Chapter 12
Chromatographic Technique:
Gas Chromatography (GC)
A.I. Ruiz-Matute, S. Rodrı́guez-Sánchez, M.L. Sanz and A.C. Soria
Instituto de Quı´mica Orgánica General (C.S.I.C.) Juan de la Cierva, Madrid, Spain
Chapter Outline
1 Introduction
2 Theory and Fundamentals
2.1 Chromatographic Flow
2.2 Retention Parameters
2.3 Migration of the Solutes
Through the Column
2.4 Efficiency and Resolution
2.5 The Mobile Phase
2.6 The Stationary Phase
2.7 Columns
3 Instrumentation
3.1 Injection Systems
3.2 Detectors
1
415
416
416
416
417
417
418
418
419
421
421
423
3.3 Multidimensional GC
4 Applications
4.1 Oils and Fats
4.2 Dairy Products
4.3 Spices and Flavors
4.4 Honey
4.5 Fruit Juices
4.6 Coffee
4.7 Wines
5 Conclusions
Acknowledgments
References
Further Reading
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431
434
437
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444
446
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INTRODUCTION
Due to the chemical complexity of foodstuffs and the sophisticated approaches
followed for food frauds, analytical techniques providing high resolution and
sensitivity are required. In this sense, a great deal of methodologies based on
the use of chromatographic techniques [gas chromatography (GC) and highperformance liquid chromatography (HPLC)] have been developed to assure
food authenticity and/or to gain insight into the most common adulteration
practices. In this chapter, we will focus on the theory, fundamentals, and instrumentation of GC, together with the use of this technique to determine the
authenticity of products that are most at risk for food fraud.
Modern Techniques for Food Authentication. https://doi.org/10.1016/B978-0-12-814264-6.00012-8
© 2018 Elsevier Inc. All rights reserved.
415
416 Modern Techniques for Food Authentication
2 THEORY AND FUNDAMENTALS
A GC system comprises two phases: an inert gas called mobile phase and an
immiscible stationary phase (solid or liquid) placed in a column. Analytes
are distributed between the mobile and the stationary phases during the process
depending on their relative affinity for both. Therefore, compounds with higher
affinity for the stationary phase are retained longer and elute later than others.
Although complex mixtures of volatile compounds can be directly analyzed
by GC, derivatization of nonvolatile polar molecules or low thermal stability
compounds is required for their analysis by this technique. The coupling of
GC to mass spectrometry (GC-MS) affords additional structural information
of usefulness to assess food authenticity.
2.1 Chromatographic Flow
Due to the compressibility of gases, the density, pressure, and velocity (u) of the
carrier gas vary in a nonlinear way through the column, and it is therefore necessary to specify at which column point they are measured or calculated.
A compression correction factor ( j) which corrects the mobile phase compressibility in the column is defined as:
j¼
3 ðpi =po Þ2 1
2 ðpi =po Þ3 1
(1)
where pi and po are the inlet and outlet pressures, respectively. pi can be adjusted
in the range of 5–700 kPa, whereas po is usually ambient pressure.
The average linear velocity of the carrier gas (
u) is proportional to the linear
velocity at the column outlet (uo):
u ¼ juo
(2)
In the practice, u is obtained by dividing the column length (L) by the retention time of an unretained compound (the hold-up time, tM):
u ¼ L=tM
(3)
The flow rate at the column outlet (Fc) can be easily measured and related to
the average flow rate through F ¼ jFc . It is also related to linear velocity:
F ¼ kuo ¼ k
u
j
(4)
2.2 Retention Parameters
Retention volume (VR) for a given compound is the volume of mobile phase
passed through the column from the injection of a compound till its detection
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(peak maximum in the cromatogram), or during its retention time (tR). The relationship between these parameters is described by:
tR ¼ VR =Fc
(5)
The net retention volume (VN) takes into account the compression correction
factor and the hold-up time:
VN ¼ jFc ðtR tM Þ
(6)
2.3 Migration of the Solutes Through the Column
When a compound is injected into a chromatographic column, it occupies a
defined zone which starts moving through the column. The function that
describes the distribution of the concentration in the zone is similar to a Gauss
function. If a mixture of compounds is subjected to GC analysis, they are distributed between the mobile and the stationary phase according to their distribution coefficients. Each compound is eluted at a different tR, and therefore they
are separated.
The relationship between the time that the sample component resides in the
stationary phase and the time that it resides in the mobile phase is measured
through the retention factor (k), which represents the extent a compound is
retained in a column.
k ¼ tR =tM
(7)
The relative retention value calculated for two adjacent peaks is also called
separation factor (α) and by definition is always 1:
α ¼ VN2 =VN1 ¼ ðtR2 tM Þ=ðtR1 tM Þ
(8)
2.4 Efficiency and Resolution
The variation of efficiency (H) with the carrier gas velocity is described by the
van Deemter equation (Van Deemter et al., 1956):
H¼A+
B
+ ðCS + CM Þu
u
(9)
where A is the eddy diffusion and B is the longitudinal diffusion. CS and CM
represent, respectively, the contribution of the stationary and the mobile phases
to the zone spreading caused by the resistance to mass transfer and other diffusion phenomena. When an open capillary column is used, the Golay equation
(Golay, 1968) can be used instead:
H¼
B
+ ðCS + CM Þu
u
In most cases, CS is negligible when compared with CM.
(10)
418 Modern Techniques for Food Authentication
The resolution (Rs) between two adjacent peaks is defined as shown in
Eq. (11) and can be easily calculated by measuring the retention times and peak
widths at base (wb) in the chromatogram.
RS ¼
ðtR2 tR1 Þ
2ðtR2 tR1 Þ
¼
ðwb1 + wb2 Þ=2
wb1 + wb2
(11)
Since the main objective of chromatography is resolving mixtures, this
parameter is quite important. Resolution is related to both the column efficiency
(which depends on the column design) and the separation factor (which depends
on the solutes and the stationary phase) through the two following expressions:
pffiffiffiffi
pffiffiffiffiffiffiffiffiffi k α 1
N k α1
and also RS ¼ 1=4 L=H
(12)
RS ¼
4 1+k α
1+k α
where N, the number of theoretical plates, is given by:
N ¼ L=H
(13)
Eq. (12) indicates that substances with similar values of α need high values
of N (i.e., long and efficient capillary columns) to be resolved. This separation
may also be achieved by choosing another stationary phase which provides a
different value of the separation factor.
2.5 The Mobile Phase
Nitrogen, helium, hydrogen, are usually employed as the mobile phase, also
known as the carrier gas. High-purity gases are necessary because traces of
impurities such as oxygen or water can react with the solutes, and spoil the
stationary phase or disturb the detector response.
Helium is recommended to achieve faster separations but nitrogen is a less
expensive alternative. Hydrogen also gives very fast separations but is harmful
and its use as mobile phase requires special care. The choice of carrier gas often
depends on the type of detector to be used.
2.6 The Stationary Phase
The stationary phase may be solid or liquid, giving rise to two main chromatographic modes: gas-solid chromatography (GSC) and gas-liquid chromatography (GLC). The first mode is especially useful for measuring physical
parameters and for the analysis of certain compounds such as permanent gases
or very polar small molecules. The analytical separations of organic substances
are mainly carried out by GLC, thus only this mode will be described here.
Liquid phases are usually polymeric molecules with enough stability and
viscosity to stand on the column at the temperature of analysis, and capable
of dissolving the solutes fast. These polymers are often cross-linked to improve
the phase stability and to extend the column life.
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The separation mechanism is mainly partition, although other mechanisms
like chirality, mesomerism, and complex formation may also contribute to the
separation. For the study of food adulteration, the most interesting are as follows:
– Partition. Compounds dissolved in the liquid phase are in equilibrium with
its vapor (Henry’s law); the elution order may be proportional to the boiling
points when the phase is nonpolar, or very different depending on the activity coefficients when the phase is polar.
– Chirality. Enantiomer pairs always co-elute on achiral phases. However, liquid phases possessing chiral groups can show a greater interaction with one
of the pair members, which is then relatively more retained. As chiral interaction energies are small, α values are usually close to 1 and high efficiency
is required, as is provided by capillary columns. Chiral phases are based on
peptides, amide-substituted polysiloxanes, or substituted cyclodextrins.
The liquids in the stationary phases have to be thermally stable over a wide temperature range. Other properties to be noted are polarity and selectivity. Polarity
depends on the functional groups present in the molecule of liquid phase, and is
usually defined by various empirical scales (e.g., that described by
McReynolds, 1970). Selectivity represents the discrimination power of a phase
toward very similar molecules due to its ability to interact in a specific way with
every solute. It can be estimated by the separation factor α described in Eq. (8).
Polysiloxanes showing a wide polarity range (with methyl, phenyl, trifluoropropyl, or cyanoalkyl substituents), and commonly known as “silicones,” are
the most widely used phases, since they possess good thermal stability and high
permeability toward solutes. Polyethylene glycols with different chain lengths
are another family of liquid phases commonly used for the separation of polar
compounds.
Ionic liquids (ILs) have also been described as GC stationary phases due to
their advantages in terms of low volatility, high thermal stability, low column
bleed, high viscosity, etc. (Poole and Lenca, 2014). The retention properties of
these stationary phases have been described to be the result of a variety of intermolecular forces and have shown to provide a different selectivity over conventional GC columns (Rodrı́guez-Sánchez et al., 2014). Moreover, their unique
and tunable physicochemical properties have promoted the development of
commercial ILs phases with different polarity and application in a number of
different studies (Fanali et al., 2017).
