution was sonicated for 10 min in an ultrasonic bath to homogenize the sample

solutions before it was injected into the GC-ECD system for quantitative analysis.

4.4 Validation of Quantitative Chromatography Method

4.4.1 Calibration Curve (Linearity)

The calibration graph of each pesticide was constructed using samples spiked with six

different concentrations of standard mixture solutions. The calibration standard mixture

solutions over the concentration range of interest were prepared by serial dilution of the

mixed standard stock solution with methanol as describe in Section 4.3.1. and then

spiked to the fruit and vegetable samples. The detector response linearity was examined

over six concentration ranges, the analyte peaks obtained were integrated and plotted as

functions of concentration. The standard mixture solutions were analyzed in triplicates

by GC-ECD and GC-MS at each concentration level.

188

4.4.2 Precision and Accuracy

The precision of an analytical method is the agreement within a series of individual

measurements of an analyte when the analytical procedure is applied repeatedly to the

multiple aliquots of a single homogeneous volume of sample matrix (Shah, 2001). The

accuracy of an analytical method is the degree of agreement between the true value of

the analyte in the sample and the experimentally determined value. Both precision and

accuracy can be calculated from the same analytical experiment.

Three different spiked concentrations of the sample and three replicates for each

concentration were analyzed at three different occasions together with a calibration

curve and the intra- and inter-day precision and accuracy were calculated. The accuracy

was determined as the mean of the measured value relative to the theoretical spiked

values and is reported as a percentage (%). The precision is denoted by the intra- and

inter-day relative standard deviation (RSD).

4.4.3 Selectivity / Specificity

Selectivity is the ability to assess unequivocally the analyte in the presence of other

components, which may be expected to be present such as impurities, degradation

products, competition between the analytes and matrix components. The selectivity of

the method was assessed by comparing the chromatograms obtained after injection of

blank samples without and with the addition of analytes. Each of the analytes was

injected separately to ensure that no interfering impurities with the same retention times

were present.

189

4.4.4 Limits of Detection (LOD) and Limits of Quantification (LOQ)

The LOD is the lowest concentration of analyte in a sample that can be detected but not

necessarily quantified, under the stated conditions of the test. The LOQ known also as

the limit of reporting, is the lowest concentration of an analyte that can be determined

with an acceptable precision and accuracy under the stated conditions of test. The LOD

and LOQ were evaluated as the signal-to-noise ratios of 3:1 and 10:1, respectively. The

LOD and LOQ in sample were evaluated for each pesticide as follows:

(a) Retention times were determined by running the chromatogram of a standard

solution.

(b) The fiber was exposed to the distilled water and a blank was performed. From

this chromatogram, the average noise levels were measured.

(c) The concentration that led to a signal three or ten times the noise level was

evaluated using the average of the peak areas obtained from three injections of

the standard solution and taking into account the values of the noise level.

To determine the LOD and LOQ in blank fruit and vegetable sample, a pesticide-free

sample was used as a blank sample and then spiked with different concentration levels

of the mixed standard stock solution that led to a signal three or ten times of the noise

level.

190

4.4.5 Recovery

Recovery tests were carried out based on the addition of known amounts of pesticides

to the fruit and vegetable samples. Since SPME is a non-exhaustive extraction

procedure and for this reason the relative recovery, defined as the ratio of the

concentration found in samples and working solution, spiked with the same amount of

analytes, instead of the absolute recovery (used in exhaustive extraction procedure) was

employed. The recoveries and linearity of the method was examined on pesticide-free

fruit and vegetable samples. The percentage recovery was determined for triplicate

samples at three concentration levels.

4.5 Pesticide Formulations

In the determination of pesticide formulations, a series of standard mixture stock

solutions for GC-ECD analysis were prepared by serially diluting with methanol until

the six concentration levels were obtained. To each calibration standard, a known

constant amount of internal standard (1-chloro-4-fluorobenzene, 200 µg/L) was added.

The response of the peak area against concentration of standard solutions and internal

standard was tabulated. The Response Factor (RF) for each analyte was calculated

using the following equation. The RF is a unitless value:

Where, AS – Response for the analyte to be measured

AIS – Response for the internal standard

CIS – Concentration of internal standard

CS – Concentration of the analyte to be measured

RF = (AS) (CIS)

(AIS) (CS)

191

The average RF can be used for calculation if the RF value within the working range is

constant (20% RSD or less). Alternatively, the result can be used to plot a calibration

curve of response ratio (AS/AIS) vs. CS. Then, the concentration of the active ingredient

in the commercial formulation was calculated from the peak area value at a particular

retention time, interpolated in a calibration graph prepared for pure standards spiked

with the internal standard and using the response ratio data for each injection.

Chromatographic method validation consisting of method specificity, linearity,

precision and accuracy was undertaken in order to demonstrate the suitability of the

analytical method for the determination of nine active ingredients in the pesticide

formulations.

192

CHAPTER 5

RESULTS AND DISCUSSION

5.1 Optimization of Chromatographic Conditions

5.1.1 Gas Chromatography – Electron Capture Detector (GC-ECD)

GC-ECD analysis requires properly optimized GC parameters to obtain the sensitivity

expected. Numerous splitless parameters, which can affect the performance of the GC-

ECD, namely injection port temperature, detector temperature, column flow and

equilibrium time need to be optimized for the best separation. The standard mixture of

11 pesticides solutions at concentrations of 0.5 – 50 µg/L were used to optimize the

performance of the GC-ECD. The standard mixture solution was run three times for

each parameter value and the three values were averaged. The average peak area values

were tabulated and the graphs were plotted. The optimum parameters were determined

from the graphs.

5.1.1.1 Injection Port Temperature

The injection port temperature must be relatively high, consistent with the thermal

stability of the sample, to give the fastest rate of the vaporization and to get the sample

into the column in a small volume. High resolution with the narrow band of the peaks is

obtained when using the high injection port temperature. However, the rubber septum

can degrade and cause the dirtying of the injection port if too high an injection port

temperature is used.

193

In this experiment, the injection port temperature was determined with the standard

mixture of 11 pesticides at the temperature values from 210 oC to 270

oC, while the

other parameters were held constant. Figure 5.1 shows a graphical presentation of the

results from the optimization of the injection port temperature. The results show that all

the OP and carbamate pesticides, acephate, chlorpyrifos, dimethoate, diazinon,

malathion, profenofos, quinalphos and carbaryl which have high vapor pressures ( > 1.0

x 10-7

mm Hg) attain the highest sensitivity at 230 – 240 oC. For the OC pesticides,

chlorothalonil, α-endosulfan and β-endosulfan with the lower vapor pressure ( < 1.0 x

10-7

mm Hg) attain the highest sensitivity at 250 oC. Thus, an injection port temperature

of 250 oC was selected for this study to ensure complete vaporization of all the

investigated compounds and to minimize the residence time in the inlet.

Figure 5.1: Effect on Peak Area at Various Injector Port Temperatures (GC-ECD)

0

1

2

3

4

5

6

7

8

9

200 210 220 230 240 250 260 270 280

Pea

k A

rea

x 10

0000

Injection Port Temperature (oC)

Acephate Carbaryl Dimethoate Diazinon

Chlorothalonil Malathion Chlorpyrifos Quinalphos

α-Endosulfan Profenofos β-Endosulfan

194

5.1.1.2 Detector Temperature

The detector temperature must be high enough to prevent condensation of the sample

components. The detector temperature was determined with the standard mixture of 11

pesticides at the temperature ranges of 180 oC to 320

oC. Temperatures above 320

oC

were not considered owing to the detector limitations. The results from Figure 5.2 show

that the effect of the detector temperature on the ECD response is compound-specific.

The OC pesticides such as chlorothalonil, α-endosulfan and β-endosulfan yielded the

highest response at a detector temperature of 300 oC. This is because the halogen

containing compounds followed the dissociative mechanism which is favoured at a

higher detector temperature ( > 250 oC). The OP and carbamate pesticides which are

based on non-dissociative process showed the highest response at a detector

temperature of 250 oC. In this study, a detector temperature of 300

oC was deemed

appropriate to ensure high response for all the investigated pesticides.

Figure 5.2: Effect on Peak Area at Various Detector Temperatures (GC-ECD)

0

1

2

3

4

5

6

7

8

9

170 190 210 230 250 270 290 310 330

Pea

k A

rea

x 10

0000

Detector Temperature (oC)




195

5.1.1.3 Column Flow Rate

The band spreading can be minimized by using the optimum column flow rate in order

to attain the maximum efficiency. Section 2.5 has detailed the effect of the column flow

rate on the separation efficiency. To optimize the column flow rate, a standard mixture

of 11 pesticides was injected three times into the GC-ECD for each flow rate value

from 0.7 mL/min to 1.7 mL/min, while holding the other parameters constant. Figure

5.3 shows the effect on peak area at various column flow rates. The results show a

flow rate of 1.3 mL/min giving the highest sensitivity for most of the investigated

pesticides. Therefore, a column flow rate of 1.3 mL/min was chosen as the optimum

column flow rate in this study.

Figure 5.3: Effect on Peak Area at Various Column Flow Rates (GC-ECD)

0

1

2

3

4

5

6

7

8

0.6 0.8 1 1.2 1.4 1.6 1.8

Pea

k A

rea

x 10

0000

Column Flow Rate (mL/min)




196

5.1.1.4 Equilibrium Time

The equilibrium time has to be long enough to assure that all the injected compounds

reach the column. Equilibrium time is critical when there are peaks of interest eluting

near the solvent tail as these peaks would be hidden under the tail. Optimizing the

equilibrium time is a compromise between the amount of sample reaching the column

and the width of the solvent peak. The optimum equilibrium time is dependent on all

other injection variables and is determined after all the other parameters are optimized.

In this study, the optimization of the equilibrium time was carried out from 0.5 min to

1.5 min. Figure 5.3 shows the graph of peak area versus equilibrium time. The results

show that the highest sensitivity was attained between 1.0 min to 1.5 min. However, an

equilibrium time of 1.0 min was chosen because the amount of contaminants

transferring from the liner to the column will increase if the equilibrium time is

prolonged and the total run time will also increase. Table 5.1 shows the optimum

parameters and the temperature programming conditions for the GC-ECD.

Figure 5.4: Effect on Peak Area at Various Equilibration Times (GC-ECD)

0

1

2

3

4

5

6

7

8

0.4 0.6 0.8 1 1.2 1.4 1.6

Pea

k A

rea

x 1

000

00

Equilibrium Time (min)




197

Table 5.1: Optimum Parameters and the Temperature Programming Conditions for the

GC-ECD

Parameter Optimum Value

Injection mode

Injection Port Temperature

Detector Temperature

Carrier Gas

Column Flow Rate

Pressure

Total Flow

Linear Velocity

Split Ratio

Equilibrium Time

Initial Oven Temperature

Hold Time

Rate 1

Final Oven Temperature

Hold Time

Total Run time

Injection Volume

Split

250 oC

300 oC

N2

1.3 mL/min

94 kPa

31.0 mL/min

24.4 cm/sec

20 : 1

1.0 min

120 oC

0 min

7 oC/min

250 oC

4.5 min

23.07 min

2 µL

5.1.2 Gas Chromatography – Mass Spectrometry (GC-MS)

GC-MS analysis at the trace level requires a system that is performing at its best. If the

gas chromatograph is not properly optimized, the mass spectrometer may not give the

sensitivity expected. This could be due to the sample not making it from the injection

port to the ion source, resulting in the absence of a signal from the detector.

Furthermore, if the chemical noise from the gas chromatograph is too high, the signal-

to-noise ratio will be reduced. The pesticide standard mixture solution at concentrations

of 1.5 – 15 mg/L was used to test the performance of the GC-MS instrument by using

the full scan mode (m/z 50 - 400 a.m.u). The standard mixture solution that was run

three times for each parameter value was then averaged and the graphs were plotted

from the average data. From the graphs, the optimum parameters were determined.

198

5.1.2.1 Injection Port Temperature

In GC, the rate of migration of a compound is controlled by the distribution equilibrium

between the stationary and the mobile phases. The rate of migration is also dependent

on the solubility in the stationary phase and its vapor pressure. Thus, an optimum inlet

temperature which must be high enough to completely vaporize the sample and

minimize its residence time in the inlet is important. However, the lowest temperature

that accomplishes this is preferred because it will reduce the sample decomposition and

minimize the flashback. A lower inlet temperature in the ranges of 200 - 270 oC can be

utilized on the splitless injection because it allows a longer time for vaporization of the

injected sample and its transfer to the column. This would reduce both sample

degradation and septum bleed. The injection port temperature was optimized in the

ranges of 210 - 270 oC while keeping the other parameters constant.

The graph of mean peak areas versus injection port temperature for all 11 pesticides

was plotted as shown in Figure 5.5. From the graph, it can be seen that the highest

sensitivity is obtained at 250 oC for most of the compounds especially the OP and

carbarmate pesticides with have high vapor pressures. However, for the OC pesticides

while have low vapor pressures, highest sensitivity is attained at 260 oC. Thus, in order

to ensure complete vaporization of all investigated pesticides and also to minimize the

residence time in the inlet, an injection port temperature of 260 oC was chosen in this

study.

199

Figure 5.5: Effect on Peak Area at Various Injection Port Temperatures (GC-MS)

5.1.2.2 Interface Temperature

The interface temperature must be high enough so as not to cause a cold spot for

condensation of the analytes. Temperature is a compromise between speed, sensitivity

and resolution. At high temperatures, the sample components spend most of their time

in the gas phase and so they are eluted quickly, but the resolution is poor. At low

temperatures, they spend more time in the stationary phase and elute slowly; resolution

is increased but sensitivity is decreased due to increase band spreading of the peaks.

For the GC-MS used in this study, there is a limited range where by the GC interface

should be operated in the ranges of 250 – 320 oC. Thus the interface temperature was

determined with the pesticide standard mixture solution at these temperature ranges of

250 – 320 oC.

0

20

40

60

80

100

120

140

160

180

205 215 225 235 245 255 265 275

x 10

0000

Acephate Dimethoate Carbaryl Chlorothalonil

Diazinon Malathion Chlorpyrifos Quinalphos


Injection Port Temperature (oC)

Pe

akA

rea

200

The graph is as shown in Figure 5.6. It shows that the interface temperatures between

250 – 320 oC are hot enough for all the investigated pesticides except malathion. For

malathion, the highest sensitivity was achieved at 300 oC and after that the sensitivity

decreases gradually. Malathion has the lowest melting point (2.85 oC) among the

pesticides. Therefore, there is a high possibility that temperatures above 300 oC can

cause decomposition of malathion. Thus, an interface temperature of 300 oC was

chosen for this study so that the ion source (its temperature is the same as the interface

temperature for this GC-MS) is not constantly overheated and its lifetime can be

prolonged.

Figure 5.6: Effect on Peak Area at Various Interface Temperatures (GC-MS)

0

20

40

60

80

100

120

140

160

180

240 250 260 270 280 290 300 310 320 330

x 10

0000




Interface Temperature (oC)

Pea

kA

rea

201

5.1.2.3 Column Flow Rate

The higher the column flow rate, the faster the analysis, but the lower the separation

between analytes. Selecting the column flow rate is therefore the same compromise

between the level of separation and length of analysis as selecting the column

temperature. Column flow rates at higher values are preferred for splitless injection.

The high column flow rate can decrease the residence time of the sample in the inlet

and reduce the flashback and decomposition of the sample. The relationships between

the column flow rate with the theoretical plate and column length has been explained

on the Section 2.5. In this study, the column flow rates from 0.6 – 1.8 mL/min were

tested while the other parameters were kept constant.

Figure 5.7 shows the effect on peak area at various column flow rates for the standard

mixture of 11 pesticides. From the graph, a flow rate of 1.3 mL/min resulting the

highest sensitivity for most of the investigated pesticides. Thus, a flow rate of 1.3

mL/min was selected as the optimum flow rate in this study.

Figure 5.7: Effect on Peak Area at Various Column Flow Rates (GC-MS)

0

20

40

60

80

100

120

140

160

180

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9

x 10

000

0




Column Flow Rate (mL/min)

Pea

kA

rea

202

5.1.2.4 Purge-off Time

In GC-MS, purge-off time also known as purge delay time or splitless valve time is one

of the most important parameters. To ensure that all the injected pesticides reach the

column, the purge-off time has to be long enough. Optimum purge-off time is a

compromise between the amount of compound reaching the column and the sharpness

of the solvent peak. Optimal purge-off time is dependent on all other injection variables

and corresponds to a transfer of 95% to 99% of the compound to the column. Purging

becomes important only when there are peaks of interest eluting near the solvent tail, as

these peaks would be hidden under the tail. When analyzing solutes that elute on the

solvent tail, a short purge delay is preferred to reduce the solvent tail. A long purge

delay is preferred for analyzing the late eluting solutes. Purge-off time is determined

after all the other inlet parameters have been optimized.

The optimization of purge-off time was carried out from 0.6 - 1.8 min. Figure 5.8

shows the graph of effect on peak area at various purge-off times. It is observed that the

highest sensitivity was attained at 1.3 min while the second highest was achieved at 1.0

min. Purge-off time of 1.0 min was selected because too long of a purge-off time will

increase the amount of contaminants transferring from the liner to the column and also

will increase the total run time. Table 5.2 shows the optimum parameters and the

temperature programming conditions for GC-MS.

