Advances in Food and Beverage Analysisfiles.alfresco.mjh.group/alfresco_images/pharma/...Dec 19,...

October 2017

Volume 30 Number s10

www.chromatographyonline.com

SUPPLEMENT TO

Advances in

Food and Beverage Analysis

Pure Chromatography

Restek products come

with an unbeatable

guarantee.

That is Restek Pure

Satisfaction.

Build a rock-solid analytical foundation with Restek reference standards:

Visit us at www.restek.com/standards

• ISO-Recognized Quality

• Full-Service Custom Flexibility

• Easy-to-Find Documentation

6 Using Headspace Gas Chromatography for the Measurement of Water in Sugar and Sugar-Free

Sweeteners and Products

Lillian A. Frink and Daniel W. Armstrong

This fast, automated method was shown to be accurate and precise for 16 liquid sweeteners, and is likely more

accurate than Karl Fischer titration.

11 Ensuring the Safety of the Food Supply: Speeding Up Arsenic Speciation Analysis

Helmut Ernstberger and Ken Neubauer This speciation method based on ion-interaction chromatography has a run time of <3 min, which is much faster

than current methods using ion-exchange techniques.

16 Determination of Cannabinoid Content and Pesticide Residues in Cannabis Edibles and Beverages

Xiaoyan Wang, Danielle Mackowsky, Jody Searfoss, and Michael J. Telepchak QuEChERS is introduced to the discipline of forensic testing as a viable method for the extraction of pesticides

and cannabinoids in various complex sample matrices.

22 Determination of α-Dicarbonyls in Wines Using Salting-Out Assisted Liquid–Liquid Extraction

Inês Maria Valente and José António Rodrigues A new methodology for the analysis of three important α-dicarbonyls (methylglyoxal, diacetyl, and

pentane-2,3-dione) in wines was developed.

Ph

oto

Cre

dit: R

om

ari

ole

n/S

hu

tte

rsto

ck.c

om

Advances in

Food and Beverage Analysis

3www.chromatographyonline.com

SUBSCRIPTIONS: LC•GC Europe is free to qualified readers in Europe. To apply for a free subscription, or to change your name or address, go to

www.chromatographyonline.com, click on Subscribe, and follow the prompts.

To cancel your subscription or to order back issues, please email your request to

[email protected], putting LCE in the subject line.

Please quote your subscription number if you have it.

MANUSCRIPTS: For manuscript preparation guidelines, visit www.chromatographyonline.com or

call the Editor, +44 (0)151 353 3500. All submissions will be handled with reasonable care, but

the publisher assumes no responsibility for safety of artwork, photographs or manuscripts. Every

precaution is taken to ensure accuracy, but the publisher cannot accept responsibility for the

accuracy of information supplied herein or for any opinion expressed.

DIRECT MAIL LIST: Telephone: +44 (0)151 353 3500.

Reprints: Reprints of all articles in this issue and past issues of this publication are available

(250 minimum). Contact Brian Kolb at Wright’s Media, 2407 Timberloch Place, The Woodlands, TX

77380. Telephone: 877-652-5295 ext. 121. Email: [email protected].

© 2017 Advanstar Communications (UK) Ltd. All rights reserved. No part of this

publication may be reproduced in any material form (including photocopying or storing it in any

medium by electronic means and whether or not transiently or incidentally to some

other use of this publication) without the written permission of the copyright owner

except in accordance with the provisions of the Copyright, Designs & Patents Act

(UK) 1988 or under the terms of a licence issued by the Copyright Licensing

Agency, 90 Tottenham Court Road, London W1P 0LP, UK. Applications for the

copyright owner’s permission to reproduce any part of this publication should be

forwarded in writing to Permissions Dept, Hinderton Point, Lloyd Drive, Ellesmere

Port, Cheshire, CH65 9HQ. Warning: The doing of an unauthorized act in relation to a

copyright work may result in both a civil claim for damages and criminal prosecution.

Published by

UBM Americas

Vice President/Group

Publisher

Mike Tessalone

[email protected]

Editorial Director

Laura Bush

[email protected]

Editor-in-Chief

Alasdair Matheson

[email protected]

Managing Editor

Kate Mosford

[email protected]

Associate Editor

Lewis Botcherby

[email protected]

Sales Manager

Oliver Waters

[email protected]

Sales Executive

Liz Mclean

[email protected]

Senior Director, Digital

Media

Michael Kushner

[email protected]

Webcast Operations

Manager

Kristen Moore

[email protected]

Project Manager

Vania Oliveira

[email protected]

Digital Production Manager

Sabina Advani

[email protected]

Managing Editor Special

Projects

Kaylynn Chiarello-Ebner

kaylynn.chiarello.ebner@

ubm.com

Art Director

Dan Ward

[email protected]

Subscriber Customer Service

Visit (chromatographyonline.com)

to request or change a

subscription or call our customer

service department on

+001 218 740 6877

Hinderton Point,

Lloyd Drive,

Cheshire Oaks,

Cheshire,

CH65 9HQ, UK

Tel. +44 (0)151 353 3500

Fax +44 (0)151 353 3601

Daniel W. Armstrong

University of Texas, Arlington, Texas, USA

Günther K. Bonn

Institute of Analytical Chemistry and

Radiochemistry, University of Innsbruck,

Austria

Deirdre Cabooter

Department of Pharmaceutical and

Pharmacological Sciences, University of

Leuven, Belgium

Peter Carr

Department of Chemistry, University

of Minnesota, Minneapolis, Minnesota,

USA

Jean-Pierre Chervet

Antec Leyden, Zoeterwoude, The

Netherlands

Jan H. Christensen

Department of Plant and Environmental

Sciences, University of Copenhagen,

Copenhagen, Denmark

Danilo Corradini

Istituto di Cromatografia del CNR, Rome,

Italy

Hernan J. Cortes

H.J. Cortes Consulting,

Midland, Michigan, USA

Gert Desmet

Transport Modelling and Analytical

Separation Science, Vrije Universiteit,

Brussels, Belgium

John W. Dolan

LC Resources, McMinnville, Oregon,

USA

Anthony F. Fell

Pharmaceutical Chemistry,

University of Bradford, Bradford, UK

Attila Felinger

Professor of Chemistry, Department of

Analytical and Environmental Chemistry,

University of Pécs, Pécs, Hungary

Francesco Gasparrini

Dipartimento di Studi di Chimica

e Tecnologia delle Sostanze

Biologicamente Attive, Università “La

Sapienza”, Rome, Italy

Joseph L. Glajch

Momenta Pharmaceuticals, Cambridge,

Massachusetts, USA

Jun Haginaka

School of Pharmacy and Pharmaceutical

Sciences, Mukogawa Women’s

University, Nishinomiya, Japan

Javier Hernández-Borges

Department of Chemistry (Analytical

Chemistry Division) University of Laguna,

Canary Islands, Spain

John V. Hinshaw

Serveron Corp., Hillsboro, Oregon,

USA

Tuulia Hyötyläinen

VVT Technical Research of Finland,

Finland

Hans-Gerd Janssen

Van’t Hoff Institute for the Molecular

Sciences, Amsterdam, The Netherlands

Kiyokatsu Jinno

School of Materials Sciences, Toyohasi

University of Technology, Japan

Huba Kalász

Semmelweis University of Medicine,

Budapest, Hungary

Hian Kee Lee

National University of Singapore,

Singapore

Wolfgang Lindner

Institute of Analytical Chemistry,

University of Vienna, Austria

Henk Lingeman

Faculteit der Scheikunde, Free University,

Amsterdam, The Netherlands

Tom Lynch

BP Technology Centre, Pangbourne, UK

Ronald E. Majors

Analytical consultant, West Chester,

Pennsylvania, USA

Debby Mangelings

Department of Analytical Chemistry

and Pharmaceutical Technology, Vrije

Universiteit, Brussels, Belgium

Phillip Marriot

Monash University, School of Chemistry,

Victoria, Australia

David McCalley

Department of Applied Sciences,

University of West of England, Bristol, UK

Robert D. McDowall

McDowall Consulting, Bromley, Kent, UK

Mary Ellen McNally

DuPont Crop Protection,Newark,

Delaware, USA

Imre Molnár

Molnar Research Institute, Berlin, Germany

Luigi Mondello

Dipartimento Farmaco-chimico, Facoltà

di Farmacia, Università di Messina,

Messina, Italy

Peter Myers

Department of Chemistry,

University of Liverpool, Liverpool, UK

Janusz Pawliszyn

Department of Chemistry, University of

Waterloo, Ontario, Canada

Colin Poole

Wayne State University, Detroit,

Michigan, USA

Fred E. Regnier

Department of Biochemistry, Purdue

University, West Lafayette, Indiana, USA

Harald Ritchie

Trajan Scientific and Medical, Milton

Keynes, UK

Koen Sandra

Research Institute for Chromatography,

Kortrijk, Belgium

Pat Sandra

Research Institute for Chromatography,

Kortrijk, Belgium

Peter Schoenmakers

Department of Chemical Engineering,

Universiteit van Amsterdam, Amsterdam,

The Netherlands

Robert Shellie

Trajan Scientific and Medical,

Ringwood, Victoria, Australia

Yvan Vander Heyden

Vrije Universiteit Brussel, Brussels,

Belgium

Editorial Advisory Board

Subscribe on-line at

www.chromatographyonline.com

The Publishers of LC•GC Europe would like to thank the members of the Editorial Advisory Board for

their continuing support and expert advice. The high standards and editorial quality associated with

LC•GC Europe are maintained largely through the tireless efforts of these individuals.

LCGC Europe provides troubleshooting information and application solutions on all aspects of

separation science so that laboratory-based analytical chemists can enhance their practical

knowledge to gain competitive advantage. Our scientific quality and commercial objectivity provide

readers with the tools necessary to deal with real-world analysis issues, thereby increasing their

efficiency, productivity and value to their employer.

4 Advances in Food and Beverage Analysis October 2017

‘Like’ our page LCGC Join the LCGC LinkedIn groupFollow us @ LC_GC

10% Post

Consumer

Waste

Our technology couldn’t be more state-of-the-art. Our business philosophy remains stuck in the 1950s.

s the only independent, family-owned and operated light scattering company in the industry, we

have no shareholders, investors or 3rd parties to please. Our sole mission for over three decades has been

to delight our customers with the finest, most versatile multi-angle light scattering (MALS) instruments

available anywhere on earth.

Our MALS detectors are most commonly coupled to our Optilab refractive index (RI) detectors and

our ViscoStar III intrinsic viscosity (IV) detectors so that you can make complete macromolecular character-

ization measurements from a single GPC/SEC run without resorting to column calibration.

We take your success personally, and we go to extraordinary lengths to make sure you’re delighted. In

our Light Scattering University, we bring you to Santa Barbara, CA for three days of intensive, hands-on training (all

expenses paid for North American customers). While you’re here, you’ll meet the people who design and as-

semble your instruments, interact with the people who code our software, and build rapport with the people

who will be answering your technical questions. We don’t just build instruments, we build relationships.

We think you’ll appreciate our old-fashioned approach. Because whether you’re buying a light scattering

instrument or a bag of groceries, it’s nice to be treated like you’re the most important person in the world.

©2017 Wyatt Technology. DAWN, Optilab, ViscoStar and the Wyatt Technology logo are registered trademarks.

Historically, honey was the primary

sweetener used to enhance many

foods; however, with advances

in technology, sweeteners with

high concentrations of fructose

or sucrose are now used (1,2).

In syrup-form, they provide an

economical, easy-to-handle

alternative to honey (2). Sweeteners

can now be found in most foods,

including carbonated beverages,

canned goods, jellies, jams, baked

goods, and dairy products, and

in many pharmaceutical products

(1–3). These additives improve the

humectancy, colour, and flavour of

food (1). Sweeteners are found in

many foods, and their compositions

are monitored and regulated (4,5).

One important component, water,

must be regulated because it affects

both the physical characteristic of

the product and consumer safety

(1,5). The water content directly

affects the viscosity of syrups

and sweeteners. When the water

content in syrup is low, the sugar

may precipitate and the ability to

easily handle the product as well

as consumer satisfaction will be

compromised (6).

In addition to human consumption,

many syrups (such as molasses and

corn syrup) are used as additives

in animal feed (2,4,5). The water

content is monitored, and if the level

exceeds the regulated range, both

mould and other microbial growth

can occur (4–6). This can lead to

significant problems because of the

toxic nature of some moulds, spores,

and their by-products (7–10).

Consumption of the mould and

spores leads to a reduction of feed

intake, which can cause weakness,

weight loss, and decreased

production in dairy cattle (7).

Furthermore, spores and mould can

cause many diseases along with

their related symptoms of vomiting,

diarrhoea, skin lesions, kidney and

liver damage, lack of muscle control,

and nervous system disorders

(7–10). Other effects are an increase

in infertility and abortions among

exposed cattle (7–10). Mould is

known to cause respiratory distress,

coughing, and shortness of breath

in both humans and livestock (7–10).

Consequently, the measurement

of water in many products is often

required by regulatory bodies

worldwide.

Water content is traditionally

measured by refractive index and

reported in degrees Brix or by

percent by weight of sucrose in

water (5,6,11,12). Degrees Brix is

used because it is a fast and easy

way to measure the moisture in

sucrose-based sweeteners (5,11).

While it is fast, many sweeteners

are fructose based or have a

combination of sucrose and other

sugars causing it to be inaccurate;

therefore, the Brix measurement is

actually an “apparent Brix” (5,11).

In addition to sugars other than

sucrose, salts are known to cause

an apparent change in the water

content (5,11). When salt is present,

the measured degrees Brix can

indicate 5–10% less water than is

actually contained in the sample (5).

