Comparison between proton transfer reaction mass spectrometry andnear infrared spectroscopy for the authentication of Brazilian coffee: Apreliminary chemometric studyMonteiro, P. I., Santos, J. S., Alvarenga Brizola, V. R., Pasini Deolindo, C. T., Koot, A., Boerrigter-Eenling, R.,van Ruth, S., Georgouli, K., Koidis, A., & Granato, D. (2018). Comparison between proton transfer reaction massspectrometry and near infrared spectroscopy for the authentication of Brazilian coffee: A preliminarychemometric study. Food Control, 91, 276-283. https://doi.org/10.1016/j.foodcont.2018.04.009

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Manuscript Number: FOODCONT-D-18-00413

Title: COMPARISON BETWEEN PROTON TRANSFER REACTION MASS SPECTROMETRY AND

NEAR INFRARED SPECTROSCOPY FOR THE AUTHENTICATION OF BRAZILIAN COFFEE: A

PRELIMINARY CHEMOMETRIC STUDY

Article Type: Research Paper

Keywords: Specialty coffee; Organic; Chemometrics; Volatile organic

compounds; Spectroscopic methods; Authenticity; PTR-MS.

Corresponding Author: Professor Daniel Granato, Ph.D

Corresponding Author's Institution: State University of Ponta Grossa

First Author: pablo Monteiro

Order of Authors: pablo Monteiro; Janio Santos; Carolina Deolindo; saskia

van Ruth; alex koot; Rita Boerrigter-Eenling; vitor brizola; Anastasios

Koidis; Konstantia Georgouli; Daniel Granato, Ph.D

Abstract: In this study, proton-transfer reaction mass spectrometry (PTR-

MS) and near-infrared spectroscopy (NIRS) were compared for the

authentication of geographical and farming system origins of Brazilian

coffees. For this purpose, n=19 organic (ORG) and n=26 conventional

(CONV) coffees from distinct producing regions were analyzed. Overall,

differences (p<0.05) in 44 and 68 ion intensities were observed between

the main producing regions and farming systems, respectively. Principal

component analysis was not effective in illustrating differences between

the coffees according to the farming system or geographical origin using

neither PTR-MS nor NIRS data. However, when the PLS-DA was applied, which

produced the best performing models overall compared to several other

techniques, the farming system was adroitly differentiated. The fact that

the classification performance (> 80%) was independent of the data

acquisition method used gives NIRS an edge over PTR-MS in the

differentiation of the farming system because of its rapid analysis and

cost. Differentiating geographic location was more complex. The PTR-MS

calibration models showed slightly better PLS-DA classification rates

compared to the NIRS models (69% vs. 61%, respectively), which is even

more evident when the alternative classifier is used (LDA-kNN, 69% vs.

39%, respectively). Coffee samples from either Minas Gerais (MG) or Sao

Paulo (SP) were differentiated from the other regions.

Suggested Reviewers: Filomena Nazarro PhD

Istituto di Scienze dell'Alimentazione, CNR-IS

filomena.nazzaro@isa.cnr.it

Expert in chemometrics, food technology and NIRS

Amin Mousavi Khaneghah PhD

University of Campinas

mousavi@fea.unicamp.br

Expert in food analysis and statistical evaluation.

Seyed Mohammad Bagher Hashemi

Fasa University

hasshemii@gmail.com

Expert in chemometrics and food technology

February, 2018

Dear Dr. R. Greiner,

I am pleased to submit on behalf of my colleagues and myself the manuscript

“COMPARISON BETWEEN PROTON TRANSFER REACTION MASS

SPECTROMETRY AND NEAR INFRARED SPECTROSCOPY FOR THE

AUTHENTICATION OF BRAZILIAN COFFEE: A PRELIMINARY

CHEMOMETRIC STUDY” for your consideration for publication in Food

Control.

1. Corresponding Author: Daniel Granato (granatod@gmail.com)

2. Significance of the manuscript: Among the analytical methods used to

evaluate the quality traits of coffee powders, proton-transfer reaction mass

spectrometry (PTR-MS) and near infrared spectroscopy (NIR) stand out. As

there is no single marker to assess the geographical and farming system origins

of high-value added foods, multiple rapid and low-cost untargeted fingerprinting

methods, such as PTR-MS and spectroscopic methods, are becoming more

popular to solve problems related to food authenticity. Therefore, the main

objectives of the present work were to characterize and classify Brazilian

coffees from ORG and conventional (CONV) farming systems, from distinct

producing regions, using PTR-MS and NIR. In addition, to date there is no

information about the discrimination of farming systems and producing region of

Brazilian coffees in the literature using PTR-MS and NIRS. Here we showed the

main differences between farming systems and producing regions. It is

important to note that the manuscript has not been submitted for consideration

in any other Journal.

Cover Letter

1

COMPARISON BETWEEN PROTON TRANSFER REACTION MASS SPECTROMETRY 1

AND NEAR INFRARED SPECTROSCOPY FOR THE AUTHENTICATION OF 2

BRAZILIAN COFFEE: A PRELIMINARY CHEMOMETRIC STUDY 3

4

Pablo Inocêncio Monteiro1, Jânio Sousa Santos1, Vitor Rafael Alvarenga Brizola2, Carolina 5

Turnes Pasini Deolindo2, Alex Koot3, Rita Boerrigter-Eenling3, Saskia van Ruth3,4, 6

Konstantia Georgouli5, Anastasios Koidis5, Daniel Granato1,2* 7

8

1Graduation Program in Food Science and Technology, 2Deparment of Food Engineering, 9

State University of Ponta Grossa. Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, 10

Brazil. E-mail: granatod@gmail.com or dgranato@uepg.br 11

3RIKILT, Wageningen University and Research, P.O. Box 230, 6700 AE, Wageningen, the 12

Netherlands. 13

4Food Quality and Design group, Wageningen University and Research, P.O. Box 17, 14

6700 AA, Wageningen, the Netherlands. 15

5Institute for Global Food Security, Queen’s University Belfast, Ireland, United Kingdom. 16

17

18

*ManuscriptClick here to view linked References

2

Abstract 19

In this study, proton-transfer reaction mass spectrometry (PTR-MS) and near-infrared 20

spectroscopy (NIRS) were compared for the authentication of geographical and farming 21

system origins of Brazilian coffees. For this purpose, n=19 organic (ORG) and n=26 22

conventional (CONV) coffees from distinct producing regions were analyzed. Overall, 23

differences (p<0.05) in 44 and 68 ion intensities were observed between the main 24

producing regions and farming systems, respectively. Principal component analysis was 25

not effective in illustrating differences between the coffees according to the farming system 26

or geographical origin using neither PTR-MS nor NIRS data. However, when the PLS-DA 27

was applied, which produced the best performing models overall compared to several 28

other techniques, the farming system was adroitly differentiated. The fact that the 29

classification performance (> 80%) was independent of the data acquisition method used 30

gives NIRS an edge over PTR-MS in the differentiation of the farming system because of 31

its rapid analysis and cost. Differentiating geographic location was more complex. The 32

PTR-MS calibration models showed slightly better PLS-DA classification rates compared 33

to the NIRS models (69% vs. 61%, respectively), which is even more evident when the 34

alternative classifier is used (LDA-kNN, 69% vs. 39%, respectively). Coffee samples from 35

either Minas Gerais (MG) or Sao Paulo (SP) were differentiated from the other regions. 36