203

Figure 5.8: Effect on Peak Area at Various Purge-off Times (GC-MS)

Table 5.2: Optimum Parameters and the Temperature Programming Conditions

for GC-MS

Parameter Optimum Value

Injection mode

Injection Port Temperature

Interface Temperature

Carrier Gas

Column Flow Rate

Purge-off Time

Initial Oven Temperature

Hold Time

Rate 1

Oven Temperature 2

Hold Time

Rate 2

Oven Temperature 3

Hold Time

Rate 3

Final Oven Temperature

Hold Time

Total Run time

Injection Volume

Splitless

260 oC

300 oC

He

1.3 mL/min

1.0 min

80 oC

2 min

30 oC/min

180 oC

0.0 min

1.5 oC/min

200 oC

0 min

20 oC/min

280 oC

8 min

30.66 min

2 µL

0

20

40

60

80

100

120

140

160

180

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9

x 10

0000




Purge-Off Time (min)

Pe

akA

rea

204

5.1.3 Gas Chromatographic Separation

The pesticides studied in this study comprise several types of compounds: OP

pesticides, namely acephate, dimethoate, diazinon, malathion, chlorpyrifos, profenofos,

and quinalphos; OC pesticides, namely chlorothalonil, α-endosulfan and β-endosulfan;

carbamate pesticide, namely carbaryl. The separation of 11 pesticides studied was

optimized initially by GC-ECD. Different internal standards were tested and 1-chloro-

4-fluoro benzene was chosen because it is not a pesticide reagent and its detector

response was good. Figure 5.9 shows the chromatogram of the standard mixture of 11

pesticides solution and the internal standard under optimum conditions.

Figure 5.9: Chromatogram of the Standard Mixture of 11 Pesticides Solution and the

Internal Standard under Optimum Conditions (GC-ECD). IS, Internal standard, 2.69

min (200 g/L); 1. Acephate, 8.64 min (200 g/L); 2. Carbaryl, 10.07 min (200 g/L);

3. Dimethoate, 13.23 min (160 g/L); 4. Diazinon, 13.56 min (160 g/L);

5. Chlorothalonil, 14.72 min (80 g/L); 6. Malathion, 16.42 min (160 g/L);

7. Chlorpyrifos, 16.65 min (4 g/L); 8. Quinalphos, 18.28 min (160 g/L);

9. -Endosulfan, 19.37 min (2 g/L); 10. Profenofos, 19.76 min (20 g/L);

11. -Endosulfan, 21.83 min (4 g/L).

100000

150000

Intensity

50000

5 10 15 20 min

1

2

3

4 5

6

7

8

9

10

11

IS

205

As far as the ECD is concerned, the response was very different among the pesticides;

the OC pesticides gave a higher response than the others. The linearity of the response

for the standard mixtures of 11 pesticides and the internal standard was studied between

0.01 – 20000 µg/L, depending on to their sensitivity to the ECD detector. The

responses of most of them were linear in the ranges studied with regression coefficient

(r2) values between 0.9972 and 0.9995. The values obtained are shown in Table 5.3.

The limits of detection (S/N=3) were between 0.0002 and 0.2 µg/L.

Table 5.3: Monitoring Parameters, Linearity Ranges, Regression Coefficients (r2), and

LOD for GC-ECD

Compound Retention

Time (min)

Linear ranges

(µg/L)

r2 LOD (µg/L)

Internal Std

Acephate

Carbaryl

Dimethoate

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

β-Endosulfan

2.69

8.64

10.07

13.23

13.56

14.72

16.42

16.65

18.28

19.37

19.76

21.83

-

10-10000

50-20000

0.3-2000

0.3-2000

0.3-2000

10-10000

0.02-100

10-10000

0.01-50

0.05-350

0.05-350

-

0.9989

0.9972

0.9991

0.9990

0.9973

0.9992

0.9985

0.9984

0.9982

0.9995

0.9987

-

0.05

0.20

0.01

0.01

0.01

0.05

0.001

0.05

0.0002

0.001

0.001

For GC-MS, different internal standards were tested and finally tetracosane was chosen

because of its detector response and it is not used on the crops. The total ion

chromatogram of the standard mixture of 11 pesticides solution and the internal

standard under optimum conditions in the full scan acquisition mode is shown in Figure

5.10

206

Figure 5.10: Total Ion Chromatogram of the Standard mixture of 11 pesticides Solution

and the Internal Standard under Optimum Conditions in Full Scan Mode (GC-MS).

1. Acephate, 7.58 min (15 mg/L); 2. Dimethoate, 7.86 min (4.5 mg/L); 3. Carbaryl,

8.03 min (15 mg/L); 4. Chlorothalonil, 8.42 min (15 mg/L); 5. Diazinon, 9.96 min (4.5

g/L); 6. Malathion, 13.34 min (7.5 mg/L); 7. Chlorpyrifos, 13.83 min (3.0 mg/L);

8. Quinalphos, 16.39 min (9.0 mg/L); 9. -Endosulfan, 17.40 min (1.5 mg/L);

10. Profenofos, 19.19 min (10.6 mg/L); 11. -Endosulfan, 21.95 min (9.0 g/L).

IS. Internal standard, 24.75 min (2.0 mg/L).

In order to enhance the limits of detection, SIM acquisition was tested by selecting two

qualifier ions of each pesticide from the spectrum of each compound under EI

ionization. The pesticides in the sample extracts were identified according to their

retention times and ion ratios. The target compound must fall within the predetermined

retention time windows (± 0.02 min) and the ratio of the qualifier ion to the target ion

must be within the expected limits (>20%) when compared with those of the standard.

This is to ensure that only molecules which has a molecular or fragment ion at that ratio

will be sensed. The mass spectrum is generally characteristic for a given compound,

giving a certain „fingerprint‟ of the peaks at various m/z ratios.

400000

800000

Abundance

200000

5 10 15 20 min

1

2

3

4

5

6

7

8

9

10

11

IS

600000

25

207

For the SIM mode, fragment ions for each pesticide in the higher mass ranges are

usually preferred and chosen. This is because the probability of matrix component

interference is much reduced at higher masses. At higher mass range, the co-extractive

interference on the pesticide peaks and the fragment ions can be minimized or

eliminated. The SIM acquisition process was time scheduled and the corresponding

ions of each pesticide are shown in Table 5.4. The linearity was checked in the interval

0.0125 - 100 mg/L and the regression coefficients were between 0.9975 and 0.9999.

The detection limits were between 0.002 mg/L and 0.1 mg/L.

Table 5.4: Monitoring Parameters, Selected ions, Linearity Ranges, Regression

Coefficients (r2) and LOD for GC-MS under SIM Acquisition

Compound Retention

Time (min)

Monitoring

Time window

(min)

Target

Ion

Qualifier

Ions

Linear

ranges (mg/L)

r2 LOD

(mg/L)

Acephate Dimethoate

Carbaryl

Chlorothalonil Diazinon

Malathion

Chlorpyrifos

Quinalphos α-Endosulfan

Profenofos

β-Endosulfan Internal Std

7.58 7.86

8.03

8.42 9.96

13.34

13.83

16.39 17.40

19.19

21.95 24.75

0-7.72 7.72-7.95

7.95-8.23

8.23-8.62 8.62-11.65

11.65-13.58

13.58-14.08

14.08-16.90 16.90-17.91

17.91-20.09

20.09-23.33 23.33-27.00

136 197

144

266 304

285

314

298 339

374

207 98

94, 183 97, 229

115, 201

264, 268 179, 152

173, 125

197, 258

146, 241 195, 263,

208, 339

239, 339 322, 66

0.25-100 0.15-30

0.25-100

0.25-100 0.03-30

0.05-50

0.02-20

0.06-60 0.01-10

0.07-70

0.06-60 -

0.9978 0.9975

0.9992

0.9983 0.9994

0.9998

0.9995

0.9999 0.9987

0.9998

0.9992 -

0.1 0.02

0.1

0.1 0.02

0.02

0.01

0.02 0.002

0.02

0.02 -

208

From the results shown in Table 5.3 and 5.4, it can be seen that the linear ranges of GC-

ECD are better than those of GC-MS. In term of the LOD of these two methods, the

sensitivity of GC-ECD is much better than that of GC-MS. Thus, GC-ECD was chosen

as a main instrument in this study for method development on multiresidue analysis of

the pesticides in fruits and vegetables.

5.2 Multiresidue Analysis of Pesticide Residues in Fruits and Vegetables

5.2.1 Solid-phase Microextraction (SPME)

Results obtained by SPME can only be correctly interpreted if the conditions of

extraction are known and fully understood. Many factors affect the SPME and are

important for successful extraction, particularly in quantitative analysis. All the affected

parameters have to be optimized before validating the analytical methodology. Table

5.5 shows the physicochemical properties of the investigated pesticides.

Table 5.5: Physicochemical Properties of the Investigated Pesticides

Name Water solubility/ mgL-1

at 25 oC

Vapor pressure/

mm Hg

log Kow

Acephate

Carbaryl

Dimethoate

Diazinon

7.0 x 105

40

3.9 x 104

40

1.7 x 10-6

1.17 x 10-6

8.5 x 10-6

9.02 x 10-5

-0.89

1.59

0.70

3.30

Chlorothalonil 0.6 - 1.2 5.7 x 10-7

3.05

Malathion 130 3.94 x 10-5

2.75

Chlorpyrifos 2 2.02 x 10-5

4.69

Quinalphos 22 2.6 x 10-6

4.44

Profenofos 28 6.23 x 10-6

4.74

-Endosulfan 0.32 3.0 x 10-6

3.83

-Endosulfan 0.32 5.96 x 10-7

3.83

(Sakamoto and Tsutsumi, 2004)

209

5.2.1.1 Direct Immersion (DI) – SPME versus Headspace (HS) – SPME

The SPME procedure can be applied to the liquid (immersion) and to the vapour

(headspace). In these experiments, the vegetable samples, cucumber, spiked with the

standard mixture of eleven pesticides solutions at concentrations of 0.5 – 50 µg/L were

used. The volume of the aqueous sample was 5 mL and the headspace volume is 10

mL. The extraction was carried out by using 100 µm PDMS fiber for 30 min at 60 oC

under control constant gentle stirring speed. The standard mixture solution was run

three times and the three values were averaged. The average peak area values were

tabulated and the graph was plotted.

The results show that only eight out of eleven pesticides were detectable by SPME

method. Acephate, carbaryl and dimethoate could not be detected under any condition

used in this study. This may because these three compounds have very low Log Kow

value which is less than 2.0 and high solubility especially for acephate and dimethoate,

7.0 x 105

mg/L and 3.9 x 10

4 mg/L, respectively. Sakamoto and Tsutsumi (2004)

reported the same result showing that acephate and dimethoate were not detected by

HS-SPME-GC-MS using five different fiber coatings. Besides, carbaryl was not

suitable to be determined by SPME method because of its small peak and the band

broadening which was very difficult for quantitative treatment. Thus, only eight out of

eleven pesticides were analyzed by using the SPME method in the subsequent studies.

Figure 5.11 shows the amount of each of the investigated pesticides extracted by DI-

SPME and HS-SPME from the spiked vegetable samples (cucumber). Both techniques

are comparable in terms of their extraction efficiency for each of the pesticides in the

vegetable samples.

210

From Figure 5.11, it was found that the performance of HS-SPME is much better with

the high peak area response, than DI-SPME. A DI-SPME extraction of vegetable

samples may be compromised by the presence of interferences, caused by suspended

solids as well as dissolved substances (in particular pectins) resulting in reduced

extraction efficiency by formation of micelles, adsorbing the analytes and slowing

down their diffusion towards the fiber (Simplicio and Boas, 1999). Besides, the

response of diazinon, malathion and chlorpyrifos obtained by HS-SPME were more

than 100% higher than those with DI-SPME, this is due to their high vapor pressure (2

x 10-5

mm Hg to 9 x 10-5

mm Hg). Whereas the extraction efficiencies of the low vapor

pressure compounds such as chlorothalonil, α-endosulfan and β-endosulfan (≈ 6 x 10-7

mm Hg) with HS-SPME showed only slightly increase, 20% higher than those with DI-

SPME. The HS-SPME has been reported to be efficient for analytes with high and

medium Henry‟s Law constants (Doong et al., 2000). The results from this study

indicate that HS-SPME could also be applied to analyze the semi-volatile organic

compounds which have a low vapor pressures ( ≤10-7

mm Hg at 25 oC).

Figure 5.11: Comparison of the Pesticides Extracted by DI-SPME and HS-SPME from

the Spiked Vegetables

2.56 3.062.11

10.89

2.14

15.49

2.34

10.87

5.283.74 4.35

22.27

3.42

18.86

3.52

13.44

0

5

10

15

20

25

x 1

000

00

DI-SPME

HS-SPME

Pea

k A

rea

211

When DI-SPME was employed for the extraction of the analytes from vegetable

samples, a high interfering background in the chromatogram was obtained. In

comparison, the background obtained from HS-SPME analysis was cleaner. A lesser

interfering background will lead to better analysis. When compared with DI-SPME,

HS-SPME can shorten the time of extraction significantly because of the faster

diffusion rate of the analytes in the gaseous phase than in the liquid phase. Because the

fiber is not in direct contact with the sample, matrix effect can be reduced, enhancing

the life expectancy of the fiber. In this respect, it should be pointed out that the use of

HS-SPME is only feasible for solid samples, such as fruits and vegetables.

5.2.1.2 Selection of SPME Coating

The physicochemical properties of OC and OP pesticides are different, OC pesticides

are hydrophobic and OP pesticides are hydrophilic. Therefore, it is difficult to select a

coating material that would be optimum for all of them. Thus, it is necessary to check

the performance of different coatings and the coating that produces the most uniform

response for all the investigated pesticides will be selected. Preliminary experiments

were carried out to evaluate the SPME method by comparing five coating materials

with different polarities and thickness. Five different fibers: 7 µm PDMS

(polydimethylsiloxane), 30 µm PDMS, 100 µm PDMS, 85 µm PA (polyacrylate), and

65 µm PDMS/DVB (divinylbenzene) were tested. PDMS is commonly used for non-

polar molecules, PA is more approapriate for more polar pesticides, and PDMS/DVB is

a mixed phase consisting of the porous polymer particles of DVB suspended in a matrix

of PDMS that has complementary properties to the DVB. All fibers were conditioned in

the injector according to the instructions provided by the manufacturer.

212

The absorption or adsorption efficiencies of five different SPME fibers were then

determined for extracting eight pesticides in this study (Figure 5.12). According to the

results shown in Figure 5.12, it could seen that the 100 µm PDMS and 85 µm PA were

the most sensitive fiber coatings for the analysis of a spiked standard pesticide

solutions. The results also showed that compounds with the higher octanol-water

partition coefficient (log Kow) and low solubilities in water, such as chlorpyrifos, α-

endosulfan and β-endosulfan were the more extensively absorbed when the 100 µm

PDMS fiber was used due to the higher affinity to the non-polar fiber coating.

Figure 5.12: Comparison of the Adsorption Efficiencies of Five Different SPME Fibers

In contrast, when the 85 µm PA fiber was used, the non-polar pesticides were less

effectively extracted with a decrease absorbed amount of 20 – 30% of the total

absorption of the PDMS fiber. Compounds with higher polarities such as malathion and

diazinon were absorbed at a higher percentage (65 – 80%) by PA in relation to PDMS

0

5

10

15

20

25

x 10

0000

7 µm PDMS

30 µm PDMS

100 µm PDMS

85 µm PA

65 µm PDMS/DVB

Pea

k A

rea

213

fiber. Generally, the 85 µm PA gives a slightly low extraction efficiency than the 100

µm PDMS fiber which can be explained not only by the nature of the fiber or

compounds, but by the slightly larger volume of the PDMS fiber with respect to the

others and hence the larger capacity to absorb the analytes. The 65 µm PDMS/DVB

showed a low extraction efficiency than that of the 100 µm PDMS and also the 85 µm

PA, only showed about 20% peak area relation to the peak area of 100 µm PDMS. The

65 µm PDMS/DVB is mixed coating in which the primary extracting phase is a porous

solid, extracting analytes via adsorption. The number of surface sites where adsorption

can take place is limited and this type of fiber is more selective for volatile compounds.

The low extraction efficiency of 65 µm PDMS/DVB may also due to the fact that this

fiber does not have a polymer at the core and it has the smallest coating volume and

surface area. Goncalves and Alpendurada (2002) have compared three PDMS/DVB

fibers, including the 65 µm PDMS/DVB, for the analysis of multiresidue pesticides in

water. They observed that the 65 µm PDMS/DVB fiber tested has the lowest extraction

ability for OC, pyrethroid, OP and triazine pesticides.

For the same kind of coating, the thickness of the film also affects the extraction

efficiency. The amount of analytes extracted by the fiber coating was directly

partitioned into the volume of the coating, which was in accord with Equation (3.8). A

large coating volume (Vc) could retain more analytes and therefore will increase the

extraction efficiency of the fiber. In this study, the results showed that the 100 µm

PDMS was more effective than its 30 µm and 7 µm coatings for all the analytes. The 7

µm PDMS has the poorest performance, its extraction efficiency is only about 8% of

the 100 µm PDMS absorption. The absorption of the 30 µm PDMS phase is about 20%

of the 100 µm PDMS phase.