Headspace gas chromatography

(GC) is another method that has

been used for the determination of

water in select foods, solvents, and

active pharmaceutical ingredients

(12–16). Early on, the amount of

water in food was measured by

the formation of a suspension in

methyl glycol and then multiple

headspace extractions were used

(12). One problem that occurred

with this early GC method was that

the various supports (for example,

diatomaceous earth and molecular

sieves) in packed columns led to

a nonideal absorption of water;

therefore these columns produced

broad tailing peaks with poor peak

area reproducibility (17–20). In

addition, these packed columns

tended to have low selectivity and

resolution between water and many

other common solvents (15–18).

In addition, air, water, and some

solvents can degrade common liquid

stationary phases at the elevated

temperatures required for the

analyses (14). New GC stationary

phases composed of ionic liquids

(IL) have been developed that allow

GC to be used for the analysis of

water (13–16,21–23). The ILs with

trifluoromethylsulfonate (TfO-) anions

Using Headspace Gas Chromatographyfor the Measurement of Water inSugar and Sugar-Free Sweeteners and ProductsLillian A. Frink and Daniel W. Armstrong, Department of Chemistry and Biochemistry, University of Texas at Arlington,

Arlington, Texas, USA

An automated method for determination of water in liquid sweeteners was developed using headspace gas chromatography (GC) and ionic liquid-based capillary GC columns. This method allowed for the rapid determination of water with minimal sample pretreatment. In addition to providing fast analysis time for the samples, the headspace GC method was found to be accurate and precise for the measurement of water in 16 liquid sweeteners. This method was shown to be widely applicable for sugar and sugar-free sweeteners and more accurate than Karl Fischer titration.

Advances in Food and Beverage Analysis October 20176

improve the water’s peak shape and

peak area reproducibility lowering

the limit of detection (14,21–23).

Further, these stationary phases are

unchanged when exposed long term

to water- and oxygen-containing

samples.

In this work, we report a simple,

effective, and accurate method

for the determination of water

content in fructose-, sucrose-,

and sucralose-based syrups. This

method, unlike previous methods,

is not affected by the sugar

composition, the presence of solid

particles in the sample, or the

presence of salts. Also, the method

does not entail multiple headspace

extractions, additional solvents,

or standards as in some of the

previous headspace GC methods.

This effective approach is easily

automated and is made possible by

the advent of advanced IL stationary

phases for GC coupled with a

specific GC system configured

for water analysis and containing

devices for the stringent reduction of

ambient moisture.

Method Materials: Fructose was obtained

from Sigma Aldrich. Blue agave nectar

was purchased from C&H Sugar.

Grandma’s Molasses was obtained

from B&G foods. Karo Light corn

syrup was obtained from ACH Food

Companies, Inc. Pancake syrup was

purchased from Safeway. Hershey’s

chocolate syrup and caramel topping

were purchased from Hersheys

Company. The Nesquik chocolate

syrup and strawberry syrup were

obtained from Nestle. Strawberry

jelly and jam were obtained from

Smucker’s. Mrs. Butterworth’s

original syrup and Mrs. Butterworth’s

sugar-free syrup were from Mrs.

Butterworth’s. Coffee creamer was

from Kahala Franchising, L.L.C.

Sugar Free Butter Flavored Syrup

was obtained from Maple Grove

Farms. Rose’s Grenadine syrup was

purchased from Mott’s LLP. Dimethyl

sulfoxide (DMSO) was purchased from

Sigma Aldrich.

Screw-thread vials (22 × 75 mm)

and magnetic screw-thread

covers for the autosampler were

purchased from Restek. The GC

columns were 30 m × 0.25 mm,

0.20-μm df Watercol 1460 and

Watercol 1900 columns along with

a 60 m × 0.25 mm, 0.20-μm df

Watercol 1910 column and were

obtained from Sigma Aldrich.

Sample Preparation: Samples

with <40% water were prepared

by adding 500 mg of sample to a

clean vial using a pipette. Samples

that contained >40% water were

prepared by adding 0.125 g of

sample and 0.375 g of DMSO

This approach is easily automated and is made possible by the advent of advanced IL stationary phases for GC coupled with a specific GC system configured for water analysis and containing devices for the stringent reduction of ambient moisture.


Frink and Armstrong

100000

80000

60000

40000

20000

00 5 10 15

Time (min)

Pe

ak

are

a o

f w

ate

r

20 25

Figure 1: The amount of water in the headspace of sealed samples at 100 ºC was evaluated every 5 min for 20 min. It can be seen that the maximum response is at 5 min and after that point the response plateaus.

80000

70000

60000

50000

40000

30000

20000

10000

0.05 0.1 0.15 0.2

Water content (g)

Pe

ak

are

a o

f w

ate

r

Water in fructose

Water in DMSO

00

Figure 2: Plots of the linear relationship between TCD response and the water content in fructose–water solutions and DMSO–water solutions. Both had a correlation of 0.99. The equation for the line produced by the fructose–water solutions is y = 429,000x and the equation of the line produced by the DMSO–water solutions is y = 403,000x.

to a clean vial. After the sample

was prepared it was immediately

capped. The vial was pressurized

to 200 kPa for 2 min at room

temperature using a Shimadzu

HS-20 headspace autosampler.

The headspace was then loaded

or extracted for 1 min. After the

purging process was complete the

vial was heated for 5 min at 100 °C.

The sample was pressurized to

100 kPa and the headspace vapour

was loaded for 2 min into a 0.2-mL

sample loop. A 0.5-min injection

was then made into the GC system.

Two external calibration curves were

produced: one for the lower water

content (<40% water) and a second

calibration curve for samples with

higher water content (>40%). The

first was used for samples with

lower water content, by combining

0.4, 0.5, 0.6, and 0.8 g of water with

2.6, 2.0, 1.9, and 1.8 g of fructose,

respectively. Samples were made in

quadruplicate by adding successive

500-mg aliquots of sample to clean

vials. Then the vials were purged,

heated, and analyzed the same as

the samples. The second calibration

curve for higher water contents

was produced by making samples

with 5%, 10%, 15%, 20%, and 25%

water in a DMSO matrix. This was

achieved by combining 0.125, 0.250,

0.375, 0.500, and 0.625 g of water

with 2.375, 2.250, 2.125, 2.000, and

1.875 g of DMSO, respectively. The

solutions were then divided into

500-mg aliquots and treated in the

same manner as the samples.

Loss on drying was measured by

weighing four clean empty vials. A

sample, ~500 mg, was added to

each vial and the new mass was

recorded. Samples were heated

for 12 h at 60 °C and then cooled

and weighted. A second 12-h

evaporation step was performed at

60 °C. The process was repeated

until a constant mass was obtained.

The Karl Fischer titration (KFT)

analyses were performed by

Robertson Microlit Laboratories. The

atmospheric moisture was measured

by adding 3–10 mg of sulfosalicylic

acid dehydrate to the Hydranal

Coulomat AG in the titration

cell. The standard was titrated

coulometrically to the electrometic

endpoint and used to determine the

response of residual moisture. The

sample, 10 mg, was then added

to the titration cell and titrated.

The atmospheric moisture was

subtracted for the reported value to

obtain the moisture in the sweetener

sample.

Apparatus and Conditions: The

analyses were performed using a

Tracera 2010 equipped with barrier

discharge ionization detection

(BID) and thermal conductivity

detection (TCD) (Shimadzu Scientific

Instruments). Labsolutions 5.82

software was used for all peak

integration. A Shimadzu HS-20

headspace autosampler was used to

purge, heat, and inject all samples.

The transfer line and sample line

were kept at 170 °C. The oven

in the autosampler was kept at

room temperature (25 °C) when

purging the vials and at 100 °C for

all analyses. A 60 m × 0.25 mm,

0.2-μm df Watercol 1910 fused-silica

capillary column coated with IL

synthesized as previously reported

or commercially acquired from

Supelco/Sigma-Aldrich (22). The GC

system oven temperature was held

isothermally at 170 °C with a run time

of 5 min. The carrier gas for all runs

was helium at a flow rate of 1.5 mL/

min (26 cm/s). The helium was dried

with a high-capacity gas purifier and

an OMI purifier tube (Supelco). The

injection port was set at 280 °C and

the TCD system was set at 200 °C

with a current of 80 mA. A split ratio

of 100:1 was used for all analyses of

the sweeteners. Selected analyses


Frink and Armstrong

Table 1: The table compares the water content, standard deviation, and relative standard deviation (RSD) for 16 syrup samples

using loss on drying, headspace GC, and Karl Fischer titration (KFT) methods

Products*Loss on Drying Headspace GC KFT

Average RSD Average RSD Average RSD

Mrs. Butterworth’s Original syrup 27.0 ± 0.1 0.4 28.7 ± 1.6 5.7 33.0 ± 0.7 2.1

Mrs. Butterworth’s Sugar Free syrup 85.5 ± 0.03 0.04 84.9 ± 2.7 3.2 91.9 ± 4.4 4.8

Sugar Free Butter Flavored syrup 89.1 ± 0.1 0.1 85.7 ± 3.4 4.0 88.3 ± 0.8 0.9

Nesquik chocolate syrup 18.3 ± 1.6† 8.5 29.0 ± 0.5 1.8 32.6 ± 1.2 3.7

Nesquik strawberry syrup 24.0 ± 0.2 1.0 28.1 ± 1.5 5.5 31.2 ± 0.6 2.1

Hershey’s chocolate syrup 27.2 ± 1.3 4.8 25.5 ± 0.9 3.6 34.6 ± 1.0 2.9

Hershey’s caramel topping 19.4 ± 1.6† 8.3 29.0 ± 1.4 4.7 24.9 ± 0.3 1.3

Rose’s Grenadine syrup 40.5 ± 0.6 1.5 46.6 ± 2.0 4.2 48.6 ± 2.7 5.5

Smucker’s strawberry jelly 29.3 ± 0.5 1.7 30.1 ± 0.9 3.0 36.7 ± 0.7 2.0

Smucker’s strawberry jam 31.4 ± 0.3 0.9 30.4 ± 1.1 3.6 35.4 ± 0.8 2.3

Pure maple syrup 26.7 ± 1.7 6.4 27.5 ± 0.5 1.9 33.7 ± 0.3 1.0

Blue Agave nectar 22.8 ± 1.4 6.1 23.6 ± 1.0 4.1 22.4 ± 1.0 4.3

French vanilla coffee creamer 36.1 ± 1.8 5.0 33.6 ± 1.9 5.6 46.1 ± 1.9 4.1

Pancake syrup 26.9 ± 0.5 1.8 27.9 ± 1.5 5.3 31.6 ± 0.7 2.2

Molasses 24.4 ± 0.03 0.1 25.4 ± 0.5 2.0 22.1 ± 0.8 3.7

Corn syrup 22.9 ± 1.4 6.1 22.4 ± 0.8 3.6 23.9 ± 0.8 3.4

*See Experimental section for sample details.

†After 7 days the mass had not stabilized, increase analysis temperature lead to degradation.

were performed using a 6890N gas

chromatograph with TCD (Agilent

Technologies Inc.) and Chemstation

plus software (Rev.B.01.03). A 1-mL

gastight syringe (Hamilton) was used

for all injections.

Results and Discussion Optimization of Separation: The

GC oven temperature, split ratio,

and GC column were evaluated,

and the optimized conditions are

specified in the “Experimental”

section. It was determined that a

temperature of 170 °C and a split

ratio of 100:1 were optimal for

these analyses. The Watercol 1910

GC column gave the best peak

symmetry for water compared to

the Watercol 1460 and Watercol

1900 columns (see “Experimental”

section). The Watercol 1900 column

gave the lowest retention time, but

the peak shape was slightly less

symmetrical than that obtained

using the Watercol 1910 column. The

improved peak shape of the water

when analyzed on the Watercol

1910 column in turn, provided more

precise water determinations (vide

infra). It was found that the water

concentrations were not in the linear

range of the sensitive BID, but they

were well within the linear range

of TCD. One of the virtues of a GC

system specifically configured for

water analysis is that it has both

of these detectors and therefore

the flexibility to handle samples

containing trace levels to higher

levels of water. In the case of these

16 sweeteners, the higher water

content in the samples allowed TCD

to give a response 4×104–8×104

times higher than the blank.

Optimization of Headspace

Conditions: The headspace

analysis of samples for water

requires the optimization of a

few parameters. These include

the purging conditions, sample

loop size, and the length of time

the sealed samples are heated

(equilibrium time). It should

be noted that the headspace

autosampler used in this study

has a unique configuration that is

advantageous for water analysis

(16). The equilibrium temperature

was set at 100 °C to reduce side

reactions (such as the Maillard

reaction), which produce water as a

by-product. The equilibrium time was

also optimized as seen in Figure 1.

The sample required 5 min at 100 °C

to reach equilibrium. Various sample

purge conditions were evaluated

using the autosampler. For example,

the vials were pressurized in a range

of 25–200 kPa and the headspace

was then removed for 6–120 s. It

was found that using a pressure

of 200 kPa and then extracting

30 s of headspace was optimal

and provided the lowest residual

moisture in the vials. Two different

sample loop sizes, 0.2 and 1.0 mL,

were compared. When the larger

sample loop was used with samples

that contained high water amounts,

the GC column was overloaded,

and the peaks were asymmetrical

because of increased tailing.

Quantitative Analysis of Residual

Water in Samples: Two calibration

curves for water were developed in

order to quantify its content in 115

syrup samples (see “Experimental”

section). Figure 2 illustrates the

linear relationship between the

peak area and the percent water

for different concentrations of water

in fructose. The correlation (r2)

was found to be 0.99. A second

calibration curve produced in DMSO

was also found to have a correlation

of 0.99. The water in 16 syrups and

sweeteners was analyzed and the

percent water therein is presented

in Table 1 (they ranged from ~20%

to 90%). The water content was also

analyzed with the loss on drying

method, which gave comparable

values but required much longer

analysis times (Table 1) and by KFT.