Finally, when addressing separations of compounds with relatively high
molecular weight, phases based on a carborane skeleton (with maximum operating temperatures up to 450°C) are specially recommended.
2.7 Columns
There are two general types of columns: open tubular (also called capillary) and
packed (Fig. 1).
420 Modern Techniques for Food Authentication
(A)
(B)
FIG. 1 Cross section of a wall-coated capillary column (A) and a packed column (B).
TABLE 1 Dimensions of GC Columns
Type
Length (m)
Diameter (mm)
df (μm)
dp (μm)
Capillary
1–100
0.1–0.5
0.1–5
–
Micropacked
2–7
0.8–1
–
80–200
Packed
2–7
2–6
–
80–500
Packed columns consist of a glass or metal tube packed with inert solid
particles (100–250 μm). Analytes are either adsorbed onto the surface of these
particles (GSC) or dissolved in the film of liquid stationary phase coating them
(GLC). The mobile phase circulates through the interstitial channels among particles. Currently, these columns are mainly used for permanent gases analysis,
and they are cheap, robust, and easy to handle.
Regarding capillary columns, the most frequently used GLC columns are
open-tubular column: the stationary phase is distributed on the inner wall,
and the mobile phase circulates through a central channel. The capillary tubing
is usually made of fused silica; this is a fragile material which requires to be
covered externally with a thin layer of polyimide to reinforce columns.
Table 1 presents the typical dimensions of the analytical columns used in GC.
2.7.1 Effect of Column Dimensions on the Separation
Since system efficiency depends on the dimensions of the analytical column,
the columns should be chosen in order to achieve the desired resolution in
the minimum time. Efficiency depends on all column dimensions: length, inner
diameter, and particle size. It is directly related to column length, as shown in
Eq. (13), and inversely related to inner diameter as its square appears in the CM
term of Golay’s equation (10). The efficiency of a packed column is strongly
Chromatographic Technique: Gas Chromatography (GC) Chapter
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421
related to the particle diameter, which appears in two terms (A and CM) of the
van Deemter Eq. (9). The efficiency of open columns is very high, since it is
possible to use very long columns with a moderate pressure drop (between
0.1 and 7 kg cm2).
Capillary columns with the inner wall covered by a porous layer (PLOT) or
by a solid particle layer (SCOT), where the stationary phase is coated, have high
sample load capacity and also high efficiency.
2.7.2 Operating Conditions
The optimization of separations is usually carried out by changing the flow rate
of the carrier gas and the column temperature; other parameters are related to
both injector and detector operation modes.
The flow rate is usually controlled by either a pressure regulator or a flow
controller. For a given compound, Eq. (5) predicts that tR is inversely proportional to the flow rate. This should be adjusted in order to achieve a linear velocity close to the van Deemter optimum (Eq. 9); higher flow rates will reduce the
analysis time but will be associated with a loss of efficiency (higher H values).
The influence of temperature on retention time is very high. When temperature is raised, both VR and tR decrease. In practice, temperature operation
should be adjusted to obtain a retention factor (k) between 1 and 15. When a
mixture contains compounds with a high span of volatility, it is not possible
to find an optimum temperature to elute all components in a reasonable time.
Since oven temperature is easily changed, it can be programmed during the
chromatographic run and thus each compound will be analyzed under the
adequate conditions.
3
INSTRUMENTATION
As shown in Fig. 2A a gas chromatograph is constituted by three main parts:
(i) the injector to introduce and volatilize the sample; (ii) the oven where the
chromatographic column is placed; and (iii) the detector which provides a signal
for each of the compounds previously separated in the column. This part of the
chapter summarizes the main characteristics of the most commonly used injection systems and detectors. Instrumentation of multidimensional GC (MDGC)
is also described as being one of the greatest advances of this technique in the
last few years (Marriott et al., 2012; Tranchida et al., 2016; Prebihalo
et al., 2018).
3.1 Injection Systems
The injector port is a hot chamber containing a glass liner where the compounds
are volatilized and swept by a stream of inert carrier gas into the column. This
chamber is closed by a rubber or microseal septum which is perforated by the
needle of a syringe to introduce the sample.
422 Modern Techniques for Food Authentication
b
a
e
d
f
c
(A)
e
b
a
g
f
d
h
i
c
(B)
FIG. 2 Schematic of a GC (A) and a GCGC system (B): (a) gas container; (b) injector; (c) oven;
(d) first dimension column; (e) detector; (f ) acquisition system; (g) modulator; (h) second dimension
column; and (i) second oven.
The most common injectors are split/splitless. In the split mode, there is an
open valve which allows the carrier gas to be split into the column and to the
split vent. The band broadening due to the differences in the column and glass
liner diameters is reduced by opening the split valve. In the splitless mode, the
valve is closed during the splitless time to introduce a higher amount of sample
and is opened again after this time. To avoid band broadening, the temperature
of the oven is decreased to concentrate the compounds at the head of the column. When the valve is opened, the oven temperature is rapidly raised according to the chromatographic program.
In the programmed temperature vaporizer (PTV) injector, the sample is
introduced into a chamber provided with a trap, the temperature of which
is progressively raised. As evaporation and venting through the split line of
the solvent takes place, preconcentration of sample components is achieved.
Therefore, PTVs are very useful for large volume injections of very diluted
samples.
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423
In on-column injection, the sample in liquid state is directly introduced into
the column by a thin (0.3 mm) injection needle, without vaporization or flow
split. The on-column injectors require the use of capillary columns with internal
diameter within the range of 0.2–0.5 mm. This injection mode avoids discrimination and minimizes degradation of thermolabile compounds; however, the
samples to be analyzed must be clean and free of nonvolatile constituents.
It is also worth mentioning the evolution taken place during the last few
years in the development of external systems coupled online to the gas chromatograph for the automated sample injection (Soria et al., 2015). Autosamplers provided with a microsyringe are commonly used nowadays for the
analysis of gas or liquid samples with high reproducibility and throughput.
However, the coupling of several volatile isolation techniques such as thermal
desorption (TD), solid-phase microextraction (SPME), purge-and-trap (P&T),
etc., has also become of great importance for the automated and solvent-free
analysis of liquid or solid food samples.
The TD involves sample heating without solvent in a flow of carrier gas to
isolate volatile compounds from the matrix and their subsequent cryogenic trapping in an adsorbent (e.g., Tenax) trap, which is in turn heated to release the
volatiles into the transfer line (connection between the TD unit and the column).
The SPME is a sample-preparation technique developed in the early 1990s
by Arthur and Pawliszyn (1990), in which a fiber coated with an extracting
phase is used to isolate volatiles from liquid or gas phases. After extraction,
SPME fiber is transferred to the injection port of a gas chromatograph where
desorption of the analytes takes place.
In P&T, an inert gas is bubbled through a liquid sample to isolate the volatile
compounds from the matrix. These volatiles are trapped on an adsorbent (or
concentrator), usually at ambient temperature, which is then heated to release
the isolated compounds into the carrier gas stream. Samples requiring volatile
isolation and preconcentration prior to GC separation can be introduced via
such a system.
3.2 Detectors
A large number of detectors are used in GC. Universal detectors are those sensitive to a wide range of components present in a variety of concentrations.
Among them, the flame ionization detector (FID) and the thermal conductivity
detector are the most common. However, there are other selective detectors
widely used in analytical laboratories for the analysis of specific substances.
These include the electron capture detector (ECD), nitrogen phosphorus detector, flame photometric detector, photoionization detector, pulsed
discharge ionization detector, etc.
The greatest advances in GC detectors arise from the use of those which provide additional structural information. That is the case of mass spectrometry
(MS), infrared (IR) and nuclear magnetic resonance (NMR) based detectors.
424 Modern Techniques for Food Authentication
The FID and MS are by far the most commonly utilized detectors for the
determination of food authenticity. In FID, the flame is produced by the combustion of hydrogen in air. A voltage of 100–300 V is applied between the flame
and an electrode, which produces a low current. When the organic compounds
moving with the carrier gas reach the flame, they are ionized, and an increase in
the current is then measured. This detector is very sensitive and has a wide linear
range.
The MS detector gives a measurement of the mass to charge (m/z) ratio of
the produced ions, with a characteristic plot (mass spectrum) being obtained for
each compound. A MS system is composed of three main parts: the ion source
which produces the ions from each compound; the mass analyzer to separate the
ions of different m/z ratios; and the detector which collects the ions. Electronic
impact and chemical ionization (CI) are the operation modes of the ion sources
used for GC analysis. Currently, there is a large number of MS analyzers [quadrupole (Q), ion trap (IT), time-of-flight (ToF), Q-ToF, triple quadrupole (QqQ),
etc.] that can be coupled to GC, but Q analyzer is the most utilized.
3.3 Multidimensional GC
The MDGC appeared due to the necessity of separating complex mixtures of
volatile compounds which cannot be resolved by only one column (dimension).
There are two kinds of MDGC: the so-called multidimensional GC or heart-cut
GC (GC-GC) and comprehensive two-dimensional GC (GCGC).
In GC-GC, two columns with different stationary phases are connected by
a valve which allows the introduction of a selected fraction (heart cut) of the
eluate from the first column (first dimension) into the second one (second
dimension) for its further separation.
