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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.
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Advances in
Food and Beverage Analysis
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H.J. Cortes Consulting,
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LC Resources, McMinnville, Oregon,
USA
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Pharmaceutical Chemistry,
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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
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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
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Ringwood, Victoria, Australia
Yvan Vander Heyden
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Belgium
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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.
7www.chromatographyonline.com
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
Advances in Food and Beverage Analysis October 20178
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
9www.chromatographyonline.com
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.
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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).
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Sidisky, LCGC North Am. 27, 596–605
(2007).
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Armstrong, Anal. Bioanal. Chem. 389,
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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.
Advances in Food and Beverage Analysis October 201710
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.
11www.chromatographyonline.com
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
12 Advances in Food and Beverage Analysis October 2017
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.
13www.chromatographyonline.com
Ernstberger and Neubauer
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
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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
14 Advances in Food and Beverage Analysis October 2017
Ernstberger and Neubauer
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.
15www.chromatographyonline.com
Ernstberger and Neubauer
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.
Advances in Food and Beverage Analysis October 201716
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
17www.chromatographyonline.com
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.
Advances in Food and Beverage Analysis October 201718
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
19www.chromatographyonline.com
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.
Advances in Food and Beverage Analysis October 201720
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.
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21www.chromatographyonline.com
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.
Advances in Food and Beverage Analysis October 201722
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
23www.chromatographyonline.com
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
Advances in Food and Beverage Analysis October 201724
Valente and Rodrigues
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
25www.chromatographyonline.com
Valente and Rodrigues
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
WW-1 WW-2 WW-3 WW-4 RW-1 RW-2 RW-3 Rosé
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.
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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.
Advances in Food and Beverage Analysis October 201726
Valente and Rodrigues
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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
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