Precision: The precision of the

headspace GC method was

evaluated by analyzing all of the

samples in quadruplicate. The

relative standard deviation (RSD) for

loss on drying, headspace GC and

KFT methods were similar in most

cases. When the average precision

of the headspace GC method was

compared to the average RSD

produced by loss on drying, there

were a few values with more than

8% RSD for the latter approach


Frink and Armstrong

15,000

(a)

(b)

Resp

on

se (

μV

)R

esp

on

se (

μV

)

2500

Air

Air

Water

Water

Time (min)

Time (min)

2000

1500

1000

500

0

11,200

7500

3750

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9

Figure 3: Typical chromatograms obtained for the analysis of water in agave nectar (a) when analyzed with the Shimadzu Tracera 2010 TCD system at 170 °C with a split ratio of 100:1 on a 60 m Watercol 1910 column, and (b) using an older, less sensitive TCD system at 150 °C and a split ratio of 5:1 on a 60 m Watercol 1910 column.

(that is, Nesquik chocolate syrup

and Hershey’s caramel topping).

It should be noted that while loss

on drying usually produced similar

RSDs, the procedure took 4–7 days

to complete whereas the headspace

GC method took 10 min. As has

been noted previously, KFT often

provides good precision while

producing inaccurate results (16).

This tendency will be discussed in

the next section.

Accuracy: The National Institute of

Standards and Technology (NIST)

does not currently provide standard

reference materials for moisture in

sugar solutions; therefore, accuracy

of the headspace GC method was

estimated by comparing it to results

obtained by two other methods,

KFT and loss on drying. The three

methods gave similar results, but

it was determined that KFT often

appeared to overestimate the water

content compared to the other two

methods. When KFT and headspace

GC were compared with a T-test

it was found that they were only

similar in the case of five samples,

and KFT was similar to three of the

loss on drying samples. When loss

on drying and headspace GC were

compared, it was found that most

of the samples were similar. When

the french vanilla coffee creamer

was analyzed with headspace

GC and loss on drying, a similar

result of ~30% water was found. In

contrast, KFT gave a significantly

higher water content (46% water).

On average, KFT yielded ~5% higher

water contents than the other two

methods. It appears likely that the

KFT reagent reacted with some of

the nonaqueous components or

constituents of many of the samples.

Results with high bias have been

previously noted for KFT for samples

that contain large amounts of sugar

(24). Loss on drying usually had

the lowest measured water content

of the three methods; however, it

has been known to underestimate

water when the viscosity of the

sample increased substantially after

heating, which, in turn, decreases

the diffusion rate of water (25). This

effect would also apply to a few

of the samples (that is, Nesquik

chocolate syrup and Hershey’s

caramel topping), which still had

small decreases in mass after being

heated for seven days. In addition,

when the incubation temperature

was further increased for a few hours

it led to sample degradation. Since

the mass did not completely stabilize

it can be assumed that there was still

some moisture present.

Instrumental Variations: The effect of

different instrumentation can affect the

GC conditions used as well as peak

shape, split ratios, and resolution. This

is particularly true when comparing

new state of the art instruments with

analogous types that are 10 or more

years old. It was found that the older

TCD system had poorer sensitivity

and therefore required a lower split

ratio, 5:1. As shown in Figure 3, the

decrease in split ratio caused the

water peak to lose symmetry via

increased tailing. The lower split

ratio also led to broader peaks and

therefore a lower resolution between

the air and water peaks. In addition,

when samples were analyzed with the

older, less sensitive TCD systems, the

analysis temperature had to be kept

slightly lower, 150 °C versus 170 °C, to

allow for baseline separation between

the water and air because of peak

broadening and increased tailing.

ConclusionsThe water content of 16 liquid

sweeteners was determined using

headspace GC. This method was

rapid, accurate, and precise. It was

shown to be broadly applicable to

a variety of sugar and sugarless

sweeteners. The method does not

require long heating periods (4–7

days) in an oven as with the loss

on drying method, so the water

content can be rapidly determined

in the syrups. KFT was shown to

overestimate the water content in most

of the samples. The ease, accuracy,

and robustness of the headspace

GC analyses are greatly enhanced

when using an ionic liquid-based

column and a GC instrument that is

specifically designed and configured

for the analysis of water.

AcknowledgementsThis work was also supported by

the Robert A. Welch Foundation

(Y0026). We would like to thank

Shimadzu Scientific Instruments for

the use of the Tracera 2010 GC.

References(1) L.M. Hanover and J.S. White, Am. J. Clin.

Nutr. 58, 724S–32S (1993).

(2) J.S. White, Am. J. Clin. Nutr. 88,

1716S–21S (2008).

(3) J. Chirfe, C.F. Fontan, and S. Vigo,

J. Agric. Food Chem. 29, 1085–1086

(1981).

(4) P. Heinze and H.D. Isengard, Food

Chem. 12, 483–86 (2001).

(5) L.R. Richardson and J.V. Halick, “Bulletin

754”, Texas Agricultural Experiment

Station: College Station, Texas, USA

(1952).

(6) D.W. Ball, J. Chem. Edu. 84, 1647–1650

(2007).

(7) R.S. Applebaum, R.E. Brackett, D.W.

Wiseman, and E.H. Marth, J. Food

Protect. 45, 725–777 (1982).

(8) J. Fink-Gremmels, Vet. J. 176, 84–92

(2008).

(9) J. Pleadin, M. Zadravec, N. Perši, A.

Vuliǅ, V. Jake, and M. Mitak, Mycotoxin

Res. 28, 157–162 (2012).

(10) A.M. Shareef, Iraqi J. Vet. Sci. 24, 17–25

(2010).

(11) R.H. King, Ind. Eng. Chem., Anal. Ed. 3,

230–232 (1931).

(12) B. Kolb and M. Auer, Fresen. J. Anal.

Chem. 336, 297–302 (1990).

(13) L.A. Frink and D.W. Armstrong, Food

Chem. 205, 23–27 (2016).

(14) D.A. Jayawardhana, R.M. Woods, Y.

Zhang, C. Wang, and D.W. Armstrong,

LCGC Europe 24, 516–529 (2011).

(15) L.A. Frink, C.A. Weatherly, and D.W.

Armstrong, J. Pharm. Biomed. Anal. 94,

111–117 (2014).

(16) L.A. Frink and D.W. Armstrong, J. Pharm.

Sci. 105, 2288–2292 (2016).

(17) H.S. Knight and F.T. Weiss, Anal. Chem.

34, 749–751 (1962).

(18) W.K. O’Keefe, F.T.T. Ng, and G.L.

Rempel, J. Chromatogr. A 1182, 113–118

(2008).

(19) H.G. Streim, E.A. Boyce, and J.R. Smith,

Anal. Chem. 33, 85–89 (1961).

(20) E.R. Quiram, Anal. Chem. 35, 593–595

(1963).

(21) D.W. Armstrong, T. Payagala, and L.M.

Sidisky, LCGC North Am. 27, 596–605

(2007).

(22) K. Huang, X. Han, X. Zhang, and D.W.

Armstrong, Anal. Bioanal. Chem. 389,

2265–2275 (2007).

(23) C.A. Weatherly, R.M. Woods, and D.W.

Armstrong, J. Agri. Food Chem. 62,

1832–1838 (2014).

(24) P. Bruttel and R. Schlink, “Water

Determination by Karl Fischer Titration,”

Metrohm Monograph 4-1 (2003).

(25) H.-D. Isengard, D. Schultheiß, B.

Radovic´, and E. Anklam, Food Control

12(7), 459–466 (2001).

Lillian A. Frink and Daniel W.

Armstrong are with the Department

of Chemistry and Biochemistry at

the University of Texas at Arlington,

in Arlington, Texas, USA.

The effect of different instrumentation can affect the GC conditions used as well as peak shape, split ratios, and resolution.


Frink and Armstrong

With the expanding knowledge of

element species and their distribution

in foods, elemental speciation analysis

has received increasingly wider

attention by the food industry. The

relevance of speciation analysis for

the elements As, Se, Cr, and Hg has

long been established based on the

significant toxicity differences between

the species of these elements. For

arsenic, the number of identified

species found in food is still growing,

as evidenced by new research

findings into arsenolipids (1); therefore,

our knowledge of the biochemistry

and toxicity of arsenic-containing

biomolecules is also still expanding.

It is a very active research field, and

most new findings have been made

in aquatic species and seafood. For

terrestrially grown food, the number

of arsenic species is more limited. For

this report we investigated rice and

juice, which have been the target of

recent regulatory oversight that singles

out and limits the toxic inorganic

arsenic forms.

The increased demand for testing

requires methods that are fast,

sensitive, and sufficiently robust

to cope with the matrix changes

associated with various food products

being subjected to extraction and

analysis. Various methods exist

to analyze for inorganic arsenic,

including hydride generation coupled

to spectrometric detection (2) and the

development of field kits (3). The gold

standard for this analysis, however,

is liquid chromatography–inductively

coupled plasma–mass spectrometry

(LC–ICP-MS). It is the most widely

used method for arsenic speciation

(4) and is investigated in this study.

The methodology to separate

and quantitate common arsenic

species by LC–ICP-MS is mature

and well established. However, one

disadvantage of current LC–ICP-MS

methods is their relatively long run

times, even for food matrices where

the speciation is not complex.

Ion-Interaction Chromatography or Ion Pairing: Differences and NoveltiesFor the food examined in this

paper, the number of arsenic

compounds quantitated is typically

restricted to four species: As3, As5,

monomethylarsonic acid (MMA),

and dimethylarsinic acid (DMA).

These are ionizable compounds,

and the LC separation techniques

are therefore based on ion-exchange

or ion-pair chromatography.

The latter is also referred to as

ion-interaction chromatography to

more correctly denote the wider

scope of the interactions encountered

beyond formation of ion pairs (5).

Nevertheless, Cecchi still titled her

book “Ion Pair Chromatography”

because of the widespread

recognition of the term (5). The term

ion-interaction chromatography is

emphasized in this article to better

highlight the difference in this

approach compared to conventional

ion-pair chromatography. Some of the

pros and cons of ion-exchange and

ion-pair chromatography have been

summarized earlier (6). Among other

factors, ion interaction approaches

may use less-expensive columns

with well-characterized stationary

phases, while methods based on ion

exchange may suffer less from matrix

interferences.

Ion-interaction chromatography

gives great flexibility in tailoring the

separation to the speciation analysis

in terms of ion-pairing reagents to

use for the separation. Whereas

ion-exchange separations are based

mainly on electrostatic interactions,

the retention behaviour of analytes

in ion-interaction mode may be more

complex and involve more than one

retention mechanism (5). In addition to

electrostatic interaction of analyte ions

with the electrical charge imparted

by the ion-pairing reagent to the

stationary phase, retention may also

occur through interaction of uncharged

analytes with the nonpolar component

of the stationary phase or interaction

with residual silanol groups.

Both traditional anion-exchange

and anion-pairing methods for the

separation of As3, As5, MMA, and

DMA use circumneutral or alkaline

conditions to ionize most of the

target analytes, resulting in run times

typically ranging from 9–15 min.

Reducing the run time required by

anion exchange is still a pressing issue

Ensuring the Safety of the Food Supply:Speeding Up Arsenic SpeciationAnalysisHelmut Ernstberger1 and Ken Neubauer2, 1PerkinElmer, Seer Green, UK, 2PerkinElmer, Shelton, Connecticut, USA

Speciation analysis of elemental contaminants in food and beverages has received a lot of attention in recent years. Recent regulations limit inorganic arsenic, taking into account that arsenic toxicity is dependent on the species present. Thus, the analysis procedure needs to be able to differentiate inorganic from organic arsenic forms. Liquid chromatography–inductively coupled plasma–mass spectrometry (LC–ICP-MS) is commonly used for the separation and detection of arsenic species, with the most widely used implementation based on ion exchange and characterized by relatively long run times. Testing of increasing sample numbers means that analysis speed becomes a focal point for potential improvements. We developed a method based on ion interaction chromatography, allowing a reduction in run times to <3 min. The method was applied to a range of food and beverages samples. Here we discuss the results of these analyses and associated method validation tests.


and the subject of recent research (7).

The traditional ion-pairing approach

for this problem involves anion-pairing

reagents, such as tetrabutylammonium

hydroxide (8). Our approach

investigated here differs in that we

use a cation-pairing reagent in a

mobile phase at acidic conditions. The

purpose of the cation-pairing reagent

is not to interact with cations, but

rather to repel like charged anions.

It has previously been noted that

when working with cation-pairing

reagents the separation of early eluted

anions or neutral molecules, such as

those studied here, can be achieved

(9). The problem was, however, that

the cationic analytes eluted at long

retention times, leading to attempts of

combining two ion-pairing reagents in

the same solution to shorten the elution

times of the analyte cations while trying

to maintain the separation of the early

eluted species (9). Similarly, Miyashita

used a mixture of ion-pairing reagents,

with a less effective, shorter-chain

cation-pairing reagent to decrease the

retention of cations (10). The resulting

compromise was, however, not ideal in

those cases and was characterized by

loss of baseline resolution for some of

the early eluted peaks. Reproducibility

problems ensued when implementation

was attempted in different laboratories,

and these techniques did not catch

on. The aim of those analyses was

to also determine cationic species

such as AsC. They, therefore, did not

focus on a dedicated optimization

for the separation of the early eluted

species for situations where cationic

species are absent as target analytes.

The benefit of the fast run times of

the approach explored here was yet

untapped.