In GCGC the whole sample is separated into two columns (generally
placed on two different ovens) and this separation should be as much
independent (orthogonal) as possible (Fig. 2B). The separation of the analytes is usually carried out using a nonpolar GC column (typically
15–30 m 0.25–0.32 mm 0.1–1 μm) in the first dimension (1D) and a short
polar column (typically 0.5–2 m 0.1 mm 0.1 μm) in the second dimension
(2D); analytes are therefore separated according to their volatility in 1D and
their polarity in 2D. However, the reverse combination can also provide
good results, depending on the compounds to be separated. In GC GC,
the two columns are coupled by an interface or modulator which focuses
the effluent of the first column in four to six narrow peaks and inject them
periodically onto the second capillary column for a very rapid separation.
There are different types of modulators: thermal, cryogenic, and valvebased modulators (Semard et al., 2009). The most widely used are the cryogenic jet systems, which retain the analytes using liquid CO2, expanding
gaseous CO2 or nitrogen cooled to 180°C. The injection into the second
dimension is carried out by switching the modulator off and eluting the
Chromatographic Technique: Gas Chromatography (GC) Chapter
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425
compounds with the oven temperature or by using a pulse of hot air. The
increase in sensitivity provided by the modulation process, together with
the high-resolution power of GCGC make this technique advantageous
over 1D GC for challenging food authentication assessments.
Detectors to be used in GCGC are conditioned by the required rapid separation in the second dimension. It means that the internal volume has to be
small and the data acquisition rate high. The FID and ECD are used, however,
couplings with MS (mainly with Q and ToF analyzers) provide greater advantages in terms of compound identification. In the last few years, advances in the
MS field exceed those experimented by GCGC and couplings with other MS
analyzers have emerged (i.e., QqQ, isotope ratio, Q-ToF, etc.) (Tranchida
et al., 2016).
4
APPLICATIONS
4.1 Oils and Fats
The authentication of edible oils and fats is one of the fields where GC first
started to achieve excellent results. They are mainly constituted by saturated
and unsaturated fatty acids (FAs) (from C12 to C22) esterified with glycerol
forming triacylglycerols (triglycerides, TAGs), and small amounts of sterols
(free and esterified with FAs), terpenic alcohols, hydrocarbons, vitamins,
etc. Since biosynthetic pathways in every animal or vegetal species are different, distinction of their fats and oils is possible by means of qualitative and
quantitative differences in these compounds. Expensive products such as cocoa
fat, olive, and almond oils are occasionally added of cheaper fats with analogous
physical properties. Similarly, the addition of pork fat to tallow is forbidden in
certain countries, whereas addition of tallow to lard is considered to be adulteration in others. Moreover, untreated oils or fats might be mixed with refined
oils, and products with protected denomination of origin (PDO) replaced with
alternatives. The analysis of the composition of FAs, TAGs, sterols, and other
minor compounds are currently carried out by GC and cover almost all the
lipidic components.
4.1.1 Fatty Acids
The FA composition is very easily determined by GC; usually a previous
conversion of glycerides into more volatile compounds is necessary. Fatty acid
methyl esters (FAMEs) are the most popular derivatives used for FA analysis,
since they are volatile enough to be separated on most GC columns. This subject
has been extensively revised by different authors (Aldai et al., 2005; RuizRodrı́guez et al., 2010). Free fatty acids (FFAs) can be methylated by acid catalysis in a methanol solution, using reagents such as boron trifluoride, HCl,
H2SO4, or diazomethane. The TAGs can be previously hydrolyzed in acid
medium to obtain FFAs, which in turn will be methylated as described above.
426 Modern Techniques for Food Authentication
However, the easiest methods for the analysis of these compounds are those
based on the direct transesterification in methanol with a basic catalyst such
as sodium methoxide, potassium hydroxide, or tetramethylammonium hydroxide. Basic catalysis has been described to be faster than acidic catalysis. However, alkali catalyst reactions require strict anhydrous conditions, a requirement
which might be difficult to fulfill depending on the sample under study. Different improvements in derivatization protocols have been reported, which mainly
differ in terms of catalyst reagent, derivatization time, and temperature (RuizRodrı́guez et al., 2010; Salimon et al., 2017).
The FAME analysis using packed columns coated with polar phases has
afforded (among others) the detection of oils and fats adulterations (Kapoulas
and Passaloglou-Emmanouilidou, 1981; Mariani and Bellan, 1997). Although
these methods can still be used, nowadays FAMEs are better separated on capillary columns coated with polar phases; those based on cyanoalkyl silicones in
particular are preferred when unsaturated isomeric FAs are present (RuizRodrı́guez et al., 2010). Recently, the use of IL columns has been proposed
for GC FAME analysis in oils of different nature (Fanali et al., 2017). The additional structural information provided by MS detectors is very useful for the
detection of very sophisticated frauds. As an example, Saba et al. (2005) used
GC coupled to acetonitrile CI MS and CI MS/MS for the detection of low-quality
deodorized olive oil in virgin olive oil. Moreover, multivariate statistical analysis
of GC data has been described as a helpful tool for the characterization and
authentication of edible fats and oils (Faria-Machado et al., 2015).
Currently, these methodologies can be improved by the use of capillary
columns with low internal diameter (close to 0.1 mm), which give similar resolution in a few minutes, saving time and carrier gas. The use of fast GC has
been proposed by Mondello et al. (2004a) as a convenient approach to detect
adulterations of nine different fats. The MDGC is also a useful tool for FAME
analysis. The differentiation of lard from other commonly animal-derived fats
was successfully achieved by the study of FAME profiles by GCGC-ToF MS
(Indrasti et al., 2010).
4.1.2 Triacylglycerols
The number of TAGs in natural fats is very high. An oil sample consisting of only
five different FAs may give 53 different triacylglycerols. However, this distribution is not random; FAs are esterified in the three positions of glycerol in a stereospecific way which is characteristic of every species. Thus, the information
obtained from TAG composition is much more complex and specific than that
supplied by the FA composition. This fact has been used to detect the presence
of transesterified fats in natural fats, using a stereospecific lipase to cleave the
FAs esterified in a specific position and analyzing FAMEs in this fraction.
Dourtoglou et al. (2003) analyzed different vegetable oils: total FAs and their
Chromatographic Technique: Gas Chromatography (GC) Chapter
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427
regiospecific distribution in positions 1 and 3 were determined using a specific
lipase from Mucor miehei; resulting data were subjected to principal component
analysis (PCA). It was possible to distinguish pure oils from mixtures and
discriminate between different types of seed oils used for adulteration.
Currently, the analysis of intact triacylglycerol affords valuable information
to characterize the purity of fats. This analysis was at first developed by HPLC,
but the use of high-temperature gas chromatography (HTGC) with short GC
capillary columns (<5 m) coated with thin films of thermally stable phases
allows very good results (Ruiz-Samblás et al., 2015). Several temperatureresistant medium polarity capillary columns have been used for TAG analysis
of different oils and fats (Ruiz-Samblás et al., 2015; Indelicato et al., 2017).
Moreover, the use of carborane-based stationary columns (which can afford
maximum temperatures up to 480°C) has also been reported for the detection
of adulterated olive oil mixed with soybean oil by using TAG profiles and
PCA (Park and Lee, 2003).
Triacylglycerol analysis requires a very careful sample introduction, since
their boiling points are very high and it is necessary to completely vaporize
the mixture, thus avoiding both thermal decomposition and discrimination by
molecular weight. The most used injection modes are on-column and PTV
(Ruiz-Samblás et al., 2015).
Among others, TAG analysis has been successfully used for the detection of
fraudulent additions of seed oils to olive oil (Antoniosi Filho et al., 1993;
Andrikopoulos et al., 2001) and for the detection of cocoa butter equivalents
(vegetable fats similar to cocoa butter) added to genuine cocoa butter or to
chocolate products (Simoneau et al., 1999; Buchgraber et al., 2004).
4.1.3 Sterols
The analysis of sterol fraction by GC was started in the 1960s as a method to
detect animal fats in vegetable oils or vice versa (Mordret, 1968). The method
required a long sample preparation time, with saponification, solvent extraction,
and thin-layer chromatography (TLC) fractionation. Several approaches omitting TLC have been attempted to find a faster and easier method such as direct
GC analysis of the unsaponifiable fraction (Giacometti, 2001), or the use of
gel-permeation chromatography (Carstensen and Schwack, 2002).
The classical methods for GC analysis of sterols require the use of packed
columns coated with nonpolar stationary phases and working at high temperature (about 270°C). Packed columns have been replaced by capillary columns
which afford better separation of isomeric sterols; a thermostable polar capillary
column (RTX-65) was used by Al-Ismail et al. (2010) for the separation of the
different sterols present in vegetable (olive, corn, soybean, sunflower, and
cottonseed) oils. The developed methodology allowed detection of additions
of 5% of the different plant oils to olive oil based on campesterol and
428 Modern Techniques for Food Authentication
stigmasterol content. Jabeur et al. (2014) used an HP-5 (5% phenyl; 95%
dimethylpolysiloxane) capillary column to identify adulterations of Chemlali
extra-virgin olive oils with sunflower oil (by the increase of Δ7-stigmastenol)
and with corn oil (by the increase of campesterol).
The coupling of liquid chromatography to GC (LC-GC) is an interesting
way to simplify the gas chromatographic analysis of minor components. Adulteration of olive oils may be carried out using desterolized sunflower oil, where
the indicator compound (a Δ7-sterol) used for their detection has been removed.
However, it has been proven that Δ7-sterols are isomerized to Δ8(14)- and Δ14sterols during the process and, therefore, the LC-GC analysis of these marker
compounds can be used to detect this kind of olive oil adulterations
(Biedermann et al., 1996).