37

Keywords: Specialty coffee; Organic; Chemometrics; Volatile organic compounds; 38

Spectroscopic methods; Authenticity; PTR-MS. 39

40

41

3

1. Introduction 42

Brazil was the largest producer (43.2%) and exporter of coffee in 2015, totaling 37.1 43

million 60 kg-sacks (US$ 6.16 billion) and is the second largest consumer of the beverage. 44

Brazilian coffees are recognized for their high organoleptic quality and the main importer 45

countries are the United States of America, Germany, Italy, Japan and Belgium (Brasil, 46

2016). Due to stresses in the global market, in the last 10 years, producers have started to 47

invest in organic farming because of the consumers’ demand for higher quality, and in 48

2016 there is an estimate of totaling 5,000 ha (~65,000 60 kg-sacks). The organic farming 49

is regulated both in European countries (i.e., EC 834/07 and EC 889/08) and in Brazil (law 50

number 10831 from 23rd December 2003) to ensure that consumers buy authentic 51

products. As coffee has a considerable high price in the international market, especially 52

organic coffee, this commodity needs to be constantly analyzed for authenticity tracing its 53

origin is an important task (Granato, Carrapeiro, Fogliano, & van Ruth, 2016). A method 54

for a rapid and/or accurate determination of authenticity of coffee would be of help for 55

producers and importers alike. For instance, the assessment of its odor components has 56

been successfully used for that purpose (Özdestan et al., 2013; Yener et al., 2015; Colzi et 57

al., 2017). 58

Among the analytical methods used to evaluate the quality traits of ground coffee, 59

proton-transfer reaction mass spectrometry (PTR-MS) and near infrared spectroscopy 60

(NIRS) emerge (Bertone et al., 2016; Danezis et al., 2016). The advantage of PTR-MS 61

and NIRS over chromatographic techniques (i.e., GC-MS or GC-FID) is that they are 62

nondestructive techniques that do not require extensive sample preparation, thus multiple 63

items may be assessed in a workday. Moreover, both techniques are highly sensitive and, 64

therefore, represent suitable alternatives to trace a product’s authenticity, typicality, and 65

geographical origin (de Toledo et al., 2017; Correia et al., 2018). 66

4

In the literature, the use of PTR-switching reagent ion-MS coupled with 67

chemometrics was reported to classify coffee brews and ground coffees from Brazil, 68

Ethiopia, Guatemala, Costa Rica, Colombia, and India (Yener et al., 2015). However, there 69

is no information available in the literature regarding the analysis of volatile organic 70

compounds (VOCs) of specialty Brazilian coffees produced in different regions with the 71

use of distinct farming systems. In addition, the use of multivariate statistical methods has 72

the ability to look at minute differences between samples, which is suited for such subtle 73

changes that are expected for ground coffee (Tavares et al., 2016). Therefore, the use of a 74

rapid and sensitive analytical method coupled with appropriate data analysis seems to be 75

a suitable approach for authentication purposes. 76

As there is no single marker to assess the geographical and farming system origins 77

of high-value added foods, multiple rapid and accurate untargeted fingerprinting methods, 78

such as PTR-MS and spectroscopic methods, are becoming more popular to solve 79

problems related to food authenticity (Santos, Lopo, Rangel, & Lopes, 2016; Botelho, 80

Oliveira, & Franca, 2017). According to Black, Chevallier, and Elliott (2016) and van Ruth, 81

Huisman, and Luning (2017), the adulteration of food products has grown considerably in 82

the last years, with all foods susceptible, especially specialty coffees because of their high 83

commercial value. Therefore, the main objectives of the present work were to characterize 84

and to authenticate Brazilian coffees from organic (ORG) and conventional (CONV) 85

farming systems and from distinct producing regions, using NIRS and PTR-MS. 86

87

2. Material and Methods 88

2.1 Coffee samples 89

A total of 45 authentic coffee samples were acquired in Curitiba/PR, Brazil, and 90

belonged to the following classes: n=19 ORG and n=26 CONV coffees. These samples 91

were produced in different Brazilian states: Minas Gerais (MG; n=13), São Paulo (SP; 92

5

n=11), Paraná (PR; n=8), Espírito Santo (ES; n=3), and Bahía (BA; n=2). Some 93

commercial blends of PR/MG/SP (n=1), MG/SP (n=6), PR/ES/Roraima (RO) (n=1) were 94

also included in the study. ORG samples were certified by third parties (IBD, TECPAR, 95

and/or Demeter) and all brands have the seal from the Brazilian Ministry of Agriculture, 96

Livestock, and Supply (MAPA) and from the Brazilian Association of Coffee Industries 97

(ABIC). Coffees belonged to Coffea arabica (n=41) or blends of Coffea arabica/C. 98

canephora var. robusta (concentration of each variety was not informed; n=4). Because of 99

the ‘not informed’ proportion of Coffea arabica/C. canephora var. robusta in those (n=4) 100

samples, the effect of the coffee species was not investigated in the current study. 101

All roasted beans were provided by the manufacturers that have certificate of 102

traceability (geographical origin or cultivation system) provided by the Brazilian Association 103

of Coffee Producers (ABIC) and beans were manufactured in each factory using the 104

‘medium roast’ technique (processing at 210-220 oC for 10-13 min). Coffee beans were 105

ground in an analytical mill (QUIMIS-6298A21) to obtain a sample size of 60 Tyler mesh. 106

107

2.2 Proton-transfer reaction mass spectrometry (PTR-MS) 108

VOCs in coffee samples were analyzed by PTR-MS in the range of m/z 20-200 109

(mass detection rate of 0.2 s/mass) using the same experimental conditions and 110

equipment (Ionicon Analytik, Innsbruck, Austria) described earlier (Granato, Koot, & van 111

Ruth, 2015). For the headspace analysis, 0.50 g of coffee was inserted in a 250 mL glass 112

flask and temperature was equilibrated at 40°C/30 min in a water bath, following the 113

procedures recommended by Özdestan et al. (2013). VOCs were analyzed via headspace 114

using five complete mass scans (the 1st and 5th scans were discarded), three times (one 115

analysis per day), in three independent samples. All of the measurements were carried out 116

under drift tube conditions (120–140 Td, Td = Townsend; 1 Td = 10−17 V cm2 mol−1) over 117

the mass range and a dwell time of 0.2 s/mass, giving a cycle time of about 40 s. For each 118

6

measurement of coffee samples, VOCs in the headspace of an empty glass flask served 119

as blank. Masses m/z 32 and m/z 37 were removed from the data set (oxygen and water 120

clusters, respectively) and mass ion intensities were converted to volume mixing ratio 121

(ppbv) values using the principles outlined by Hansel et al. (1995). 122

123

2.3 Near infrared spectroscopy (NIRS) 124

NIRS is a spectroscopic method that uses the near-infrared region of the 125

electromagnetic spectrum (from about 750 to 2500 nm) to study vibrational frequencies of 126

chemical bonds, enabling one to identify functional groups present in a matrix. In the 127

current study, NIRS spectra were recorded by means of a Buchi NIR-Flex system (BÜCHI 128

Labortechnik GmbH, Essen, Germany) equipped with a holder for a small Petri dish 129