Ion-Interaction Chromatography: Method DevelopmentTo make the output from the column

amenable to introduction to the

ICP-MS system, we considered in our

optimization both peak separation

and minimizing the dissolved solids

content of the mobile phase. Using the

conditions in the study by Miyashita

and colleagues (10) as a starting

point, we changed the cation-pairing

reagent to octane sulfonate (OSA),

omitted the anion pairing reagent,

retained malonic acid as a buffer, and

used the same column. However, the

pH, OSA concentration, and malonic

acid concentration were reoptimized

to separate the four species As3,

As5, MMA, and DMA. Furthermore,

arsenobetaine was included in the

study on the sideline to possibly serve

as an internal standard and explore

the potential to expand the technique

to seafood analysis. Accurate species

quantitation in seafood was hindered

by the early elution of large amounts

of AsB in anion exchange techniques,

prompting the development of fast

techniques that aim to only quantitate

inorganic As in LC runs (11). The

late elution of AsB after the other

analyte peaks may be an attractive

feature here. Proper pH selection is

crucial as it is in other ion-pairing or

ion-exchange applications. At the

experimentally determined optimum

pH 4.0, As5 is anionic (pKa1 = 2.3),

MMA is mostly deprotonated

(pKa1 = 3.6), and As3 (pKa1 = 9.3)

and DMA (pKa = 6.2) are neutral

(acidity constants taken from reference

12). Thus, the anions arsenate (As5)

and MMA are eluted first as a result

of electrostatic repulsion from OSA

adsorbed onto the C18 surface. The

neutral species arsenous acid (As3)

and DMA are retained stronger and

are eluted later. Retention of those

species is likely based on weak

partitioning. AsB is zwitterionic at this

pH (pKa = 2.2) and is eluted last. Its

stronger retention may be influenced

by electrostatic attraction between


Ernstberger and Neubauer

Stationary-phase

support

C18 coating

Ion pairing reagent

HO

HO

OH

OH

As

As

As

OH

O

O

O

O-

O-

CH3

CH3

CH3

OH

OH

As (III)DMA

MMA

As(v)

AS

Figure 1: Ion interaction chromatography mechanism. Repulsive forces between anions and a negatively charged surface lead to their early elution and separation from neutral molecules, which are weakly retained by partitioning. The diagram includes the four most abundant arsenic species in apple juice and rice.

6000.0

5000.0

4000.0

3000.0

2000.0

1000.0

0.0

AJ5AJ5spk2AsB

0.00 0.50 1.00 1.50 2.00

Time (min)

Resp

on

se (

cps)

2.50 3.00 3.50

Figure 2: Sample chromatogram of an apple juice sample (20 μL injection) with and without AsB spike. AsB may be used as an internal standard.

its cationic moiety with the negatively

charged stationary phase. In summary,

a variety of interaction mechanisms,

both attractive and repulsive, take part

in the separation. Overall, the limited

interaction by attractive forces results

in desirable short run times while

maintaining baseline separation of the

species. Understanding the separation

mechanism may be aided by the

illustration provided in Figure 1.

Analysis of Apple JuiceThe regulatory bodies initially focused

on the need for testing apple juice,

therefore, we first applied our method

to the analysis of apple juice.

The analysis was carried out on

an Altus-10 HPLC system coupled

to a NexION 350D ICP-MS system

(both from PerkinElmer). The mobile

phase for the apple juice analysis

consisted of 2 mM octanesulfonate,

2 mM malonic acid, adjusted to pH 4.0

and blended with 1% methanol. The

separation was carried out on a C18

column, monitoring As75 in standard

mode. The injection volume was 20 μL.

Complete operational details are given

elsewhere (13).

Standards were prepared in mobile

phase, and the apple juice samples

were injected directly without further

dilution. The only sample treatment

was filtration, if required. Figure 2

shows the peaks are well resolved.

Not only is the run time very short, but

also DMA is well resolved from As3,

whose adequate separation is typically

challenging in anion-exchange

chromatography. Furthermore, AsB,

which is not present in the samples, is

eluted well after the analyte peaks and

may be used as an internal standard.

Several store bought apple juice

samples were analyzed, and the

results (Figure 3) show that both

inorganic arsenic forms dominate.



3.5

2.5

1.5

0.5A

s co

nce

ntr

ati

on

(p

pb

)

0Aj1 Aj2 Aj3 Aj4 Aj5 Aj6 Aj7

DMA

MMA

As5

As31

3

2

Figure 3: Apple juice arsenic speciation results.

whether 5 or 1000 samples

ₔ�YVIV[PJ�Z`YPUNL�L_JOHUNL�ₔ�

HKHW[HISL�YHJR�]PHS�WSH[MVYT�ₔ�

ₔ�TPJYV�:7,�HUK�ÄS[LY�JHY[YPKNLZ�ₔ�

�+PZ[YPI\[VY�,UX\PYPLZ�>LSJVTL�

Effortless Accuracy Consistent results

�� PUMV'LWYLW�JVT�H\

highly capable and easy to use

sample preparation for all analytical laboratories

ₔ�HUHS`[PJHS�WYLJPZPVU�ₔ�ZPTWSL�>VYRÅV^�ZVM[^HYL�ₔ�

ₔ�^PKL�YHUNL�VM�Z`YPUNLZ��[VVSZ�HUK�HJJLZZVYPLZ�ₔ

Automation for Every Laboratory

^^ �̂LWYLW�JVT�H\�L7YLW

Levels of inorganic arsenic are well

below the action limit of 10 ppb

proposed by the United States Food

and Drug Administration (US FDA).

The check standards analyzed

periodically throughout the run showed

6% maximum deviation over an 8 h

period, thus obviating the need for an

internal standard.

Arsenic Speciation in Rice: Method OptimizationArsenic speciation in rice is

receiving a lot of attention because

of legislation being implemented in

Europe and China limiting the content

of permissible inorganic arsenic.

The most stringent limit of 100 ppb

inorganic arsenic applies to rice for

production of food for infants and

young children.

We investigated arsenic speciation

in rice after extraction with 0.28 M

nitric acid according to the method

published by Huang and colleagues

(14). This extraction method preserves

species identity and has been

thoroughly validated (15). Initial tests

with direct injection of the acidic

extract impacted the chromatography:

The DMA peak broadens and shifts to

a longer retention time, while the As5

peak shape deteriorated. There is also

a very strong effect on AsB retention

time and peak shape. Those effects

were less pronounced upon dilution,

but not eliminated (Figure 4[a]).

Neutralizing the sample extract

restored peak shape and retention

times for DMA and also allowed

AsB to be used as internal standard

(Figure 4[b]), indicating that the

adverse effect on those peaks

was caused by the introduced

acidity. However, the As5 peak

shape remained poor (Figure 4[b]),

suggesting this effect is related to ionic

strength. In a subsequent optimization

of the mobile phase, we aimed to

reduce the difference between sample

matrix and mobile phase to improve

the As5 peak shape. We obtained both

optimum separation and peak shape

for all four analyte peaks with the

addition of 50 mM ammonium nitrate to

the mobile phase. Those experiments

were carried out with twofold diluted,

neutralized extract matrix at 10 and

20 μL injection volumes.

The results achieved with

neutralization allow for lower

detection limits (because of limited

sample dilution) and display good

method robustness. The benefits

of a neutralization step led to

its incorporation in the arsenic

speciation protocol used by the

FDA (16). However, the addition of a

neutralization step also increases the

analysis complexity and needs more

user awareness than a direct analysis

of acidic extracts. We therefore tested

the possibility of analyzing extracts

simply diluted with deionized water

without further neutralization. The

results show that the improvement to

As5 peak shape brought about by

the ammonium nitrate addition to the

mobile phase persisted, corroborating

the earlier indication that the As5

peak shape had been affected by

ionic strength differences between

samples and mobile phase. However,

at low dilutions (twofold), the DMA

peak broadened and even split. The

effect was seen in both rice extract

and extract matrix and persisted when

the DMA solution was prepared fresh

from the solid chemical, eliminating

the possibility that the peak split

would be attributable to ageing of

solutions. Increasing the dilution ratio

with deionized water alleviated the

issue, and 10-fold diluted extracts

were successfully analyzed at 10- and

20-μL injection volumes (Figure 5).

The results of the optimization

experiment show that a mobile phase

modified by the addition of 50 mM

ammonium nitrate provides both good

peak separation and peak shape of all

four analyte peaks for twofold diluted

neutralized extracts and 10-fold diluted

extracts without neutralization, allowing

flexibility in the sample preparation

protocol to accommodate both

approaches. If dilution with deionized

water is selected as the sole analysis

approach, a lower ammonium nitrate

concentration in the mobile phase may

also be used.

Analysis of Rice SamplesUsing the mobile phase composition

of the apple juice analysis with the

modification of 50 mM ammonium



6000.0

5000.0

4000.0

3000.0

2000.0

1000.0

Resp

on

se (

cps)

0.0

0.00 0.50

As5

As3

MMA

DMA

20 μL

10 μL

1.00 1.50

Time (min)

2.00 2.50

Figure 5: Extract matrix, 10-fold diluted with deionized water, and spiked with 1 ppb As species.

60000.0

(a) (b)

50000.0

40000.0

30000.0

Re

spo

nse

(cp

s)

Re

spo

nse

(cp

s)

20000.0

10000.0

0.0

60000.0

50000.0

40000.0

30000.0

20000.0

10000.0

0.0

0.00 0.50 1.00

Smp 10-fold 5ppb; 20.00Smp 5-fold 5ppb; 20.00Smp 2-fold 5ppb; 20.00

Smp pH7.2 2-fold spk5ppb; 20.00Smp pH7.2 5-fold spk5ppb; 20.00Smp pH7.2 10-fold spk5ppb; 20.00

1.50 2.00 2.50 3.00 3.50 4.00 4.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

Figure 4: Effect of acidity: Analysis of diluted rice extracts (a) without and (b) with neutralization. Neutralization eliminates adverse effects on DMA and AsB. Solutions were spiked with 5 ppb for all As species. 20-μL injections.

nitrate addition, we injected 10 μL of

1:10 diluted sample extracts. National

Institute of Standards and Technology

(NIST) 1568b rice flour reference

material was digested in duplicate and

analyzed to test the applicability of the

method for rice analysis. The results

agreed well with certified values with

recoveries averaging 99% for MMA

and DMA, and 97% for inorganic

arsenic, thus validating the method.

We analyzed two rice samples

intended for baby food production

and five additional rice-derived

products aimed at babies that are

4 or 7 months old. The results are

displayed in Figure 6. Inorganic

arsenic is given as the sum of As3

and As5. Both species are plotted as

adjacent bar segments so inorganic

As can be easily read off the graph

(Figure 6). The white rice sample

(R1) was far below the relevant EU

limit of 100 ppb inorganic arsenic,

while the brown rice sample (R2)

was above the limit. The rice cereal

(R3) and porridge samples (R4, R5)

range from 67 to 117 ppb, and the

two rice cake samples (R6, R7) are

142 and 116 ppb, respectively, both

below the total inorganic arsenic limit

of 300 ppb set by the EU legislation.

Inorganic arsenic was the dominant

arsenic form. DMA is the major

organic arsenic species, and MMA is

a minor constituent of some samples.

The mobile phase worked as

a calibration matrix with species

remaining stable during the run.

Reanalyzing standards prepared in

mobile phase in the range 0.05–5 ppb

one day after preparation confirmed

the species are stable.

Detection limits expressed as parts

per billion in solid were 2.5 μg/kg for

As5 and As3 and 2.0 μg/kg for MMA

and DMA (expressed on elemental

As basis), demonstrating adequate

sensitivity of the analysis. Detection

limits were calculated based on 3*SD

of analysis of seven replicates of a

0.05 ppb As standard. Detection limits

may be further lowered by increasing

the injection volume from 10 to 20 μL

if desired, or by neutralizing samples

before injection to utilize lower dilution

ratios.

Spike recoveries (1 ppb spike level)

ranged from 94–115% for all samples

and all species, with averages for all

samples being 102%, 104%, 102%,

and 102% for As5, MMA, As3, and

DMA, respectively.

SummaryAn analysis method for arsenic in

apple juice and rice by ion-interaction

chromatography has been developed

that uses electrostatic repulsion of

analyte anions from a negatively

charged stationary phase in addition

to partitioning of neutral analytes. The

limited role of attractive forces results

in very short run times, substantially

shorter than what is currently used

for ion-exchange techniques. We

adapted the technique for the analysis

of apple juice and rice extracts and

demonstrated accurate results and

method robustness to cope with

variations in sample type. Early

elution of analyte peaks leads to tall

and narrow peaks, which allow lower

levels to be measured and increases

quantitation accuracy. This approach

provides advantages compared to

anion-exchange methodology, which

not only has longer run times, but also

reverses elution order with As5 eluted

as the last peak. With substantially

shortened run times, recalibrations are

much less time consuming (12 min for

four standards). Also check standards

and quality control solutions can be

analyzed more frequently, improving

data quality. Furthermore, higher

sample throughput is desirable to meet

the increased testing needs generated

by the introduction of recent legislation

for arsenic speciation in food.

References(1) K.O. Amayo, A. Raab, E.M. Krupp, and

J. Feldmann, Talanta 118, 217–223

(2014).

(2) S. Musil, A.H. Petursdottir, A. Raab,

H. Gunnlaugsdottir, E. Krupp, and J.

Feldmann, Anal. Chem. 86(2), 993–999

(2014).

(3) E. Bralatei, S. Lacan, E.M. Krupp,

and J. Feldmann, Anal. Chem. 87(22),

11271–11276 (2015).

(4) M.M. Nearing, I. Koch, and K.J. Reimer,

Spectrochim. Acta Part B-Atomic

Spectrosc. 99, 150–162 (2014).

(5) T. Cecchi, Ion-Pair Chromatography

and Related Techniques (CRC Press,

Boca Raton, Florida, USA, 2010).

(6) K. Neubauer, Spectroscopy 24(11),

30–33 (2009).

(7) B.P. Jackson, J. Anal. At. Spectrom.

30(V), 1405–1407 (2015).

(8) S. Afton, K. Kubachka, B. Catron, and

J.A. Caruso, J. Chromatogr. A 1208(1–

2), 156–163 (2008).

