Open access peer-reviewed chapter
Literature examples of authentication and adulteration detection procedures of olive oil and other edible oils and fats.
Abstract
Authentication of food products and food fraud detection are of great importance in the modern society. The application of sophisticated instrumentation, such as gas chromatography (GC), with this aim helps to improve the protection of consumers. Gas chromatography mostly combined with the most powerful detector, a mass spectrometer (MS), and various multivariate data processing tools is in the last few decades being increasingly applied in authenticity and traceability of a wide spectra of food products. These include animal and plant products, beverages and honey. This chapter gives an overview of the most recent applications of gas chromatography technique in determining food authenticity, described in scientific literature.
Keywords
- food products
- authenticity
- food fraud
- consumer protection
- gas chromatography
1. Introduction
The adulteration practices on food product market are known since ancient times [1, 2]. It was found that, during the nineteenth century, gypsum and alum were added to bakery flour to increase weight, strychnine was added to beer to increase bitterness, and salts of copper, lead, and mercury were added to sweets in order to get a beautiful color and gloss [3, 4, 5]. Consumer interest in safety, authenticity and quality of food products is constantly increasing [6]. Authenticity is related to truthfulness, so a food product can be said to be authentic if it was not subject to any fraud [7]. European and global food policies require food on the market to be authentic. This means that the label on the product must match its actual composition, origin (geographical, botanical and genetic) and the process of production (conventional, organic and traditional) [2, 8, 9]. With globalization, market development and rapid distribution systems, as well as expanding the range of food items, counterfeiting and contamination of food products, are becoming international in character, and the possible consequences are far-reaching [2, 4, 9, 10, 11]. The most common type of adulteration—economically motivated food adulteration—is defined as
This chapter represents a thorough overview of the analytical methods employing a GC technique that are dealing with authentication and adulteration detection of various types of foodstuffs. The methods described are published in scientific literature in the last two decades.
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2. Authentication and adulteration detection in various food products
2.1 Olive oil and other edible vegetable oils
Edible vegetable oils represent a matrix which is usually analyzed with the application of GC. That is why there are a large number of papers dealing with authentication and adulteration detection in this type of food, using GC. Among them, extra virgin and virgin olive oils are definitely the most investigated. The suggested analytical methods are focused on the determination of constituents in oil mixtures of high prices and quality, the discrimination of extra virgin olive oils from defected oils, the possibilities of the authentication of various edible oils and fats and the determination of geographical origin. Triacylglycerol composition, fatty acid composition, 13C/12C and 2H/1H ratios and enantiomeric distributions of certain compounds, and just in some cases volatile organics and phenolic compounds, are usually considered as discrimination factors. Considering that this kind of analysis provides a large amount of data, the recently published papers are almost exclusively coupling GC with various unsupervised and supervised techniques of multivariate chemometric data analysis. Among unsupervised principal component analysis is definitely the mostly used, and among supervised techniques and machine learning algorithms, there are many different described: LDA and SLDA, PLS-DA, OPLS-DA, SIMCA, ANN-MLP, R-SVM and OC-SVM and some other. Table 1 lists chronological literature data on authentication and adulteration detection procedures of the most commonly investigated olive oil, and also edible oils of other plant species, and some examples of animal fats.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Discrimination of “Ligurian” from “non-Ligurian” olive oils | HS-SPME/GC-ITMS | LDA, ANN-MLP | [18] |
| Differentiation of monovarietal olive oils according to olive variety | HS-SPME/GC × GC-TOF-MS HT-GC-ITMS HS-SPME/GC-MS GC-TOF-MS |
PCA PCA, HCA, PLS-DA — PLS-DA |
[19] [20] [21] [22] |
| Differentiation of extra virgin and virgin olive oils according to geographical origin (various regions in Spain, Italy) | HS-SPME/GC-MS HS-SPME/GC-MS GC-C/P-IRMS |
SLDA — — |
[23] [21] [24] |