(diameter of 35 mm) over the NIRS range of 800 – 2500 nm with a 2.5 nm sampling 130

interval in the reflectance mode. Measurements were taken on 0.5-1 cm-thick portion 131

(about 2.5 g), evenly distributed into Petri dishes. All the NIRS measurements were 132

performed in duplicate for each sample on two independent samples, totaling 4 133

results/sample. The final spectra that were used for chemometric modeling were the 134

average of those replicates. 135

136

2.4 Statistical analysis 137

2.4.1 Spectral data pre-treatment 138

The NIR spectral data initially contained 2051 variables resulting from spectral 139

acquisition from 819.67 to 2500 nm. The visible and shortwave part (400-1098 nm) of the 140

spectra were cut and then Standard Normal Variance (SNV) (Barnes, Dhanoa and Lister, 141

1989), detrending (Barnes, Dhanoa and Lister, 1989) and Savitzky-Golay smoothing 142

(polynomial order=2, frame size=9) (Savitzky and Golay, 1964) were applied to the signal 143

resulting in 1275 pre-processed variables. The PTR-MS mass spectrometry data were 144

7

used without preprocessing (in total 177 variables) after removing the values for m/z 32 145

and m/z 37. 146

147

2.4.2 Exploratory data analysis 148

Two discrimination factors were used to explore the data: a) the farming system of 149

the coffee where samples grouped into two classes (ORG and CONV) and b) the 150

producing regions. In the latter case, samples were distributed in five subclasses 151

corresponding to four distinct coffee producing areas of Brazil: Minas Gerais (n=13); São 152

Paulo (n=11); combined São Paulo and Minas Gerais (n=6), Paraná (n=8) and one 153

additional group (OTHER, n=7) that includes the rest of the regions in the dataset. 154

Especially for PTR-MS data, inferential analysis aiming to compare the VOCs (m/z) 155

ORG and CONV coffees as well as samples from the four main regions (Minas Gerais, 156

São Paulo, Paraná, and Espírito Santo) was performed according to the procedures 157

outlined by Granato, de Araújo Calado, and Jarvis (2014). Probability values below 5% 158

were used to highlight differences between farming systems or between producing 159

regions. For both PTR-MS and NIRS data, principal component analysis (PCA) was also 160

performed according to standard practices to explore the data structure and look for 161

obvious clustering (Nunes et al., 2015; Margraf et al., 2016). 162

163

2.4.3 Classification of coffees 164

Several supervised classification methods were employed in the dataset (partial 165

least squares – discriminant analysis, PLS-DA; soft independent modeling of class 166

analogy, SIMCA; k-nearest neighbors, kNN;, principal component analysis coupled with 167

kNN, PCA-kNN; linear discriminant analysis coupled with kNN, LDA-kNN; Support Vector 168

Machines, SVM, and LDA-SVM with multiple configuration variables). The best performing 169

methods presented here were PLS-DA and LDA-kNN. The latter technique first uses LDA 170

8

to reduce the dimensionality of the data and then classifies into the define groups non-171

parametrically using the best k factor (Georgouli, Martinez Del Rincon & Koidis, 2017). 172

Two validation procedures were performed to estimate the classification models: one 173

validation step was carried out with an arbitrary dataset (validation set) consisting of 30% 174

of the all spectral data and containing samples from all groups in proportion (while 70% of 175

the data is left for calibration of the model) and another validation with venetian blinds 176

cross validation, where 1/7th of all data in each iteration (7 splits) predicted with the 177

remaining 6/7th of the samples. The results were evaluated using the correct classification 178

rate also known as accuracy (%). Specifically, in cross validation procedure, the mean 179

correct classification rate and the standard deviation over these iterations are the main 180

evaluation metrics. All chemometric data preprocessing was performed by means of in–181

house Matlab routines (The MathWorks Inc., USA). 182

183

3. Results and Discussion 184

3.1 Exploratory data analysis 185

PTR-MS mean data are shown in Figure 1. In a general view, ORG samples had 186

higher ion intensities. Using inferential analysis (one-way ANOVA/unpaired t-test), a total 187

of 68 ion intensities were found to be different (p<0.05) between farming systems in the 188

m/z 20-200 (Supplementary material). In a study with organic and conventional coffees 189

marketed in the Netherlands, Özdestan et al. (2013) found seventeen ions out of the range 190

m/z 26–200 showed significant differences between organic and regular coffees from 191

various locations (Americas, Asia, and Africa). These differences may be due to the 192

various biosynthesis pathways regulators in response to biotic interactions and 193

environmental/agronomical factors employed in the ORG system, such as low sun 194

exposure, differences between soil type, chemical composition, and nutritional status 195

compared to CONV and ORG produce (Dudareva et al., 2013). Two other hypotheses 196

9

arise: i) The samples come from commercial companies and, as such, it is known that 197

different roasting history of coffee may result in distinct volatile composition and that may 198

have interfered with the results; ii) During roasting, many compounds may be decomposed 199

because of the high temperature, so the ideal analytical approach to know the exact 200

compounds in our samples would be the use of GC-FID or GC-MS or even PTR–TOF–MS 201

(Colzi et al., 2017). PTR-MS data provide relative abundance values for selected ions (m/z 202

20-200) but PTR-MS cannot distinguish isobaric compounds, therefore PTR-MS should be 203

used as an untargeted method to characterize complex and different food samples. 204

Coffees from Minas Gerais and Espírito Santo had the highest ion intensities as 205

compared to the other regions (Figure 1). Differences (p<0.05) in 44 ion intensities were 206

observed between the main Brazilian producing regions (Table 1 - Supplementary 207

material) and most differences were located in m/z<121. These results are mainly related 208

to the terroir effect, that is, differences in geomorphology, soil type and its 209

chemical/biological conditions, and climate conditions, especially temperature, sunlight 210

exposure, humidity, and irrigation between regions (Muilwijk et al., 2015). These results 211

also suggest that the sunlight exposure, water stress, and temperature play an important 212

role in the biosynthesis of specific compounds as the samples from Paraná (the southern 213

state) had the lowest mean ion intensities. Unlike PTR-MS data, analyzing the NIRS 214

spectrum of coffees from different geographical locations (Figure 2B) it is not possible to 215

establish a pattern, indicating that a complex statistical data treatment is required for these 216

results (see below). 217

Similarly to what was observed for PTR-MS, NIRS data showed it is not so easy to 218

differentiate the farming systems (Figure 2A). Indeed, CONV coffee has a higher intensity 219

in the range 800 – 1100 nm, which may be related to the degree of roasting (Maillard and 220

caramelization reactions) at high temperatures. Similarly, ORG coffee presented a higher 221

absorbance in the 1400-1600 nm range, which might be related to the presence of 222

10

carbohydrates. The region between 1200 and 1450 nm was already shown to be related to 223

differences between C. arabica and C canephora coffees (Correia et al., 2018), but in 224

Figures 2A and 2B, the differences in absorbance between coffees from distinct locations 225

and farming systems are negligible. The band in the 1400–1500 nm region refers to the 226

first overtone of the OH functional group of chlorogenic acid isomers present in coffees, 227

which are the main phenolic compounds in coffee. For instance, ORG samples (Figure 2A) 228

and coffee produced in Paraná state (Figure 2B) have a higher absorbance (Workman & 229