(9) X.C. Le, Anal. Chem. 68, 4501–4506

(1996).

(10) S. Miyashita et al., Chemosphere 75(8),

1065–1073 (2009).

(11) J.J. Sloth, E.H. Larsen, and Y.

Julshamn, J. Agric. Food Chem. 53(15),

6011–6018 (2005).

(12) M. Leermakers et al., Trends Anal.

Chem. 25(1), 1–10 (2006).

(13) H. Ernstberger and K. Neubauer,

“Accurate and Rapid Determination

of Arsenic Speciation in Apple Juice,”

PerkinElmer application note (2015).

(14) J.-H. Huang, G. Ilgen, and P. Fecher,

J. Anal. At. Spectrom. 25, 800–802

(2010).

(15) J.H. Huang, P. Fecher, G. Ilgen, K.N.

Hu, and J. Yang, Food Chem. 130(2),

453–459 (2012).

(16) K.M. Kubachka, N.V. Shockey,

T.A. Hanley, S.D. Conklin, and D.T.

Heitkemper. US Food and Drug

Administration, “Elemental Analysis

Manual for Food and Related

Products,” Section 4.11 (FDA, Rockville,

Maryland, USA, 2012).

Helmut Ernstberger is with

PerkinElmer in Seer Green, UK.

Ken Neubauer is with PerkinElmer in

Shelton, Connecticut, USA.



180.0

160.0

140.0

120.0

100.0

80.0

60.0

40.0

20.0

00.0

R1

As

con

cen

tra

tio

n (

pp

b)

R2 R3 R4 R5 R6 R7

DMA

MMA

As5

As3

Figure 6: Distribution of arsenic species in rice and rice derived products for baby food.

To date, 25 states and the District of

Columbia in the United States have

legalized the medical use of marijuana,

while four states and the District of

Columbia have also legalized the

recreational use of marijuana (1). Although

the federal government still classifies any

use or possession of the drug as illegal, all

50 states are starting to see an increase in

the number of edible marijuana samples

within their borders. As a result, many

testing laboratories are looking for fast,

reliable, and cost-effective methods

to determine cannabis potency and

chemical residues in cannabis edibles

and beverages. The pros and cons

of legalization are still heavily debated

throughout the country, but all scientists

agree that uniform testing policies and

procedures need to be established as

soon as possible and that overall sample

cleanup is the main issue within these

analyses. Many states legalized the use

of recreational and medicinal marijuana

without establishing any analytical

protocols that are commonplace and

routine in other scientific industries.

Without any sort of regulatory control,

laboratories in these states can analyze

samples and report results without any

fear of repercussion.

For states where any consumption or

possession of marijuana is still illegal,

forensic laboratories are saddled with the

task of analyzing this contraband. Even

though it is extremely common for drug

chemistry laboratories to directly test plant

material and even oil-based products for

the presence of tetrahydrocannabinol

(THC) and related compounds, the use

of edibles and beverages recreationally

has brought along a new set of analytical

challenges. Drug chemistry departments

are not necessarily equipped to handle

this type of analysis. In spite of forensic

toxicology laboratories being capable

of handling extractions from various

biological matrices, baked goods and

hard candies have few similarities to their

everyday case load.

One concept that does appear to be

readily universal in the edible marijuana

community is the establishment that

10 mg of THC is equal to one dose for

this drug (2). While this standard works

in theory, many edible manufacturers

produce products that range from

multiple doses in one package to just

containing one individual dose. If proper

interpretation of dosing instructions is

not noted, some consumers may be

put at risk for either undesirable effects

from consuming too much in one

sitting or no relief of symptoms at all for

medicinal users. An imperative concept

that novice edible users need to keep in

mind is that digesting marijuana is not

the same as smoking it when discussing

the plant’s pharmacology. Those who

have prior experience with smoking

marijuana may assume that one brownie

or one package of gummy bears equals

one marijuana cigarette, though that

is far from the case. Employees at

marijuana shops may not be educated

enough on this issue or be aware that

edibles do not necessarily contain the

THC amount as advertised on their

packaging. This fact alone makes it

even more critical that effective testing

measures are put in place to ultimately

protect the end users.

The development of an extraction

method that can be used in a wide

variety of laboratory settings is critical

to the emerging fields of recreational

and medicinal marijuana testing.

Within environmental and food testing

laboratories, the QuEChERS (quick,

easy, cheap, effective, rugged, and

safe) sample preparation method has

been widely used for the past 13 years.

In 2003, Anastassiades and Lehotay

published the first QuEChERS application,

which discussed the determination of

pesticide residues in produce (3). Since

then, QuEChERS has become the

analytical gold standard for the testing

and analysis of a wide variety of edible

matrices, including oil, egg, meat, fish,

wine, and beverage samples (4–9). Using

disposable consumables, hundreds of

pesticides can be analyzed in a single

extraction with the QuEChERS approach.

In addition to pesticide residues, other

chemical residues such as antibiotics,

veterinary drugs, mycotoxins, polycyclic

aromatic hydrocarbons (PAHs), bisphenol

A, and phthalates are routinely monitored

using this technique (10–14). The solvent

waste generated is much less than what is

typically associated with complex organic

extractions. This technique is a relatively

easy analytical method for technicians

to learn, allowing for laboratories to

effortlessly adopt this system of sample

preparation.

Neither traditional solid-phase extraction

(SPE) columns nor liquid–liquid extraction

techniques can successfully provide

laboratories with the reproducible and

fast results needed for cannabis food

analysis. Unlike biological matrices, edible

Determination of Cannabinoid Content and Pesticide Residues in Cannabis Edibles and BeveragesXiaoyan Wang, Danielle Mackowsky, Jody Searfoss, and Michael J. Telepchak, UCT, Bristol, Pennsylvania, USA

As a result of the rapid growth of the cannabis industry, many testing laboratories are looking for efficient, reliable, and cost-effective analytical methods to analyze chemical residues, such as pesticides, mycotoxins, solvent residues, terpenes, and heavy metals, as well as cannabinoid concentration in cannabis-infused edibles and beverages. In this article, QuEChERS (quick, easy, cheap, effective, rugged, and safe), a sample preparation technique widely adopted in the food testing industry, is introduced to the discipline of forensic testing as a viable method for the extraction of pesticides and cannabinoids in various complex sample matrices. The claimed amounts of cannabinoids versus the actual amounts are compared, as well as the pesticide residue levels in edible and beverage samples.


products do not easily pass through the

porous frits and sorbent of an SPE column.

In addition, they do not contain the same

endogenous matrix interferences found

in biological samples that ultimately need

to be removed for accurate quantitation.

Lastly, when analyzing the cannabinoid

content in edible samples, the final

extract often needs to be diluted rather

than concentrated before instrumental

analysis. This is in stark contrast to forensic

samples, which often require concentration

of target analytes at trace levels. Liquid–

liquid extraction often requires large

amounts of undesirable and toxic solvents

to be used. The above limiting factors

allow for QuEChERS to make a desirable

transition to the forensic community.

ExperimentalReagents and Standards: High

performance liquid chromatography

(HPLC)-grade acetonitrile, HPLC-grade

methanol, and American Chemical

Society (ACS)-grade acetic acid were

purchased from Spectrum. Also, 35 neat

pesticide standards were purchased

from Sigma-Aldrich, Chem Service, or

Ultra Scientific. A 2-ppm working solution

containing 35 pesticides was prepared in

acetonitrile. Three cannabinoids including

THC, cannabidiol (CBD), and cannabinol

(CBN) were purchased from Cerilliant in

1-mg/mL solutions. A 10-ppm mixture of

the three cannabinoids was prepared in

acetonitrile. Neat triphenyl phosphate was

purchased from Cerilliant and diluted to

10 ppm in acetonitrile.

Sample Preparation: Baked goods,

chocolate bars, and hard candies were


Wang et al.

Figure 1: Photographs of a hard candy (a) before and (b) after freezer mill grinding; (c) after QuEChERS extraction; and (d) the extracts before (left) and after (right) dSPE cleanup.

(a)

(c) (d)

(b)

Figure 2: Chromatograms of 35 pesticides and triphenyl phosphate (IS) retained on the UCT Selectra Aqueous C18 HPLC column.

ground into a fine powder using a SPEX

6770 freezer mill before extraction. A

hard candy sample before and after

grinding is shown in Figures 1(a) and 1(b).

Gummy-based candies can be ground

into powder with the presence of liquid

nitrogen; however, the powder will return to

its elastic gel state when the temperature

rises. Thus, gummy-based samples were

cut into fine pieces instead of using a

freezer mill. Carbonated beverages, such

as sodas, were degassed for 30 min before

analysis, while oil samples were extracted

without any sample pretreatment.

Homogenized 1-g samples (baked

goods, chocolate bars, hard candies,

gummy bears, or oil samples) were

weighed into a 50-mL centrifuge tube.

To each of these samples, 10 mL of

reagent water was added. Samples

were hydrated for 1 h using a horizontal

shaker. For beverage samples, 10 mL of

degassed sample was added to 50-mL

tubes without the water addition and

the 1-h hydration step. Internal standard

and 10 mL of acetonitrile containing 1%

acetic acid were added to all samples,

which were then shaken for 1 min using

the SPEX Geno/Grinder homogenizer.

A proprietary blend of QuEChERS

extraction salts supplied by UCT was

added to each tube, and the tubes were

shaken vigorously to break up any salt

agglomerates. The extraction salts help

to facilitate phase separation and partition

target analytes from the aqueous layer

into the acetonitrile layer. After shaking,

the samples were centrifuged for 5 min at

3000 rcf. Three distinct layers are formed

after centrifugation as demonstrated

in Figure 1(c). The top layer is the

organic phase (acetonitrile) containing

pesticide residues, cannabinoids, and

the organic-soluble matrix coextractives;

the middle layer is the insoluble matrix

components and water containing the

water-soluble matrix components, such

as sugars; and the bottom layer is the

undissolved excess extraction salts.

For pesticide residue analysis, 1 mL

of the supernatant was transferred to a

2-mL dispersive solid-phase extraction

(dSPE) tube containing a proprietary blend

of sorbents (UCT), and shaken for 1 min

using the Geno/Grinder, then centrifuged

for 5 min at 3000 rcf. This process removes

chlorophyll, sugars, organic acids, and

fatty compounds from the sample extracts

by retaining them onto the sorbents.

The resulting clean extract, illustrated in

Figure 1(d), is then diluted 2× with reagent

water and analyzed by LC–MS/MS.

For cannabinoid content analysis,

dSPE was not necessary because of

the high cannabinoid concentration in

the acetonitrile extract. Instead, serial

dilutions ranging from 200× to 20,000×

were carried out to obtain a concentration

(a few hundred parts per billion) that is

suitable for LC–MS/MS analysis.

Instrumental: A Thermo Scientific

Dionex UltiMate 3000 LC system

coupled to a Thermo Scientific TSQ

Vantage tandem mass spectrometer

was used for pesticide and cannabinoid

analysis. Xcalibur (Version: 2.2) software

was used for data acquisition and

processing. A 100 mm × 2.1 mm, 3-μm

dp Selectra Aqueous C18 HPLC column

and 10 mm × 2.1 mm, 3-μm dp guard

column supplied by UCT were used

for analyte retention and separation.

The aqueous C18 HPLC column was

selected because of the high polarity of

several pesticides covered in this study,

such as methamidophos and acephate.

Chromatograms of 35 pesticides and

triphenyl phosphate (IS) are shown in

Figure 2, with the retention order listed

in Table 1. The same HPLC column

and mobile phases were also used

for cannabinoid analysis to provide

ease without the need of switching the

HPLC column and mobile phases when

analyzing the pesticide and cannabinoid

samples in succession. The aqueous C18

HPLC column showed great separation

of CBD and THC, two compounds

that have the same multiple reaction

monitoring (MRM) transitions. An example

chromatogram of a diluted mint milk

chocolate sample spiked with 70 ppb of

three cannabinoids using this aqueous

C18 column is shown in Figure 3.

The column oven was maintained at

40 °C, and the samples in the autosampler

were kept at 10 °C. The injection

volumes were 2 and 5 μL for pesticide

and cannabinoid analysis, respectively.

Mobile-phase A was 10 mM ammonium

acetate in Milli-Q water, and mobile-phase

B was methanol with 0.1% formic acid.

The mobile-phase flow rate was 300 μL/

min. The gradient for pesticides was

programmed as follows: 0 min, 0% B;

1–3.5 min, 50% B; 6–9 min, 95% B;

and 9.1–14 min, 0% B. For cannabinoid

analysis, the gradient program was as

follows: 0–0.5 min, 60% B; 3–7 min, 95% B;

and 7.1–10 min, 60% B.


Wang et al.

100

80

60

40

20

0100

80

60

40

20

0100

Rela

tive A

bu

nd

an

ceR

ela

tive A

bu

nd

an

ceR

ela

tive A

bu

nd

an

ceR

ela

tive A

bu

nd

an

ce

80

60

40

20

0100

80

60

40

20

00.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Time (min)

4.5 5.0 5.5

5.53

4.97

4.34

4.304.464.60

5.115.205.375.53

5.78

4.78

5.89

4.704.825.10 5.47 5.63

5.75CBN

CBD

TPP (IS)

5.26

THC

5.145.29 5.43

5.91

5.936.10

6.396.49 6.77

5.626.10

6.506.60

6.0 6.5

Figure 3: Chromatogram of a diluted (2000×) mint milk chocolate sample spiked with 70 ppb of three cannabinoids.