The analysis of sterols is also useful for detecting admixtures of processed
fats in natural fats. Bleaching of oils during refining results in the dehydration of
sterols to give hydrocarbons, which are not present in the original fats and,
therefore, can be considered as indicators of industrially refined oils. They have
been useful for detecting refined fats added to chocolate (Crews et al., 1997) and
for refined oils added to virgin oils (Cert et al., 1994).
4.1.4 Other Minor Compounds
Triterpenic alcohols and tocopherols are characteristic compounds of the different vegetable species. Their analysis is carried out in a similar way to sterols;
thus, a fractionation step by TLC or on silica columns prior to GC analysis is
required. Triterpene alcohols allowed the detection of sal fat, shea fat, and illipe
butter in cocoa butter (Soulier et al., 1990) and the detection of virgin olive oil
adulteration with 5% olive pomace oil (Ntsourankoua et al., 1994). Erythrodiol
and uvaol have been described as useful markers for the geographical differentiation of Tunisian virgin olive oils (Ouni et al., 2011), while tocopherol was
used for the authentication of sesame seed oils (Aued-Pimentol et al., 2006).
Lupeol and an unknown compound containing a lupane skeleton were also
detected (after saponification, extraction, TLC, and silylation) in the 4,40 dimethylsterol fraction of hazelnut oils; both compounds proved to be good
markers for detecting <4% of hazelnut oil in olive oil (Damirchi et al., 2005).
4.1.5 Volatiles
Volatile analysis has been scarcely used for the purpose of detecting adulterations in oils, but in those cases where the composition of FAs and sterols is very
similar, their study could be a useful alternative. Different volatile compounds
have been proposed as markers of adulterations such as α-pinene, used for the
detection of an admixture of sunflower oil with poppy seed oil in relevant (5%–
40%) amounts (Krist et al., 2006) or filbertone (E-5-methylhept-2-en-4-one),
proposed for detecting the addition of hazelnuts oil to olive oil (Blanch
et al., 1999).
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
429
Headspace (HS) coupled to GCGC-ToF MS has been recently applied for
the detection of adulteration of sesame and peanut oil with soybean oil (Zhao
et al., 2013). The multivariate statistical analysis of volatile flavor compounds
allowed the discrimination between the three types of pure vegetable oil samples under study, and distinguishing sesame oils and peanut oils adulterated
with 5% and 10% of soybean oils.
4.2 Dairy Products
The analytical methods used to determine the authenticity and to detect adulterations of dairy products have been recently reviewed (Kamal and Karoui,
2015). Adulterations may be carried out by substituting part of the fat or protein;
adding low-cost dairy products (mainly whey derivatives); mixing milk of different species; mislabeling products protected by PDO; and selling conventional milk as organic. The GC is especially well suited for detecting foreign
fat in milk fat and for distinguishing between organic and conventional milk
through fat analysis; it has also been used for the authentication of cheeses
and to detect butter adulterations (Kim et al., 2014; Naviglio et al., 2017).
4.2.1 Foreign Fat in Milk Fat
The major adulterants of milk fat are vegetable oils (soybean, sunflower,
groundnut, coconut, palm, and peanut oil) and animal fat (cow tallow and pork
lard) (Trbovi et al., 2017). The GC methods vary depending on the compounds
to be determined as potential adulteration markers.
FAMEs
The methodologies used for FAME analysis are those described above for fats
and oils; nevertheless, it is necessary to take into account the special composition of ruminant milk fats, which contain relevant quantities of short-chain FAs,
as well as a very complex mixture of cis- and trans-unsaturated acids with
18 carbons. Thus, FAME analysis requires the use of long, very polar columns:
formerly polyester-based stationary phases were used, but nowadays cyanoalkyl silicones within 30–60 m in length are recommended (Derewiaka
et al., 2011). A programmed temperature is always necessary, starting at low
temperature (about 70°C) to separate methyl butyrate from methanol and other
solvents. Separation of all the isomers of oleic and linoleic acid (which have
nutritional relevance, and can also help to characterize the origin of dairy products) is very difficult to accomplish. Fortunately, detection of foreign fats
usually does not require a complete separation of these isomers. More recently,
the use of IL-coated capillary GC columns has been recommended for the analysis of FAMEs. Commercial SLB-IL111 capillary column (100 m 0.25 mm,
0.2 μm thickness; Supelco) showed advantages over cyanopropyl siloxane columns for the analysis of mixtures containing geometric and positional isomers
430 Modern Techniques for Food Authentication
of FAMEs (Delmonte et al., 2011, 2012; Yoshinaga et al., 2014). De la Fuente
et al. (2015) has also proposed the complementary use of SLB-IL111 and
cyanoalkyl polysiloxane stationary phases for the analysis of FAMEs in milk
samples.
Concentration ranges of the major FAs and certain FA ratios to identify foreign fats in mixtures with milk fat were tested (Ulberth, 1994). Linear discriminant analysis was successful in distinguishing pure from adulterated milk fats;
detection limits were between 3% and 10%. Nevertheless, the natural variability
of milk fat makes this approach useless when the level of adulteration is low.
Thus, addition of vegetable fats mainly relies on sterol analysis, whereas
addition of animal fats is better detected by TAG analysis.
TAGs
The TAG analysis of milk fat displays a complex profile, which has not been
entirely resolved either by GC or by HPLC. The GC method developed by
Precht (1991) became the EU’s official method to detect foreign fat adulterations. It was based on the use of packed columns which only allowed the separation of TAG families. This method was updated using capillary columns and
at present the resolution achieved by GC is enough to resolve most adulterations
with foreign fat (Molkentin, 2007; AOCS, 2009; Kail et al., 2012). The HTGC
(separations above 300°C) with FID is also used to determine TAGs in milk and
milk products (ISO 17678 IDF 202, 2010; Ruiz-Samblás et al., 2015) and
methods based on this technique have been successfully applied to detect milk
and butter adulterations (Povolo et al., 2008; Naviglio et al., 2017). Mixtures of
fats from milks of different mammal species (Goudjil et al., 2003; Smiddy et al.,
2012) and adulterations with nonmilk fats such as palm, peanut, fish, corn oils,
etc. (Goudjil et al., 2003; Gutierrez et al., 2009) can also be detected by GC.
Authenticity of different milk-based products such as ghee-based sweets has
also been evaluated by the GC analysis of TAGs (Amrutha Kala et al., 2016).
Sterols
Detection of vegetable oils in milk fat is easy by sterol analysis (Chmilenko
et al., 2011), since the main compound in the latter is cholesterol, and only small
quantities of minor precursors (desmosterol, lathosterol, and lanosterol) have
been reported.
Detection of vegetable fats is indeed standardized (IDF, 1992). As indicated
above the preparation method requires saponification of fat, extraction of unsaponifiable fraction, and TLC fractionation to obtain a pure sterol fraction, which
is then injected in GC; this is a long and tedious procedure. Several methods
have been proposed to simplify this: some of them propose to suppress TLC
fractionation and to analyze free sterols (Precht, 2001) or their trimethylsilyl
(TMS) derivatives (EU, 1992; Derewiaka et al., 2011). Another procedure,
useful for testing the purity of milk fat, is to convert triacylglycerols into their
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
431
methyl esters (one-pot reaction) and to analyze the reaction mixture, since sterols elute after FAMEs (Alonso et al., 1997). A faster but more expensive
method is the use of LC-GC coupling (Kamm et al., 2002).
Volatiles
Volatile compounds determined by GC have been proposed for the authentication of cheeses: as an example, high mountain cheeses have a higher level of
terpenes than those manufactured with milk from animals grazing at the plain
or in a farm (Mariaca et al., 1997). Volatiles have also been described to be useful for distinguishing the thermal treatment undergone by milk (Contarini
et al., 1997).
4.2.2 Differentiation of Organic Milk and Conventional Milk
Organic milk is produced following “organic farming methods” and its price is
considerably higher than that of conventional one. Therefore, the selling of conventional milk as organic one is a fraud which should be controlled (Kamal and
Karoui, 2015). The GC analysis of both organic and conventional milks
revealed that both types of milk showed a similar content of saturated FAs.
However, organic milk samples had higher concentrations of polyunsaturated
FAs, α-linonenic acid, and branched FAs (Collomb et al., 2008; Molkentin,
2009; Butler et al., 2011). The use of these compounds as authentication
markers of organic milks has been proposed.
Recently, controversy has arisen about the use of phytanic acid (3,7,11,15tetramethylhexadecanoic acid) as a potential marker to differentiate organic and
conventional milks. Vetter and Schr€
oder (2010) observed that concentrations of
this acid were significantly higher in organic over conventional dairy products
(milks, cheeses, and butter) from the German market. A target value of 200 mg
of phytanic acid/100 g lipids was suggested to guarantee authenticity of organic
dairy products. Schr€
oder et al. (2012) found that organic milks showed higher
phytanic acid and a lower ratio of SRR/RRR-diastereomers of phytanic acid
than conventional milks. However, Capuano et al. (2014) did not find significant differences in phytanic acid concentrations between bovine organic and
conventional milks, while the diastereomers ratio could be a good marker to
differentiate these products.
4.3 Spices and Flavors
Spices and flavors, traditionally used to impart an appealing aroma and to mask
off-flavors in food, are expensive additives, and therefore have been frequently
targeted for adulteration. Main fraudulent practices involve mixing spices with
other less valuable natural substances (e.g., other plant parts, inexpensive
varieties, etc.), whereas essential oils are usually adulterated with synthetic
components. Two different approaches are generally followed for authenticity
432 Modern Techniques for Food Authentication
assessment: (i) the search for specific markers of the adulterant, and (ii) the
study of differences in the analytical fingerprints gathered for authentic and
adulterated samples.