Weyer, 2008). 230

Principal component analysis (PCA) as an unsupervised pattern recognition 231

technique was also applied for exploratory purposes to the PTR-MS and to the NIRS 232

datasets for both classification criteria separately (Figure 3). The first three principal 233

components (PCs) that compose the PCA scatter plot explain the majority of the variation 234

observed in the data (>95% in all cases). The mapping of the samples indicated that there 235

is no clear differentiation of the groups using either the producing systems (Figure 3A, B) 236

or the producing regions (Figure 3C, D). In other words, there is no evident pattern in the 237

spectral data from the two techniques (PTR-MS and NIRS) both in terms of farming 238

systems or producing regions. Similarly, another hypothesis is plausible: the first 239

dimensions may be explained by the roasting process (the exact temperature and time 240

each coffee lot was subjected to) or brand, while the provenance and farming systems 241

may be explained in higher dimensions (projection of the samples in a more complex 242

structure). Our results support the findings obtained by Colzi et al. (2017) who used PTR-243

TOF-MS (m/z 20-210) coupled with PCA aiming to distinguish Arabica and Robusta 244

coffees: two principal components were able to explain more than 94% of data variability 245

and partial-least squares-discriminant analysis classified 100% of coffee samples based 246

on their taxonomic category. From this perspective, it is evident that the differentiation of 247

11

coffee from taxonomic categories is a better strategy than the differentiation between 248

farming systems and producing regions. 249

250

3.3 Classification of coffees 251

Table 1 and 2 show the classification results obtained using the two discrimination 252

factors, namely farming system (two systems: organic and conventional) and the 253

producing region of coffee samples (5 geographic locations of Brazil, see material and 254

methods) for PTR-MS and NIRS data, respectively. The tables provide both the average 255

accuracy and the sensitivity per group for all the groups of the chemometric models. 256

Firstly, in terms of overall accuracy, the model performance with PTR-MS data is almost 257

identical to the performance with NIRS data which is rather unusual taking into account 258

that NIRS spectroscopy is a rapid method, more accessible, and less chemical 259

‘information-heavy’ compared to the PTR-MS analysis (Colzi et al., 2017). In particular, 260

when the PLS-DA is applied, which produces the best performing models overall, the 261

classification rate is exactly the same for prediction of the farming systems with results 262

only varying slightly according to the validation method (arbitrary chosen dataset vs cross 263

validation) used. Nonlinear classification methods, such as SVM, did not produce good 264

classification results (>50%, detailed data not shown) which indicates that the both the 265

mass and the spectral data are rather linear and linear classifiers (LDA, PLS-DA, LDA-266

kNN) work very well. This is due to the nature of the chemometric data (Osorio, Haughey, 267

Elliott, & Koidis, 2015). Results showed that prediction of the conventional and organic as 268

farming systems of coffees is relatively better due to the very good chemometric models 269

produced independently of the data acquisition method used (PTR-MS and NIRS) 270

compared to the prediction of the geographic origin within Brazil. 271

Using NIRS and PLS-DA (Table 2), all ORG coffee samples were discriminated, but 272

the classification results were fair different when LDA-kNN was applied to NIRS data. It 273

12

has to be highlighted, both PLS-DA and LDA-kNN cannot perform very well in case of 274

large imbalances in the sample size of the classes which cause a biased estimation of the 275

class membership towards the class with the most members (Berrueta, Alonso-Salces & 276

Héberger, 2007). Although the classes are mostly balanced in this problem (19 ORG vs 26 277

CONV) it appears that there is less variance in the organic class (the chemical 278

composition of these samples proved to be more standardized) and more 279

variance/variability can be found in the conventional coffee class where multiple types of 280

processed coffees are included from a wide variety of sources/locations. 281

The prediction of the geographic origin is obviously a more difficult classification 282

problem due the relatively higher number of classes is involved (5 vs. 2) and therefore a 283

drop in model’s performance is expected compared to the first problem. Apart from the 284

lower overall performance, the PTR-MS calibration models have slightly better PLS-DA 285

classification rates compared to that of NIRS models (69% vs. 61%, respectively), which is 286

even more evident when the alternative classifier is used (LDA-kNN, 69% vs. 39%, 287

respectively). The cross-validated rates are significantly lower than their non-cross 288

validated counterparts and consistent with the trends mentioned above which is again 289

expected. However, they provide a better picture of the models’ performance especially if 290

the standard deviation is taken into account. 291

Coffee samples from either Minas Gerais (MG) or São Paulo (SP) were more 292

discriminative, and thus better predicted, than samples from other Brazilian regions using 293

PTR-MS data. In these cases, PTR-MS had a slight advantage in prediction accuracy over 294

NIRS. These results reiterate that NIRS accuracy is equally good as PTS-MS, indicating a 295

clear advantage of NIRS as the preferred screening technique. Using PTR-MS data, all 296

samples from Minas Gerais were adroitly classified using both the PLS-DA and LDA-kNN 297

classifiers. Coffee from Paraná state seems to be better distinguished from the other 298

regions using NIRS and PLS-DA (100% accuracy), while coffee samples from Minas 299

13

Gerais were again well distinguished from the other regions using NIRS coupled with LDA-300

kNN (100% accuracy). The blends of coffees from São Paulo and Minas Gerais were not 301

differentiated neither using PTR-MS nor NIRS measurements (accuracy below 70%). It is 302

important to note that each Brazilian state has similar dimensions to those of other 303

countries: Minas Gerais (586,522 km2; Spain), Bahia (564,733 km2; France), São Paulo 304

(248,222 km2; United Kingdom), Paraná (199,307 km2; Senegal), and Espírito Santo 305

(46,095 km2; Switzerland). Therefore, any classification methods proposed for Brazilian 306

coffees may be compared to other classification methods for intraregional areas in Europe. 307

Additionally, this work is the first report in the literature comparing the VOCs of coffees 308

coming from the main producing regions in Brazil. This characterization may help the 309

sector to propose flavor compounds unique to certain regions and establish the typicality 310

of Brazilian coffee. 311

According to Botelho, Oliveira, and Franca (2017), the coffee origin has become 312

increasingly relevant for the producers, since it allows the consumers to relate the singular 313

characteristics of their preferred product to its respective provenance. These authors 314

evaluated the feasibility of fluorescence spectroscopy to differentiate the geographical 315

origin of Brazilian coffees cultivated in micro regions of Minas Gerais state (Cerrado 316

Mineiro, Matas de Minas, North, and South) and observed that Unfolded Partial Least 317

Squares with Discriminant Analysis (UPLS-DA) was able to discriminate coffees from 318

Cerrado Mineiro and those from the South. N-way partial least squares - discriminant 319

analysis (NPLS-DA) and Parallel Factor Analysis (PARAFAC) were not able to classify the 320

analyzed coffees using fluorescence spectroscopy. 321

In comparison to the results obtained here, De Luca et al. (2016) evaluated the 322

effects of coffee variety (C. arabica or C. robusta) and roasting time on NIRS and high-323

performance liquid chromatography (HPLC) profiles of coffee beans. Authors used PLS-324

DA to build classification models aiming to authenticate the samples and verified that PLS-325

14

DA resulted in 100% of correct classification of Arabica and 95% correct classification of 326