Tandem MS was operated with heated

electrospray ionization (HESI) in positive

mode. The MS/MS conditions were set as

follows: spray voltage at 3500 V; sheath

gas of nitrogen at 50 arbitrary units;

auxiliary gas of nitrogen at 40 arbitrary

units; vaporizer temperature at 450 °C;

ion transfer capillary temperature at

350 °C; collision gas of argon at 1.5 mTorr;

Q1 peak width of 0.4 Da full width half

maximum (FWHM); and Q3 peak width of

0.7 Da FWHM. The optimization of the

MS/MS transitions was performed

individually for each analyte by infusing

1 μg/mL of standard in acetonitrile at

10 μL/min in a 50:50 A–B mobile phase

at a flow rate of 300 μL/min. Scheduled

selected reaction monitoring (SRM) with

a cycle time of 0.5 s was set for data

acquisition. The precursor and product

ions, collision energies, and S-Lens RF

values of the 35 pesticides, IS, and three

cannabinoids are listed in Table 1.

Results and DiscussionSemiquantitative Determination of

Pesticide Residues: Pesticide residues

were analyzed semiquantitatively

because of the lack of negative control

samples for each sample matrix, as well

as the large number of pesticides at

different concentration levels making the

standard addition method impractical.

Matrix-matched calibration curves

were generated using the post-spiked

blank extracts of green tea sample to

semiquantify the pesticide residues in

edibles and beverages. Appropriate

amounts of the pesticide spiking solution

were added into the tea extracts to

generate six-point matrix-matched

calibration curves with concentrations

at 5, 10, 25, 50, 100, and 250 ng/mL.

The responses were found to be linear

(R2 > 0.99) over the entire concentration

range. Accuracy and precision results at

10-ng/g and 50-ng/g spiking levels are

shown in Table 2.

A variety of edibles, including chocolate

bars, brownies, hard and gummy candies,

CBD oil, and beverage samples, were

analyzed for pesticide residues using

the QuEChERS extraction and dSPE

cleanup method described above, and

the results are summarized in Table 3. Four

products were found to contain bifenazate

at concentrations ranging from 10 to

1221 ng/g, although some of them claimed

to be “organically grown” products. The

highest concentration was found in a CBD

oil sample, where the pesticide might have

been enriched when manufacturing the

concentrated oil product from the raw plant

materials. Bifenazate is commonly used

to control mites on agricultural products.

Though it is not terribly harmful to humans

in comparison to other pesticides, there is

a lack of research regarding the health risk

of constant consumption of this pesticide.

Most pesticide research does not take

into account the oral dosing over a long

period of time, especially in people with

compromised immune systems.

Quantitative Determination of

Cannabinoid Content: For cannabinoid

analysis, content was quantitated

by the standard addition method.

Diluted QuEChERS extracts were


Wang et al.

Table 1: SRM transitions of pesticide and cannabinoids

Pesticide Precursor Product 1 CE1 Product 2 CE2 S-lens RF

Metamidophos 142.0 94.1 14 125.0 13 50

Acephate 184.0 143.0 6 95.0 25 33

Aldicarb sulfoxide 207.1 89.1 13 69.1 16 32

Oxydemeton methyl 247.0 169.0 13 109.0 27 57

Pymetrozine 218.1 105.1 20 176.1 17 63

Dichrotophos 238.1 112.1 12 127.0 18 52

Triethylphosphorothioate 199.0 125.0 16 143.0 14 55

Dimethoate 230.0 125.0 22 171.0 15 50

Carbendazim 192.1 160.1 18 132.1 29 60

Dichlorvos 220.9 109.0 17 127.0 13 62

Thiabendazole 202.0 175.1 25 131.1 31 70

Fenamiphos sulfone 336.1 266.0 19 188.0 26 75

Fenamiphos sulfoxide 320.1 233.0 24 108.1 40 60

Simazine 202.1 132.0 19 124.1 16 66

Tebuthiuron 229.1 172.1 16 116.0 26 55

Carbaryl 202.1 145.1 11 127.1 30 38

Flutriafol 302.1 70.1 17 123.0 28 69

Famphur 326.0 217.0 20 93.0 30 68

Thionazin 249.0 113.0 23 97.0 28 58

DEET 192.1 119.1 17 91.1 29 64

Atrazine 216.1 174.1 16 68.1 34 66

Malathion 331.0 127.0 12 99.0 25 55

Triadimefon 294.1 197.1 14 69.1 20 65

Pyrimethanil 200.1 107.1 24 183.1 23 68

Bifenazate 301.1 170.1 18 198.1 6 48

Acetochlor 270.1 224.1 10 148.1 18 58

Sulfotep 323.0 97.0 37 115.0 30 60

Tebuconazole 308.1 70.1 21 125.0 33 66

Zoxamide 336.0 187.0 21 159.0 38 74

Diazinon 305.1 169.1 20 153.1 20 68

TPP (IS) 327.1 152.1 35 77.1 38 95

Cyprodinil 226.1 93.1 33 77.1 43 70

Pyrazophos 374.1 222.1 20 194.1 31 100

Profenofos 372.9 302.9 17 128.0 42 73

Ethion 385.0 142.9 26 199.0 6 56

Chlorpyrifos 349.9 97.0 32 197.9 19 67

Cannabinoids

CBD 315.0 193.1 20 123.0 30 77

CBN 311.1 223.1 19 293.2 14 73

THC 315.2 193.1 19 123.1 31 73

spiked at +50% and +150% of the

stated cannabinoid content. Plotting

the peak areas of the diluted sample

and the two aforementioned spiked

samples allowed for the calculation

of cannabinoid concentrations in the

diluted samples. Excellent linearity

(R2 > 0.995) was observed for all

samples tested. An example of the

standard addition calibration curve is

demonstrated in Figure 4. Calculation of

the cannabinoid contents in the original

samples was based on the dilution

factors and unit conversion factors. The

results (in milligrams per pack) were

then compared to the stated content

on the packaging of each product, as

summarized in Table 4. For all of the

cannabis samples tested in this study,

only 31% (four out of 13 samples) had

accurately labelled the total cannabinoid

content within ±20% of the stated values.

Although the US Food and Drug

Administration takes care of monitoring

the safe production of the majority of

edible products in the United States,

marijuana-infused foods do not fall under

their jurisdiction because the drug is still

considered to be illegal in the eyes of the

federal government. Recreational users

may be primarily focused on getting their

money’s worth from these products, but

medicinal users have additional, notable

concerns with edibles. Many medicinal

consumers do not necessarily want to feel

the psychoactive effects of marijuana, but

rather just want to combat the symptoms

of their specific ailment. If a product is

inaccurately labelled, medicinal users run

the risk of consuming too much marijuana

and thus having undesirable side effects.

On the contrary, they also could be

unnecessarily suffering from side effects

of their illnesses because of an edible

product containing less cannabinoids

than the suggested dosage amount.

These concerns are in addition to the

possible presence of unwanted chemical

residues, such as pesticides, mycotoxins,

and heavy metals.

ConclusionA fast, reliable, and cost-effective

analytical method has been developed

for the determination of pesticide

residues and cannabinoid contents in

cannabis infused edibles and beverages.


Wang et al.

Table 2: Accuracy and precision results of pesticides in spiked samples

Compound

Spiked at 10 ng/g Spiked at 50 ng/g

Recovery%RSD% (n = 6)

Recovery%RSD% (n = 6)

Methamidophos 80 11 83 12

Acephate 81 14 93 12

Aldicarb sulfoxide 93 13 95 23

Oxydemeton_methyl 74 16 80 23

Dichrotophos 90 15 75 14

Pymetrozine 57 20 59 10

Dimethoate 105 16 87 12

Triethylphosphorothioate 97 14 82 14

Carbendazim 98 15 74 12

Dichlorvos 97 12 97 11

Fenamiphos sulfone 121 11 108 15

Fenamiphos sulfoxide 99 14 96 16

Simazine 121 14 107 14

Carbaryl 93 10 103 14

Tebuthiuron 105 9 105 17

Thiabendazole 70 7 78 8

Famphur 101 13 101 13

Flutriafol 92 14 96 10

Thionazin 103 11 99 12

Atrazine 99 24 95 13

DEET 105 30 97 12

Malathion 102 23 115 14

Triadimefon 97 21 101 18

Bifenazate 154 23 98 21

Pyrimethanil 83 14 84 16

Acetochlor 96 16 101 12

Sulfotep 100 15 99 13

Tebuconazole 85 2 87 5

Zoxamide 86 3 91 5

Diazinon 92 4 92 3

Cyprodinil 77 5 77 3

Pyrazophos 94 4 97 3

Ethion 92 3 92 5

Profenofos 87 8 88 6

Chlorpyrifos 90 9 93 9

Table 3: Pesticide residues

semi-quantitatively detected in

beverages, oil, and edibles

Cannabis Samples

Detected Pesticide Residues

(Semi-Quantitative)

Elixirs (beverage) 14 ng/g bifenazate

Orange kush

(soda)10 ng/g bifenazate

Cola (soda) None detected

CBD oil 1221 ng/g

bifenazate

Cookie and

cream (bar)None detected

Fantastic brownie 97 ng/g bifenazate

Mint milk

chocolate None detected

Monkey bar None detected

Mixed drops

(hard candy)None detected

Nectarbee (hard

candy)None detected

Sour gummies None detected

Sour fruit ring

(gummy)None detected

Sweet'n sours

(gummy)None detected

This method uses the advantages of

QuEChERS products to extract 35

pesticides and three cannabinoids (CBD,

CBN, and THC) from complex food and

beverage matrices. This extraction is

then followed by either serial dilution for

cannabinoid content analysis, or a dSPE

cleanup for the efficient removal of various

matrix coextractives, resulting in extracts

for trace-level pesticide residue analysis.

This hybrid method allows QuEChERS,

which is extensively used in the food

testing industry, to be used in a forensic or

private cannabis laboratory setting.

The developed method has been

successfully applied to the analysis of

13 real cannabis edible and beverage

samples. For pesticide analysis,

bifenazate was present in four out

of the 13 tested samples at varied

concentrations ranging from 10 to

1221 ng/g. For cannabinoid content

analysis, only 31% (four out of 13) of the

samples had accurately labelled the

total cannabinoid content within ±20%

of the stated value, while the majority of

samples (eight out of 13, or 62%) were

overlabelled (more than 20% below the

labelled amount). One sample was found

underlabelled which actually contained

40% more THC than stated.

Acknowledgements Keith Tucker with SPEX SamplePrep LLC

is acknowledged for providing the 6770

Freezer mill and 2010 Geno/Grinder for

this study. Erik Swiatkowski with UCT is

thanked for his help in handling liquid

nitrogen when using the SPEX 6770

freezer mill.

References(1) A.A. Monte, R.D. Zane, and K.J. Heard,

JAMA 313(3), 241–242 (2015).

(2) State Medical Marijuana Laws, National

Conference of State Legislators 18 April

2016.

(3) M. Anastassiades, S.J. Lehotay, D.

Stajnbaher, and F.J. Schenck, J. AOAC Int.

86(2), 412–431 (2003).

(4) S.W.C. Chung and B.T.P. Chan, J.

Chromatogr. A 1217, 4815–4824 (2010).

(5) S.C. Cunha, S.J. Lehotay, K. Mastovska,

J.O. Fernandes, M. Beatriz, and P.P Oliveira,

J. Sep. Sci. 30(4), 620–626 (2007).

(6) Y. Sapozhnikova, J. Agric. Food Chem. 62,

3684–3689 (2014).

(7) X. Wang and M.J. Telepchak, LCGC Europe

26, 66–77 (2013).

(8) J. Wang and W. Cheung, J. AOAC Int. 99(2),

539–557 (2016).

(9) M. Villar-Pulido, B. Gilbert-Lopez, J.F.

Garcia-Reyes, N.R. Martos, and A.

Molina-Diaz, Talanta 85, 1419–1427, (2011).

(10) B. Kinsella, S.J. Lehotay, K. Mastovska, A.R.

Lightfield, A. Furey, and M. Danaher, Anal.

Chim. Acta 637, 196–207 (2009).

(11) L. Vaclavik, M. Zachariasova, V. Hrbek, and

J. Hajslova, Talanta 82, 1950–1957 (2010).

(12) T.H. Kao, S. Chen, C.J. Chen, C.W. Huang,

and B.H. Chen, J. Agric. Food Chem. 60,

1380–1389 (2012).

(13) S.C. Cunha, C. Cunha, A.R. Ferreira, and

J.O. Fernandes, Anal. Bioanal. Chem. 404,

2453–2463 (2012).

(14) Y. Ma, Y. Hashi, F. Ji, and J.M. Li, J. Sep.

Sci. 33, 251–257 (2010).

Xiaoyan Wang, Danielle Mackowsky,

Jody Searfoss, and Michael J.

Telepchak are with UCT in Bristol,

Pennsylvania, USA.


Wang et al.

Figure 4: Standard addition calibration curve of THC in a cookie and cream bar sample (500× dilution of the QuEChERS extract, 70 ppb cannabinoids were added for the 50% spiked sample and 210 ppb were added for the 150% spiked sample).

Table 4: Comparison of labelled and detected cannabinoids (mg/pack)

Cannabis SamplesCBD CBN THC Total Cannabinoids Accuracy

(%)Labelled Detected Labelled Detected Labelled Detected Labelled Detected

Elixirs (beverage) NA ND NA ND 90 60 90 60 67

Orange kush (soda) NA ND NA ND 10 6 10 6 60

Reef cola (soda) NA ND NA ND 10 7 10 7 70

CBD oil 500 493 < 5 ND 5 12 505–510 505 99–100

Cookie and cream (bar) NA ND NA ND 30 29 30 29 97

Fantastic brownie NA ND NA ND 10 14 10 14 140

Mint milk chocolate NA ND NA ND 100 74 100 74 74

Monkey bar NA ND NA ND 100 69 100 69 69

Mixed drops (hard candy) NA ND NA ND 100 49 100 49 49

Nectarbee (hard candy) NA ND NA ND 10 6 10 6 60

Sour gummies NA ND NA ND 100 95 100 95 95

Sour fruit ring (gummy) NA ND NA ND 10 8 10 8 80

Sweet'n sours (gummy) NA 28 NA ND 100 31 100 59 59

NA: not available; ND: not detected

The α-dicarbonyl compounds have

great importance in food quality since

they result from enzymatic and chemical

processes, namely the Maillard reaction.