214

The 100 µm PDMS and 85 µm PA fibers showed the best extraction efficiency among

the five fibers that were studied. They are also the most popular coatings and are used

in the real samples analysis. The following experiments for optimizing the parameters

influencing the HS-SPME process were checked with these two types of fibers.

5.2.1.3 Effect of Extraction Time

Since the HS-SPME technique is an equilibrium process of the analytes between the

vapor phase and the fiber coating, it is important to determine the time required to reach

the equilibrium. When the analytes have low Henry‟s Law constant values and low

vapor pressures, they will need longer periods to reach the equilibrium. Furthermore,

analytes with high molecular masses are expected to require longer equilibrium times,

due to their lower diffusion coefficients because the equilibrium time is inversely

proportional to the diffusion coefficient (Bras et al., 2000). The effect of the extraction

time in the extraction yield was investigated by varying the times between 5 min to 150

min with a constant extraction temperature of 60 oC.

Under the above observed optimum conditions, extraction time profiles for PDMS and

PA fibers were generated for each of the pesticides and are presented in Figure 5.13 and

5.14., respectively. Each data point is the average of three independent measurements.

An unique absorption-time curve was produced, reflecting the affinity of the

investigated pesticides for the SPME fiber coating and the ECD response.

215

Figure 5.13: Effect of Extraction Time on Peak Area using a 100 µm PDMS Fiber

Figure 5.14: Effect of Extraction Time on Peak Area using a 85 µm PA Fiber

0

5

10

15

20

25

30

0 20 40 60 80 100 120 140 160

x 10

0000

Diazinon Chlorothalonil Malathion Chlorpyrifos

Quinalphos α-Endosulfan Profenofos β-Endosulfan

Pe

ak A

rea

Extraction Time (min)

0

5

10

15

20

25

0 20 40 60 80 100 120 140 160

x 10

0000




Pe

ak A

rea

216

For the PDMS fiber, the equilibrium time of most of the analytes is shorter and almost

reached after 60 min (Figure 5.13). The results showed that the equilibrium is

compound-dependent and can vary significantly between the different compounds.

Chlorpyrifos, α-endosulfan and β-endosulfan practically reached equilibrium after 30

min despite the high molecular mass especially for endosulfan compounds with a 100

µm PDMS fiber. These compounds have a better affinity with the PDMS fiber because

they have low polarities and better hydrophobicities. This also been observed by

Aguilar et al. (1998) who state that the more hydrophobic compounds (less polar) were

absorbed more readily by the polymeric phase.

The detector response for the 85 µm PA fiber is proportional to the absorption for the

first 60 min for all the analytes, reaching a plateau for most of the analytes after 90 min

which corresponds to the equilibration time (Figure 5.14). The state of matter of both

fibers phases is an important factor that influences the attainment of the equilibrium.

Since the PDMS coating is a viscous liquid polymer and the diffusion coefficient of the

analyte in it will be orders of magnitude higher than its diffusion coefficient in a solid

polymer of PA. Therefore, since the dynamics of mass transport in a well-stirred

solution is controlled by the diffusion coefficient of the analyte in the coating, the

extraction time required with a liquid polymer coating will be considerably less than

that required with a solid-phase polymer (Pawliszyn, 1997). Thus the longer

equilibrium time for the PA coating can be explained. Another limitation of PA for the

extraction of OC and OP pesticides is the more polar characteristic of its coating.

217

Although it is better to provide longer time for all the analytes reach their equilibrium,

it was important also to take into consideration other factors concerning sample

preparation time, loss of analyte during extraction due to their high hydrophobicities

and the sensitivity of the SPME method when deciding on the final extraction time.

Based on the previous reports, it is not required in the SPME analysis that the

equilibrium be reached, as long as the extraction is carefully timed and the mixing

conditions and extractions volumes remain constant (Santos et al., 1996). Also, the use

of the equilibrium time in the absorption step is not necessary if the limits of detection

(LOD) and relative standard deviation (RSD) values obtained are acceptable (Valor et

al., 1997). Since the above LOD and RSD limitations were fulfilled for the investigated

pesticides, an extraction time of 30 min has been selected for the extraction for both

PDMS and PA fibers.

5.2.1.4 Effect of Extraction Temperature

The extraction temperature plays an important role in the extraction process by

controlling the diffusion rate of the analytes into the coating. The effect of the

extraction temperature in the extraction yield was investigated by varying the

temperatures between room temperature (25 oC) to 95

oC with a constant extraction

time of 30 min.

Extraction curves are as shown in Figure 5.15 and 5.16, obtained with a 100 µm PDMS

and 85 µm PA fibers show clearly an increase in the amount of the analytes absorbed

when the temperature increases. However, when the sampling temperature exceeded 60

oC, there was a decrease in the amount of pesticides extracted except for chlorothalonil,

218

α-endosulfan and β-endosulfan. For more volatile compounds which show high vapor

pressures such as malathion and diazinon, their sensitivity is decreased after 60 oC. This

is because at the higher temperature, the partition coefficient from the gas phase in the

headspace into the fiber was reduced and the lower signal that was obtained could also

be attributed to the analyte instability, since malathion and diazinon are reported to

decompose at high temperatures (Tsoukali et al., 2005). For the less volatile

compounds which are more difficult to extract into the headspace such as

chlorothalonil, α-endosulfan and β-endosulfan, increasing the extraction temperature

enhances their sensitivity until 70oC.

Theoretically, there are three parameters affecting the absorption efficiency directly

which are affected by temperature that can account for the observed phenomenon. The

first parameter is the vapor pressure of the analytes in the headspace of the samples. At

higher temperature, the concentration of the analytes in the headspace is increased and

therefore, the extraction is more efficient at higher temperature (Lambropoulou and

Albanis, 2001).

219

Figure 5.15: Effect of Extraction Temperature on Peak Area using a 100 µm PDMS

Fiber

Figure 5.16: Effect of Extraction Temperature on Peak Area using a 85 µm PA Fiber

0

5

10

15

20

25

30

35

20 30 40 50 60 70 80 90 100

x 10

0000



Extraction Temperature (oC)

Pe

ak A

rea

0

1

2

3

4

5

6

7

8

9

20 30 40 50 60 70 80 90 100

x 10

0000




Pea

k A

rea

220

The second parameter is the absorption process on the SPME fiber coating. The rate of

diffusion of the analytes is increased at elevated temperature and therefore, the rate of

absorption of the analytes on the fiber is increased. At the same time, as the

temperature increases, the ability of SPME fiber coating to absorb organic compounds

begins to decrease. This is because absorption is an exothermic process and therefore,

disfavored at high temperatures. Thus, there would be an optimum temperature

whereby the extraction efficiency is maximized. Increasing the temperature beyond the

optimum temperature will have a negative effect on the extraction process (Valor et al.,

1997).

The final parameter is the stability of the compounds under high temperature

conditions. Degradation of the pesticides by hydrolysis may be accelerated at elevated

temperatures and thus, contributes to the fall in extraction efficiency. Therefore, for

analysis of the thermally labile compounds, it may be advisable to employ low

temperatures. Furthermore, an increase in water vapor pressure in the gas tight vial is

another cause of decrease in the sensitivity of HS-SPME when the extraction

temperature exceeds 60 oC. It was observed that at temperatures higher than 70

oC,

some air bubbles appeared in the solution and adhered on the fiber surface, which

might result in peak broadening and tailing when such fiber was directly introduced

into the GC column. From the results obtained in the study, the optimum extraction was

achieved at 60 oC and this temperature was selected for the subsequent experiments.

221

5.2.1.5 Effect of Stirring Rate

The intensity of the stirring is one of the important parameters that can affect the time

profile. In the headspace extraction, stirring of the sample matrix will accelerate the

migration of analytes from the aqueous sample to the gaseous phase by constantly

generating a fresh surface. When the analytes reach the gaseous phase, they will be

rapidly transported to the fiber by air convection. For the headspace study, stirring

should be vigorous and has to be maintained constant in all experiments. The actual

stirring rate required depends on the dimensions of the vial (15 mL) and the magnetic

stirring bar (5 x 10 mm). The optimum stirring rate was determined by analyzing the

spiked standard pesticide solutions at different stirring rates between 400 rpm and 1000

rpm. From the obtained results (Figure 5.17) it can be stated that the response increases

when the stirring speed is increased which agrees with the fact that SPME is a

technique based on equilibrium and that good diffusion through the phases is essential

to reach the equilibrium faster. Although the equilibrium time progressively decreases

with increasing agitation rate, the amount of the analyte extracted decreases at speeds

higher than 800 rpm. This is because at the maximum speed, the stirring bar begins to

vibrate and agitation of the sample is not uniform. This faster agitation tends to be

uncontrollable and the rotational speed might cause a change in the equilibrium time

and poor measurement precision. Moreover, it was observed that the signals are more

difficult to reproduce with high agitation speed, perhaps because some drops of the

liquid might be deposited on the surface of the fibers and alter its behavior. Thus, a

constant gentle stirring speed at 800 rpm was considered as the most adequate and was

used in all subsequent experiments to increase the rate of extraction.

222

Figure 5.17: Effect of Stirring Speed on Peak Area using a 100 µm PDMS Fiber

5.2.1.6 Effect of Ionic Strength

In the SPME procedure the salting out effect can be employed to modify the matrix by

adding salt, such as NaCl, Na2CO3 and (NH4)2SO4 to increase the ionic strength of the

water so as to decrease the solubility of the analytes and release more analytes into the

headspace, thereby contributing to enhanced absorption on the fiber. Saturation with a

salt can be used not only to enhance the detection limits of determination, but also to

normalize the influence of a random salt concentration in sample matrix. In order to

investigate the salting out effect, working water was prepared with salted water instead

of pure water. First, the effect of different types of salt in the extraction of the analytes

by the fiber was tested. A salted spiked standard pesticide solution samples with NaCl,

Na2CO3 and (NH4)2SO4 (10% w/v) was extracted using a 100 µm PDMS fiber. The

results from Figure 5.18 indicated that NaCl (10% w/v) was most effective in

increasing amount of the analytes extracted by the fiber. This effect from the addition

0

5

10

15

20

25

350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050

x 10

0000



Stirring Speed (rpm)

Pe

ak A

rea

223

of NaCl additives was also reported in other studies with various pesticides (Berrada et

al., 2000; Yao et al., 2001; Tsoukali et al., 2005; Scheyer and Morville, 2006).

The results on the effect of NaCl concentration added to the spiked standard pesticide

solution as the salting out agent for the tested fibers are shown in Figure 5.19 and 5.20.

The concentrations of NaCl added were varied between 0% and 30% (w/v). The

maximum concentration was 30% (w/v) because at this level, the saturation level of the

solution was reached. Beyond this level it was impossible to solubilise any more salt

crystals.

Figure 5.18: Effect of Various Types of Salt (10%, w/v) on Peak Area using a 100 µm

PDMS Fiber

0

20

40

60

80

100

120

140

x 10

0000

NaCl

Na2CO3

(NH4)2SO4

Pea

k A

rea

224

Figure 5.19: Effect of NaCl (%) on Peak Area using a 100 µm PDMS Fiber

Figure 5.20: Effect of NaCl (%) on Peak Area using a 85 µm PA Fiber

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30

x 10

0000



NaCl (%)

Pe

ak A

rea

0

5

10

15

20

25

30

0 5 10 15 20 25 30

x 10

0000



NaCl (%)

Pea

k A

rea

225

The salting out effect on the analytes has a relationship with their solubilities in the

aqueous phase (Santos et al., 1996). The greater the solubility of the analytes in water,

the greater the influence on absorption will be by the addition of salt. Thus, with

reference to the PDMS fiber the compounds with higher water solubilities such as

diazinon and malathion showed an increase in the extraction yield with the addition of

the concentration increasing of the NaCl until 30% (w/v). However, no effect or even a

decrease in extraction yield was observed for compounds with have low water

solubility after 10% (w/v) of NaCl. For the PA fiber, a similar behavior was observed

(Figure 5.20). These variable effects of the salt additives were also reported in other

studies with various pesticides (Santos et al., 1996; Scheyer and Morville, 2006). Thus

the optimum NaCl concentration for the extraction of the investigated pesticides was

fixed at 10% (w/v).

The resulting increase of extraction yield following an increase in salt concentration,

reaches a maximum, followed by a decrease in the amount extracted with further

increase in salt concentration. This behavior can be explained by considering two

simultaneously occurring processes. Initially, the analyte recovery is enhanced due to

the “salting out” effect, whereby water molecules from the hydration spheres surround

the ionic salt molecules. These hydration spheres reduce the concentration of water

available to dissolve more analyte molecules; which will drive additional analytes into

the extraction phase (Boyd-Boland and Pawliszyn, 1995). In competition with this

process, the molecules may participate in the electrostatic interactions with the salt ions

in solution, thereby reducing their ability to move into the extraction phase.

226

Initially, it would be the interaction of the ionic salt species with water that is the

predominant process. As salt concentration increases further, ionic salt species will

begin to interact with the analyte molecules. Thus it is reasonable that there should be

an initial increase in the analyte extracted with increasing salt concentration. This is

followed by a decrease of the extraction efficiency because of the predominant salt

interaction with the analytes in solution.

5.2.1.7 Effect of pH

An adjustment of the pH may improve the extraction yield for the compounds that can

be protonated. In most cases, the pH is adjusted in order to obtain the analyte in its

neutral undissociated form to enhance extraction yield, because only this form is

extracted in the absorption fiber. Because a PDMS fiber is not resistant to media at pH

below 4 or above 10 (Huang et al., 2004), hence the more alkaline solutions were not

assayed because of the alkaline hydrolysis of OP pesticides. Thus, in our studies, the

pH was varied from pH 4 to pH 10 to evaluate the effect of the pH on the extraction. A

series of pH buffer solutions were prepared as shown in Table 5.6.

Table 5.6: Buffer Solutions from pH 4 to pH 10

pH Chemicals pKa

4

5

6

7

8

9

10

0.10 M CH3COOH + 0.018 M CH3COONa

0.10 M CH3COOH + 0.18 M CH3COONa

0.10 M H2CO3 + 0.04 M Na2CO3

0.10 M NaH2PO4 + 0.064 M Na3PO4

0.10 M NaH2PO4 + 0.64 M Na3PO4

0.10 M NaHCO3 + 0.005 M Na2CO3

0.10 M NaHCO3 + 0.05 M Na2CO3

4.745

4.745

6.40

7.21

7.21

10.32

10.32

227

Figure 5.21 shows the effect of the pH value on the extraction efficiency for a 100 µm

PDMS fiber. The extraction for α-endosulfan and β-endosulfan decreased significantly

in the acidic or basic solution. The optimum extraction efficiency was obtained at the

pH values of 6.0 to 7.0. The amounts extracted for chlorothalonil remained the same

from pH 4 to 8. In this pH range, chlorothalonil is in its neutral (molecular) form, thus,

there is no significant variation of recovery for chlorothalonil. Beyond pH 8, the

extraction efficiency decreases, because chlorothalonil is not stabile in the basic

solution. The OC pesticides are not significantly affected by pH because they are

nonionizable compounds in aqueous solutions.

Figure 5.21: Effect of pH on Peak Area using a 100 µm PDMS Fiber

0

5

10

15

20

25

3 4 5 6 7 8 9 10 11

x 10

0000



pH

Pea

k A

rea

228

As shown in Figure 5.21 there is no significant effect on the extraction of most of the

OP pesticides except for diazinon. All the pesticides showed maximum sensitivity at

pH values of 6.0 to 7.0, depending on the compound. However, the extraction

efficiency of diazinon decreased in the acidic solution and increased in the basic

solution. Acidification resulted in reduced signals for diazinon probably due to the

protonation of the two pyrimidine N-atoms. Therefore, acidification of the sample was

omitted. Lambropoulou and Albanis (2001) reported that the variation of pH over a

range from 2 to 11 did not significantly affect the extraction by the fiber for the OP

pesticides and thus the pH of the water samples was not adjusted in that study.

According to the results obtained, the pH for the simultaneous extraction of the

investigated pesticides was not adjusted and it was carried out using double distilled

water which near to pH 7 because most of the analytes have an optimum response at

this value and increasing or decreasing the pH did not improve their extraction

efficiency.

5.2.1.8 Effect of Desorption Temperature

After the analytes have been trapped on the fiber, the desorption conditions, such as the

temperature and the time required to completely desorb all the analytes from the fiber

coating were optimized. Although the analyte can be desorbed effectively at a higher

temperature in a shorter time, the stability of the fiber will be affected and the analyte

may be decomposed if the desorption temperature is too high. Thus, the desorption

temperature was studied in the ranges of 200 - 270 oC for a 100 µm PDMS fiber and

220 - 300 oC for a 85 µm PA fiber, working with a spiked standard pesticide solution

229

for triplicate injections while maintaining a constant desorption time of 10 min. The

maximum temperature in each fiber is as specified by the manufacturer.