Solvent extraction, steam distillation, micro-simultaneous distillationsolvent extraction, etc., have been traditionally used for the isolation of the
aroma of spices (Tarantilis and Polissiou, 1997; Bononi et al., 2010); their main
handicaps are the degradation of thermolabile components and the overenrichment of the extracts obtained in high boiling-point constituents. In an attempt
to overcome the limitations of these traditional methods in terms of speed,
efficiency and requirement of relatively large solvent volumes, advanced
extraction techniques such as ultrasound-assisted extraction, supercritical fluid
extraction, etc., have also been described for the authentication and quality control of species and plant sources of food aromas (Flores et al., 2005; Anastasaki
et al., 2009; Bononi et al., 2015).
The HS techniques, although biased toward the fractionation of highvolatility components, do not require the use of organic solvents and minimize
artifacts, with advantages in reproducibility being obtained if they are automated (Garcı́a and Sanz, 2001). Online coupling of TD to GC-MS (TD-GCMS) has been applied, among others, to determine the authenticity of Spanish
saffron (Crocus sativus L.) (Alonso et al., 1998).
The GC has been extensively used for the detection and identification of
adulterations of essential oils (Bonaccorsi et al., 2012; Do et al., 2015; Ng
et al., 2016). Spencer et al. (1997) reported high concentrations of isopulegol
and another compound, tentatively identified as neoisopulegol, as indicators
for the detection of cornmint oil (from Mentha arvensis) in the more highly
priced peppermint (Mentha piperita) oil. GC-olfactometry (GC-O) and
aroma-extract dilution analysis (AEDA) have also been applied to analyze
differences in composition and odor potency of unrectified Yakima peppermint
oil and a dementholized Chinese cornmint oil (Benn, 1998).
Due to the economic importance of authentication and/or classification of
essential oils, more complex approaches based on the combination of data gathered by different analytical platforms have been developed. As an example,
Mehl et al. (2014) evaluated the potential of volatile fraction analyzed by
GC-FID/GC-MS and Fourier transform-middle infrared spectrometry (FTMIR), and that of data for nonvolatile residues collected by FT-MIR, 1H nuclear
magnetic resonance (1H NMR), and ultrahigh-performance liquid chromatography coupled to mass spectrometry (UHPLC-ToF MS) for the classification
of cold-pressed lemon oils. Successful data treatment in this metabolomic
approach allowed the discrimination of samples under study according to their
origin of production or extraction process.
A different approach for the assessment of authenticity involves GC analysis
of the enantiomeric distribution of chiral compounds in both authentic and
adulterated samples, with modified cyclodextrins being the stationary phases
of choice for most applications (Chanotiya and Yadav, 2009). Coleman and
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
433
Lawrence (2000) developed a fast, sensitive, and miniaturized SPME-GC-MS
method for the analysis of the enantiomeric excess of chiral monoterpenes in
peppermint, spearmint, and rosemary (Rosmarinus officinalis) essential oils
from different origin. Changes in enantiomeric patterns were used to ascertain
the origin and authenticity of these oils. Similarly, the enantiomeric ratios of
(3S)-(+)-linalool and (3R)-()-linalool determined by chiral GC-FID/GC-MS
have also been proposed by Chanotiya and Yadav (2009) for the authentication
of various essential oils of Indian origin.
Despite its high resolution power, monodimensional GC sometimes proves
to be insufficient for the complete separation of complex essential oils, with
MDGC affording advantages over 1D GC in terms of resolution and sensitivity.
Bonaccorsi et al. (2011) reported the enantiomeric distribution of fourteen
chiral volatiles in essential oils of different Citrus species by GC and, in the case
of complex samples, by MDGC using a chiral column in the second dimension.
The MDGC allowed the separation of camphor and citronellal isomers, otherwise not separated by 1D GC, and to determine for the first time the enantiomeric excess of camphene, α- and β-phellandrene in lime essential oil.
The requirement of additional procedures to isolate, and in some cases, to
preconcentrate the compounds of interest, together with the possibility of
unwanted changes in the enantiomeric ratio associated with these preparation
procedures, are limitations to be taken into account in the analysis of chiral compounds. In this sense, online coupling of reversed-phase liquid chromatography
(RP-LC) to GC-MS has been described by Barba et al. (2012) as a useful integration of sample preparation and analysis for the determination of the enantiomeric composition of target chiral compounds in essential oils.
On the other hand, the previously described advantages provided by
GC GC have also been explored with a view to authenticate different essential
oils such as those from peppermint and spearmint (Mentha spicata) (Dimandja
et al., 2000) and from Citrus (Dugo et al., 2005).
Online coupling of GC with isotope ratio mass spectrometry (GC-IRMS)
has also gained importance in authenticity control of flavors. Combination of
isotopic data [stable isotope ratios for carbon (δ13C), nitrogen (δ15N), and
hydrogen (δ2H)] with gas chromatographic data for characteristic aroma compounds has been reported for origin-specific analysis and authenticity control of
mandarin (Faulhaber et al., 1997), lemon (Citrus limon), and lemongrass (Cymbopogon winterianus) (Nhu-Trang et al., 2006a) essential oils. Bonaccorsi et al.
(2012) compared the results obtained by multidimensional enantio-GC-mass
spectrometry and GC-IRMS for the genuineness assessment of lime oils. The
simultaneous use of these two techniques provided advantages for the reliable
detection of subtle adulterations of Citrus essential oils, otherwise not detected
by any of these single techniques. More recently, Pellati et al. (2013) also
described the use of GC-combustion-isotope ratio mass spectrometry (GC-CIRMS), in combination with GC-MS and GC-FID, for authentication of commercial Rosa damascene Mill. essential oil. The unusual δ13C values of geranyl
434 Modern Techniques for Food Authentication
acetate determined in most of the essential oils were proposed as markers of the
undeclared addition of a natural cheaper oil from a C4 plant (Cymbopogon martinii, palmarosa). Finally, GC-pyrolysis-isotope ratio mass spectrometry
(GC-Py-IRMS) has also been considered for studies on authenticity of a variety
of essential oils (Nhu-Trang et al., 2006b).
4.4 Honey
Honey is a natural substance produced by honey bees from the nectar of plants
or from the secretions that are found over them. Its composition is variable and
depends on numerous factors—floral type, climate, processing and storage
conditions, etc.
Honey has been reported to have different biological properties, together
with a high nutritional value and a characteristic flavor (Siddiqui et al.,
2017). Taste and aroma, the two most significant attributes of honey, contribute
to honey quality and may also help to determine its authenticity. As honey is a
product of limited supply and relative high price, its quality assurance becomes
extremely important. Honey fraud can involve the addition of industrial sugar
syrups, cheaper or lower quality honey, or its sale under a false or doubtful
labeling (Trifkovic et al., 2017). Corn syrups (CS), invert syrups (IS) obtained
by acidic or enzymatic hydrolysis from refined beet or cane sugar, high fructose
corn syrups (HFCS) mainly produced by enzymatic hydrolysis and isomerization of corn starch, high fructose inulin syrups (HFIS) from partial enzymatic
hydrolysis of inulin, and rice syrup are the most common sweeteners that can be
added directly to honey after harvest or fed to bees during the harvest to improve
yield (Wu et al., 2017).
Recently, several reviews have been focused on the most common honey
frauds and adulterations as well as the different analytical methodologies used
for their detection (Pita-Calvo et al., 2017; Soares et al., 2017; Siddiqui et al.,
2017;Wu et al., 2017; Trifkovic et al., 2017).
4.4.1 Volatiles
The GC profile of honey volatiles represents a “fingerprint” of the product
which can be used to determine its floral origin. The GC analysis of honey
requires a prior stage to isolate the volatiles from the sugar matrix, and if possible, to preconcentrate them. Several studies have been reported on optimization and application of different techniques for this purpose such as solvent
extraction (Rowland et al., 1995), simultaneous steam distillation-extraction
(SDE) (Bouseta and Collin, 1995), P&T (Soria et al., 2008), etc. Among them,
SPME has become one of the most used techniques for the isolation of honey
volatiles because of its advantages in terms of speed, easy to use, affordability,
etc. (Soria et al., 2003, 2009; Plutowska et al., 2011; Chen et al., 2017).
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
435
The botanical origin of honey has been extensively addressed by GC analysis of its volatile composition. Although certain compounds have been suggested as markers of honeys from a specific floral source (e.g., acyloins for
eucalyptus honeys; De la Fuente et al., 2007), the natural variability of honey
makes difficult to find a reliable marker for botanical discrimination, as volatiles not only depend on floral origin but also on other factors such as geographical origin, collection season, storage conditions, etc. In fact, different honey
markers for the same floral origin have been reported or even the same volatile
compound has been suggested as a characteristic constituent for honeys of different floral origin (Kaškoniene and Venskutonis, 2010, Soares et al., 2017).
Thus, most of the studies consider groups of compounds rather than single
volatiles as indicators of floral origin (Guyot et al., 1998; Castro-Vázquez
et al., 2009). Recently, Chen et al. (2017) have proposed a methodology for
the botanical differentiation and classification of honeys based on the nontargeted volatile profiles obtained by SPME-GC-MS in combination with chemometrics. In this study, volatiles of 87 honeys from four different floral origins
were isolated using a divinyl benzene/carboxen/polydimethylsiloxane (DVB/
CAR/PDMS)-coated fiber and separated on a HP-5 MS capillary column.
Chemometric methods were used to establish prediction models that allowed
honey botanical classification with 100% accuracy.
