A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

Esteki, Mahnaz; Simal-Gándara, Jesús; Shahsavari, Zahra; Zandbaaf, Shima; Dashtaki, Elham; Heyden, Yvan Vander

doi:10.1016/j.foodcont.2018.06.015

Cited by 141 publications

(52 citation statements)

References 242 publications

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“…OPLS-DA is a supervised data classification technique in which the relationship between two data matricesas measured X giving information about the chromatographic and spectral data, and as dummy Y possessing user-defined class informationwas investigated. 40,41 In geographical discrimination, a calibration dataset of total of 60 samples were divided into three classes as 17 Middle (class M), 19 North (class N), and 24 South (class S) samples, whereas 31 samples (9 M, 10 N, and 12 S) were used as a validation set. In differentiation of harvesting year, a calibration dataset (60 samples) belonging to two consecutive years (36 samples for the first harvesting year (class 1) and 24 samples for the second harvesting year (class 2)) and a validation set (31 samples) from the same harvesting years (18 and 13 samples for classes 1 and 2 respectively) were used.…”

Section: Discussionmentioning

confidence: 99%

“…Orthogonal partial least‐squares (OPLS) discriminant analysis (DA) was used to visualize the separation of olive oil samples according to geographical origin and harvesting year by using the pretreated data. OPLS‐DA is a supervised data classification technique in which the relationship between two data matrices – as measured X giving information about the chromatographic and spectral data, and as dummy Y possessing user‐defined class information – was investigated . In geographical discrimination, a calibration dataset of total of 60 samples were divided into three classes as 17 Middle (class M), 19 North (class N), and 24 South (class S) samples, whereas 31 samples (9 M, 10 N, and 12 S) were used as a validation set.…”

Section: Methodsmentioning

confidence: 99%

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Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques

2020

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BACKGROUND Minor compounds of olive oil could have discriminatory characteristics in the authentication of this product. It was aimed to determine the detailed pigment profiles of Turkish olive oils and use them in differentiation of the samples in comparison to fast, reliable, and environmentally friendly Fourier‐transform infrared (FTIR) and ultraviolet (UV)–visible spectroscopic techniques. Pigment contents of 91 olive oils obtained from different locations for two consecutive harvesting years were determined with chromatographic analysis and FTIR and UV–visible spectra of these samples were also obtained. All data were analyzed with orthogonal partial least‐squares discriminant analysis to investigate the differentiation ability of these methods with regard to their detailed pigment and spectroscopic profiles. RESULTS Pheophytin a (2.78–8.98 mg kg−1) and lutein (1.19–4.07 mg kg−1) were the major pigments in all samples. Pigment profiles provided successful classification of olive oils with respect to their designated origins and harvesting year with average correct classification rates of 97%. UV–visible spectroscopy has quite similar results with pigment profiles in terms of its discriminatory power. In addition, FTIR and fused data were slightly better in discrimination of the samples, and the fused dataset has the highest correct classification rate of 100%. CONCLUSION Use of detailed pigment profiles is quite promising in authentication of olive oils. However, UV–visible and FTIR spectroscopic techniques could be reliable alternatives for the same purposes. All of the techniques studied have great potential in ‘protected designation of origin’ certification studies. © 2020 Society of Chemical Industry

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques

2020

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“…Grouping of honey with similar floral sources could be predicted via pattern of recognition through multivariance analysis technique. Moreover, classification of honey in association with floral source using chromatographic profiling combined with chemometrics analysis has been recently introduced (Zhou et al, 2014;Esteki et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of botanical source and processing conditions on physicochemical properties and antioxidant activity of honey in the northern part of Thailand

Hempattarasuwan

Settachaimongkon

Duangmal

2019

Int J of Food Sci Tech

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Summary Phenolics, melissopalynological analysis, and selected physicochemical characteristics could differentiate floral honey origin. Botanical sources (longan, multifloral and Manuka) had more pronounced effect on honey properties than processing conditions. The conductivity was related to ash content and colour of honey. Total sugar, fructose and glucose content in all samples were not significantly different. Melissopalynological analysis was used to verify the botanical source of honey samples. Predominant pollen was identified. Results characterising antioxidant properties (DPPH radicals, hydroxyl radicals, and carbon‐centred radicals scavenging activity) were confirmed via both Electron Spinning Resonance and oxygen radical absorbance capacity (ORAC). They showed that only hydroxyl radicals scavenging activity and ORAC were significantly different. Type and amount of phenolics in honey samples identified using HPLC could differentiate botanical source. Gallic acid and kaempferol were potential marker phenolics for longan honey. Multivariate statistical analysis allowed discrimination of honey from different botanical sources based on their phenolic profiles.

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“…However, this technique allows to solve problems in which only one known category is involved, contrary to DC techniques (Rodionova, Titova, & Pomerantsev, 2016). This characteristic is what strongly suits OCC as a tool for tracking food frauds and food authentication (Esteki et al, 2018;Esteki, Regueiro, & Simal-Gándara, 2019;Granato, Putnik, et al, 2018). Examples of OCC tecnhiques include unequal dispersed classes (UNEQ) and soft independent modeling of class analogies (SIMCA).…”

Section: Classification Methodsmentioning

confidence: 99%

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Mutz

Rosário

Conté-Júnior

2020

Comp Rev Food Sci Food Safe

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Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

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A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

Cited by 141 publications

References 242 publications

Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques

Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques

Impact of botanical source and processing conditions on physicochemical properties and antioxidant activity of honey in the northern part of Thailand

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

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