Figure 5.22 shows that in the case of the PDMS fiber, desorption at 200 oC to 230

oC

was not capable of desorbing completely the analytes; they were completely removed

from the coating at 240 – 270 oC but little significant difference were observed within

these ranges of temperature. For the PA fiber (Figure 5.23), the peak areas of all the

analytes increased as the desorption temperature increased, and these areas gradually

decreased when the temperature exceeded 260 oC. According to these results,

desorption temperature was set at 240 oC for PDMS and 260

oC for PA since high

temperature can shorten the coating lifetime and can result in the bleeding of the

polymer, causing problems in the separation and quantification (Beltran et al., 2003).

Figure 5.22: Effect of Desorption Temperature on Peak Area using a 100 µm PDMS

Fiber

0

5

10

15

20

25

190 200 210 220 230 240 250 260 270 280

x 10

0000



Pea

k A

rea

Desorption Temperature (oC)

230

Figure 5.23: Effect of Desorption Temperature on Peak Area using a 85 µm PA Fiber

5.2.1.9 Effect of Desorption Time

Desorption time is also an important parameter to ensure that pesticides are completely

desorbed from the fiber to attain the highest sensitivity and to avoid carry-over.

Desorption times from 1 – 15 min were tested setting the injector temperature to 240 oC

for the PDMS fiber and to 260 oC for the PA fiber. From the Figure 5.24, it was

observed that a 6 minute-period was sufficient to desorb all the pesticides in the GC

injector port. After this period of time, all the pesticides are completely desorbed and

by increasing the value of this parameter the response is kept constant.

0

1

2

3

4

5

6

7

220 230 240 250 260 270 280 290 300 310

x 10

0000



Desorption Temperature (oC)

Pea

k A

rea

231

Figure 5.24: Effect of Desorption Time on Peak Area using a 100 µm PDMS Fiber

Another important consideration of the desorption process is the presence of carry-over.

That is to say, if the analytes are not completely desorbed they are left in the coated

phase and may give false signals in subsequent analyses. Hence the fiber is left in GC

injector port for another 4 min to eliminate all residues on the fiber to guarantee a

reproducible desorption. The results from the carry-over profiles showed that all the

investigated pesticides were efficiently desorbed from the fiber during the 10 min

injector desorption for GC-ECD. The PA fiber coating showed the same result with the

PDMS and no carry-over effect was observed and all the analytes are completely

desorbed from the fiber during the 10 min desorption time. Besides, the use of a longer

desorption time permitted the reduction of the injection port temperature. This will

reduce the possibility of the bleeding of fiber material and also prolong the lifetime of

the fiber. Hence a 10 min desorption time was chosen to desorb the analytes from both

fibers.

0

5

10

15

20

25

0 2 4 6 8 10 12 14 16

x 10

0000



Pea

k A

rea

Desorption Time (min)

232

5.2.1.10 Effect of Fiber Depth in the Injector

The effect of the fiber depth into the liner of the GC injector port was also checked. For

this study, the retractable fiber inside the adjustable needle of the SPME device was

placed into the injector port. Then, this needle was set at 1 cm to 4.5 cm. The results

obtained from Figure 5.25 showed that peak areas increased when the depth of fiber

into the injector glass-liner was longer until 3.5 cm, which is close to the column

entrance and the center of the hot injector zone. The peak areas gradually decreased

when the depth of fiber exceeded 3.5 cm. Thus, 3.5 cm fiber depth was chosen because

in this position the length of the exposed fiber resulted in good sensitivity and

reproducibility. A longer fiber depth in the injector resulted in stress fiber and has

carry-over effect, whereas shorter depths caused loss of response. The needle position

in the injector could be important for certain compounds, probably because the injector

is not uniformly heated. Although for some analytes this factor is minor importance, the

best reproducibility is to keep the needle position always constant.

Figure 5.25: Effect of Fiber Depth in the Injector Port on Peak Area using a 100 µm

PDMS Fiber

0

5

10

15

20

25

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 10

0000



Fiber Depth (cm)

Pe

ak A

rea

233

5.2.1.11 Fiber Coating Lifetime

The lifetime of the fiber coating is important for practical applications. An important

quality factor is the number of extractions that can be performed with the fiber. The

coating is damaged mainly during the extraction due to the interference between the

matrix of the samples and the fiber. This effect is more pronounced when the sampling

is performed directly from the aqueous solution for DI-SPME. In contrast, in the HS-

SPME mode the fiber is suspended in the headspace above the liquid layer of the

samples and there is no interference between the matrix of the samples and the coating.

Thus the coating is protected and the lifetime is increased. In conventional SPME

process (immersion technique) each fiber can be re-used for approximately 30 times for

surface water samples and 27 times in run-off water (Dugay et al., 1998).

The extraction capability of the fiber coating was determined by duplicate extractions

in a vial with the spiked cucumber sample and extracted using a 100 µm PDMS fiber

until 130 extractions were completely with the same fiber. The experiment was carried

out within 13 days, 5 vials and 10 extractions each day. As found in this study using

headspace technique, the fibers can be re-used up to 100 – 120 times with the RSD

value < 20%. Some loss of capacity and slow decrease of the absorbed amount (~30%)

of the analytes by the fiber was observed after 100 - 120 uses (Figure 3.26). The

influence of this effect on analyzing the precision is important and clearly suggests that

any routine use of the HS-SPME approach for complex matrix should include frequent

calibration runs.

234

Figure 5.26: Effect of Number of Extractions on Peak Area using a 100 µm PDMS

Fiber

5.2.1.12 Effect of Dilution on Sample Extraction

The effect of dilution for the extraction of some pesticides from aqueous samples using

DI-SPME have been previously demonstrated (Simplicio and Boas, 1999; Beltran et

al., 2003; Berrada et al., 2004; Sanusi et al., 2004; Vazquez et al., 2008). In this study,

the application of HS-SPME for the extraction of eight OC and OP pesticides in three

types of fruits, namely strawberry, starfruit and guava and three types of vegetables,

namely cucumber, tomato and pakchoi were selected for the evaluation of the effect of

dilution on different matrices. It was found that the recoveries were low in fruit and

vegetable samples without any dilution when compared to that in aqueous samples.

0

5

10

15

20

25

0 20 40 60 80 100 120 140

x 10

0000



Number of Extractions

Pe

ak A

rea

235

Assuming that the pesticide is distributed in the sample between a “free” form and a

“bound” form with the matrix components, sample dilution should increase the

extraction efficiencies as a consequence of the displacement of the equilibrium towards

the free form of the pesticide due to a reduced matrix effect. However, diluting the

samples reduces the sensitivity. Therefore, it is important to find an optimal dilution

factor. The effect of adding water to the samples in order to favor the release of analyte

from the matrix was established by using different amounts of water ranging from a

dilution factor of 1 to 10. The final volume of the samples was kept at 5 mL spiked

with the same amount of pesticides.

Figure 5.27 shows the graph of average recovery (%) versus the dilution factor for the

cucumber samples (recoveries were calculated by comparing the peak ratio of the

relevant chromatographic peak to the spiked sample and an aqueous solution at the

same pesticides concentration level that was progressively subjected to the same

dilution). From the results, a dilution factor of 2 increased the recovery of all the

investigated pesticides from 25 – 32% to 65 – 75%. Hence, a dilution factor of 2 was

chosen for cucumber as the optimum dilution factor to increase the extraction

efficiency and it was then adopted for further work on the cucumber samples. The

detection response of all pesticides was enhanced with the addition of water and

decreased when the amount of water added exceeded a dilution factor of 2.

236

Figure 5.27: Effect of Dilution on the Extraction of Pesticides from Cucumber

Figure 5.28 shows the average recovery (%) of diazinon for all the investigated fruit

and vegetable samples. The optimum dilution factor for cucumber and tomato were 2

and 3, respectively. For the other samples it was a dilution factor of 5. This could be

due to the different amounts of water in the fruits and vegetables. The HS-SPME

process is affected by the suspended matter and dissolved compounds (sugar, pectins

etc) contained in the fruit and vegetable samples which could adsorb the analytes,

forming micelles and thus making it difficult for the analytes to reach the fiber because

it is interfering with diffusion (Lambropoulou and Albanis, 2003). Since the analytes

were analyzed by HS-SPME, the addition of larger amounts of water would dilute the

concentration of the analytes and increase the diffusion barrier of the pesticides from

aqueous phase to gaseous phase.

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6

diazinon chlorothalonil malathion chlorpyrifos


Dilution Factor (water/sample)

Ave

rage

Re

cove

ry (%

)

237

Figure 5.28: Effect of Dilution on the Extraction of Diazinon from Various Fruits and

Vegetables

Figure 5.29 shows that the increase in average recovery (%) was compound and

structure dependent. It is clear that the recovery for β-endosulfan was much higher than

for malathion. This discrepancy may be attributed to the different water solubilities of

the pesticides. β-Endosulfan has low water solubility (0.32 mg/L). The desorbed

pesticides will be easily released from aqueous solution to the gaseous phase. However,

malathion compounds have relatively high water solubility (130 mg/L). The malathion

compounds released from the samples will be retained in the aqueous solution and

subsequently the recovery is low when compared to the recovery of β-endosulfan.

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Cucumber Tomato Pakchoi Strawberry Guava Starfruit

Ave

rage

Re

cove

ry (%

)

Dilution Factor (water/sample)

238

Figure 5.29: Comparison of the Recovery (%) of Malathion and β-Endosulfan with

Dilution Factor of 5 on Strawberry

5.2.1.13 Effect of the Organic Solvent on Sample Extraction

The addition of an organic solvent could also promote the release of organic

compounds from the fruit and vegetable samples because it can enhance the diffusion

of analytes from the sample to the fiber coating. However, the presence of a high

concentration of an organic solvent would lead to a significant decrease in the

extraction efficiency of the analytes (Doong and Liao, 2001; Lambropoulou and

Albanis, 2003). Therefore, only a small amount of solvent is recommended for use as

the additive.

38.8

27.525.6 26.8

30.7

25.8

55.7

40.9

46.8

32.7

42.8

36.2

0

10

20

30

40

50

60

Cucumber Tomato Bakchoy Guava Strawberry Starfruit

Malathion

β-Endosulfan

Re

cove

ryIn

cre

me

nt

(%)

239

Because of the wide range of polarity and solubility exhibited by the compounds

investigated, a single neat solvent system cannot provide acceptable recoveries for all

compounds. For this study, the halogenated solvent such as dichloromethane was

eliminated from consideration because of hazards and disposal costs. Toluene, propanol

and cyclohexane were eliminated because of insufficient volatility. Based on previous

studies related to the analysis of pesticides in complex matrices (Lambropoulou and

Albanis, 2003), the organic solvents tested were methanol, acetone, acetonitrile, ethyl

acetate, methanol / acetone (1:1), methanol / acetonitrile (1:1), methanol / ethyl acetate

(1:1) and acetone / acetonitrile (1:1). In this study, 2% (vol/weight) of organic solvent

was added to the fruit and vegetable samples.

Table 5.7: Boiling Point, Vapor Pressure and Polarity of the Tested Solvents

Solvent Boiling Point (oC) Vapor Pressure (kPa) at

20 oC

Polarity (P‟)

Acetone

Acetonitrile

Ethyl acetate

Methanol

56.2

81.6

77.1

64.7

24.6

9.6

9.7

12.8

5.1

5.8

4.4

5.1

The influence of water (dilution factor of 5) and the addition of an organic solvent in

guava samples are shown in Figure 5.30. From the results, it can be seen that the

addition of an organic solvent increases the average recovery of all the investigated

pesticides. The polar solvent such as acetonitrile with the polarity index of 5.8 is

suitable for extracting the polar compounds such as diazinon and malathion but shown

poor results for extracting non-polar compounds. However, the non-polar compounds

such as chlorothalonil, α-endosulfan and β-endosulfan are most effectively extracted in

ethyl acetate which is non-polar solvent with the polarity index of 4.4. but this solvent

240

was not suitable for polar compounds. Thus, these two solvents are not suitable for the

multiresidue analysis for the investigated pesticides. Methanol and acetone have lower

boiling points and higher vapor pressures than both ethyl acetate and acetonitrile are

suitable to extract all the investigated pesticides.

Figure 5.30: Effect of Organic Solvents Addition on Extraction Efficiency

in Guava Samples

Overall, extraction solvents consisting of a mixture of methanol/acetone (1:1 v/v)

exhibited the best recoveries for all the investigated pesticides. The average recoveries

for all the investigated pesticides were in the ranges of 90% to 97% with the RSD

values of less than 3% for three levels of concentrations. Even in the case of the lowest

recovery (90%), the overall repeatability and sensitivity of the method were acceptable

to ensure a reliable determination at levels lower than the respective MRLs allowed by

40

50

60

70

80

90

100

No solvent Methanol Acetone Acetonitride EthylAcetate

MeOH/Ace MeOH/N MeOH/E Ace/ethylA

Avera

ge R

ecovery (

%)

241

the Codex Alimentarius (European Union, 2004). Besides the extraction efficiency, a

mixture of methanol/acetone (1:1) was selected because of its effectiveness for mid-

polar and non-polar pesticides from a diverse range of matrices. Its other advantages

include low toxicity and cost, easy to volatilize and readily obtainable in the laboratory.

Table 5.8 shows the comparison of average recovery (%) of the fruit and vegetable

samples between condition 1 (without dilution or organic solvent added) and condition

2 (optimum dilution and 2% (vol/weight) of methanol/acetone (1:1) added). Significant

differences were found between the results obtained from condition 1 and condition 2.

The recoveries obtained from condition 1 were very low, ranging from 5% to 44%.

This could be due to the fact that removing pesticides from a complex matrix is not

very effective because the suspended matter interferes in the extraction process. In the

attempt to reduce the matrix effect and to ameliorate the analyte recovery, the sample

matrix was diluted with distilled water together with the addition of a small amount of

the organic solvents. The addition of aliquots of water and organic solvents increased

the extraction recoveries to between 70% and 99% for all the investigated pesticides in

all the fruit and vegetable samples studied. The increase in the average recovery (%)

was from 59.0% to 72.3%. The increase in the average recovery (%) for α-endosulfan,

profenofos and β-endosulfan were greater than 70% may be due to their lower water

solubility and non-polar characteristics. The relative standard deviations for triplicate

experiments were less than 10% and the calibration curves were linear for the full range

with regression coefficient values greater than 0.9900.

242

Table 5.8: Comparison of Average Recovery (%) of the Fruit and Vegetable Samples

between Condition 1 (without dilution or organic solvent added) and Condition 2

(optimum dilution and 2% (vol/weight) of methanol/acetone (1:1) added)

Recovery (%)

Condition 1 Condition 2 Average

Increment (%) Ranges

(%)

Average (%) Ranges

(%)

Average (%)

Diazinon

20 - 40

29.7

83 - 95

88.7

59.0

Chlorothalonil 5 - 44 28.0 75 - 93 87.0 59.0

Malathion 13 - 33 26.2 81 - 97 90.5 64.3

Chlorpyrifos 7 - 34 18.0 74 - 94 82.0 64.0

Quinalphos 12 - 37 22.0 81 - 97 90.0 68.0

α-Endosulfan 5 - 29 14.3 74 - 92 85.7 71.4

Profenofos 8 - 33 20.2 82 - 99 90.5 70.3

β-Endosulfan 7 - 24 15.7 70 - 99 88.0 72.3

Ranges (%) 5 - 44 14.3 – 29.7 70 -99 82.0 – 90.5 59.0 – 72.3

5.2.1.14 Effect of Washing on Pesticide Residues by Different Solutions

All the studies in this section were performed with previously analyzed pesticide-free

vegetable (cucumber) and fruit (strawberry) samples. The linearity in the response was

studies by using the spiked matrix calibration solutions. Six point calibration curves

were constructed. The resulting regression coefficients (r2) were higher than 0.9900 in

all cases. The calibration curves were used for quantification purposes. The effect of

washing by using 5% and 10% of acetic acid, sodium carbonate, sodium chloride and

tap water for 10 min and 30 min on pesticide residues in cucumber and strawberry are

presented in Table 5.9.

243

Table 5.9 indicates that the washing process including tap water, and different

concentrations of acetic acid, sodium chloride and sodium carbonate solutions is

effective in reducing OC and OP pesticides. Acetic acid is the most effective in

removing residues of the investigated pesticides, with 44 - 70% of the residues being

eliminated from the samples, followed by sodium carbonate with 30 - 50% of residues

being eliminated and by sodium chloride with 23 - 40% reduction of residues. Among

these washing methods, washing with tap water proved the least effective, it only

reduced the residues from 10 – 20% as a whole. These results are in agreement with

those obtained by Abou-Arab (1999), Soliman (2001), Zohair (2001) and Pugliese et al.

(2004). It is clear that the effect of washing with similar solution concentration at the

same treatment time for the removal of OP pesticides were greater than those for the

OC pesticides. In addition, there was a gradual increase in the percentage reduction due

to the increase of concentration of acetic acid, sodium carbonate and sodium chloride

for the same time treatment period. Besides, there was also a gradual increase in the

percentage reduction due to the increase in treatment time at the same concentration.

There was no significant difference for the reduction of pesticides in the two samples -

cucumber and strawberry.

From the results, it is clear that pesticides should be applied correctly according to good

agricultural practice, using only the amounts recommended. Washing with tap water,

acetic acid, sodium chloride or sodium carbonate can be effective to decrease the intake

of pesticide residues. The acidic solutions are more effective in the elimination of the

OC and OP pesticides under investigation when compared to alkaline and neutral

solutions.