Although different studies have been focused on searching appropriate
volatile markers to determine the geographical origin of honey (Kaškoniene
and Venskutonis, 2010), establishment of fully validated markers for sample
classification is yet to be achieved.
A common practice of honey adulteration is the addition of artificial aromas
or natural essential oils (Alissandrakis et al., 2010; Kružik et al., 2017). Mannas
and Altuğ (2007) used SPME-GC-MS for the estimation of thyme honey
authenticity and for the detection of its adulterations. Although high amounts
of volatiles such as thymol and carvacrol indicated honey adulteration with
thyme essential oil, 3,4,5-trimethoxybenzaldehyde was proposed as a possible
marker for determining thyme honey authenticity.
Due to honey aroma complexity, co-elution of some compounds may occur,
making their identification difficult. Recently, MDGC has been applied to
characterize the botanical origin of honey as well as to detect frauds.
The use of SPME-GCGC-ToF MS by Cajka
et al. (2007) allowed a rapid
and comprehensive examination of honey volatile profile (164 volatiles
identified). The best sorption capacity was obtained by a DVB/CAR/PDMS
fiber and the best resolution for the studied compounds was provided by a
DB-5SUPELCOWAX-10 column set. Although this methodology has also
been used to distinguish Corsican honeys from honeys harvested in other
European countries (Cajka
et al., 2009), the examination of a large set of honey
volatile profiles is still needed to assess the feasibility of this strategy for
traceability purposes.
436 Modern Techniques for Food Authentication
4.4.2 Carbohydrates
Regarding carbohydrate fraction, honey is probably the most complex mixture
of oligosaccharides in the nature and the study of these compounds has been
used to identify frauds. However, this is not a trivial task due to the high variability of honey sugar composition (which depends, among others, on its botanical or geographical origin) and due to the use of more sophisticated sugar
syrups as adulterants to mimic the chemical composition of natural honey
(Ruiz-Matute et al., 2007a).
Carbohydrates must be transformed into appropriate volatile compounds for
GC analysis and several methods of derivatization have been developed (RuizMatute et al., 2011). The TMS ethers (Sweeley et al., 1963), trifluoroacetates
(Sullivan and Schewe, 1977), and alditol acetates (Bj€orndal et al., 1967) are
the most popular derivatives. These derivatization procedures provide a different compound for each anomeric form of the sugar (up to five). Converting
sugars into oximes prior to trimethylsilylation reduces the peaks formed for
each sugar to two: E- and Z-oxime isomers (Laine and Sweeley, 1971; Low
and Sporns, 1988); this might be advantageous for the analysis of complex
carbohydrate mixtures as those present in honey.
Many authors have developed GC methods to characterize sugars in monofloral and honeydew honeys and to establish honey authenticity (e.g., De la
Fuente et al., 2011); combination of sugar and physicochemical data was, however, necessary in most of the studies to obtain reliable results (Astwood et al.,
1998; Sanz et al., 2004).
Different sugar concentration ratios have also been proposed as markers for
the control of honey authenticity such as maltose/isomaltose (Doner et al.,
1979), maltose/turanose, sucrose/turanose, and maltotriose/raffinose + erlose
+ melezitose (Horváth and Molnár-Perl, 1997). However, due to the variable
concentration of carbohydrates in honeys, this strategy is only valid for the
detection of highly hydrolyzed starch syrups such as CS, with high concentration of maltose or maltotriose.
Cotte et al. (2003) combined the use of high-performance anion-exchange
chromatography with pulsed amperometric detection (HPAEC-PAD) and
GC-FID data with a statistical processing by PCA to demonstrate the addition
of IS and CS to honey samples. Application of this approach to acacia, chestnut,
and lavender honeys allowed the detection of adulterations resulting from
additions as low as 5%–10% of sugar syrups.
Specific markers of honey adulterations have also been reported. Difructose
anhydrides (DFAs), nonfermentable pseudo-disaccharides produced during
heating of sugars, were proposed for the first time as possible markers of honey
adulteration with IS and HFCS by Ruiz-Matute et al. (2007a). The detection of
DFAs in the adulterated honeys required a previous enrichment step based on
incubation with yeast (Saccharomyces cerevisiae) to remove major sugars and
to preconcentrate these compounds. This methodology allowed the detection of
this sort of adulterants at levels as low as 5% of adulteration.
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
437
Inulotriose has been reported to be an appropriate marker for the detection of
honey adulterations with HFIS, as this compound is present in these syrups and
it is not a natural component of honey (Ruiz-Matute et al., 2010a). The detection
limit (LOD) for inulotriose was 0.03 mg g1 honey, which indicates that <1%
syrup addition would be detectable.
Ruiz-Matute et al. (2010b) studied the influence of caged bees fed with
HFCS and sucrose syrup (SS) in the carbohydrate composition of the resulting
honeys. As Fig. 3 shows, the main changes in honey carbohydrate profiles
obtained by GC-MS were detected in disaccharide region. The SS honeys only
differed from natural honeys in the content of sucrose. However, fructosylfructoses were detected in honeys from bees fed with HFCS, which allowed
differentiating them from those natural honeys produced by free-flying bees.
However, a higher number of honeys from confined and free-flying bees should
be analyzed in order to confirm these results.
4.5 Fruit Juices
Fruit juices are complex mixtures of sugars, organic acids, volatile flavors, FAs,
sterols, amino acids, flavonoids, pigments, etc., in water. Adulteration of industrially processed juices usually consists of substitution of the authentic material
with cheaper alternatives. Partial replacement of fruit juice with water, sugar,
fruit-derived products (pulpwash, peel, etc.) or with less expensive fruit
varieties (e.g., addition of grapefruit to orange juice) are common adulteration
practices (Fanali et al., 2016; Rinke, 2016). It is permitted to bulk fruit juices
with these substances for different purposes (e.g., to correct the acidity or for
sweetening), but such additions are only allowed if they are properly labeled.
Although juices from a single fruit are the most extensively consumed, compounded juices from different fruits have started being commercialized to
expand the market. In order to guarantee the authenticity of compounded
beverages and to avoid adulteration, the declared presence of every single juice
needs to be confirmed. Recently, undeclared replacement of organic juices by
conventional products has also been targeted due to the added-value of these
premium quality products.
4.5.1 Addition of Sweeteners
Carbohydrates account for >98% of the total soluble solids in fruit juices.
Therefore, the replacement of natural sugars by inexpensive sweeteners with
a composition resembling that of the authentic sugars is the most common
and profitable adulteration practice. Similar to honey, adulteration of juices
mainly involves the undeclared addition of industrial syrups such as IS
and HFCS.
Oligosaccharide fingerprints gathered by GC-FID have been used to detect
the adulteration of orange and apple juices with both IS and HFCS at
Abundance
1
2
10,000,000
8,000,000
6,000,000
4,000,000
4
3
2,000,000
5.00
(A)
Abundance
10.00
15.00
20.00
25.00
30.00
35.00
40.00
min
1
30,000,000
3
20,000,000
2
10,000,000
4
5.00
(B)
Abundance
10.00
15.00
20.00
25.00
30.00
35.00
40.00
min
1
2
4,000,000
3,000,000
2,000,000
4
1,000,000
3
(C)
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
min
FIG. 3 Gas chromatographic profile of trimethylsilyl oxime derivatives of disaccharide region in
honeys obtained from caged bees fed with HFCS (A) and with SS (B) and from naturally fed bees
(C). (1) Monosaccharides, (2) phenyl-β-D-glucoside (intenal standard), (3) sucrose, and (4) other
disaccharides. (Reproduced with permission from Ruiz-Matute, A.I., Rodrı´guez-Sánchez, S., Sanz,
M.L., Martı´nez-Castro, I., 2010a. Detection of adulterations of honey with high fructose syrups from
inulin by GC analysis. J. Food Compos. Anal. 23, 273–276; Ruiz-Matute, A.I., Weiss,
M., Sammataro, D., Finely, J., Sanz, M.L., 2010b. Carbohydrate composition of high-fructose corn
syrups (HFCS) used for bee feeding: effect on honey composition. J. Agric. Food Chem. 58,
7317–7322; ©American Chemical Society.)
Chromatographic Technique: Gas Chromatography (GC) Chapter
12
439
percentages as low as 5% (Low, 1995, 1996). Sample preparation included dilution with water to 5.5°Brix, freeze-drying and trimethylsilylation of the carbohydrates prior to their gas chromatographic analysis using a DB-5 column. The
addition of IS to juice was detected by the presence of two unidentified marker
peaks (IS1 and IS2) thought to be formed during sucrose inversion (Prodolliet
and Hischenhuber, 1998). IS1 and IS2 were identified by Thavarajah and Low
(2006) as turanose and O-β-D-fructofuranosyl-(2 ! 6)-D-glucose, respectively.
Although IS1 and IS2 may also be present in thermally processed authentic
juices, the ratio IS1/IS2 in heated juices is significantly different from that
observed for apple and orange juices adulterated with IS, with the critical
IS1/IS2 value not being generally agreed at this time. Additionally, the areas
for both marker peaks in juices subjected to excessive heating are considerably
lower than those corresponding to 5% IS adulteration.
Gansser and Busmann (1998) observed the interference of a natural constituent of apples (catechin) with the IS2 marker peak when applying the method
suggested by Low (1995, 1996) to detect the addition of IS to apple juice
concentrates. Inclusion of solid-phase extraction on RP-18 cartridge as part
of the sample preparation allowed the removal of catechin prior to the GC
analysis of juice without interfering with the IS1/IS2 ratio.