| Discrimination of extra virgin olive oils from defected oils | HS-SPME/GC × GC-MS | PCA, PLS-DA | [25] |
| Detection of extra virgin olive oil, virgin olive oil and olive oil adulteration (with various types of edible oils) | LC-GC-ITMS LC-chiral-GC-ITMS GC-MS SPME/GC × GC-MS GC-MS SPME/GC-MS GC-FID GC-MS GC-MS |
— — PCA — SIMCA, kNN, PLSR — PCA, PLS PCA, TFA, SIMCA, PLS OC-SVM |
[26] [27] [28] [29] [30] [31] [32] [33] [34] |
| Discrimination of various vegetable oils according to botanical origin (sunflower, corn, sesame, soybean, olive, rapeseed, camellia, peanut, canola, palm, rice bran, coconut, grapeseed, hazelnut, walnut, apricot seed, red pepper seed, prikachberry, pumpkin) | GC-C-IRMS GC-C-IRMS HT-GC-FID GC-FID GC-FID HT-GC-MS GC-MS GC-MS GC-MS GC-MS |
CDA — PCA PCA, KNN, CNN PCA, PLS SIMCA, PLS, GA-PLS PCA, PLS-DA, OPLS-DA PCA, HCA, RF LDA, GA-SVM |
[35] [36] [37] [38] [32] [39] [40] [41] [42] [43] |
| Differentiation of almond oils according to almond variety | HS-SPME/GC-MS | SLDA | [44] |
| Detection of corn oil adulteration | GC-C-IRMS | — | [45] |
| Detection of flaxseed oil adulteration | GC-MS | PCA, R-SVM | [46] |
| Detection of sesame oil adulteration | GC-FID GC-FID GC-MS |
— SVM OC-SVM |
[47] [48] [49] |
| Authenticity and geographical origin of pumpkin seed oil | GC-FID GC-C-IRMS |
PCA, RDA | [50] |
| Authenticity of cocoa butter | LC-GC-MS | — | [51] |
| Discrimination of various edible oils and fats (pig, mutton, beef and chicken) | GC-MS | PCA, PLS-DA, OPLS-DA | [40] |
Table 1.
2.2 Honey and other bee products
The authenticity of honey and other bee products has two aspects. Authenticity in respect of production, i.e., to prevent adulteration by the addition of other food ingredients (various types of sugar syrups), and authenticity of botanical and geographical origin. The GC method for determining the addition of sugar syrups relies on carbohydrate profiling in combination with classical statistical procedures for data processing. However, methods for authentication of geographical and botanical origin of honey samples usually employ more complex sample preparations, such as solid-phase microextraction in a headspace mode, and more sophisticated instrumentation, such as multidimensional GC. These methods mostly rely on the analysis of volatile organic compounds and also usually involve the application of multivariate chemometric tools for data analysis—unsupervised and supervised pattern recognition techniques. Unsupervised techniques, PCA and HCA, are more commonly used, but some studies also report the application of supervised tools: LDA and SLDA, OPLS-DA, SIMCA and ANN-MLP. Table 2 lists examples from literature data on authentication and adulteration detection procedures of honey and other bee products, such as beeswax, propolis and royal jelly.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Detection of the addition of sugar syrups to honey (high-fructose corn syrup and inverted syrup) | GC-FID GC-FID/MS GC-FID |
PCA — — |
[52] [53] [54] |
| Differentiation of four types of multifloral Portuguese honeys (produced in Madeira Island) | HS-SPME/GC-MS | PCA, SLDA | [55] |
| Authenticity of “Corsica” honey | HS-SPME/GC × GC-TOF-MS HS-SPME/GC × GC-TOF-MS |
PCA, ANN-MPL LDA, SIMCA, SVM, DPLS |
[56] [57] |
| Detection of honey adulteration with high-fructose inulin syrups | GC-MS | — | [58] |
| Authenticity of thistle honey | HD-SPME/GC-MS | — | [59] |
| Authenticity of botanical origin of unifloral chestnut ( |
GC-MS | — | [60] |
| Differentiation between lemon blossom honey ( |
GC-MS | PCA | [61] |
| Geographical origin identification of honey (samples from various regions of Greece; samples from various Mediterranean countries: Egypt, Greece, Morocco, Spain) | HS-GC-MS HS-SPME/GC-MS HS-GC-MS HS-SPME/GC-Q-TOF-MS HS-SPME/GC-MS SPME-GC/MS |
— HCA, SLDA, kNN OPLS-DA, SIMCA, OPLS-HCA PCA LDA LDA |
[62] [63] [64] [65] [66] [67] |
| Differentiation of honeys according to botanical origin: heather, raspberry, rape, alder buckthorn, lime, rosemary, chestnut, sunflower, acacia, thyme, orange, linden, amaranth, honeydew, citrus, |
HS-GC-MS SPME/GC-MS HS-GC-MS SPME/chiral-GC × GC-MS SPME/GC-MS/O SPME-GC/MS |
— LDA OPLS-DA, SIMCA, OPLS-HCA — AHC, CA PCA, HCA |
[62] [68] [64] [69] [70] [71] |
| Establishment of orange honey authenticity | SPME/GC-MS | — | [72] |
| Authenticity of royal jelly; detection of the addition of sugar syrups | HR-GC | — | [73] |
| Characterization of traditional plant syrups from Spain, namely, palm honey (miel de palma), must syrup (arrope) and sugarcane honey (miel de caña) | GC-MS | — | [74] |
| Detection of adulterated beeswax from |
HT-GC-FID/MS HT-GC-FID/MS |
— HCA, PCA, LDA |
[75] [76] |
| Geographical origin identification of propolis | HS/GC-MS/O | PCA | [77] |
| Establishment of sugarcane honey authenticity | HS-SPME/GC-MS | PCA, LDA | [78] |
Table 2.