244

Table 5.9: The Effect of Washing on Pesticide Residues in Cucumber by

Different Solutions

compound Time

(min)

Amount of Pesticide Residues Removed Expressed in %

Acetic

Acid

Sodium

Carbonate

Sodium

Chloride

Tap

Water

5%

10%

5%

10%

5%

10%

Diazinon

10

30

53.3

60.3

65.1

69.0

39.5

43.1

46.0

46.9

31.6

33.6

36.6

39.6

15.0

17.4

Chlorothalonil 10

30

44.6

54.0

56.0

58.6

32.3

34.1

36.9

39.4

21.5

23.4

25.8

26.7

13.7

15.7

Malathion 10

30

61.0

64.1

65.6

69.8

46.3

49.9

52.4

53.7

25.8

30.0

33.4

38.6

17.5

18.4

Chlorpyrifos 10

30

53.9

56.1

58.9

61.9

39.7

43.6

48.6

49.5

23.2

31.3

36.4

40.9

15.2

18.2

Quinalphos 10

30

60.5

60.7

62.2

63.4

41.7

44.7

47.9

52.7

25.8

38.1

41.9

43.1

14.0

18.9

α–Endosulfan 10

30

49.8

51.3

53.3

58.3

32.2

39.0

41.5

44.2

20.5

22.6

27.9

30.5

11.1

14.3

Profenofos 10

30

55.2

60.9

63.3

67.8

38.3

43.6

43.6

46.7

30.4

33.6

36.2

38.5

15.0

18.0

β–Endosulfan 10

30

47.7

51.4

52.1

57.0

31.1

39.9

42.4

45.5

21.5

22.4

24.4

29.8

10.2

14.3

Ranges 44.6 – 69.8 31.1 – 53.7 20.5 – 43.1 10.2 – 18.9

245

5.2.2 Validation of Quantitative Chromatography Method

When a method has been developed, it is important to validate it to confirm that it is

suitable for its intended purpose. The validation shows how reliable the methods are,

specifically for its intended application. In this study, the analytical performance

characteristics of the optimized HS-SPME method were validated. The optimized HS-

SPME conditions are as follows: a homogenized spiked sample was added with 2%

(vol/weight) of methanol/acetone (1:1) and optimum dilution was made with distilled

water containing 10% NaCl until 5.00 g. Then, an internal standard was added and the

sample was extracted by the headspace of a 100 µm PDMS fiber at 60 oC for 30 min;

with sample agitation at 800 rpm without pH adjustment. Desorption was done at 240

oC for 10 min.

5.2.2.1 Calibration Curve (Linearity)

The linearity of an analytical method is its ability to produce test results that are

directly proportional to the concentration of the analyte in the samples within a given

ranges. For the establishment of linearity, a minimum of five different concentrations

should be used. It is also recommended that a specific range, normally from 80 – 120%

of the expected concentration range be employed (Shah, 2001).

Usually, the spiked solutions are made with a known amount of a mixture of the

analytes and calibration curves are drawn by relating the peak areas obtained when

desorption occurs at the concentrations used for spiking the samples. However, in real

samples, the number of analytes present and their concentrations are unknown, and

some matrix effect exist that can modify the calibration curves. In order to minimize

246

the competition between the analytes in the partition process, the calibration curves of

the analytical method in this study was determined under three conditions, (a) only one

pesticide spiked in the samples (cucumber), (b) a mixture of eight pesticides spiked in

the samples (cucumber), and (c) a mixture of eight pesticides spiked in distilled water.

The internal standard quantification was carried out at six levels of concentrations using

three different conditions in triplicates. The peak area ratio (peak area of analytes / peak

area of internal standard) was used for each compound. Table 5.10 shows the

calibration curve for the three different conditions.

Table 5.10: Calibration Curve for Three Different Conditions

Compound One pesticide in

sample

Mixture of pesticides

in sample

Mixture of pesticides in

distilled water

Calibration

Curve

r2 Calibration

Curve

r2 Calibration

Curve

r2

Diazinon

y=0.0057x

0.9993

y=0.0047x

0.9982

y=0.0057x

0.9991

+0.1076 +0.1429 +0.0750

Chlorothalonil y=0.0075x 0.9976 y=0.0075x 0.9971 y=0.0082x 0.9974

+0.4768 +0.4787 +0.3841

Malathion y=0.0008x 0.9969 y=0.0007x 0.9974 y=0.0008 0.9990

+0.1623 +0.2145 +0.0849

Chlorpyrifos y=0.0981x 0.9975 y=0.0951x 0.9977 y=0.1012x 0.9988

+0.7782 +0.7804 +0.665

Quinalphos y=0.0007x 0.9981 y=0.0006x 0.9973 y=0.0007x 0.9980

+0.0663 +0.0791 +0.0918

α–Endosulfan

y=0.4857x 0.9987 y=0.4592x 0.9953 y=0.4956x 0.9980

+1.8836 +1.9572 +1.7778

Profenofos y=0.0171x 0.9991 y=0.0167x 0.9990 y=0.0184x 0.9993

+0.1481 +0.1422 +0.0973

β–Endosulfan y=0.0624 0.9985 y=0.0563x 0.9960 y=0.0653x 0.9985

+2.0881 +2.0613 +1.9922

247

The results show that the three calibration curves are almost identical with the r2 value

> 0.9950 for all the calibration curves. These results are important and therefore, the

partition process is reproducible using these conditions, indicating that the technique is

quantitative for these pesticides.

Table 5.11 shows the comparison of the linearity, r2 and RSD value of the investigated

pesticides in distilled water and in the cucumber sample. The linear ranges, r2

and RSD

values were slightly better in distilled water compared to the cucumber sample

demonstrating that the vegetable sample has a small matrix effect in the analysis of

investigated pesticides. Overall, the linearity obtained by using both conditions were

acceptable and the regression coefficients were better than 0.9950 in all cases with the

RSD values less than 7% for all the investigated pesticides.

Table 5.11: Comparison of the Linearity, r2 and RSD (%) Values of the Investigated

Pesticides in Distilled Water and in the Cucumber Sample

Compound In distilled water In cucumber sample

Linear ranges

(µg/L)

r2 RSD

(%)

Linear ranges

(µg/L)

r2 RSD

(%)

Diazinon

0.3-2000

0.9991

4.09

10-1000

0.9982

4.71

Chlorothalonil 0.3-2000 0.9974 3.20 10-1000 0.9971 6.50

Malathion 10-10000 0.9990 3.68 50-5000 0.9974 6.09

Chlorpyrifos 0.02-100 0.9988 4.36 0.5-50 0.9977 5.80

Quinalphos 10-10000 0.9980 3.59 50-5000 0.9973 4.16

α–Endosulfan 0.01-50 0.9980 3.03 0.1-20 0.9953 3.98

Profenofos 0.05-350 0.9993 2.11 1-100 0.9990 6.96

β–Endosulfan 0.05-350 0.9985 3.09 1-100 0.9960 3.58

248

5.2.2.2 Precision

The precision of an analytical method is the closeness of a series of individual

measurements of an analyte when the analytical procedure is applied repeatedly to

multiple samplings of a homogeneous sample. The precision is usually expressed as the

relative standard deviation (RSD). The measured RSD can be subdivided into three

categories: repeatability (intra-day precision), intermediate precision (inter-day

precision) and reproducibility (inter-laboratory precision, e.g., in a collaborative study).

In this study, repeatability and intermediate precision of the developed HS-SPME

method were investigated.

The accuracy of an analytical method is the degree of closeness between the true value

of analytes in the sample and the value determined by the method and is sometimes

called trueness (Shah, 2001). Accuracy can be measured by analyzing samples with

known concentrations and comparing the measured values with the true values.

5.2.2.2 (a) Repeatability

The repeatability of an analytical method refers to the use of the procedure within a

laboratory over a short period of time, and carried out by the same analyst with the

same equipment. According to the International Conference on Harmonization (ICH)

documents, it is recommended that repeatability be assessed using a minimum of nine

determinations covering the specified ranges such as three concentrations and

three replicates for each concentration or a minimum of six determinations of 100% of

the test concentration (ICH-Topic Q2B, 1996).

249

The intra-day accuracy and repeatability was assessed, at three concentration levels

and three replicates for each concentration on the same day. The repeatability of the

investigated compounds in the spiked cucumber and strawberry samples as shown in

Table 5.12. The intra-day accuracies varied between 71.4% and 81.9% with the RSD

values between 0.4% and 3.7% for the cucumber sample. For the strawberry sample,

the intra-day accuracies ranged from 70.0% to 83.5% with the RSD values from 0.3%

to 2.5%. For the analysis of pesticide residues at the ppb/ppm levels, accuracy and

recovery of 70% to 120% are considered acceptable (Herdman et al., 1988). So, the

results obtained above for the concentration levels investigated are in accordance with

acceptable practice.

5.2.2.2 (b) Intermediate Precision

The intermediate precision (Figure 5.13) shows the variations from day-to-day

analysis. The intermediate precision in this study was based on the mean repeatability

values of a set of spiked samples at three concentration levels for a period of 4 days.

The inter-day accuracy varied from 70.7% to 83.9% with the RSD values ranging

from 0.8% to 2.5% for both samples and this indicates that the proposed HS-SPME

method shows acceptable intermediate precision.

250

Table 5.12: Repeatability of the Optimized HS-SPME Method in the Spiked


compounds Spiking

levels (µg/L)

Cucumber

(n=3)

Strawberry

(n=3)

Accuracy,%

RSD,%

Accuracy,%

RSD,%

Diazinon

40

160

480

79.1

81.1

80.7

2.5

1.9

1.5

74.7

79.9

81.9

0.9

0.9

0.8

Chlorothalonil

20

80

240

78.9

80.8

81.7

2.0

1.5

1.6

70.5

74.4

71.1

1.2

1.5

1.2

Malathion

50

250

600

77.7

70.6

73.8

1.5

2.0

1.8

75.1

83.5

75.1

2.5

2.2

1.1

Chlorpyrifos

1

4

12

79.6

71.4

77.6

0.4

3.7

2.0

70.0

70.3

74.8

1.0

0.7

0.8

Quinalphos

50

250

600

76.6

74.8

73.9

1.7

2.6

2.0

71.5

80.2

81.3

0.9

1.2

1.4

α–Endosulfan

0.5

2

6

81.4

77.4

80.3

1.5

1.5

1.7

72.8

71.0

81.8

0.7

1.0

0.8

Profenofos

5

20

60

78.9

78.0

80.8

1.9

0.8

0.4

78.3

80.9

81.6

1.4

0.9

2.0

β–Endosulfan

1

4

12

81.9

80.7

73.0

1.8

2.5

2.8

73.4

82.8

78.7

0.3

0.8

0.8

Ranges 0.5 - 480 71.4 – 81.9 0.4 – 3.7 70.0 – 83.5

0.3 – 2.5

251

Table 5.13: Intermediate Precision of the Optimized HS-SPME Method in the Spiked


compounds Spiking

levels (µg/L)

Cucumber

(n=3 x 4 days)

Strawberry

(n=3 x 4 days)

Accuracy,% RSD,% Accuracy,% RSD,%

Diazinon

40

160

480

74.4

80.9

80.8

1.5

1.5

1.3

74.0

80.4

81.5

0.8

1.0

1.5

Chlorothalonil

20

80

240

74.3

79.8

81.2

1.9

1.0

1.2

70.7

77.4

73.0

1.3

1.5

1.0

Malathion

50

250

600

74.3

70.8

73.1

1.6

1.6

1.4

81.9

83.9

74.6

1.9

2.1

1.6

Chlorpyrifos

1

4

12

72.2

71.0

81.0

0.8

2.2

1.7

75.3

72.6

75.5

1.8

1.7

1.3

Quinalphos

50

250

600

72.2

72.5

73.3

1.4

2.1

1.8

71.3

80.7

80.8

1.5

1.6

1.9

α–Endosulfan

0.5

2

6

75.6

77.9

78.8

1.0

1.1

1.1

76.4

72.5

81.9

1.6

1.9

2.5

Profenofos

5

20

60

75.2

74.3

74.3

1.8

1.4

1.5

79.1

79.3

79.6

0.9

1.6

1.9

β–Endosulfan

1

4

12

75.3

82.5

78.3

1.4

2.1

1.5

78.2

79.2

76.6

0.8

0.8

1.6

Ranges

0.5 - 600

70.8 – 82.5

0.8 – 2.2

70.7 – 83.9

0.8 – 2.5

252

5.2.2.3 Selectivity / Specificity

The ICH documents define specificity as the ability to assess unequivocally the analyte

in the presence of other components, such as impurities, degradation products and

matrix components which may be expected to be present. Other international

organizations such as IUPAC and AOAC have preferred the term selectivity, reserving

specificity for those procedures that are completely selective (Soh and Abdullah, 2005).

The selectivity of the analytical method in this study was determined by comparing the

chromatograms of a blank matrix solution with the spiked matrix solutions. Figure 5.31

shows the chromatograms of the spiked cucumber sample and the blank cucumber

sample by GC-ECD. The analytes of interest were well separated from the other

components present in the samples. SPME is an equilibrium method which is more

selective when compared to other exhaustive methods as it takes full advantages of the

difference in extracting phase/matrix distribution constants to separate the mixture of

pesticides from the interferences. This SPME technique has demonstrated its selectivity

as it does not require an additional cleanup step to remove any interference.

253

Figure 5.31: Selectivity Chromatograms (a) Spiked Cucumber Sample (b) Blank

Cucumber Sample. IS (internal standard), 2.68 min; 1. Diazinon, 13.58 min;

2. Chlorothalonil, 14.74 min; 3. Malathion, 16.42 min; 4. Chlorpyrifos, 16.65 min;

5. Quinalphos, 18.30 min; 6. α-Endosulfan, 19.37 min; 7. Profenofos, 19.76 min;

8. β–Endosulfan, 21.83 min.

0

0

10 5 15 20

50000

min

100000

150000

Intensity

a IS

1

2

3

4

5

6

7

8

b

0

0

10 5 15 20

50000

min

100000

150000

Intensity

IS

254

5.2.2.4 Limits of Detection (LOD) and Limits of Quantification (LOQ)

The LOD is defined as the concentration of analyte that results in a peak height three

times the noise level when injected into the chromatographic system. The LOD is the

lowest concentration of the analyte in a sample that can be detected but not necessarily

quantifiable. The LOQ is the lowest concentration of the analyte in a sample that can be

quantified with an acceptable degree of accuracy and precision. The LOQ should have

an accuracy of 80 – 120% and a precision with a maximum of 20% RSD (Shah, 2001).

The LOD & LOQ values obtained (Table 5.14) are below the first calibration level.

These values are lower than the Maximum Residue Levels allowed by the Codex

Alimentarius (European Union, 2004).

5.2.2.5 Recovery

High recovery of the analyte(s) from the matrix is a desirable outcome of the sample

preparation, and is therefore an important characteristic of the extraction procedure.

The relative recovery was applied to instead of absolute recovery as used in exhaustive

extraction procedures because SPME is a non-exhaustive extraction procedure.

According to the expected levels of real concentrations, the spiking was performed at

three fortification levels - high, middle and low regions of the linear ranges. The

recovery of each pesticide at each fortification level was evaluated. Three pesticide-free

vegetable and three pesticide-free fruit samples were spiked with pesticides at three

fortification levels. The peak areas obtained on these samples were analyzed and

compared with the peak areas obtained when analyzing standard solutions with the

same concentration by the same procedure.

255

Table 5.14: Limits of Detection (LOD), Limits of Quantification (LOQ) and Maximum

Residue Levels (MRL) from Codex Alimentarius of the Investigated Pesticide

using the Optimized HS-SPME Method

Pesticide Pesticide level

(µg/L)

Pesticide Level (µg/L) Cucumber Tomato Pakchoi Guava Starfruit Straw

berry

Diazinon

LOD

LOQ

MRL

0.2

1

20

0.2

1

50

0.2

1

20

0.2

1

20

0.2

1

20

0.2

1

20

Chlorothalonil

LOD

LOQ MRL

0.2

1 1000

0.2

1 2000

0.2

1 5000

0.2

1 3000

0.2

1 3000

0.2

1 3000

Malathion

LOD

LOQ MRL

1

5 3000

1

5 3000

1

5 3000

1

5 500

1

5 500

1

5 500

Chlorpyrifos

LOD LOQ

MRL

0.02 0.1

50

0.02 0.1

50

0.02 0.1

50

0.02 0.1

200

0.02 0.1

200

0.02 0.1

200

Quinalphos

LOD

LOQ

MRL

1

5

50

1

5

50

1

5

50

1

5

50

1

5

50

1

5

50

-Endosulfan

LOD

LOQ

MRL

0.01

0.05

50

0.01

0.05

500

0.01

0.05

50

0.01

0.05

50

0.01

0.05

50

0.01

0.05

50

Profenofos

LOD

LOQ MRL

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

-Endosulfan

LOD

LOQ MRL

0.1

0.5 50

0.1

0.5 500

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

0.1

0.5 50

Ranges

LOD LOQ

0.01 – 1 0.05 – 5

256

Mean recovery data and relative standard deviations (RSD) obtained in the analysis of

fortified fruit and vegetable samples are listed in Table 5.15. Acceptable relative

recoveries were obtained, ranging between 71 - 97% for the vegetable samples with the

RSD values ranging from 0.1 – 4.7%, and the relative recoveries of 76 - 98% for the

fruit samples with the RSD values ranging from 0.3 - 4.7%. The percentage relative

recoveries and RSD values obtained for the fruit samples were slightly better than those

obtained for the vegetable samples. This is probably due to the higher total suspended

solids present in the vegetable samples. When all the fruit and vegetable samples were

compared, it appears that the relative recoveries obtained in pakchoi were lower than

the other samples. This could be due to the water content of the pakchoi is the lowest

among the samples. As can be seen, the matrix has little effect on the developed HS-

SPME method.