Robusta coffees, concluding that chemometrics may be used to authenticate the varietal 327

origin of coffee. Özdestan et al. (2013) tried to n = 110 differentiate specialty market 328

coffees (CONV, ORG, espresso, and Kopi Luwak coffee) based on headspace PTR-MS 329

data and PLS-DA and verified that 98% ORG (n = 42 out of 43) and 95% CONV (n = 63 330

out of 67) coffees were correctly classified. Additionally, PTR-TOF-MS was used to identify 331

the main VOCs of those coffees. Authors concluded that PTR-MS data coupled with 332

chemometrics is a promising strategy to authenticate the farming system used to cultivate 333

coffees. 334

335

Conclusions 336

Having screened several multivariate classification techniques, the most efficient 337

classification model for the prediction of the farming system of the coffee samples could be 338

developed with either PTR-MS or NIRS data using the typical band of the spectra and the 339

PLS-DA classifier. Our results indicate that the NIRS classification model - which is much 340

simpler to develop and deploy - can provide equally good results (up to 89% prediction 341

success) with less instrumentation complexity and at a reduced cost. For the prediction of 342

the geographic location, a PTR-MS model can provide some confidence (approx. 70% 343

correct classification) due to the complexity of the analytical problem with NIRS following 344

closely. More coffee samples should be included in the study in order to build a robust 345

authentication analytical method based on NIRS and PTR-MS. In order to give more 346

resolution to the chemometric model and represent emerging coffee farming systems, the 347

biodynamic coffee class can be introduced and characterized with enough authentic 348

samples and fresh approaches to model design such as data augmentation (Georgouli et 349

al. 2018). Moreover, our approach may be used to assess the farming system and 350

15

geographical origins of other high-value added foods especially if it is further validated 351

with additional samples. 352

353

Acknowledgements 354

Authors thank Coordination for the Improvement of Higher Education Personnel, 355

CAPES, for one M.Sc scholarship (P.M. I.), and Fundação Araucária for one PhD 356

scholarship (J. S. S.) and The Brazilian National Council for Scientific and Technological 357

Development, CNPq, for a productivity grant (process number 303188/2016-2) and a B.Sc 358

grant (C. T. P. Deolindo). 359

360

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442

FIGURE CAPTIONS 443

444

Figure 1: PTR-MS fingerprinting of Brazilian coffees from different farming systems (A and 445

B) and distinct producing regions (C). 446

447

Figure 2: Near infrared spectra (NIRS) of Brazilian coffee powders according to the 448

farming systems (A) and main producing regions (B). 449

450

19

Figure 3. Principal Components Analysis (PCA) scatter plots of all coffee samples (n=45) 451

with the PTR-MS (3A, 3C) and NIRS (3B, 3D) variables. Different colors represent different 452

discrimination factors: producing system (in 3A, 3B) and producing locations (3C, 3D). 453

454

HIGHLIGHTS

Brazilian coffee from different origins and farming systems were

assessed by PTR-MS and NIRS

68 ion intensities were found to be different between organic and

conventional coffees

Differences in 44 ion intensities were observed between the main

producing regions in Brazil

Coffee samples from either Minas Gerais or São Paulo were more

discriminative

GRAPHICAL ABSTRACT

*Highlights (for review)

A1

Figure

A2

// // //

A3

A4

A5 // // //

A6

A

Figure

B

(A) (B)

(C) (D)

Figure

Table 1: Classification rate for the discrimination of farming systems and

producing regions of Brazilian coffee using PTR-MS data.

Discrimination factor

PLS-DA PLS-DA (c.v.) LDA-KNN LDA-KNN (c.v.)

ORG 80 58 80 63

CONV 87 77 87 85

Total 85 69 ± 18 85 76 ± 16

MG 100 62 100 69

SP+MG 50 33 50 67

SP 67 54 67 46

PR 50 38 50 25

Other 50 43 50 52

Total 69 49 ± 30 69 53 ± 22 1 Takes into account both average sensitivity and average precision. All other values

present the sensitivity (%) of the particular class or group (i.e. number of class member

correctly predicted / total number of class members).

Table

Table 2: Classification rate for the discrimination of farming systems and

producing regions of Brazilian coffee using NIRS data.

Discriminating factor

PLS-DA PLS-DA (c.v.) LDA-KNN LDA-KNN (c.v.)

ORG 100 84 60 37

CONV 75 92 87 77

Total 85 89 ± 12 77 60 ± 11

MG 50 62 100 39

SP+MG 50 33 0 0

SP 67 36 0 82

PR 100 75 50 13

Other 50 29 0 0

Total 62 49 ± 29 39 33 ± 12 1 Takes into account both average sensitivity and average precision. All other values

present the sensitivity (%) of the particular class or group (i.e. number of class member

correctly predicted / total number of class members).

Table

Table 1: Ion intensities (given in ppbv) measured by proton-transfer reaction mass spectrometry of Brazilian coffees from different

cultivation systems and producing regions.

m/z Cultivation systems Producing regions

Organic (n=19) Conventional (n=26) p-Value Minas Gerais (n=13) Espírito Santo (n=3) São Paulo (n=11) Paraná (n=8) p-Value

26 0.2±0.1 0.2±0.1 0.857 0.3±0.1 0.3±0.0 0.2±0.1 0.2±0.1 0.240

27 6.6±1.5 6.8±1.0 0.635 7.4±1.0 6.1±0.9 6.4±1.0 6.0±1.2 0.001

28 0.7±0.2 0.6±0.2 0.099 0.6±0.2 0.7±0.1 0.6±0.1 0.6±0.1 0.029

29 57.3±12.4 56.7±8.0 0.852 60.4±8.3 54.5±2.0 54.1±5.8 52.4±11.3 0.004

30 31.3±11.0 30.5±7.6 0.791 32.1±9.1 36.7±1.1 30.1±5.2 24.7±8.5 0.012

31 20.4±4.3 18.2±2.5 0.031 18.4±2.3 17.0±2.0 18.3±3.1 19.0±4.0 0.038

33 4456.9±1152.8 3734.0±665.6 0.011 3713.4±512.3 3307.8±271.1 3921.9±925.6 3945.3±1093.0 0.043

34 53.7±14.5 44.3±8.2 0.009 43.7±6.3 39.9±3.4 46.8±11.9 47.6±13.1 0.038

35 9.5±2.7 7.9±1.6 0.016 8.0±1.1 6.9±0.5 8.2±2.1 8.3±2.4 0.034

36 0.1±0.1 0.1±0.1 0.278 0.1±0.1 0.1±0.0 0.2±0.1 0.1±0.1 0.050

38 0.5±0.2 0.7±0.3 0.016 0.7±0.3 0.3±0.2 0.8±0.2 0.4±0.2 0.004

39 54.3±9.9 49.7±6.4 0.062 52.3±7.1 49.1±8.9 48.6±6.0 49.8±9.9 0.030

40 2.5±0.5 2.4±0.3 0.210 2.5±0.4 2.4±0.2 2.2±0.4 2.5±0.6 0.201

41 180.3±31.6 163.9±19.9 0.038 170.4±22.1 162.8±30.0 161.8±21.0 166.0±30.6 0.044

42 33.4±20.5 29.8±15.3 0.498 35.3±20.0 27.9±3.0 27.4±13.4 25.8±5.3 0.525

43 5999.5±1764.2 6248.4±1268.1 0.584 6731.5±1159.9 6837.7±360.9 6027.2±1011.5 4838.2±1376.9 0.013