Literature examples of authentication and adulteration detection procedures of honey and other bee products.
2.3 Milk and dairy products
Authenticity of milk and dairy products, such as cheese and fermented milk, using GC, is usually based on the determination of fat content of samples: triacylglycerols and fatty acids. Therefore, it is usually enough to combine GC with FID, to perform a successful analysis. In some particular cases, MS or olfactometry is used (if the analytical method is based on determining volatile profiles of the samples). Methods described in the literature rarely use chemometric data analysis, in some cases PCA, LDA and PLS-DA, but rather rely on the application of classical statistics. Papers describing the authentication of milk and dairy products usually deal with discriminating organic from conventionally produced ones, discriminating samples according to geographical origin and according to the animal breed they are produced of. Table 3 shows literature examples of authentication and adulteration detection practices in milk and dairy products, such as cheese.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Authenticity of goat milk | GC-FID | — | [79] |
| Differentiation between cow milk produced in the lowlands, mountains and highlands of Switzerland | HR-GC-FID | — | [80] |
| Authenticity of milk fat: detection of foreign fat in milk fat (such as pork lard, bovine tallow, fish oil, peanut oil, corn oil, olive oil, soy oil, sunflower oil, coconut fat) | GC-FID UFM-GC-FID GC-FID GC-FID GC-FID |
— — LDA — — |
[81] [82] [83] [84] [85] |
| Differentiation of milk produced under conventional and organic management | GC-FID GC-FID GC-MS |
— — — |
[86] [87] [88] |
| Differentiation of cow, goat, sheep, water buffalo, donkey, horse and camel milk | GC-FID | PCA | [89] |
| Determining the origin of milk samples: hay milk vs. conventional (silage) milk | GC-FID | PCA, PLS-DA | [90] |
| Geographic origin of Emmental cheese | GC-FID HS-GC-FID/MS |
— PCA |
[91] [92] |
| Differentiation of Grana Padano, Parmigiano-Reggiano and Grana Trentino cheeses | GC-O | PCA | [93] |
| Examining foreign fat origin in cheese from cow milk fat | GC-FID | — | [94] |
| Differentiation between certified organic and conventional probiotic fermented milks | GC-FID | — | [95] |
| Quality control for Parmigiano-Reggiano cheese | GC-MS | PCA | [96] |
Table 3.
Literature examples of authentication and adulteration detection procedures of milk and various dairy products.