257

Table 5.15: Spiked Concentration Levels and Relative Recoveries over Fortified Fruits

and Vegetables using GC-ECD

Pesticide Spiking

levels (µg/L)

Recovery,% (RSD %, n=3)

Cucumber Tomato Pakchoi Guava Starfruit Straw

berry

Diazinon 40 84 (1.9) 94 (1.5) 88 (0.1) 76 (3.1) 84 (1.1) 82 (0.6)

160 88 (2.3) 88 (1.5) 94 (2.5) 87 (2.1) 84 (4.3) 83 (2.5)

480 94 (2.2) 92 (0.9) 90 (0.4) 83 (4.7) 89 (1.5) 93 (1.1)

Chlorothalonil 20 88 (3.1) 88 (0.6) 86 (1.0) 82 (0.6) 78 (3.1) 76 (0.8)

80 88 (0.4) 92 (2.4) 94 (4.7) 85 (1.0) 79 (1.1) 78 (2.9)

240 97 (0.6) 96 (1.2) 85 (2.0) 85 (0.8) 74 (4.6) 78 (1.2)

Malathion 50 82 (3.2) 97 (2.8) 97 (3.7) 94 (1.0) 90 (1.1) 84 (3.2)

250 80 (3.9) 91 (1.4) 96 (1.8) 95 (0.9) 90 (1.2) 90 (0.9)

600 86 (3.0) 94 (1.2) 95 (1.7) 95 (0.5) 87 (2.0) 84 (2.6)

Chlorpyrifos 1 83 (1.0) 86 (2.1) 77 (1.1) 94 (0.7) 78 (3.1) 76 (0.8)

4 88 (2.1) 80 (4.6) 84 (0.5) 95 (0.5) 82 (1.1) 80 (0.6)

12 91 (2.6) 80 (0.8) 74 (1.7) 94 (0.3) 81 (1.7) 81 (1.8)

Quinalphos 50 80 (3.2) 96 (0.6) 93 (2.7) 95 (1.0) 90 (3.4) 79 (2.7)

250 84 (1.6) 94 (0.1) 89 (1.7) 93 (3.4) 94 (2.7) 97 (2.0)

600 86 (4.0) 95 (0.2) 96 (1.7) 88 (1.3) 90 (2.2) 91 (2.8)

Endosulfan 0.5 88 (3.0) 94 (0.6) 71 (2.8) 95 (1.0) 78 (2.3) 80 (4.2)

2 89 (4.4) 92 (0.2) 78 (1.6) 92 (1.0) 77 (1.9) 78 (3.8)

6 95 (4.5) 93 (1.3) 76 (3.1) 89 (1.6) 79 (2.6) 86 (4.4)

Profenofos 5 85 (1.5) 87 (1.9) 88 (1.1) 93 (0.3) 88 (1.2) 87 (1.9)

20 84 (1.6) 93 (1.3) 91 (3.7) 93 (1.1) 93 (1.0) 88 (0.6)

60 81 (3.6) 90 (1.0) 81 (1.7) 96 (1.0) 88 (2.9) 95 (1.0)

Endosulfan 1 89 (0.8) 81 (0.9) 72 (1.0) 97 (1.2) 89 (1.2) 83 (0.5)

4 94 (1.5) 82 (1.1) 78 (1.6) 96 (1.0) 90 (1.2) 91 (2.0)

12 97 (1.5) 80 (0.4) 71 (3.2) 98 (0.5) 88 (1.5) 85 (1.1)

Ranges 0.5 - 600 Vegetables: 71-97 (0.1-4.7) Fruits: 76-98 (0.3-4.7)

258

5.2.2.6 Confirmation of Pesticide Residue Determination by GC-MS

Confirmatory analyses were carried out using a gas chromatograph with a mass

spectrometry detector. Mass spectrometry is characterized by a high degree of

specificity. The relative retention time, linear ranges, regression coefficient (r2), limits

of detection, limits of quantification and MRL values for GC-MS are as shown in Table

5.16.

When compared to the liquid-liquid extraction method which showed the LOD ranging

from 0.02 – 0.15 mg/L (Tan and Tang, 2005), the values obtained were much better.

Besides, the values of the LOQ and LOD from HS-SPME are acceptable because they

are lower than the MRL values set by Codex Alimentarius (European Union, 2004).

Table 5.16: GC-MS Retention Time, Linear Ranges, r2 Value, LOD, LOQ and MRLs

from Codex Alimentarius in Fruits and Vegetables.

Compound Retention

Time

(min)

Linear

Ranges

(mg/L)

r2 LOD

(mg/L)

LOQ

(mg/L)

MRL

(mg/L)

Diazinon

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

β-Endosulfan

Internal Std

9.96

13.34

13.83

16.39

17.40

19.19

21.95

24.75

0.075-15

0.125-25

0.05-10

0.15-30

0.025-5

0.175-35

0.015-30

-

0.9952

0.9966

0.9872

0.9955

0.9900

0.9964

0.9911

-

0.01

0.01

0.005

0.01

0.001

0.01

0.002

-

0.05

0.05

0.02

0.03

0.005

0.03

0.01

-

0.5

0.5

0.2

0.05

0.05

0.05

0.05

-

259

Relative recoveries of seven pesticides were obtained at three levels of fortifications.

Three replicates at each fortification level were prepared. The mean relative recoveries

and RSD values from the spiked samples are shown in Table 5.17. Satisfactory

recoveries with the great majority above 85% were obtained from the six pesticide-free

commodities spiked in triplicate at 0.05 – 3.5 mg/L. The results are similar to those

obtained with the GC-ECD, percentage relative recoveries and the RSD values obtained

for the fruit samples being slightly better than those obtained for the vegetable samples.

This may due to the higher total suspended solids present and less water content in the

vegetable samples.

When compared to the GC-MS method, the GC-ECD results only provide quantitative,

elemental and peak retention time data, but lack the specificity necessary for molecular

structural identification. Mass spectrometry is a two-dimensional detection method and

provides both peak retention time and mass spectrum. The full spectrum profile in the

computer database is a finger print identification for final confirmation. GC-MS is a

powerful tool for residue identification or confirmation purposes. The sensitivity and

selectivity of GC-ECD for a rapid and reliable quantitative result makes the MS system

a logical complementary instrument in trace residue confirmation.

260

Table 5.17: Spiked Concentration Levels and Relative Recoveries over Fortified Fruits

and Vegetables using GC-MS

Pesticide Spiking

levels (mg/L)

Recovery,% (RSD %, n=3)

Cucumber Tomato Pakchoi Guava Starfruit Straw Berry

Diazinon

0.15 0.75

1.5

88.2(0.8) 90.6 (2.0)

91.8 (2.5)

94.8 (0.8) 96.3 (0.6)

91.2 (1.4)

91.6 (3.7) 95.1 (1.1)

93.9 (0.9)

78.8 (0.7) 83.9 (0.9)

86.9 (0.9)

90.2 (0.8) 90.9 (0.8)

88.5 (2.0)

82.8 (0.6) 87.7 (2.2)

86.9 (2.7)

Malathion 0.25

1.25

2.5

80.7 (2.4)

82.5 (0.6)

83.6 (1.3)

91.1 (1.1)

92.6 (2.1)

88.5 (0.5)

93.3 (3.7)

97.6 (2.0)

97.5 (1.8)

95.7 (2.2)

98.1 (1.1)

96.5 (1.8)

88.8 (0.7)

87.5 (1.9)

89.5 (0.5)

95.8 (2.3)

91.6 (2.1)

95.5 (0.5)

Chlorpyrifos 0.1

0.5

1.0

86.5 (2.2)

90.8 (0.7)

88.7 (1.7)

88.9 (0.9)

87.3 (0.7)

78.9 (1.6)

77.8 (2.7)

79.3 (0.7)

79.4 (0.6)

95.7 (2.2)

96.7 (2.1)

94.6 (2.0)

84.8 (2.5)

81.4 (0.6)

78.5 (2.1)

80.2 (1.0)

80.3 (0.7)

80.4 (0.6)

Quinalphos 0.3

1.5

3.0

84.7 (2.3)

88.5 (1.8)

82.5 (1.4)

97.1 (1.1)

95.5 (1.9)

95.8 (0.8)

85.7 (2.3)

83.8 (0.7)

92.4 (1.7)

91.4 (0.6)

92.8 (0.8)

92.8 (2.4)

88.3 (0.7)

89.0 (1.1)

88.4 (1.7)

95.3 (0.7)

90.5 (1.9)

92.4 (1.7)

-Endosulfan 0.05

0.25 0.5

92.4 (0.6)

97.5 (1.9) 93.2 (1.1)

93.1 (1.1)

93.6 (2.0) 94.6 (0.5)

75.3 (0.9)

75.5 (0.5) 78.5 (0.6)

90.9 (1.4)

92.6 (2.1) 91.8 (0.8)

77.7 (2.4)

82.5 (2.0) 79.5 (0.6)

90.5 (0.5)

91.4 (0.6) 90.5 (2.0)

Profenofos 0.35 1.75

3.5

85.3 (0.7) 87.8 (1.6)

87.1 (1.0)

88.7 (2.3) 89.3 (1.5)

93.6 (3.9)

86.9 (0.8) 87.5 (2.0)

94.7 (3.9)

96.1 (0.9) 97.6 (1.9)

96.0 (1.1)

91.7 (2.3) 92.2 (1.4)

87.6 (2.2)

96.7 (2.3) 93.3 (3.4)

93.3 (2.3)

-Endosulfan 0.30 1.5

3.0

97.7 (2.1) 94.3 (0.7)

97.9 (1.0)

88.3 (0.7) 85.8 (0.7)

81.2 (3.0)

76.7 (2.5) 74.6 (0.5)

82.3 (3.6)

98.7 (2.1) 98.6 (0.5)

96.0 (2.7)

85.1 (1.1) 86.3 (1.6)

84.8 (0.6)

94.8 (3.1) 91.8 (0.7)

96.2 (1.4)

Ranges 0.05-3.5 Vegetables: 75.3-97.9 (0.5-3.7) Fruits: 77.7-98.7 (0.5-3.4)

261

5.2.2.7 Application of HS-SPME on Real Samples

The developed HS-SPME method was subsequently applied to the analysis of ten fruits

and vegetables purchased from a local wet market. Tables 5.18 and 5.19 show the

pesticide levels detected in the investigated fruits and vegetables and the MRLs from

the Codex Alimentarius (European Union, 2004). All the pesticides were detected at

levels that were lower than the MRLs.

To assure the quality of the results when the proposed method is applied to routine

analysis, the following internal quality control criteria are applied in order to check if

the system is under control:

(a) A blank extract to eliminate a false positive by contamination in the extraction

process, instrument or reagents used.

(b) A blank extract spiked at the concentration of the second calibration level in

order to assess the extraction efficiency. Recovery rates between 70% and 120%

are considered as acceptable.

(c) Calibration curves prepared weekly to check both, sensitivity and linearity in

the working range of concentrations in order to avoid quantitation errors caused

by possible matrix effect and instrument fluctuation (r2 > 0.9900 are requested).

262

Table 5.18: Pesticide Level Detected in Investigated Fruits and Vegetables

Pesticide Pesticide Level, µg/L (RSD, %, n=3)

Cucumber Tomato Pakchoi Chili Spinach Guava Starfruit Strawberry Mango Papaya

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

Β-Endosulfan

n.d

n.d

n.d

6.2 (1.4)

n.d

n.d

n.d

n.d

9.4 (1.5)

n.d

n.d

n.d

21.4 (2.0)

n.d

n.d

n.d

n.d

n.d

56.8 (0.7)

n.d

n.d

n.d

17.4 (3.6)

n.d

n.d

n.d

n.d

n.d

35.3 (0.6)

n.d

n.d

n.d

n.d

n.d

53.5(1.0)

n.d

n.d

n.d

19.7 (0.7)

n.d

7.0 (0.6)

n.d

n.d

n.d

20.5 (2.2)

n.d

n.d

n.d

n.d

n.d

n.d

4.9 (0.5)

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

41.3 (2.2)

n.d

15.0 (0.7)

n.d

n.d

n.d

n.d

6.0 (0.6)

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d

n.d – not detected

Table 5.19: Maximum Residue Levels (MRL) from Codex Alimentarius (European Union, 2004)

Pesticide Maximum Residue Level (MRLs), µg/L

Cucumber Tomato Pakchoi Chili Spinach Guava Starfruit Strawberry Mango Papaya

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

Β-Endosulfan

20

1000

3000

50

50

50

50

50

50

2000

3000

50

50

500

50

500

20

5000

3000

50

50

50

50

50

20

2000

3000

50

50

50

50

50

20

5000

3000

50

50

50

50

50

20

3000

500

200

50

50

50

50

20

3000

500

200

50

50

50

50

20

3000

500

200

50

50

50

50

20

3000

500

200

50

50

50

50

20

3000

500

200

50

50

50

50

263

5.3 Comparison of HS-SPME, SPE and HS-SDME for the Determination of

Pesticide Residues in Fruits and Vegetables

Since solid phase extraction (SPE) is a well-established method and single drop

microextraction (SDME) is the latest method for the determination of pesticide residues

in food, a comparison of the overall performance of the HS-SPME method developed

in this study with that of SPE and HS-SDME was undertaken.

5.3.1 SPE Method

The SPE procedure of extracting pesticide residues from fruits and vegetables is based

on the literature review (Lal et al., 2008). The method employed acetone: ethyl acetate:

n-hexane (10: 80: 10, v/v/v) as the extraction solvent. A 5% acetone in n-hexane was

used as the eluent on a RP LC18 SPE cartridge and a gas chromatograph with an

electron capture detector was used for the determination of the investigated pesticides.

5.3.2 HS-SDME Method

The determination of pesticides in the food samples by SDME has received only very

limited attention. There has been no study on the extraction of pesticide residues from

fruits and vegetables employing HS-SDME as the sample preparation. In this study, the

parameters affecting the extracting process of HS-SDME were investigated and

optimized. Subsequently, the performance of the optimized HS-SDME was compared

to that of the developed HS-SPME and SPE methods.

264

5.3.2.1 Effects of Solvent Types and Drop Volume

The first step in the HS-SDME method is the selection of an appropriate extraction

solvent. Selection of a suitable solvent is very important to achieve good selectivity and

improve extraction efficiency. The selection of the extraction solvent was based on the

principle of “like dissolves like”. The extraction solvent must have low water solubility,

extract analytes well and have good drop stability during stirring and has a low level of

toxicity (Psillakis and Kalogerakis, 2002). Several types of organic solvents including

n-hexane, isooctane and toluene were tested for the HS-SDME. The chemical

characteristics of three extraction solvents are listed in Table 5.20.

Table 5.20: The Chemical Characteristics of Three Extraction Solvents

Extraction

Solvent

Log P Surface Tension

(dyn/cm)

Viscosity

(cP)

Boiling Point

(oC)

n-Hexane 3.90 17.91 0.08 68.7

Isooctane 4.09 18.77 0.50 99.2

Toluene 2.73 28.53 0.59 110.6

The results showed that, n-hexane had the tendency to evaporate at a faster rate once

exposed to the air among the three tested solvents. It may most probably be due to the

fact that it had the low boiling point. Isooctane was found to be more resistant to

evaporation due to its higher boiling point and resulted in enhanced extraction of target

analytes when compared to n-hexane. Overall, toluene exhibited the highest extraction

efficiency for all the target analytes (Figure 5.32). So, toluene was the solvent of choice

since it has a high boiling point reducing evaporative loss, high surface tension and

viscosity increasing cohesive forces at the interface and thus reducing solvent re-

dissolution. Moreover, the small log P value shows the non-polar character is very

265

suitable for extracting all the analytes studied. Besides, toluene is also a very suitable

solvent for pesticide GC injection (Mastovska and Lehotay, 2004). Thus, toluene was

selected for the subsequent HS-SDME experiments.

Figure 5.32: Effect of Solvent Types on Peak Area in HS-SDME

Generally, the use of a large organic drop results in an increase in the analytical

response of the instrument. However, large drops are difficult to manipulate and are

less reliable. In addition, the analytes diffuse into the drop through the diffusion process

when the drop volume increases, and it takes a longer time to reach equilibrium.