Among these compounds, diacetyl is the

most representative, being known as an

important quality marker in food products

such as beer, wine, butter, yogurt, and

cheese (1–5).

In wines, the major α-dicarbonyls are

glyoxal, methylglyoxal, diacetyl, and

pentane-2,3-dione, which are formed

by malolactic fermentation (6). These

compounds have a significant importance

in the characteristics of such products

because of their sensorial activity, high

reactivity with other wine components,

and their role in biological processes.

The impact of α-dicarbonyls, namely

methylglyoxal, in human health has been

extensively studied in recent years (7–9).

The determination of α-dicarbonyls is

usually performed by chromatographic

analysis, either by liquid chromatography

(LC) (5,6,10–16) or gas chromatography

(GC) (1,6,17,18). The analytes’ extraction

is recommended because of their low

concentration in the samples and high

reactivity of the carbonyl group. The

most frequent extraction procedures

for GC analysis include solid-phase

microextraction (SPME) (1,3,18,19),

gas-diffusion extraction (20), and

liquid–liquid extraction (LLE) (6,21). For

LC analysis it is common to analyze the

samples without extraction; α-dicarbonyls

are derivatized in-sample and directly

injected into the LC system (10,16,22–24),

as described in the reference analytical

method for α-dicarbonyls analysis of the

Organization of Vine and Wine (OIV) (25).

These α-dicarbonyl compounds can

be successfully quantified at very low

levels in wines (above 0.05 mg/L) (25).

However, the use of an extraction step

can enhance analyte recovery while

avoiding the interference and deleterious

effect of several matrix components

(such as sugars, lipids, pigments, and so

forth) on the chromatographic systems.

To address these issues, solid-phase

extraction (SPE) (11,13,15) and

gas-diffusion extraction (12) procedures

were proposed for the analysis of

α-dicarbonyls aiming their LC analysis.

In the last decade a well-known

but underexplored procedure, called

salting-out liquid–liquid extraction (SALLE)

(26,27), has been increasingly used

in food analysis namely through the

QuEChERS (quick, easy, cheap, effective,

rugged, and safe) procedure (28). SALLE

is a homogeneous liquid–liquid extraction

technique in which a water-miscible

organic solvent is separated from an

aqueous solution by the addition of a salt.

Simultaneously, the extraction of analytes

to the organic phase is attained when the

liquid phases’ separation occurs. In this

work, a sample preparation procedure

based on SALLE was developed for the

analysis of three important α-dicarbonyls

(methylglyoxal, diacetyl, and pentane-2,3-

dione) in wines.

Materials and MethodsChemicals and Samples: High-purity

water (resistivity not lower than

18.2 MΩ-cm) from a Direct-Q 3 ultraviolet

(UV) water purification system (Millipore)

was used throughout all the studies. High

performance liquid chromatography

(HPLC)-grade acetonitrile was from

Fisher. All eluents were filtered through a

nylon filter (0.45 μm pore size, Whatman)

before use.

Methylglyoxal (40% in water),

diacetyl (97%), pentane-2,3-dione

(97%), hexane-2,3-dione (90%), and

o-phenylenediamine (OPDA, 98%) were

purchased from Sigma-Aldrich. The salts

used (sodium chloride, sodium acetate,

and sodium carbonate) were of analytical

grade and were also purchased from

Sigma-Aldrich.

Stock standard solutions of

methylglyoxal (1 g/L) were prepared by

diluting the appropriate volume of the

commercial reagent in ultrapure water

and were stored at 4 °C. Stock standard

solutions of diacetyl, pentane-2,3-dione

(1 g/L), and hexane-2,3-dione (125 mg/L,

internal standard) were prepared in

acetonitrile and stored at -20 °C.

The extracting solution containing

OPDA (0.5%, m/v) was prepared daily by

dissolution of the appropriate amount of

the commercial reagent in acetonitrile and

stored in the dark.

Wine samples used in this work were

of Portuguese origin and were purchased

in local markets. The alcohol content in

the samples varied between 9.0% and

13.0%, and the pH varied between 3.1

and 3.7.

Extraction Procedure: Wine samples

were diluted (2:5, v/v) with acetate

Determination of α-Dicarbonyls in Wines Using Salting-Out Assisted Liquid–Liquid ExtractionInês Maria Valente1,2 and José António Rodrigues1, 1REQUIMTE/LAQV – Departamento de Química e Bioquímica, Faculdade

de Ciências, Universidade do Porto, Porto, Portugal,2Departamento de Clínicas Veterinárias, Instituto de Ciências Biomédicas

Abel Salazar, Universidade do Porto, Porto, Portugal.

A method based on salting-out assisted liquid–liquid extraction for the analysis of α-dicarbonyls in wines was developed. The sample preparation procedure consists of a single step, involving the simultaneous extraction and derivatization of the analytes using an o-phenylenediamine–acetonitrile solution with sodium chloride as the salting-out agent. The obtained organic phase is collected and directly analyzed by liquid chromatography with spectrophotometric detection. The studied α-dicarbonyls were determined in eight wines. The developed methodology substantially reduces the complexity of the sample matrix, which is a very important aspect in routine analysis, especially to ensure long-lasting and reliable functioning of the chromatographic systems, while being a new and attractive methodology for the analysis of α-dicarbonyls.


buffer (0.2 mol/L, pH 4) and internal

standard (hexane-2,3-dione) was added

to a final concentration of 0.25 mg/L.

In a 10-mL tube, 2 mL of the diluted

sample was mixed with 2 mL of OPDA

solution and 0.13 g of sodium chloride.

After vigorous shaking until an almost

complete dissolution of the salt was

attained, the mixture was kept in the

dark for 1 h at room temperature to

simultaneously promote the derivatization

of the α-dicarbonyls and their extraction

to the acetonitrile phase. The tubes were

centrifuged at 6000 rpm for 2 min to

improve phase separation and finally an

aliquot of the upper phase was collected

for HPLC–UV analysis. A simplified

scheme of the experimental procedure is

shown in Figure 1.

HPLC–UV Analysis: HPLC–UV analysis

was performed using a PerkinElmer S200

chromatographic system with a S200 UV

detector. Separation of the derivatized

α-dicarbonyls (quinoxalines) was

performed using a 250 mm × 4.0 mm,

5-μm dp Phenomenex Gemini C18

column at room temperature. The mobile

phase was composed of acetonitrile

(mobile-phase A) and 10 mM acetate

buffer, pH 4.8 (mobile-phase B). The flow

rate was 0.8 mL/min. A gradient was used

as follows: 0–5 min 30% A; 5–15 min

linear increase of A to 50%; 15–20 min

linear decrease of A to 30%; 20–35 min

30% A. The injection volume was 20 μL

and UV detection was performed at

315 nm.

Results and DiscussionEffect of the Derivatizing Reagent on

the Extraction: In a previous work (26),

the application of SALLE to the extraction

of α-dicarbonyl compounds from

aqueous solutions was studied, opening

the possibility to use this technique for

beverage analysis. In the present work,

three extraction procedures were tested:

• The analytes were initially derivatized


Valente and Rodrigues

temperature

1 h in the dark at room

HPLC–UV analysis

(2 min at 6000 rpm)

CentrifugationShaking

+ 2 ml OPDA

+ 0.13 g NaCI

2 mL

(0.5% m/v in acetonitrile)

Sample dilution with acetate buffer

(0.2 mol/L, pH 4)

+ internal standard

(hexane-2,3-dione; 0.25 mg/L)

Figure 1: Simplified scheme of the sample preparation procedure.

5000

10,000

15,000

20,000

30,000

40,000

25,000

35,000

0Diacetyl

Procedure A

Procedure B

Procedure C

Standard solutionStandard solution

Standard solution

Derivatized solution

+ NaCl+ NaCl+ NaCl

Extract + OPDA+ OPDA

+ acetonitrile+ acetonitrile

+ OPDA

Methylglyoxal

Peak a

rea (

μV

•s)

Pentane-2,3-dione

Extraction + derivatizationExtractionExtraction DerivatizationDerivatization

A: In-sample derivatization and subsequent extraction B: Extraction and subsequent derivatization C: Simultaneous extraction and derivatization

Figure 2: Study of the presence of the derivatizing agent on the extraction using a standard solution of α-dicarbonyls (0.5 mg/L). The three tested experimental procedures (A, B, and C) and the results obtained for each one are depicted. Results are expressed as the mean value of three replicates.

in-sample with OPDA for 1 h; the

resulting quinoxalines were then

extracted by SALLE using acetonitrile

and sodium chloride; finally the upper

phase was collected and analyzed by

HPLC–UV (procedure A in Figure 2);

• α-dicarbonyls were first extracted from

the aqueous sample to acetonitrile

using sodium chloride; then the upper

phase was collected and derivatized

with OPDA before HPLC–UV analysis

(procedure B in Figure 2); and

• α-dicarbonyls were extracted and

derivatized simultaneously using an

OPDA solution prepared in acetonitrile

using sodium chloride for the phase

separation (procedure C in Figure 2).

The schemes of the three tested

procedures using α-dicarbonyls standard

solutions (0.5 mg/L) and the obtained

results are shown in Figure 2. The results

indicated that the use of the derivatizing

agent during the extraction (procedure C)

enhanced the recoveries of the analytes,

compared to procedure B in which

OPDA was absent during the extraction.

The presence of OPDA in the extraction

medium likely displaced the chemical

equilibria towards the acetonitrile

phase, favouring the overall extraction

of α-dicarbonyl compounds. As

expected, the major differences between

procedures B and C were observed for

the most polar analyte, methylglyoxal

(a fourfold increase). The quantity of

analytes extracted using procedure C

was very similar to that obtained with

procedure A. Procedure C (simultaneous

extraction and derivatization) was used for

the subsequent experiments considering

its advantages compared to procedure A

in terms of simplicity of the experimental

procedure and time saving. The three

procedures were also performed with

spiked wine samples (0.5 mg/L of each

α-dicarbonyl), and the results were similar

to those obtained for standard solutions.

Effect of the Salt on the Extraction:

The salt used in SALLE as the salting-out

agent influenced both the extraction

efficiency and the phase separation

between acetonitrile and water (26).

In this work, three salts were tested

(sodium chloride, sodium carbonate,

and sodium acetate) using procedure

C (Figure 2). Initial experiments with

standard solutions (0.25 mg/L) showed

that the extraction–derivatization of the

analytes was not affected by the salt

used. The same experiments were

performed using wine (white and red)

samples spiked with 0.25 mg/L of each

studied carbonyl compound. The results

(Figure 3) showed that the determination

of α-dicarbonyl compounds in wine

samples is influenced by the salt used,

suggesting that a chemical modification

of the sample matrix is occurring. The

first hypothesis put to the test was the

possibility of a pH alteration of the

sample because of the salt addition (in

the case of sodium acetate and sodium

carbonate). In fact, it is known that

α-dicarbonyls are prone to react with or

bind to several wine components such as

sulfites (29) and amino acids (30). Since

the majority of those binding reactions

are pH-dependent, the variation of this

parameter can affect the determination of

α-dicarbonyl compounds. For this reason,

the influence of the wine sample’s pH was

studied.

Effect of Sample pH: The effect of pH on

the extraction of α-dicarbonyls was tested

in the pH 2–8 range, using α-dicarbonyls

model standard solutions (0.25 mg/L)

and wine samples. The results for the

extraction of α-dicarbonyls from model

standard solutions (data not shown)

revealed that peak areas of methylglyoxal

were higher at pH above 5 than for pH

below 5. The results for diacetyl and

pentane-2,3-dione were slightly different



60,000

1,000,000

White wine Red wineNaClCH

3COONa

Na2CO

3

100,000

50,000

0

950,000

Methylglyoxal Diacetyl Pentane-2,3-dione Methylglyoxal Diacetyl Pentane-2,3-dione

Peak a

rea (

μV

•s)

50,000

40,000

30,000

20,000

10,000

0

Figure 3: Results obtained from the study of the influence of the salt on the extraction of α-dicarbonyls in spiked white and red wine (0.25 mg/L). NaCl, CH3COONa, and Na2CO3 were used at a concentration of 1 mol/L. Results are expressed as the mean value of three replicates.

2

0.0

0.2

0.4

0.6

Co

nce

ntr

ati

on

(m

g/L

)

0.8

1.0

1.2

1.4 Methylglyoxal

Diacetyl

Pentane-2,3-dione

3 4 5

pH

6 7 8

Figure 4: Determined concentration of the studied α-dicarbonyls extracted from a red wine sample with adjusted pH values between 2 and 8. The quantification was performed by the standard additions method and results are expressed as the concentration ± standard deviation. pH adjustment was performed using the following buffer solutions: 0.2 M HCl for pH 2; 0.2 M phosphate buffer for pH 3, 6, 7, and 8; and 0.2 M acetate buffer for pH 4 and 5.

showing less interference of pH on the

extraction of these compounds. The

effect of pH was also studied in diluted

wine samples (2:5, v/v) in a buffer solution

before the extraction. These studies were

performed with the addition of standard

solutions to the wine samples and the

results (Figure 4) are presented as the

concentration determined by the standard

additions method. The results showed

some influence of pH in the extraction

of diacetyl and pentane-2,3-dione in

the tested pH range (from pH 2 to 8); for

methylglyoxal no significant differences

were observed.

The effect of pH on the extraction

of α-dicarbonyls, and other carbonyl

compounds, from complex matrices

such as wine is still poorly understood.

Several discussions about pH influence

on the interaction between carbonyls

and other wine components, and its

impact on the analytical determination

of these compounds are available (30).