2.4 Fruits and fruit-made beverages
Most of the papers dealing with fruit authenticity testing using GC are focused on determining discriminating factors that will enable discrimination of varieties of certain fruit species. These factors are mostly constituted of free and bound volatile compounds belonging to different chemical groups, namely, linear and branched esters, terpenes, alcohols and others. The paper published by Kurz et al. [97] is an exception, which is dealing with the analysis of neutral sugars of cell wall polysaccharide profiles of apricots, peaches and pumpkins using GC-FID. In some cases GC was also combined with other analytical techniques, thus enabling the wider spectra of chemical species to be included in the analysis, such as LC, in order to include nonvolatile carbohydrates, fatty acids and organic acids. The obtained data were mainly processed using multivariate data analysis techniques, such as HCA, PCA, PLS-DA, LDA and OPLS-DA. Older investigations usually do not include multivariate data analysis. Schmarr and Bernhardt [98] used image processing techniques in order to process the data obtained after comprehensive two-dimensional GC analysis. Table 4 chronologically lists some literature examples on authentication and adulteration procedures of various fruit species and fruit-made juices.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Differentiation of blackcurrant ( |
SPME/GC-FID | — | [97] |
| Differentiation of apricots ( |
GC-FID | — | [99] |
| Differentiation between |
HS-SPME/GC-MS | PCA | [100] |
| Discrimination of red grape varieties of southern Italy (Aglianico, Uva di Troia, Negroamaro, Primitivo) | GC-MS | PCA | [101] |
| Differentiation of apples, pears and quince fruit | HS-SPME/GC × GC | — | [98] |
| Classification of apple varieties (Golden Delicious, Granny Smith, Pinova and Stark Delicious) | HS-SPME/GC-TOF-MS | PCA, PLS-DA | [102] |
| Differentiation between grape varieties: |
GC-MS | HCA | [103] |
| Differentiation of Chinese bayberry cultivars ( |
HS-SPME/GC-MS HS-SPME/GC-MS/O |
PCA — |
[104] [105] |
| Discrimination of nine passion fruits: yellow, purple, lemon, orange, pineapple, peach, melon, banana and tomato | HS-SPME/GC-MS | PCA, PLS-DA | [106] |
| Differentiation between Tanzanian grown fruits: mango, pineapple, jackfruit, baobab and tamarind | GC-MS | HCA, PCA | [107] |
| Discrimination of |
HS-SPME/GC-MS | PCA, HCA | [108] |
| Differentiation between date palm fruit ( |
SPME/GC-MS | PCA, HCA, OPLS-DA | [109] |
| Characterization of organic oranges ( |
HS-SPM/GC-MS | PLS-DA | [110] |
| Differentiation of sun-dried raisins made from different grape varieties | HS-SPME/GC-TOF-MS | — | [111] |
| Differentiation between citrus species: mandarin, sweet orange, sour orange, papeda, pummelo, lemon, |
GC-MS | HCA | [112] |
| Differentiation of apple cultivars from geographical origin and growing conditions (organic and conventional) | HS-SPME/GC-MS | PLS-DA | [113] |
| Differentiation of Chinese Jujube varieties | HS-SPME/GC-MS | HCA | [114] |
| Detecting adulteration of blackcurrant juice | GC-FID | — | [115] |
| Authentication of apple and orange juice | GC-FID | — | [116] |
| Detecting the addition of aromas to fruit beverages | SPME/chiral-GC-MS | — | [117] |
| Citrus juice classification (lemon, grapefruit, mandarin, orange, lime) | HS-SPME/GC-MS | LDA | [118] |
| Assessment of premium organic orange juices authenticity | HS-SPME/GC-MS | PLS-DA | [119] |
Table 4.
Literature examples of authentication and adulteration detection procedures of various fruits and fruit juices.
2.5 Cereals and bakery products
Cereals, pseudocereals, flours and bread, as mostly used bakery products in human nutrition, are usually differentiated according to varietal, botanical or geographical origin by combining GC analysis with chemometric processing of the obtained data. The chemical compounds that have the role of discriminating factors usually involve small molecules, such as simple soluble sugars and free fatty acids. Chemometric methods involve most often exploratory data analysis techniques, such as PCA, PCO and HCA, but in some cases also classification methods of LDA and QDA were applied to measure the classification and prediction abilities. Table 5 chronologically lists some literature examples of authentication and adulteration detection practices of cereals, flour and the most commonly used bakery product in human nutrition-bread.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Differentiation between |
GC-FID | PCA, LDA, QDA | [120] |
| Differentiation between hexaploid ( |
GC-MS | — | [121] |
| Classifications of cereals (wheat and corn) used in DDGS material by geographical and botanical origin | GC-FID | PLS-DA | [122] |
| Differentiation of corn and small grain flour (wheat, rye, triticale, barley, oats) | GC-MS | HCA, PCO HCA, PCA |
[123] [124] |
| Differentiation of corn and oat flour, from other small grains (wheat, barley, triticale, rye) | GC-MS | HCA, PCO | [125] |
| Differentiation of flours of corn, spelt, buckwheat, amaranth and small grains (wheat, rye, triticale, oats, barley) | GC-MS | HCA, PCA | [126] |
| The content of buckwheat flour in wheat bread | GC-MS | HCA HCA, PCA |
[127] [128] |
Table 5.
Literature examples of authentication and adulteration detection procedures of cereals, flour and bakery products.