Therefore, in order to increase the sensitivity of the SDME procedure, the organic drop

volume must be optimized experimentally. Figure 5.33 shows that the analytical signal

increased with increasing drop volume from 1.0 µL to 1.5 µL, after that it levels off,

and after 1.5 µL the peak areas for all the investigated pesticides decrease with any

further increase in the drop volume. Therefore, the organic drop volume of 1.5 µL was

0

5

10

15

20

25

x 10

0000

n-hexane

isooctane

toluene

Pe

ak A

rea

266

used to ensure the formation of a stable and reproducible microdrop and to allow for

fast stirring speeds.

Figure 5.33: Effect of Solvent Drop Volume on Peak Area in HS-SDME

5.3.2.2 Effects of Extraction Time and Temperature

The effect of extraction time on extraction efficiency was investigated with the time

varying from 5 min to 30 min. The extraction efficiency increases with extraction time

in HS-SDME method. The extraction time should be sufficient for the microdrop to

extract a finite quantity of the target analytes. The results (Figure 5.34) showed that the

rapid initial increased in the amount of analyte extracted followed by a slower

increased lasting a long time and the equilibrium was not yet attained for all the

investigated pesticides after 30 min extraction reflects the processes taking place in the

system. The first stage corresponds to the analyte extraction from the headspace only.

As soon as the headspace concentration of the analyte falls below the equilibrium value

with respect to the aqueous phase, the analyte molecules begin to diffuse from the

0

2

4

6

8

10

12

14

16

18

0 0.5 1 1.5 2 2.5 3

x 10

0000



Solvent Drop Volume (µL)

Pe

ak A

rea

267

aqueous phase to the gaseous phase, which is a rate-determining step. The overall

extraction rate has two rate-determining steps: aqueous-phase mass transfer and

diffusion of solutes into the extracting solvent. However, longer extraction times were

avoided as they typically resulted in significant solvent evaporation. Nonetheless, for

the quantitative HS-SDME analysis, it is not necessary for the analytes to have reached

equilibrium, only to allow sufficient mass transfer into the microdrop and exact

reproducible extraction time (Shariati-Feizabadi et al., 2003; Yamini et al., 2004; Vidal

et al., 2005). Moreover, a phenomenon of microdrop dissolution was observed where

approximately 0.5 µL extraction solvent was lost during the 30 min extraction time,

owing to longer exposure times. An extraction time of 15 min was selected in order to

make the HS-SDME more reliable.

Figure 5.34: Effect of Extraction Time on Peak Area in HS-SDME

0

5

10

15

20

25

0 5 10 15 20 25 30 35

x 10

0000




Pe

ak A

rea

268

When the microdrop is in the headspace, analytes are removed from the headspace first,

followed by indirect extraction from the matrix. Therefore, volatile analytes are

extracted faster than semivolatiles, since they are at a higher concentration in the

headspace, which contributes to faster mass transport rates through the headspace.

Temperature has a significant effect on both kinetics and thermodynamics of the

extraction process. Temperature affects the kinetics of sorption in the microdrop by

determining the vapor pressure of analytes and diffusion coefficient values in all three

phases. (Pawliszyn, 1997). Figure 5.35 shows that the extraction efficiency of most

pesticides decreased as the temperature increased. It is because the process of analyte

absorption in the microdrop is exothermic and at the high temperature, the amount of

the extracted analyte decreases due to that partition coefficient of the analyte to the

extraction phase decreases. Besides, the high temperatures can also cause the solvent

drop damage and loss which will then decrease the response too. To simplify the

method, subsequent experiments were performed at room temperature.

Figure 5.35: Effect of Extraction Temperature on Peak Area in HS-SDME

0

2

4

6

8

10

12

14

16

18

20 25 30 35 40 45 50 55 60

x 10

0000




Pe

ak A

rea

269

5.3.2.3 Effect of Stirring Rate

The effect of agitation on the extraction of pesticides was also studied. Fast agitation of

the sample could be employed to enhance the extraction efficiency because agitation of

the aqueous sample results in a degree of convection of the headspace. Increasing the

speed of sample agitation is expected to enhance the rate of extraction of all

investigated analytes, suggesting thus that the aqueous-phase mass transfer

corresponding to a limiting step in extraction (Przyjazny and Kokosa, 2002). To

evaluate the effect of stirring rate, sample solutions were continuously agitated at

different stirring rates from 400 rpm to 1000 rpm. The results (Figure 5.36) show that

the relative peak areas of all the analytes increased with the increase of stirring rate

from 400 rpm to 800 rpm. However, when the stirring rate was greater than 800 rpm,

the precision of the method was unacceptable and the microdrop in the needle was

unstable. Nonetheless, at speeds exceeding 800 rpm, the formation of air bubbles was

promoted increasing the incidents of drop loss or dislodgement. Therefore, the

optimum stirring rate of 800 rpm was selected and was used in all subsequent

experiments.

Figure 5.36: Effect of Stirring Rate on Peak Area in HS-SDME

0

2

4

6

8

10

12

14

16

18

350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050

x 1

000

00



Stirring Speed (rpm)

Pe

ak A

rea

270

5.3.2.4 Effect of Ionic Strength

Addition of salt such as NaCl to the sample may have several effects on SDME

(Psillakis and Kalogerakis, 2002). It can improve the extraction of analytes since high

ionic strength due to the salt addition reduces their water solubility. However, the

presence of salt was found to restrict extraction of analytes. Apart from the salting out

effect, the presence of salt can reduce the diffusion rates of the anaytes into the drop.

The effect of salt concentration on the extraction efficiency of pesticides is illustrated in

Figure 5.37. As can be seen, the addition of salt caused little reduction in the extraction

efficiency except for diazinon and malathion. This means that with increased salt

concentration the diffusion of analytes towards the organic drop becomes more difficult

thus limiting the extraction. In contrast, the extraction efficiency for diazinon and

malathion increased with increasing salt content from 0 to 30% of NaCl due to its high

water solubility behavior. Based on the experimental results obtained, the direct sample

analysis without the addition of salt was employed in this study.

Figure 5.37: Effect of NaCl (%) on Peak Area in HS-SDME

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30

x 10

0000



NaCl (%)

Pe

ak A

rea

271

Overall, the optimum extraction conditions found in the present HS-SDME studies are

as follows: a 1.5 µL toluene microdrop was exposed for 15 min to the headspace of a 5

mL aqueous sample in a 15 mL vial at room temperature and stirred at 800 rpm.

5.3.3 Analytical Performance of the HS-SPME, SPE and HS-SDME Methods

The analytical parameters for HS-SPME, SPE and HS-SDME procedures were

obtained by the analysis of different spiked cucumber and strawberry samples using

internal calibration curves for three concentration levels of standard pesticide mixtures.

The linearity of the detector response using all three extraction techniques was verified

in the concentration ranges from 0.0001 mg/L to 500 mg/L. Triplicate analyses were

run for each of the six concentration levels chosen within these ranges. The precision

(repeatability) of each method was determined by performing five consecutive

extractions at the middle concentration level. The results are summarized in Table 5.21.

For HS-SPME, the regression coefficient (r2) ranged from 0.9969 to 0.9990 and for

SPE, the r2 ranged from 0.9981 to 0.9996. For the HS-SDME method, the values

ranged from 0.9834 to 0.9949. Overall, the repeatability expressed as the relative

standard deviation (RSD) was found to be satisfactory for HS-SPME which ranged

from 1.30% to 5.93%, with a mean value of 3.87% and for SPE, it ranged from 0.70%

to 2.69%, with a mean value of 1.63%. However, for the HS-SDME method, the RSD

values varied between 5.88% and 15.15% with a mean value of 10.62%. In the HS-

SPME and HS-SDME extraction techniques, higher RSDs are expected when, as in this

study, the extraction were carried out under non-equilibrium conditions. It is evident

that, with HS-SPME and SPE, better precision and linearity are obtained for all

272

investigated pesticides compared to HS-SDME. This observation is based on the fact

that HS-SDME requires more elaborate manual operations when pushed down the

plunger to expose the microdrop in the stirred solution, held the microsyringe at a

certain level and retracted back the microdrop into the microsyringe. All these manual

operations are giving rise to less repeatable results.

The limits of detection (LOD) for all the investigated pesticides at a signal-to-noise

(S/N) ratio of 3 : 1 using all three methods were then determined. The results from

Table 5.22 show clearly that, under the present experimental conditions, HS-SPME is

the most sensitive among the three techniques. The detection limit for HS-SPME is one

order of magnitude lower than that for SPE, although a 10 - fold sample volume was

used for SPE. This can be overcome by increasing the volumes for SPE, but in the

present study where sample volume is limited a higher sensitivity would be a

considerable advantage. In comparison to HS-SPME, the limits of detection for HS-

SDME is 10 – 100 times higher than HS-SPME. For HS-SDME, lower LODs are

expected by prolonging the extraction times. However, prolonged sampling times may

result in drop dissolution and dislodgment for HS-SDME.

Pesticide-free cucumber and strawberry samples were spiked at three concentration

levels and analyzed using the SPE and HS-SDME methods in order to evaluate the

effect of the matrix and compare the results with those obtained with HS-SPME.

Similar to HS-SPME, HS-SDME is an equilibrium technique and not an exhaustive

method such as SPE. In contrast to SPE which measures the absolute recovery, HS-

SDME and HS-SPME measure the relative recovery. The results of the mean recovery

(%) at the three concentration levels are given in Table 5.22.

273

For SPE, the average absolute recoveries ranged from 86.5% to 104.0% with the RSD

values of less than 3%. The relative recoveries of HS-SPME and HS-SDME ranged

from 84.2% to 96.6% and 71.8% to 95.8%, respectively. However, the RSD values

obtained with the HS-SDME method (4.7 – 13.6%) were higher than those obtained

with HS-SPME (1.2 – 3.0%), demonstrating again the fact that HS-SDME is a more

elaborate method requiring more manual operations.

In terms of sample preparation time, this parameter mainly depends on the extraction

time which can be chosen within certain boundaries by the analyst in the case of HS-

SPME. The equilibrium is not yet attained in less than 1 hour and quite often it takes

several hours to establish, but for practical reasons the extraction time between 20 min

and 1 hour is employed for most cases. Quite often the extraction time chosen depends

on the duration of a GC run to shorten the overall time of analysis. In this study this

was also the main reason for choosing an extraction time of 30 min, as equilibrium was

reached only after an extraction time of 60 min and the resulting sensitivity was

sufficient after 30 min. Sample preparation by SPE also takes about 2 hours with

a greater number of steps had to be carried out in that time. HS-SDME is a much faster

extraction method given that the results were obtained after sampling the samples for

only 15 min instead of 30 min used in the case of HS-SPME. Automation, although not

applied in this work, would be possible for SPME but would be difficult for SPE and

SDME methods.

274

Table 5.21: Monitoring Parameters, Linearity Ranges, Regression Coefficients, and Mean RSD (%) for HS-SPME, SPE and HS-SDME

Linear ranges (µg/L) Regression coefficients, r2 Precision (RSD, %, n=5)

Compound HS-SPME SPE HS-SDME HS-SPME SPE HS-SDME HS-SPME SPE HS-SDME

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

β-Endosulfan

10-1000

10-1000

50-5000

0.5-50

50-5000

0.1-20

1-100

1-100

100-10000

100-10000

500-50000

5-500

500-50000

1-200

10-1000

10-1000

1000-100000

1000-100000

5000-500000

50-5000

5000-500000

10-2000

100-10000

100-10000

0.9985

0.9977

0.9973

0.9969

0.9972

0.9982

0.9990

0.9990

0.9996

0.9991

0.9981

0.9986

0.9992

0.9987

0.9996

0.9987

0.9876

0.9912

0.9966

0.9834

0.9949

0.9945

0.9946

0.9918

1.30

5.93

3.93

5.71

4.82

4.25

2.75

2.30

1.61

2.17

2.69

1.29

1.91

1.25

0.70

1.39

8.33

12.46

12.31

13.15

5.88

15.15

7.44

10.20

Table 5.22: Monitoring Parameters: Limits of Detection (LOD), and Mean Recovery (%) for HS-SPME, SPE and HS-SDME

Compound LOD (µg/L) Mean Recovery, % (RSD,%, n=3 x 3 levels)

Cucumber Strawberry

HS-SPME SPE HS-SDME HS-SPME SPE HS-SDME HS-SPME SPE HS-SDME

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

α-Endosulfan

Profenofos

β-Endosulfan

0.2

0.2

1

0.02

1

0.01

0.1

0.1

2

2

10

0.2

10

0.1

1

1

200

200

1000

2

1000

1

10

10

96.0 (1.4)

89.9 (2.2)

88.3 (1.5)

90.7 (2.5)

92.3 (1.8)

95.4 (1.5)

88.8 (2.2)

95.1 (2.2)

90.0 (0.9)

92.4 (1.6)

97.1 (1.0)

86.5 (1.8)

89.5 (3.0)

102.1 (1.8)

98.5 (2.7)

92.6 (0.9)

76.7 (6.7)

77.4 (7.3)

91.9 (6.4)

81.9 (9.6)

77.0 (4.7)

95.8 (8.1)

89.5 (4.8)

93.0 (10.0)

90.4 (2.1)

96.1 (1.6)

86.7 (1.5)

84.2 (1.2)

91.9 (3.0)

86.9 (1.9)

96.6 (2.6)

94.9 (1.7)

104.0 (2.3)

96.5 (1.2)

94.5 (1.9)

88.8 (2.0)

92.3 (2.3)

94.5 (1.0)

95.5 (1.9)

94.8 (1.0)

75.0 (8.5)

81.6 (6.8)

84.7 (13.6)

75.7 (6.7)

87.8 (6.0)

89.0 (12.7)

85.3 (8.4)

71.8 (4.8)

275

Based on the analytical performance results, it can be seen that HS-SPME and SPE

showed good linearity, precision, LODs and recoveries for extracting the pesticide

residues in fruit and vegetable samples. In comparison to SPE, SPME also offers

another distinct advantage since HS-SPME is almost free of any organic solvent, using

100 times less organic solvent than SPE. In addition, the total sample preparation time

is much less with SPME than with SPE. SPME fibers are re-usebale whereas SPE

cartridges are designed for single use applications. The advantages thus conferred by

SPME allow for increased sample throughput, along with concomitant decrease in

both the expense and the amount of waste generated. Therefore, it can be concluded

that SPME can be used as an alternative method to replace SPE method which is a

well-established method for the determination of pesticide residues in food.

276

5.4 Pesticide Formulations

Method validation for the quantitative determination of nine active ingredients in

pesticide formulations is also presented. Method validation was carried out by

determining the parameters required by EC (1991) and CIPAC (1999) guidelines.

According to the above-mentioned guidelines specificity, linearity, repeatability,

precision and accuracy were established for the method validation studies.

5.4.1 Specificity

The ability of an analytical method to distinguish the analyte to be determined from its

degradation products, metabolites or known additives were investigated (EC, 1991;

CIPAC, 1999). For this purpose, concentrated sample extracts as well as a standard

mixture of pesticides were analyzed. It was found that there was no interference since

no other peaks appeared at the regions of the pesticide and the targeted internal

standard. This lack of interference was also demonstrated by the application of the

above-mentioned analyses to a confirmation method by using GC-MS.

5.4.2 Linearity of Response and Range

The linearity response was determined by analyzing in triplicates five working

solutions of different concentrations for each of the tested active ingredients. For this

purpose the ratio of the peak areas of the active ingredients and that of the internal

standard was plotted against their concentration ratio. After the multi-point calibration

was plotted, the calibration curve value, regression coefficient and linearity ranges were

determined and are shown in Table 5.23. In the case of pesticide formulations analysis,

the results can be considered as acceptable if the regression coefficients, r2 exceeds

277

0.9970. Using this criterion, the calibration shown in Table 5.23 was considered

acceptable as the regression coefficients were greater than 0.9972.

Table 5.23: Statistical Parameters of Calibration and Repeatability for Pesticide

Formulations

Compound Calibration Curve r2 Linearity Ranges

(mg/L) Repeatability,

RSD (%)

(n=5)

Acephate

Carbaryl

Dimethoate Diazinon

Chlorothalonil

Malathion Chlorpyrifos

Quinalphos

Profenofos

y=0.5981x+0.0862

y=0.2552x+0.1519

y=4.8751x+0.1541 y=5.2087x+0.1418

y=7.3235x+0.4078

y=07553x+0.1371 y=84.8472x+0.9388

y=0.6011x+0.1097

y=16.2964x+0.1718

0.9980

0.9977

0.9993 0.9994

0.9972

0.9982 0.9998

0.9981

0.9972

0.016-10

0.08-20

0.0032-2 0.0032-2

0.0032-2

0.016-10 0.0002-0.1

0.016-10

0.0007-0.35

0.25

0.16

0.57 0.98

0.31

0.38 0.33

0.86

0.69

5.4.3 Repeatability of Injections

The repeatability of the injection technique was tested for each active ingredient

separately, using the intermediate level working standard solution. Five replicate

determinations were made. In the case of pesticide formulations analysis, the

repeatability is considered as acceptable if the relative standard deviation (RSD) of the

peak area ratios is less than 1%, which was demonstrated in this study (Table 5.22).