The detection of the two tautomeric forms of isomaltose has also been proposed to trace the adulteration of apple and orange juices with high fructose
syrup from corn and palm (Low, 1995, 1996). Oligosaccharide fingerprinting
by GC of white grape juices from Argentina showed that maltose and isomaltose are not natural constituents of grape juice, but they can be originated from
extended storage of lees juice in SO2. Therefore, the presence of isomaltose may
not necessarily indicate the adulteration of white grape juice with HFCS (Low,
1998). More recently, Willems and Low (2014) developed for the first time
oligosaccharide profiling methodologies by GC-FID and by HPAEC-PAD to
detect the debasing of pear juice from different production regions and years.
Although the GC-FID method allowed the detection of adulterations with commercially available sweeteners (HFCS, HFIS, and IS) at levels of 0.5%–3.0%
(v/v) (Fig. 4), HPAEC-PAD profiling could only be used to confirm the presence of HFCS and HFSI in pear juice. In addition, the GC-FID methodology
showed the ability to detect, within a single chromatographic analysis, the addition of pear to apple juice and juices produced using a total fruit liquefaction
process by measuring the content of arbutin and cellobiose, respectively.
Several carbohydrates naturally present in juices have also been proposed as
authenticity markers. Myo-inositol and the myo-inositol/fructose ratio have
been reported as indices to assess the quality and genuineness of orange juices
(Villamiel et al., 1998). GC-FID analysis of carbohydrates after derivatization
with trimethylsilylimidazole and trimethylchlorosilane showed that both indices were lower in samples made from concentrates than in freshly squeezed or
straight-processed orange juices.
440 Modern Techniques for Food Authentication
pA
140
3
120
6
100
1
4
8
9
80
5
7
2
60
40
20
25
30
35
40
45
50
55
min
FIG. 4 GC-FID chromatogram of pear juice to which HFCS 55, HIS, TIS, and cellobiose have
been added. Identified peaks are as follows: (1) α-inulobiose (HIS marker); (2) arbutin (pear
marker); (3) β-inulobiose (HIS marker); (4) α-cellobiose (liquefaction marker); (5) unidentified
TIS marker; (6) β-cellobiose; (7) O-β-D-fructofuranosyl-(2 ! 6)-D-glucose (TIS marker); (8) αisomaltose; and (9) β-isomaltose (HFCS marker). (Reproduced with permission from Willems, J.L.,
Low, N.H., 2014. Authenticity analysis of pear juice employing chromatographic fingerprinting.
J. Agric. Food Chem. 62, 11737–11747; ©ACS Publications.)
4.5.2 Authentication of a Declared Fruit Juice
Sterols have been proposed as useful markers for detecting juices of pineapple,
passionfruit, orange, and grapefruit in compounded beverages (Ng and Hupe,
1998). One-step liquid-liquid extraction into hexane, with ethanol being added
to precipitate pectins and other emulsifying agents, followed by GC-MS was
carried out to analyze sterols in juices. Overlapped peaks corresponding to
structurally similar compounds were common in sterol GC-MS profiles, with
extracted-ion chromatograms being a valuable tool for improving their separation. Ergostanol and stigmastanol were found to be markers for pineapple juice,
whereas passionfruit was characterized by the presence of an unidentified compound with mass spectra m/z 424, 165, 205, 69, and 41. Higher stigmasterol/
campesterol ratios were found for both orange and grapefruit juices, and the
differentiation of both citrus juices required the additional estimation of the
ratio of two semivolatile flavors (valencene/nootkatone).
4.5.3 Addition of Synthetic Aromas
Stereochemistry has been demonstrated to be a useful tool for food authentication. The study of the enantiomeric composition of 10 chiral terpenes
was proposed by Ruiz del Castillo et al. (2003a) to detect the addition of
synthetic aromas to commercial fruit beverages. SPME using a 100-μm
poly(dimethylsiloxane) fiber was used for volatile isolation under nonracemization conditions. GC-FID separations were performed on a chiral capillary
column coated with Chirasil-β-Dex. The enantiomeric excess of limonene,
linalool, and α-terpineol in natural products was found to be 100%, 100%,
and 80% for the (+)-enantiomer, respectively. Any deviation from these values
Chromatographic Technique: Gas Chromatography (GC) Chapter
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441
was attributed to the addition of synthetic aromas or inappropriate thermal
processing of fruit beverages under analysis. With a similar purpose, online
coupling of RP-LC with GC (RP-LC-GC) has been used to determine the enantiomeric composition of chiral terpenes in fruit drinks, orange aroma, and
orange essential oils without previous sample pretreatment (Ruiz del Castillo
et al., 2003b).
4.5.4 Authentication of Organic Juices
Despite the increasing consumer demand for organic foodstuffs, few studies
have yet addressed the assessment of authenticity of juices from different management systems (organic vs conventional). With the aim of authenticating
European premium organic orange juices, Cuevas et al. (2017) developed
a chemometric approach based on data obtained by two analytical platforms:
HS-SPME-GC-MS and HPLC-HR-MS. Mid-level data fusion strategies
identified some aldehydes and esters, together with several flavonoids and
FAs, as potential markers for the sensitive and specific differentiation of
organic juices. Moreover, this robust chemometric approach has resulted
advantageous over models based on individual techniques.
4.6 Coffee
Coffee is an expensive product that can be target of frauds by means of adulterations with cheaper commodities or false designations regarding its variety or
geographical origin, which affects its final price.
4.6.1 Fraud in Coffee Varieties
Coffea arabica and Coffea robusta, the two main coffee species, have different
value for consumers due to their sensorial properties and, therefore, different
prices in the market. Although coffee blends of these two varieties are preferred,
as they combine both characteristic flavors, it is necessary to define the composition of these blends because of the higher value of C. arabica beans, which
makes it a target for fraud. Unroasted coffees can easily be differentiated by its
volatile compounds, sugar, and amino acid contents (Knysakv, 2017); however,
these compounds are modified during their processing. Therefore, it is important to establish analytical methodologies to discriminate between coffee
species after roasting with the aim of detecting potential adulterations of
high-quality coffee brews. GC is an appropriate technique to detect these frauds
(Risticevic et al., 2008). Although there are some studies based on FA composition of Arabica and Robusta coffees (Alves et al., 2003; Romano et al., 2014),
most of the GC analyses are focussed on the determination of the volatile
compounds characteristic of coffee aroma. Different approaches, such as HS
(Kallio et al., 1988), on-column injections (Shimoda and Shibamoto, 1990),
and P&T systems (Liu et al., 2004) have been applied for the isolation of these
442 Modern Techniques for Food Authentication
compounds; however, HS-SPME is the most widely used technique for this purpose since it is simple, rapid, solvent-free, and inexpensive (Risticevic et al.,
2008; Toci and Farah, 2014). Several studies have compared the effectiveness
of SPME fibers with different coatings (Freitas et al., 2001; Rocha et al., 2003),
PDMS is the one commonly chosen for the characterization of the volatile
composition of coffee varieties (Zambonin et al., 2005). However, Mondello
et al. (2004b) demonstrated the suitability of a triple-phase coating (DVB/
CAR/PDMS) for the isolation of compounds within a wide range of volatility.
Recently, laboratory made cross-linked polymeric ionic liquid (PIL)-based
sorbent coatings have been used to extract volatile aroma-related compounds
from Arabica coffee (Toledo et al., 2014). The GC-MS analysis allowed the
detection of frauds down to 1% (w/w) of adulterant and accurately determined
the degree of coffee adulteration.
Around 150 compounds from different chemical families (aldehydes, alcohols, pyrazines, pyrroles, etc.) have been identified and quantified in blends of
roasted C. arabica and C. robusta and significant differences in their contents
have been observed (Sanz et al., 2002). It is worth noting that blends containing
high proportions of C. robusta showed greater concentrations of guaiacol
(Mondello et al., 2005) and sulfur compounds (mainly methanethiol)
(Holscher and Steinhart, 1992) than those with high percentages of
C. arabica. In general, the last blends showed more elevated concentrations of
other volatiles such as ketones, alcohols, pyrroles, furans, etc. (Sanz et al.,
2002). However, some discrepancies regarding the contents of aldehydes or
pyrazines that may assist in discriminating between coffee varieties have been
reported by different authors (Sanz et al., 2002; Zambonin et al., 2005).
A simple GC-FID method based on the determination of the ratio between
the integrated peak areas of kahweol (K) divided by the sum of K and 16-Omethylcafestol (16MCF) has been recently proposed to determine the authenticity of commercial blends used for the Italian Espresso coffee (Pacetti
et al., 2012).
HS-SPME coupled to GCGC has also been used to determine coffee volatile composition (Mondello et al., 2004b; Ryan et al., 2004; Cordero et al.,
2008). GCGC-FID analysis, using a Supelcowax-10BPX-5 column set,
provided the separation of nearly a thousand volatiles present in Arabica and
Robusta coffees, and allowed the discrimination of both coffee varieties based
on quantitative data (Mondello et al., 2004b). Ryan et al. (2004) continued these
studies by analyzing the same coffee bean samples by GCGC-ToF MS. Two
sets of columns, polar/nonpolar (SolGel-WAXBPX-5) and nonpolar/polar
(BPX-5BP-20) were tested; the first combination was more effective in the
separation of coffee volatiles. This technique was shown to be suitable for accurate peak identification and quantitation, although some assays using GCGCQ MS at a reduced mass scanning range (40–400 m/z) demonstrated that it can
be an alternative to GCGC-ToF MS for the analysis of target analytes.