However, a more detailed study of

pH-dependent reactions of carbonyls

and their implications on the extraction

is still scarce. Previous works have

already addressed this by studying the

pH effect on the extraction of carbonyls

considering their interaction with sulfites

(29,31,32). However, according to the

results obtained in the present work, other

interactions are also present and can

influence the determination of carbonyl

compounds. For this reason, in this

work pH control of the sample before

the extraction was carefully performed.

A pH of 4 was chosen since it is the

approximate value of the analyzed

samples’ pH.

Methodology Figures of Merit: The

linearity of the methodology was studied

by the establishment of calibration



0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0

WW-1 WW-2 WW-3 WW-4 RW-1 RW-2 RW-3 Rosé



2.0

0.5

1.0

1.5

2.5

3.0

3.5

0

1

2

3

4

5

6

Co

nce

ntr

ati

on

(m

g/L

)C

on

cen

trati

on

(m

g/L

)C

on

cen

trati

on

(m

g/L

)

Methylglyoxal

Diacetyl

Pentane-2,3-dione

Figure 5: Concentrations of the studied α-dicarbonyls on the analyzed samples (WW = white wine; RW = red wine; Rosé = rosé wine). Experimental conditions used are described in the text and Figure 1.

Table 1: Figures of merit of the developed methodology

ParameterCalibration Curve Equation

(y = mx + b)*r

2† LOD (mg/L)‡

Repeatability RSD (%)§

Reproducibility RSD (%)§

Recovery ± RSD (%)§

White Wine

Red Wine

White Wine

Red Wine

White Wine

Red Wine

Methylglyoxal y = (2.85 ± 0.03)x + (0.12 ± 0.08) 0.999 0.008 4.9 2.6 5.0 4.6 111 ± 8 99 ± 2

Diacetyl y = (3.96 ± 0.08)x + (0.22 ± 0.09) 0.999 0.007 7.6 2.6 9.5 3.3 90 ± 5 78 ± 6

Pentane-2,3-dione y = (1.69 ± 0.08)x – (0.02 ± 0.02) 0.993 0.011 4.6 5.5 4.7 8.1 93 ± 4 88 ± 8

*m: slope ± standard deviation (n = 3) expressed in L/mg; b: intercept ± standard deviation (n = 3) expressed in μV s.

†Determination coefficient

‡Limit of detection (LOD) was calculated as the concentration that produced a chromatographic signal equal to three times the

standard deviation of the intercept–slope of the calibration curves.

§RSD: relative standard deviation expressed as percentage of the mean value

curves using model standard solutions

prepared in buffer solution pH 4

covering α-dicarbonyls concentration

ranges normally found in beverages

(methylglyoxal, 0.09–0.5 mg/L; diacetyl,

0.2–2.2 mg/L; and pentane-2,3-dione,

0.04–0.2 mg/L). Each concentration

level was tested in triplicate. Internal

standard (hexane-2,3-dione) was used

to compensate any variability on the

extraction process. The figures of merit

determined for the methodology are

summarized in Table 1.

The influence of the sample matrix on

the extraction procedure was evaluated

by the analysis of two different wine

samples (white wine and red wine). Each

sample was spiked at four concentration

levels (0.1–0.5 mg/L) and analyzed in

triplicate. Average values of recoveries

ranged from 78% to 111%.

The slopes of the standard addition

curves were compared with those of the

calibration curves using the Student t-test

(99% confidence level). In some cases,

significant differences between the

slopes were verified. For this reason, the

standard additions method was used for

the quantification of all α-dicarbonyls in

the wine samples.

The repeatability (intraday precision)

of the methodology was obtained

by the analysis, in the same day, of

five replicates of spiked samples. For

reproducibility (interday precision), five

replicates of the same sample were

analyzed in three different days. The

relative standard deviation (RSD) values

were below 9.5% (Table 1) and were

considered satisfactory for the levels

in which the compounds were found in

samples, showing that the method has

good precision.

Application of the Methodology to

Samples: The developed methodology

was applied to the quantification of the

studied α-dicarbonyls in eight wines

from Portuguese origin. The results are

summarized in Figure 5.

The methylglyoxal content found

in wines was on average 0.7 mg/L,

except for one of the red wine samples

in which a higher concentration was

determined (3.2 ± 0.2 mg/L). The diacetyl

concentration was very distinct in white

and red wines. The obtained values in

white wines (around 1.4 mg/L) were much

lower than the concentrations determined

in red wines (around 5.6 mg/L). In

general, pentane-2,3-dione was the

least concentrated α-dicarbonyl found

in samples with concentration values

ranging from 0.16 ± 0.02 mg/L to 0.62 ±

0.07 mg/L.

ConclusionsA new methodology for the analysis

of three important α-dicarbonyls

(methylglyoxal, diacetyl, and pentane-2,3-

dione) in wines was developed. The

sample preparation procedure involves

the use of SALLE and combines the

extraction and derivatization of the

analytes in the same step. The developed

methodology proved that it is possible

to perform an extraction aided by

derivatization, allowing an increase on the

analytes recoveries. It was also verified

that pH influences the extraction process,

becoming a necessary careful control of

sample’s pH. The figures of merit of the

methodology were evaluated and the

studied α-dicarbonyls were determined in

eight Portuguese wines.

AcknowledgementsThis work received financial support

from FCT/MEC through national

funds and co-financed by FEDER,

under the Partnership Agreement

PT2020 - UID/ QUI/50006/2013

-POCI/01/0145/FEDER/007265. IMV

(SFRH/BPD/111181/2015) wishes to

acknowledge FCT for her postdoctoral

grant funded by the Portuguese Ministry

of Education and Science and by the

European Social Fund within the 2014–

2020 Strategic Framework.

References(1) D. Saison, D. Schutter, F. Delvaux, and F.

Delvaux, J. Chromatogr. A 1216, 5061–5068

(2009).

(2) E.J. Bartowsky and P.A. Henschke, Int. J.

Food Microbiol. 96, 235–252 (2004).

(3) Y. Chen, R. Shirey, and L. Sidisky,

Chromatographia 72, 999–1004 (2010).

(4) E.J.G. Hernandez, R.G. Estepa, and I.R.

Rivas, Food Chem. 53, 315–319 (1995).

(5) G. Zeppa, L. Conterno, and V. Gerbi, J.

Agric. Food Chem. 49, 2722–2726 (2001).

(6) G.D. Revel, L. Pripis-Nicolau, J.-C. Barbe,

and A. Bertrand, J. Sci. Food Agr. 80,

102–108 (2000).

(7) C.-Y. Lo, W.-T. Hsiao, and X.-Y. Chen, J.

Food Sci. 76, H90–H96 (2011).

(8) C.-Y. Lo, S. Li, D. Tan, M.-H. Pan, S. Sang,

and C.-T. Ho, Mol. Nutr. Food Res. 50,

1118–1128 (2006).

(9) M.W. Poulsen, R.V. Hedegaard, J.M.

Andersen, B. de Courten, S. Bügel, J.

Nielsen, L.H. Skibsted, and L.O. Dragsted,

Food Chem. Toxicol. 60, 10–37 (2013).

(10) P. Li, Y. Zhu, S. He, J. Fan, Q. Hu, and Y.

Cao, J. Agric. Food Chem. 60, 3013–3019

(2012).

(11) A. Barros, J.A. Rodrigues, P.J. Almeida, and

M.T. Oliva-Teles, J. Liq. Chromatogr. Relat.

Technol. 22, 2061–2069 (1999).

(12) J.G. Pacheco, I.M. Valente, L.M. Gonçalves,

P.J. Magalhães, J.A. Rodrigues, and A.A.

Barros, Talanta 81, 372–376 (2010).

(13) S.L. McCarthy, J. Am. Soc. Brew. Chem. 53,

178–181 (1995).

(14) W. Bednarski, L. Jedrychowski, E.G.

Hammond, and Z.L. Nikolov, J. Dairy Sci.

72, 2474–2477 (1989).

(15) A.C.D.S. Ferreira, S. Reis, C. Rodrigues, C.

Oliveira, and P.G. De Pinho, J. Food Sci. 72,

S314–S318 (2007).

(16) M. Yamaguchi, J. Ishida, Z.X. Xuan,

A. Nakamura, and T. Yoshitake, J. Liq.

Chromatogr. 17, 203–211 (1994).

(17) OIV, Method OIV-MA-AS315-21.

Compendium of International Methods

of Wine and Must Analysis, International

Organisation of Vine and Wine. Paris,

France, 2013.

(18) M.-l. Bao, F. Pantani, O. Griffini, D. Burrini, D.

Santianni, and K. Barbieri, J. Chromatogr. A

809, 75–87 (1998).

(19) J. Beránek and A. Kubátová, J. Chromatogr.

A 1209, 44–54 (2008).

(20) C. Mathis, M.N. Pons, J.M. Engasser, and

M. Lenoel, Anal. Chim. Acta 279, 59–66

(1993).





France, 2013.

(22) J. Degen, M. Hellwig, and T. Henle, J. Agric.

Food Chem. 60, 7071–7079 (2012).

(23) S. Gensberger, S. Mittelmaier, M. Glomb,

and M. Pischetsrieder, Anal. Bioanal. Chem.

403, 2923–2931 (2012).

(24) M.I. Rodríguez-Cáceres, M.

Palomino-Vasco, N. Mora-Diez, and M.I.

Acedo-Valenzuela, Food Chem. 187,

159–165 (2015).





France, 2013.

(26) I.M. Valente, L.M. Gonçalves, and J.A.

Rodrigues, J. Chromatogr. A 1308, 58–62

(2013).

(27) R.E. Majors, LCGC North Am. 27(7),

526–533 (2009).

(28) M. Anastassiades, S.J. Lehotay, D.

Štajnbaher, and F.J. Schenck, J. AOAC Int.

86, 412–431 (2003).

(29) R.M. Ramos, J.G. Pacheco, L.M.

Gonçalves, I.M. Valente, J.A. Rodrigues,

and A.A. Barros, Food Control 24, 220–224

(2012).

(30) L. Pripis-Nicolau, G. de Revel, A. Bertrand,

and A. Maujean, J. Agric. Food Chem. 48,

3761–3766 (2000).

(31) L.C. Azevedo, M.M. Reis, L.F. Motta, G.O.

Rocha, L.A. Silva, and J.B. de Andrade, J.

Agric. Food Chem. 55, 8670–8680 (2007).

(32) M. Cruz, I. Valente, L. Gonçalves, J.

Rodrigues, and A. Barros, Anal. Bioanal.

Chem. 403, 1031–1037 (2012).

Inês Maria Valente is with the

REQUIMTE/LAQV – Departamento

de Química e Bioquímica, Faculdade

de Ciências, and the Departamento

de Clínicas Veterinárias, Instituto de

Ciências Biomédicas Abel Salazar at the

Universidade do Porto in Porto, Portugal.

José António Rodrigues is with

the Departamento de Química e

Bioquímica, Faculdade de Ciências at the

Universidade do Porto.



CHROMacademy Lite members have access

to less than 5% of our content.

Premier members get so much more !

Ask the Expert

Video Training coursesFundamental HPLC

Fundamental GC

Fundamental LCMS

Fundamental GCMS

HPLC Method Development

GC Method Development

www.chromacademy.com

To find out more about Premier Membership contact:

Glen Murry: +1 732.346.3056 | [email protected]

Peter Romillo: +1 732.346.3074 | [email protected]

The world’s largest e-Learning website for analytical scientists

We are always on hand to help fix your

instrument and chromatographic

problems, offer advice on method

development, help select a column for

your application and more.

?

powered by

SPEMSGCHPLC IR BLS BIO

&OOD�3AFETY�s�&LAVOR�AND�&RAGRANCE�s�1UALITY�#ONTROL

GC-MS and LC-MS

www.gerstel.com

Fully Automated Determination of 3-MCPD and Glycidol in Edible Oils by GC–MS Based on the Commonly Used Methods ISO 18363-1, AOCS Cd 29c-13, and DGF C-VI 18 (10)

Automated determination of 3-MCPD and glycidol in edible oils by GC–MS. An evaporation step helps reach the required LODs using a standard MSD, while removing excess derivatization reagent for improved uptime and stability.

Automated determination of Acrylamide in Brewed Coffee samples by Solid Phase Extraction (SPE)–LC–MS/MS

A manual SPE method used for the determination of acrylamide in brewed coffee was automated. Calibration standards prepared in freshly brewed green (unroasted) coffee produced good linearity and precision.

Characterization of Aroma Compounds in Bread by a 2-Step Multi-Volatile Method (MVM)

A dual step multi-volatiles method (MVM) based on Dynamic Headspace (DHS) analysis provides uniform enrichment of aroma compounds across a wide range of polarities, while eliminating ethanol and water. Bread samples were analyzed.

Analysis of Aroma Compounds in Edible Oils by Direct Thermal Desorption GC–MS Using Slitted Micro-Vials

Hexanal, 2-(E)-nonenal and 2,4-(E,E)-decadienal, edible oil off-flavors derived from unsaturated fatty acid degradation were determined by direct thermal desorption in disposable micro-vials.

Qualitative Analysis of Coconut Water Products Using Stir Bar Sorptive Extraction (SBSE) combined with Thermal Desorption-GC–MS

Flavor compounds, off-flavors, pesticides, antioxidants, and compounds migrating from packaging materials were success-fully determined in coconut water products by stir bar sorptive extraction (SBSE)-TD-GC–MS.

For more information about these and other GERSTEL applications, please go to www.gerstel.com/en/apps-food-beverages.htm

7HAT�CAN�WE�DO�FOR�YOU�

Advances in Food and Beverage Analysisfiles.alfresco.mjh.group/alfresco_images/pharma/...Dec 19,...

Documents

Transcript of Advances in Food and Beverage Analysisfiles.alfresco.mjh.group/alfresco_images/pharma/...Dec 19,...