278

5.4.4 Precision of the Method

Precision is the degree of agreement between independent analytical results obtained

under the same analytical conditions (EC, 1991). It is a measure of random errors, and

may be expressed as repeatability and reproducibility. Precision is an important

characteristic in the evaluation of all quantitative methods. Repeatability and

reproducibility are expressed as relative standard deviation (RSD) of a number of

samples (EC, 1991; CIPAC, 1999). The expected repeatability and reproducibility

values can be obtained from the Horwitz equation (Equation 5.1) and the modified

Horwitz equation (Equation 5.2) (EC, 1991; CIPAC, 1999). The results are considered

acceptable if they are smaller than the values calculated by the Horwitz equation.

RSDR = 2(1-0.5 log C)

(5.1)

RSDr (%) = RSDR (%) x 0.67 (5.2)

Where C is the concentration of the analyte in the sample expressed as a decimal mass

fraction (1 mg/L = 10-6

), RSDR is the inter-laboratory relative standard deviation and

RSDr is the repeatability relative standard deviation. The data obtained from the

analysis of triplicate samples were used to calculate the experimental RSDr values. The

Horwitz equation (Equation 5.1) and the modified Horwitz equation (Equation 5.2)

were applied for the calculation of the expected values of RSDR and RSDr respectively.

Table 5.23 shows the comparison of the experimental RSDr values and the theoretical

RSDr values. It can be seen that the repeatability of the method is acceptable as the

measured values are not outside the recommended theoretical values.

279

5.4.5 Accuracy of the Method and Sample Analysis

The accuracy of a procedure may be determined by the determination of a number of

samples containing a known quantity of the analyte. The mean recovery (%) for the

synthetic formulation is as follows:

Mean recovery (%) = x 100

Three concentration levels - at low, middle and high regions of the linear ranges were

determined and the mean percentage recovery was calculated for each concentration

level. Table 5.24 shows the results of nine pesticide formulations. The analytical results

of these investigated pesticides were within the specifications for the commercial

pesticide formulations.

These mean recoveries (%) should be within the following ranges:

Active ingredient, nominal (%) Mean recovery (%)

>10

1 – 10

<1

98.0 – 102.0

97.0 – 103.0

95.0 – 105.0

(CIPAC, 1999)

Mean content determined (%)

Theoretical content (%)

280

Table 5.24: Results of Nine Pesticide Formulations Determination at Three

Concentration Levels

Compound Active

Ingredient

(%)

RSDr (%)

Conc (mg/L)

Content in Formulation

(%)

Accuracy, % (RSD, %)

Acephate

Carbaryl

Dimethoate

Diazinon

Chlorothalonil

Malathion

Chlorpyrifos

Quinalphos

Profenofos

73

85

40.0

55.0

12.3

84.0

37.1

10.9

45.1

1.41

1.37

1.56

1.84

1.47

1.54

1.38

1.51

1.87

0.05

0.5

5.0

0.1

1.0

10.0

0.01

0.1

1.0

0.01

0.1 1.0

0.01 0.1

1.0

0.05 0.5

5.0

0.001

0.01

0.10

0.05

0.5

5.0

0.001

0.01 0.10

72.7

73.0

72.8

85.8

85.1

84.0

39.6

39.9

40.3

54.8

55.0 55.0

12.1 12.2

12.3

85.1 84.0

84.2

37.4

36.4

37.0

10.8

10.9

10.9

44.8

44.5 46.0

99.5 (1.0)

100.0 (1.2)

99.7 (1.0)

100.9 (1.0)

100.2 (1.0)

99.6 (1.3)

99.0 (1.0)

99.8 (0.7)

100.7 (0.6)

99.7 (1.0)

100.1 (1.1) 99.9 (1.2)

98.3 (1.2) 99.2 (1.2)

100.1 (1.3)

101.3 (1.1) 100.0 (1.1)

100.3 (1.1)

100.7 (1.3)

98.1 (1.3)

99.8 (1.2)

98.8 (0.3)

99.9 (1.5)

100.2 (1.0)

99.3 (1.4)

98.8 (1.1) 101.9 (1.6)

281

It should be stressed that by using the internal standard method, the error due to sample

manipulation can be eliminated when taking extremely small sample volumes (2 µL).

The manual injection technique was applied in this study to introduce liquid samples in

the GC system. This method has significant discrimination since the uneven injection

volume and injection speed will directly affect the outcome and also the precision of

the results. By using the internal standard method, the error arising out of these

inconsistent injections can be eliminated or minimized. The precision (RSD from 0.3%

to 1.6%) obtained in this study are better than the precision reported by Skoulika et al.

(2000) (RSD, 0.1 – 7.8%), Quintas et al. (2003b) (RSD, 1.1 – 2.6%), and Kumar et al.

(2007) (RSD, 0.87 – 2.57%). Therefore, the internal standard procedure developed in

this study is suitable for the determination of the active ingredients in commercial

pesticide formulations.

The selection of an appropriate internal standard is an important job. The nature and the

concentration of the substance selected depend on several factors. The main

requirement is that the substance must have a good response to the detector so that a

high signal can be obtained. It also can give a good peak shape and is resolved from the

analytes of interest and any other peaks in the separation. Other requirements are the

internal standard must be sufficiently stable in the sample dissolving solvent to prevent

the formation of degradation products, which would interfere with the integration

results. The substance selected should be cheap and readily available in a high-purity

form from commercial suppliers so that the method can be readily reproduced

elsewhere. The toxicity of the internal standard must be minimal to reduce any handling

precautions that may be required. 1-chloro-4-fluoro benzene is met all the above

requirements and was chosen as the internal standard in this study.

282

CHAPTER 6

CONCLUSION

Solid-phase microextraction has been introduced as a modern alternative to traditional

sample preparation technology, and it is able to address many of the requirements for

accurate analytical results. This technique eliminates use of organic solvents, and

substantially shortens the time of analysis and is a convenient sample preparation step.

The application of HS-SPME for trace analysis of multiresidue pesticides in fruits and

vegetables has been demonstrated in this study. There have been no reports about HS-

SPME of multiclass and multiresidue pesticides from this matrix without any

pretreatment of the samples, resulting in a drastic reduction of working time and

organic solvent consumption. The addition of water and small amounts of organic

solvents were needed to enhance the analyte release from the matrix. To optimize the

HS-SPME process, the effects of some experimental parameters, namely extraction

time and temperature, stirring rate, ionic strength, pH, fiber depth, desorption time and

temperature were evaluated. The proposed analytical method is as follow: a

homogenized spiked sample is added with 2% (vol/weight) of methanol/acetone (1:1)

and optimum dilution is made with distilled water containing 10% NaCl until 5.00 g.

Then, an internal standard is added and the sample is extracted by the headspace of a

100 µm PDMS fiber at 60 oC for 30 min; with sample agitation at 800 rpm without any

pH adjustment. Desorption was done at 240 oC for 10 min. The selectivity and capacity

of the fiber coating used in SPME are important factors in matching the fibers with the

analyte type. In this study, it was found that the 100 µm PDMS is a good fiber coating

283

for non-polar and semi-polar volatile and semi-volatile compounds; whereas, the 85 µm

PA is good for extracting polar compounds.

HS-SPME analysis is an excellent technique for volatile or semi-volatile compounds

and can be used for “dirty” matrices either in the liquid or solid state. The technique is

characterized by simple sample preparation and the analytes can be transferred directly

to a GC. The method developed in this study reduces the tedious sample preparation

procedures, such as derivatization, separation and concentration for trace analysis.

Since all the sample processing occurs in an enclosed vial, sample loss is also

minimized. Low ppb levels of pesticide residues until 0.01 as found from this study can

be determined accurately.

A novel and straightforward analytical method for the determination of pesticide

residues in fruits and vegetables has been developed by using HS-SPME. The

validation parameters according to the ICH recommendations were applied and it was

demonstrated that the proposed new method is specific, accurate and precise, within the

established linearity range. The recoveries for the 0.5 µg/L to 600 µg/L fortification

levels ranged from 71% to 98%. The recoveries obtained in this study are comparable

with the recovery values reported by Berrada et al. (2004) (76% to 95%), Cai et al.

(2006) (55.3% to 106.4%), Dong et al. (2005b) (78.4% to 119.3%), and Lambropoulou

et al. (2003) (74% to 91%). Therefore, the developed HS-SPME procedure is suitable

for the determination of the multiresidue analysis of pesticides in fruits and vegetables.

This analytical procedure is also characterized by its high accuracy since confirmatory

analyses were also carried out using a gas chromatograph with a mass spectrometric

detector.

284

The developed HS-SPME method can be used to determine the pesticide residues in

local fruits and vegetables since the LOQ and the LOD are much lower than the MRLs

as specified in the Codex Alimentarius. Besides, the developed HS-SPME method

showed results comparable to those obtained with the established SPE method.

However, from time-saving and waste-reduction considerations, the developed HS-

SPME method is superior to the SPE procedure, which is reflected in lower costs and

less environmental pollution. HS-SPME and HS-SDME are two fast microextraction

methods. HS-SPME can be easily used for headspace analysis and yields lower

detection limits for the tested analytes. HS-SDME, on the other hand, requires more

elaborate manual operations, which can affect the linearity and precision.

There are some practical problems when the SPME technique is employed such as the

quality of the needles is not consistent and it always depends on the manufacturer, and

sometimes the performance of the fiber may differ from batch to batch. The carry-over

effects of the fiber are also a problem which in some cases is difficult to eliminate, even

at high temperatures. Samples with a high percentage of suspended matter can present a

serious problem because the fiber coating can be damaged during agitation; similarly

high molecular mass compounds can be adsorbed irreversibly to the fiber, thus

changing the properties of the coating and making it unusable when it becomes black.

The problems mentioned above might be some of the reasons for the poor

reproducibility and non-linearity encountered with SPME. These problems can be

solved with optimization of each fiber before use and at the same time employing the

internal standard method to get the relative recovery. Conditioning and calibration

should be always performed on each new fiber and also when a fiber has not been used

285

for some time. A blank GC run should be performed with the fiber between sampling.

In the case of complex matrices, optimum dilution and with the addition of an organic

solvent with the HS-SPME technique must be used.

The GC-ECD determination with an internal standard method has been successfully

used for the rapid quality control of commercially available formulations of pesticides.

The proposed method is a fast alternative to the FTIR procedures which is usually

employed in the quality control process of commercial formulations. The main

advantages of the developed GC-ECD procedure are that: (a) it can be performed

without any sample pre-treatment. (b) it provides a high sampling throughput, because

it only needs 5 min sample preparation and 20 min for the GC analysis. (c) it reduces

drastically the amount of solvent used.

The analytical results of the investigated pesticides were within specifications for nine

commercial pesticide formulations. The sensitivity of this method was excellent for all

the investigated compounds. The accuracies obtained were within 98.1% to 101.9%

with the relative standard deviation (RSD) between 0.3% and 1.6%. Validation of the

analytical method for pesticide formulations is based on a series of experimental

procedures to establish specificity, linearity, repeatability, precision and accuracy

according to international guidelines namely, CIPAC guidelines (1999); and EC

guidelines (1991). This technique can be used for the quantitative determination as well

as for positive identification of the active ingredients in the pesticide formulations. It

can also be used to determine the percentage of active ingredients for the non-

scheduled pesticides which might be used illegally in Malaysia.

286

SUGGESTIONS FOR FUTURE WORK

One of the limitations of this method is its inability to detect very soluble and non-

volatile pesticides such as acephate and dimethoate. Further studies need to be carried

out to develop a new coating for these types of compounds from aqueous matrices for

quantitation and speciation.

In addition, the procedure described in this study can be also be automated and placed

on-line with the GC instrument by using an autosampler system, taking advantage of

the fact that the SPME device is analogous to a syringe in its operation and that after

desorption the coating is cleaned and ready for re-use. It is recommended that further

work using the 96-pin SPME replicator device for the extraction of non-volatile species

and automated analysis by GC be investigated. A customized robotic system which can

guide the SPME replicator device through the entire process including extraction with

agitation, solvent desorption, and sample reconstitution prior to interfacing with GC

platforms for analysis to reduce human error and operator handling time can be

employed.

Further studies need to be carried out to develop an effective multiresidue analysis

method for the determination of pesticide residues in food matrices which exceed the

MRLs or used illegally in Malaysia, namely fenobucarb, parathion-methyl, phenthoate,

and other dithiocarbamate pesticides.

287

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310

LIST OF PUBLICATIONS AND PRESENTATIONS

A. International Journals

1. Mee Kin, Chai. and Guan Huat, Tan. (2009). Validation of a headspace solid-

phase microextraction procedure with gas chromatography-electron capture

detector of pesticide residues in fruits and vegetables. This manuscript is

accepted by International Journal of Food Chemistry, Elsevier on April 2009.

2. Mee Kin, Chai. And Guan Huat, Tan. Chai, M.K., Tan, G.H. (2008).

Comparison of headspace single-drop microextraction with solid-phase

microextraction and solid-phase extraction for the determination of eight

organochlorine and organophosphorus pesticide residues in food matrices. This

manuscript is accepted by International Journal of Chromatographic Science on

Dec 2008.

3. MeeKin, Chai., GuanHuat, Tan. And Lal, A. (2008). Optimization of

headspace solid phase microextraction for the determination of pesticide

residues in vegetables and fruits. The International Journal of Analytical

Sciences. 24 (2). 273-276.

4. Lal, A., GuanHuat, Tan, and MeeKin, Chai. (2008). Multiresidue analysis of

pesticide in fruits and vegetables using solid-phase extraction and gas

chromatographic method. The International Journal of Analytical Science.

24(2). 231-236.

311

B. National Journals

5. Chai, M.K. and Tan, G.H. (2009). Comparison of different types of coating in

headspace solid-phase microextration for the analysis of pesticide residues in

vegetables and fruits. Malaysian Journal of Analytical Science, 12(2), 444-

450.

6. Chai, M.K., Tan, G.H. and Kumari, A. (2009). Application of solid-phase

microextraction for the determination of pesticides in vegetables samples by

gas chromatography with an electron capture detector. Malaysian Journal of

Analytical Science, 12(1), 1-9.

7. Chai, M.K., Tan, G.H. and Kumari, A. (2007). Headspace solid-phase

microextraction in combination with gas chromatography-mass spectrometry

for the rapid screening of pesticide residues in vegetables and fruits. Malaysian

Journal of Chemistry. 9(1). 010-015.

8. Chai, M.K., Tan, G.H. and Kumari, A. (2006). Method development of

determination of pesticide residues in vegetables and fruits by using solid-

phase microextraction. Malaysian Journal of Chemistry. 8(1). 067-071.

9. Kumari, A., Tan, G.H. and Chai, M.K. (2006). Simultaneous determination of

diazinon, malathion and quinalphos pesticide formulations by gas

chromatography with an electron capture detector. Malaysian Journal of

Science. 25 (2). 131-138.

10. Chai, M.K., Tan, G.H. and Kumari, A. (2005). Determination of active

ingredients in pesticide formulation by gas chromatography with an electron

capture detector. Malaysian Journal of Science. 24(2). 59-63.

11. Kumari, A., Tan, G.H. and Chai, M.K. (2005). Determination of quinalphos

and endosulfan pesticide formulation by gas chromatography-mass

spectrometry. Malaysian Journal of Analytical Science. 9(2), 28-33.

312

C. International Conferences

12. Chai M. K. and Tan, G. H (2008). Application of headspace single-drop

microextraction and comparison with solid-phase microextraction and solid-

phase extraction for the determination of pesticide residues in fruits and

vegetables. Oral presentation and proceeding in The International Conferences

on Science & Technology. Universiti Teknologi Mara, Penang, Malaysia.

13. Chai, M.K. and Tan, G.H. (2007). Comparison of different types of coating in

headspace solid-phase microextration for the analysis of pesticide residues in

vegetables and fruits. Oral presentation and proceeding in The International

Symposium on Environmental and Green Chemistry (EGC), 12th

Asian

Chemical Congress (12ACC), Putra World Trade Centre, KL. Malaysia.

14. Chai, M. K., Tan, G.H. and Kumari, A. (2006). Solid-phase microextraction

gas chromatography- analysis of pesticide residues in vegetables and fruits.

Poster presentation at The 2nd

Maths and Physical Science Graduate

Conference. National University of Singapore, Singorpore.

D. National Conferences

15. Chai M.K., Tan, G.H. and Kumari, A. (2006). Development of a headspace

solid-phase microextraction for the determination of pesticide residues in

vegetables and fruits by gas chromatography with an electron capture detector.

Bronze Medal in the category of fundamental research, UM Ekspo

Penyelidikan, rekacipta dan inovasi, Malaysia.

16. Chai, M. K., Tan, G.H. and Kumari, A. (2006). Headspace solid-phase

microextraction-gas chromatograph mass spectrometry: a fast and simple

screening method for the assessment of pesticide residues in vegetables and

fruits. Oral presentation at 19th Malaysian Analytical Chemistry Symposium

(SKAM 19) and 2nd

Malaysian Conference on Catalyst (MyCat 2), Melaka,

Malaysia.

17. Chai, M.K., Tan, G.H. and Kumari, A. (2005). Application of solid-phase

microextraction for the determination of pesticides in vegetables samples by

gas chromatography with an electron capture detector. Oral presentation at 18th

Malaysian Analytical Chemistry Symposium (SKAM 18). Johor Bahru,

Malaysia. Published on Malaysian Journal of Analytical Science, 12(1), 1-9.

development and validation of a solid phase microextraction method for simultaneous

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