2.6 Meat, fish and seafood
The studies of authenticity of seafood and meat products using a GC technique usually focus on the determination of freshness of a seafood or meat product. Chemometric techniques, such as PCA, were able to successfully discriminate between fresh samples, deteriorated samples and gradually decaying samples of seafood, and ANN were employed in order to classify samples of fresh meat, frozen-thawed meat and spoiled meat. The PCA of gas chemometric fingerprints was able to show separation not only between oyster species but also between oysters originating from different cultivation areas, as well as oysters harvested at different time intervals. There was only one paper found in the literature that deals with differentiation of meat according to the breed origin. The PCA was successfully applied to discriminate between samples of pork, chicken, beef and mutton meat. Table 6 represents a chronological list of examples of authentication and adulteration detection procedures of various types of meat, fish and seafood.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Differentiation between fresh and deteriorated oyster |
HS-SPME/GC-MS | PCA | [129] |
| Differentiation between fresh and frozen-thawed cultured gilthead sea bream fish ( |
SPME/GC-MS | — | [130] |
| Razor clam ( |
HS-SPME/GC-MS | PCA | [131] |
| Differentiation of European flat oyster ( |
GC-FID GC-MS |
PCA | [132] |
| HS/GC-MS | PCA | [133] | |
| Differentiation of fresh and frozen pork | UFGC | PCA, ANN | [134] |
Table 6.
Literature examples of authentication and adulteration detection procedures of meat products and seafood.
2.7 Coffee and tea
Differentiation of coffee samples is based mostly on fatty acid profiles and volatile and semi-volatile compounds (organic acids, sugars, terpenoids). Differentiations of various tea plants were based exclusively on volatile components. In order to enable differentiations and classifications of investigated samples of beverages, the data obtained after GC analysis were combined with various chemometric techniques: HCA, PCA, SLDA and OPLS-DA. Table 7 represents chronological literature data on the authentication and adulteration detection procedures of coffee and tea from various plant species.
| Purpose of the study | Analytical technique | Chemometric technique | Ref. |
|---|---|---|---|
| Differentiation between arabica ( |
HR-GC-FID | HCA, CVA, DA | [135] |
| Determining the geographical origin of coffee samples | HS-SPME/GC-TOF-MS | PCA | [136] |
| Differentiation of |
GC-MS | HCA, PCA, LDA | [137] |
| Discrimination of oolong tea ( |
HS-SPME/GC-MS | PCA, HCA, SLDA | [138] |
| Discrimination of two roselle ( |
SPME/GC-MS | PCA, HCA, OPLS-DA | [139] |
| Discrimination of different teas ( |
HS-SPME/chiral-GC-MS | HCA, PLS-DA | [140] |
| Discrimination of American ginseng ( |
GC-MS | PCA, PLS | [141] |
Table 7.
Literature examples of authentication and adulteration detection procedures of coffee and tea.
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3. Conclusions
Gas chromatograph, as a common instrument in most analytical laboratories worldwide, can be successfully applied in authentication and fraud detection procedures of various food and beverage products, such as olive oil and other edible vegetable oils, honey and other bee products, milk and dairy products, cereals and bakery products, meat, fish and seafood, as well as coffee and tea. In this manner, gas chromatograph is coupled to flame ionization detector or single/tandem mass spectrometers. It can be concluded that utilization of a GC device in further development of authentication methodologies could provide us with meaningful results, thus representing a significant contribution to this emerging field in the future.
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Acknowledgments
The authors would like to acknowledge the support from the Ministry of Education, Science and Technological Development of the Republic of Serbia.
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Acronyms and abbreviations
agglomerative hierarchical clustering artificial neural networks combustion isotope ratio mass spectrometry correspondence analysis canonical discriminant analysis counterpropagation neural network canonical variates analysis discriminant analysis discriminant partial least squares flame ionization detector genetic algorithm gas chromatography fast multiple heart-cut enantioselective multidimensional gas chromatography enantioselective comprehensive two-dimensional gas chromatography hierarchical cluster analysis high resolution headspace high temperature isotope ratio ion-trap k-nearest neighbors Kohonen neural network liquid chromatography linear discriminant analysis multilayer perceptron Monte Carlo one-class partial least squares mass spectrometry olfactometry one-class support vector machine orthogonal projections to latent structures discriminant analysis pyrolysis isotope ratio mass spectrometry principal component analysis principal coordinate analysis principal least squares regression quadrupole accurate mass time-of-flight mass spectrometry quadratic discriminant analysis recursive support vector machine regularized discriminant analysis random forests soft independent modeling of class analogy stepwise linear discriminant analysis solid-phase microextraction support vector machine time-of-flight ultrafast module
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