44 143.5±43.5 149.0±30.7 0.623 161.4±29.1 163.5±8.1 143.2±24.4 114.7±33.0 0.013

Table

45 314.0±70.7 280.0±42.3 0.051 293.1±44.0 260.6±45.2 281.2±52.4 283.6±74.3 0.093

46 9.7±2.1 8.8±1.1 0.060 9.2±1.3 8.6±1.1 8.5±1.5 8.5±2.0 0.037

47 320.1±214.5 431.7±206.2 0.085 509.4±269.0 424.4±76.5 377.9±108.3 225.9±158.7 0.059

48 4.2±2.8 5.4±2.5 0.147 6.4±3.3 5.4±1.0 4.8±1.3 2.8±1.9 0.063

49 1.9±1.0 2.4±1.0 0.116 2.8±1.3 2.3±0.3 2.1±0.6 1.5±0.7 0.084

50 0.1±0.1 0.1±0.1 0.940 0.1±0.1 0.1±0.1 0.1±0.1 0.1±0.1 0.261

51 12.2±3.3 9.8±2.2 0.005 9.7±1.8 9.1±1.2 10.5±2.8 10.3±3.1 0.021

52 0.4±0.1 0.4±0.1 0.019 0.4±0.1 0.4±0.0 0.4±0.1 0.4±0.1 0.034

53 30.9±5.7 28.7±3.8 0.141 30.5±4.5 29.4±5.1 27.7±3.7 28.1±4.6 0.022

54 1.6±0.4 1.4±0.3 0.058 1.5±0.3 1.4±0.3 1.5±0.3 1.3±0.4 0.371

55 38.8±7.9 37.0±7.6 0.457 39.7±9.0 33.8±4.8 37.1±6.3 33.7±7.5 0.187

56 2.9±0.7 2.7±0.5 0.107 2.9±0.6 2.5±0.4 2.7±0.6 2.6±0.6 0.236

57 577.7±115.2 553.2±69.3 0.380 584.0±71.8 545.9±28.1 540.2±57.4 512.6±105.4 0.031

58 21.2±4.3 20.5±2.4 0.472 21.5±2.4 20.3±2.0 19.9±2.0 19.1±3.8 0.024

59 120.7±33.7 109.8±26.2 0.228 117.4±28.5 99.6±26.2 111.8±31.4 101.3±30.2 0.146

60 14.1±3.0 13.0±1.9 0.161 13.5±2.3 13.0±1.4 12.7±2.0 12.4±2.9 0.032

61 6949.9±2272.1 7475.7±1680.3 0.377 8188.7±1455.4 8376.4±201.3 7096.6±1298.3 5520.2±1895.9 0.007

62 175.7±61.3 190.7±44.9 0.349 209.7±39.6 213.9±5.1 179.9±35.2 137.5±49.0 0.005

63 33.2±11.9 35.9±8.2 0.369 39.3±7.3 40.3±1.4 34.2±6.6 25.8±8.7 0.005

64 0.7±0.3 0.8±0.2 0.310 0.9±0.2 0.9±0.1 0.7±0.1 0.6±0.3 0.031

65 1.4±0.6 1.6±0.6 0.282 1.8±0.8 1.7±0.2 1.5±0.4 1.1±0.4 0.223

66 0.1±0.1 0.1±0.0 0.057 0.1±0.0 0.1±0.0 0.1±0.0 0.1±0.1 0.332

67 9.2±2.1 7.9±1.3 0.015 8.4±1.4 7.7±1.9 7.8±1.6 8.3±2.1 0.075

68 81.9±19.6 69.7±10.2 0.009 72.3±11.8 65.0±10.1 69.2±11.2 73.8±16.5 0.014

69 459.4±82.6 428.4±59.7 0.150 454.3±70.7 440.8±83.9 412.8±57.9 418.6±72.3 0.078

70 22.9±4.1 21.3±3.0 0.141 22.4±3.7 21.9±3.9 20.6±2.9 20.9±3.6 0.108

71 98.9±20.2 87.8±11.8 0.025 92.3±13.2 86.5±14.8 86.6±13.5 87.8±19.3 0.036

72 4.8±1.0 4.2±0.6 0.020 4.5±0.7 4.0±0.7 4.1±1.0 4.2±0.9 0.014

73 171.4±56.5 198.1±87.1 0.250 227.5±104.4 142.0±41.7 191.7±50.3 140.5±62.3 0.145

74 16.3±4.1 16.0±4.8 0.806 17.5±6.2 13.7±1.1 15.2±2.4 14.7±4.7 0.314

75 976.8±216.1 953.6±135.6 0.661 1007.4±133.9 939.7±26.7 927.3±103.2 858.4±200.2 0.054

76 34.7±7.8 33.7±4.8 0.584 35.7±4.7 33.1±1.0 32.9±3.7 30.3±7.2 0.053

77 4.8±1.1 4.6±0.7 0.399 4.9±0.7 4.6±0.2 4.6±0.6 4.2±1.0 0.015

78 0.2±0.1 0.2±0.1 0.264 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.670

79 12.2±2.9 10.7±1.4 0.021 11.1±1.6 11.1±1.7 10.8±2.1 10.6±2.5 0.059

80 444.1±178.5 315.0±82.1 0.002 327.3±84.8 268.2±78.4 334.9±134.6 394.6±192.2 0.089

81 2224.7±401.6 2082.3±284.1 0.170 2172.8±328.1 2143.5±394.2 2017.8±269.6 2032.1±341.8 0.058

82 137.5±25.3 128.6±17.8 0.175 134.0±20.2 133.0±26.1 124.0±16.4 125.9±21.5 0.042

83 54.6±10.0 51.7±6.3 0.251 52.6±6.9 56.5±6.3 50.3±7.4 50.3±8.8 0.036

84 4.3±0.9 3.9±0.5 0.057 4.0±0.7 4.0±0.8 3.9±0.6 3.8±0.9 0.179

85 88.9±17.8 87.8±9.1 0.784 90.6±9.6 94.4±6.4 84.1±10.5 80.0±13.4 0.006

86 7.1±1.5 6.4±0.7 0.068 6.7±0.8 6.6±0.7 6.4±1.0 6.2±1.3 0.036

87 584.3±137.4 479.0±98.5 0.004 492.2±97.3 466.6±74.7 474.9±97.8 567.1±185.7 0.136

88 29.6±7.3 24.1±5.9 0.007 24.8±5.7 23.5±4.0 23.7±5.7 29.0±10.2 0.183

89 109.6±23.2 97.2±12.8 0.027 103.4±14.2 97.7±11.6 94.6±14.9 96.4±21.4 0.022

90 5.1±1.1 4.5±0.6 0.018 4.8±0.6 4.5±0.4 4.3±0.7 4.4±1.0 0.023

91 3.1±0.6 2.7±0.5 0.020 3.0±0.6 3.0±0.2 3.0±0.4 2.7±0.3 0.073

92 0.3±0.1 0.2±0.1 0.003 0.2±0.1 0.2±0.1 0.2±0.1 0.3±0.1 0.092

93 2.1±0.5 1.9±0.3 0.019 2.0±0.3 2.0±0.5 2.0±0.3 2.0±0.5 0.258

94 6.9±3.0 4.7±1.7 0.003 4.9±1.6 3.9±1.3 4.9±2.2 6.5±3.6 0.103

95 165.3±45.8 137.4±25.7 0.013 145.0±28.8 121.4±35.4 140.0±34.6 149.0±46.7 0.099

96 28.8±5.9 27.4±4.3 0.354 29.0±4.8 29.0±7.2 26.5±3.8 25.4±5.1 0.046

97 344.1±70.0 312.1±44.3 0.068 326.5±49.5 303.5±53.3 309.4±45.8 310.3±68.2 0.048

98 28.5±5.7 25.7±3.4 0.046 26.8±3.6 24.9±4.2 25.5±4.0 25.9±5.7 0.067

99 159.5±27.6 149.3±19.3 0.153 157.2±22.8 155.0±26.1 144.5±20.3 144.8±25.0 0.114

100 12.6±2.4 11.4±1.6 0.044 12.1±2.0 11.6±2.2 11.0±2.0 11.5±2.3 0.068

101 166.2±36.2 148.2±21.4 0.043 156.1±25.2 144.9±23.9 147.7±28.5 145.3±32.6 0.062