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Coffee beans can be roasted either by adding sugar during the process (torrefacto coffees) or without sugar addition (conventional or natural coffees). Previous studies have shown that torrefacto roasting masks the poor sensorial
properties of Robusta coffee (Maeztu et al., 2001) and could be a fraudulent
practice to hide the low-quality beans. GC-MS studies have detected a higher
content of pyrazines, furans, and pyridines in torrefacto coffees as compared to
natural roasted coffees (Sanz et al., 2002; López-Galilea et al., 2006). The presence of DFAs has also been described in torrefacto coffees. These compounds
have been described to provide useful information related to the degree of torrefacto roasting and be used as quality markers to detect frauds (Montilla et al.,
2006). GC analysis of DFAs requires a previous derivatization process to obtain
their TMS derivatives.
4.6.2 Authentication of Geographical Origin
Prices of coffee are also dependent on its geographical origin. The volatile composition of these products can be different, and both producers and consumers
select them mainly in terms of their aroma. However, conclusive geographic
markers is yet to be identified (Burns et al., 2017). Zambonin et al. (2005) combined HS-SPME-GC-MS with multivariate analysis to discriminate among coffees from Africa and Central and South America. Similarly, Choi et al. (2010)
developed an integrated metabolomic approach based on statistical analysis of
data gathered by different analytical techniques (GC-FID, among others) to successfully identify coffees from Asia, South America, and Africa. A rapid
HS-SPME-GC-ToF MS method has also been developed for the verification
of geographical origin traceability of coffee samples (Risticevic et al., 2008).
4.6.3 Adulteration with Cheaper Products
It has been reported that commercial coffees have been adulterated with coffee
husks, cereals (barley, wheat, corn, etc.), malt, maltodextrins, cocoa and soy
bean, chicory root, acai berries, woody tissue straw, and caramel (Prodolliet
and Hischenhuber, 1998; Burns et al., 2017). Carbohydrates, in particular, free
fructose and glucose, sucrose, mannitol, total glucose, and total xylose, have
been proposed as markers of these adulterations (Burns et al., 2017). More
recently, a GC method has been optimized to determine the sugar and polyalcohol (mainly bornesitol) mainly of coffee and its substitutes (from cereals,
carob, and chicory). Differences in chromatographic profiles were found to
be useful for detecting adulterations of coffee with these products (RuizMatute et al., 2007b).
The SPME-GC-MS analysis of the HS from ground roasted coffee has also
been proposed to detect coffee adulterations with roasted barley (Oliveira et al.,
2009). By this method, adulterations down to 1% (w/w) with roasted barley
were detected in coffees with high degrees of roast.
444 Modern Techniques for Food Authentication
4.6.4 Authentication of Palm Civet Coffee
Palm civet coffee or kopi luwak is a coffee which includes partially digested
coffee cherries eaten and defecated by Asian palm civet (Paradoxurus hermaphroditus). The obtained coffee has a unique flavor as consequence of the
modifications produced by the civet’s digestive enzymes on the chemical composition of coffee beans. Due to this singular production process, cost of this
coffee is extremely high and this product can be target of frauds (Burns
et al., 2017). Several approaches based on the treatment of coffee beans with
different microorganisms and enzymes to mimic taste and smell of civet coffee
have been reported (Song, 2014; Jung, 2015). The analysis by GC-MS and
GC-FID of derivatized freeze-dried coffee extracts have allowed the selection
of discriminant markers (e.g., citric acid, malic acid, and inositol/pyroglutamic
acid ratio) to detect potential civet coffee frauds (Jumhawan et al., 2013;
Jumhawan et al., 2015). The adulteration degree of kopi luwak with regular
coffee has also been determined by GC-MS (Jumhawan et al., 2016).
4.7 Wines
Adulteration of wines is mainly based on the use of grapes of different varieties
from vineyards or geographical origins, although dilution of wine with inferior
products, falsification of the race of wine and/or the manufacture method, and
the preparation of artificial wines are other practices which affect the quality of
wine (Savchuk et al., 2001).
The authenticity of wine is regulated by authorities and many authors have
focused their efforts on finding new methodologies to detect these frauds.
Although the application of GC for these purposes is limited, some studies concerning the analysis of volatile compounds in wine need to be highlighted
(Langen et al., 2013; Gómez-Meire et al., 2014; Langen et al., 2016). Garcı́aJares et al. (1995) reported a P&T-GC method that allowed the distinction of
white wines produced in different regions of Spain. Other works have been
focused on the distinction of grape varieties: SPME-GC-FID was used to study
the volatile compounds of 16 wines from four different varieties (Pozo-Bayón
et al., 2001). The use of stepwise discriminant analysis allowed a 100% correct
classification of these wines using four variables: 1-propanol, ethyl octanoate,
cis-3-hexen-1-ol, and octanoic acid. The usefulness of the direct coupling
SPME-MS has also been evaluated to differentiate between varieties used for
wine production and also to differentiate wines by country of origin. SPMEMS has been shown to be a faster method and to provide much better sample discrimination than conventional SPME-GC-MS analysis (Ziółkowska et al., 2016).
Different substances have also been proposed as markers of the contact of
wine with oak barrels; oak lactones were the most important (Singleton, 1995).
More recently, quercitol was found to be a potential new indicator of the contact
of wine with oak wood (Carlavilla et al., 2006).
Chromatographic Technique: Gas Chromatography (GC) Chapter
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12
Gambier
Yes
Bornesitol
No
Yes
Oak
No
Quercitol
Quebracho
Grape
Chestnut
Yes
No
Pinitol
Yes
Tara
No
Arabinose and xylose
Yes
Quebracho
Chestnut
No
Muco-inositol and chiro-inositol
No
Quebracho
Grape
Yes
Chestnut
FIG. 5 Scheme of tannins classified according to their monosaccharide and polyalcohol composition. (Reproduced with permission from Sanz, M.L., Martı´nez-Castro, I., Moreno-Arribas, M.V.,
2008. Identification of the origin of commercial enological tannins by the analysis of monosaccharides and polyalcohols. Food Chem. 111, 778–783; ©Elsevier.)
Other studies have been focused on the evaluation of the authenticity of tannins used during winemaking. Sanz et al. (2008) proposed a new procedure
based on GC-MS analysis of sugars (arabinose, xylose, fructose, and glucose)
and polyalcohols (arabitol, quercitol, pinitol, chiro-inositol, muco-inositol,
scyllo-inositol, and myo-inositol) to differentiate commercial enological tannins
extracted from different sources. Differentiation of tannins could be done taking
into account their monosaccharide and polyalcohol composition as indicated in
the scheme of Fig. 5.
The proportions of myo- and scyllo-inositol have also been proposed, in a
multiparametrical approach along with isotopic methods, for the control of
adulteration of concentrated rectified musts with sugar (Monetti et al., 1996).
The GC-MS methods have been developed to detect the addition of industrial glycerol to wines (Lampe et al., 1997). 3-Methoxy-1,2-propanediol and
cyclic diglycerols are present in synthetic glycerol but not in grapes or authentic
wines. Therefore, their presence can be considered as an indication of these
adulterations. Modifications of the method (extraction of compounds with chloroform, silylation before analysis or use of a silicone oil stationary phase) have
been carried out by Otteneder et al. (1999) and Bononi et al. (2001) to improve
the separation of the compounds and to reduce the detection limits.
446 Modern Techniques for Food Authentication
Other frauds are based on the addition of nongrape ethanol. A new procedure
for wine ethanol 13C/12C isotope ratio determination by GC-IRMS has been
proposed (Cabañero et al., 2008). This method showed several advantages
for wine adulteration detection over previously reported isotopic methods such
as accuracy, speed, and simplicity.
The establishment of potential age markers is also important to detect frauds
and to ensure the authenticity of wine. In this sense, Perestrelo et al. (2011)
proposed a new methodology based on the use of SPME and GC GC-ToF
MS to determine furans, lactones, volatile phenols, and acetals as potential
age markers of Madeira wines.
5 CONCLUSIONS
New and challenging frauds regarding food authenticity are continuously
emerging propitiated, among others, by the global market. Moreover, the
authentication of samples is greatly difficult owing to the wide number of factors affecting food composition. With regard to analytical approaches, gas chromatographic techniques (mainly GC-MS) are the tools of choice for assessing
the authenticity of foodstuffs. They are at present the most widely used techniques for the authentication of oils, fats, and dairy products, by means of
the analysis of FAs and TAGs, as well as sterols and other minor components.
Other fraudulent practices involving plant-derived products (spices, coffees,
essential oils, etc.) are commonly determined by GC/GC-MS; although the
volatile isolation technique required prior to their analysis is a key factor to
be optimized depending on the sample. Adulteration of honeys and fruit juices
involving the addition of industrial syrups is also usually carried out by these
techniques after a required derivatization process. For other foods such as
wines, the use of GC is limited despite this technique has allowed the authentication of samples according to different criteria. Moreover, the use of
GCGC, although presently relegated to research laboratories, is proposed
for the sensitive identification of authentication markers in a variety of food
samples. Finally, the complementary information provided by data fusion
approaches from different analytical platforms is gaining wide acceptance
for the reliable detection of challenging adulterations.
ACKNOWLEDGMENTS
This work has been funded by Fundación Ramón Areces (project CIVP17A2843) and Ministerio de Economı́a, Industria y Competitividad (MINECO) of Spain (project AGL201680475-R). the authors also thank financial support from the Comunidad Autónoma of Madrid
and European funding from FEDER program (project S2013/ABI-3028, AVANSECAL).
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FURTHER READING
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