102 10.4±2.3 9.1±1.5 0.023 10.0±2.0 9.0±2.0 9.1±2.0 9.2±2.1 0.131

103 149.5±25.1 141.2±19.9 0.221 146.0±21.0 162.4±19.6 139.6±19.6 129.8±16.4 0.023

104 8.5±1.4 8.1±1.3 0.311 8.4±1.4 9.3±1.0 8.0±1.0 7.4±1.0 0.014

105 3.9±0.7 3.5±0.5 0.023 3.6±0.6 3.4±0.8 3.4±0.5 3.5±0.5 0.058

106 0.9±0.3 0.7±0.2 0.007 0.7±0.2 0.7±0.2 0.7±0.2 0.8±0.2 0.031

107 6.9±1.4 6.2±0.9 0.038 6.5±1.0 5.9±1.5 6.1±0.7 6.7±1.5 0.165

108 7.8±3.3 5.6±2.6 0.018 5.6±1.8 4.8±1.7 5.7±2.1 8.0±5.1 0.216

109 150.9±40.8 126.1±24.6 0.015 131.0±25.9 113.3±36.1 127.1±26.2 138.8±45.5 0.089

110 59.5±10.4 54.0±8.0 0.050 55.3±9.3 53.8±12.1 53.6±7.3 55.8±10.4 0.203

111 282.0±50.7 260.6±40.6 0.124 269.7±41.2 255.5±68.4 255.9±30.9 261.5±52.8 0.104

112 23.1±4.4 21.2±3.2 0.101 21.6±3.3 20.5±4.8 20.8±2.5 21.5±4.3 0.062

113 75.9±14.3 70.9±9.4 0.157 74.1±10.9 70.9±14.3 69.3±11.2 69.7±12.2 0.134

114 8.4±1.9 7.2±1.2 0.019 7.4±1.4 73.3±1.5 7.1±1.3 7.7±1.8 0.066

115 33.8±6.9 30.2±4.4 0.039 31.5±5.0 28.6±5.2 29.8±5.2 31.2±6.8 0.098

116 3.1±0.7 3.0±0.4 0.065 3.0±0.5 2.6±0.3 2.8±0.5 2.9±0.7 0.044

117 52.1±10.4 46.8±7.4 0.055 49.4±8.4 46.0±9.6 45.3±7.6 47.1±9.2 0.069

118 3.6±0.6 3.3±0.4 0.042 3.4±0.5 3.4±0.7 3.1±0.4 3.3±0.6 0.041

119 1.9±0.4 1.7±0.3 0.211 1.8±0.3 1.6±0.3 1.7±0.2 1.7±0.3 0.393

120 1.6±0.6 1.3±0.4 0.034 1.3±0.4 1.1±0.4 1.3±0.2 1.7±0.6 0.039

121 12.4±2.7 11.1±1.9 0.052 11.2±1.9 10.6±3.4 10.7±1.5 12.1±2.8 0.095

122 7.0±1.6 6.0±1.1 0.021 6.1±1.4 5.8±1.7 6.0±0.7 6.8±2.0 0.214

123 79.1±20.3 66.3±13.2 0.014 68.0±14.4 62.1±21.3 66.0±11.3 74.7±22.3 0.081

124 43.3±7.3 38.6±5.0 0.014 39.8±5.9 37.7±8.7 38.5±4.1 40.7±7.3 0.115

125 49.0±9.0 42.6±6.5 0.009 42.8±6.9 41.5±10.8 43.0±6.0 48.6±10.0 0.196

126 10.6±2.2 9.5±1.4 0.042 9.5±1.7 8.9±2.4 9.4±1.3 10.1±2.0 0.140

127 85.9±18.5 78.7±13.3 0.132 80.6±16.5 83.6±22.0 77.3±13.6 81.7±16.1 0.388

128 10.3±2.2 9.1±1.7 0.035 9.2±1.8 9.6±2.7 9.0±1.7 9.9±2.3 0.233

129 17.7±3.6 18.4±2.2 0.407 19.0±2.7 19.0±1.5 17.8±2.6 16.7±3.4 0.140

130 1.9±0.4 1.8±0.3 0.176 1.8±0.4 1.9±0.3 1.7±0.3 1.9±0.4 0.256

131 10.6±2.1 9.6±1.4 0.077 10.0±1.5 9.8±1.9 9.4±1.6 9.6±2.0 0.147

132 1.0±0.2 0.9±0.2 0.037 0.9±0.1 0.9±0.2 0.8±0.2 0.9±0.2 0.167

133 2.0±0.4 1.8±0.4 0.128 1.8±0.4 1.6±0.4 1.8±0.3 2.0±0.6 0.431

134 1.6±0.4 1.4±0.4 0.153 1.4±0.3 1.5±0.5 1.4±0.2 1.6±0.5 0.591

135 9.2±2.2 7.8±1.5 0.015 7.8±1.7 7.5±2.2 7.9±1.2 8.7±2.2 0.156

136 5.6±1.2 5.0±0.9 0.041 5.0±0.9 5.0±1.5 4.9±0.7 5.6±1.4 0.318

137 40.7±9.5 35.0±6.6 0.022 35.4±7.7 34.1±10.3 35.0±5.2 37.7±9.2 0.143

138 17.9±3.4 16.0±2.5 0.031 16.2±2.8 16.2±3.6 15.9±2.4 17.3±3.4 0.309

139 24.2±4.4 21.0±3.2 0.007 21.5±3.7 20.8±5.6 21.5±3.4 22.9±4.5 0.355

140 5.4±1.1 4.6±0.8 0.005 4.7±0.9 4.5±1.4 4.7±0.8 5.0±1.1 0.153

141 35.0±6.2 30.6±5.0 0.011 31.4±5.4 30.4±9.6 30.7±4.6 33.4±6.9 0.370

142 6.0±1.1 5.1±0.8 0.002 5.3±1.0 5.0±1.3 5.2±0.8 5.6±1.1 0.168

143 7.3±1.6 6.5±1.0 0.039 6.7±1.2 6.963±1.7 6.4±1.3 6.8±1.5 0.502

144 1.2±0.3 1.1±0.2 0.177 1.1±0.3 1.2±0.2 1.2±0.2 1.2±0.3 0.454

145 2.9±0.6 3.0±0.4 0.710 3.0±0.4 3.0±0.4 2.9±0.5 2.8±0.6 0.301

146 0.4±0.1 0.4±0.1 0.582 0.4±0.1 0.4±0.1 0.4±0.1 0.4±0.1 0.678

147 2.2±0.4 2.0±0.4 0.044 1.9±0.5 2.1±0.6 2.0±0.3 2.0±0.4 0.304

148 5.4±0.9 4.8±0.8 0.030 4.9±1.1 4.7±1.0 4.9±0.5 5.3±1.0 0.597

149 4.9±0.9 4.4±0.8 0.107 4.4±0.8 4.3±0.9 4.4±0.4 4.8±1.2 0.318

150 1.9±0.5 1.8±0.4 0.187 1.7±0.4 1.8±0.5 1.7±0.3 1.9±0.6 0.401

151 16.1±3.0 14.5±2.3 0.048 14.7±2.7 14.2±2.9 14.4±2.4 14.4±2.7 0.173

152 6.2±1.2 5.5±1.0 0.029 5.5±1.2 5.5±1.3 5.6±0.9 5.7±1.2 0.472

153 19.4±3.9 16.5±3.2 0.009 16.6±3.4 16.7±5.0 16.8±3.0 18.6±4.8 0.382

154 3.1±0.7 2.6±0.6 0.021 2.6±0.7 2.5±0.7 2.6±0.5 3.0±0.9 0.287

155 6.4±1.6 5.3±1.3 0.021 5.3±1.3 5.3±1.1 5.5±1.1 6.4±2.3 0.505

156 1.6±0.4 1.4±0.3 0.010 1.4±0.4 1.2±0.3 1.4±0.2 1.5±0.4 0.137

157 2.9±0.6 2.7±0.6 0.251 2.7±0.7 2.7±0.9 2.7±0.4 2.8±0.8 0.885

158 0.7±0.1 0.6±0.2 0.188 0.6±0.2 0.6±0.1 0.6±0.1 0.7±0.2 0.288

159 1.2±0.3 1.3±0.3 0.191 1.3±0.3 1.6±0.6 1.2±0.2 1.1±0.2 0.159

160 0.6±0.2 0.4±0.1 0.008 0.5±0.2 0.4±0.1 0.4±0.1 0.6±0.3 0.116

161 1.2±0.3 1.1±0.2 0.090 1.1±0.3 1.1±0.3 1.2±0.2 1.2±0.3 0.243

162 1.3±0.3 1.2±0.3 0.037 1.1±0.3 1.2±0.4 1.1±0.2 1.4±0.4 0.261

163 1.8±0.4 1.5±0.3 0.004 1.4±0.3 1.6±0.4 1.5±0.3 1.8±0.5 0.096

164 1.0±0.3 0.9±0.2 0.078 1.0±0.3 1.0±0.4 0.9±0.2 1.0±0.2 0.644

165 2.1±0.6 1.7±0.5 0.019 1.7±0.5 1.8±0.3 1.7±0.4 2.1±0.7 0.613

166 1.6±0.4 1.5±0.3 0.051 1.4±0.3 1.4±0.4 1.6±0.2 1.5±0.4 0.552

167 3.2±0.8 2.9±0.6 0.157 2.9±0.8 3.3±0.9 2.9±0.7 2.9±0.8 0.775

168 0.7±0.2 0.6±0.2 0.038 0.6±0.2 0.7±0.1 0.6±0.2 0.8±0.3 0.316

169 1.9±0.6 1.6±0.5 0.049 1.6±0.5 1.5±0.3 1.7±0.5 1.9±0.8 0.777

170 0.5±0.1 0.4±0.1 0.042 0.4±0.1 0.4±0.1 0.5±0.2 0.5±0.2 0.806

171 1.3±0.4 1.5±0.3 0.025 1.0±0.3 1.0±0.3 1.0±0.3 1.3±0.5 0.312

172 0.2±0.1 0.2±0.1 0.578 0.2±0.1 0.3±0.1 0.2±0.1 0.2±0.1 0.191

173 0.4±0.1 0.4±0.1 0.235 0.4±0.1 0.4±0.1 0.4±0.1 0.4±0.1 0.642

174 0.1±0.1 0.1±0.1 0.648 0.1±0.0 0.1±0.1 0.1±0.1 0.1±0.1 0.794

175 0.5±0.1 0.4±0.1 0.157 0.5±0.1 0.4±0.2 0.5±0.1 0.5±0.1 0.895

176 0.6±0.2 0.6±0.2 0.594 0.6±0.2 0.7±0.4 0.5±0.1 0.5±0.1 0.011

177 0.4±0.2 0.4±0.1 0.280 0.3±0.1 0.5±0.1 0.4±0.1 0.4±0.1 0.068

178 0.2±0.1 0.2±0.1 0.041 0.2±0.1 0.1±0.1 0.2±0.1 0.2±0.1 0.480

179 0.7±0.2 0.6±0.3 0.134 0.6±0.2 0.6±0.1 0.6±0.2 0.7±0.4 0.982

180 0.5±0.2 0.6±0.1 0.287 0.6±0.1 0.7±0.2 0.5±0.2 0.6±0.1 0.681

181 0.5±0.2 0.5±0.1 0.564 0.5±0.1 0.5±0.3 0.5±0.1 0.5±0.2 0.308

182 0.2±0.1 0.2±0.1 0.337 0.1±0.1 0.1±0.0 0.2±0.1 0.2±0.1 0.490

183 0.5±0.1 0.4±0.2 0.213 0.4±0.2 0.5±0.2 0.5±0.2 0.4±0.2 0.995

184 0.1±0.1 0.1±0.0 0.018 0.1±0.1 0.1±0.0 0.1±0.0 0.1±0.0 0.486

185 0.2±0.1 0.2±0.1 0.251 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.904

187 0.1±0.1 0.1±0.1 0.315 0.1±0.1 0.1±0.1 0.1±0.1 0.1±0.0 0.543

189 0.1±0.1 0.2±0.1 0.978 0.2±0.1 0.1±0.1 0.1±0.0 0.2±0.1 0.085

190 0.1±0.1 0.1±0.1 0.088 0.1±0.0 0.1±0.1 0.1±0.0 0.1±0.1 0.150

191 0.3±0.1 0.3±0.1 0.688 0.2±0.1 0.3±0.2 0.2±0.1 0.3±0.1 0.314

192 0.1±0.0 0.1±0.1 0.665 0.1±0.1 0.1±0.1 0.1±0.0 0.1±0.1 0.707

193 0.2±0.1 0.2±0.1 0.181 0.2±0.1 0.2±0.1 0.2±0.1 0.2±0.1 0.691

194 0.1±0.1 0.1±0.0 0.973 0.1±0.1 0.1±0.0 0.1±0.1 0.1±0.1 0.783

195 0.1±0.1 0.1±0.1 0.553 0.1±0.0 0.1±0.1 0.1±0.1 0.1±0.1 0.498

196 0 0.1±0.0 0.382 0.1±0.1 0 0.1±0.0 0.1±0.0 0.673

197 0.1±0.1 0.1±0.1 0.520 0.1±0.1 0.1±0.1 0.1±0.0 0.1±0.0 0.839

199 0.1±0.1 0.1±0.1 0.474 0.1±0.1 0.1±0.0 0.1±0.1 0.1±0.0 0.816

Note: Probability values obtained by Stydent-t test for independent samples (n=2 groups) or one-way ANOVA (n≥3 groups)