Verifying the red wines adulteration through isotopic and chromatographic investigations coupled with multivariate statistic interpretation of the data

Geană, Elisabeta-Irina; Popescu, Raluca; Costinel, Diana; Dinca, Oana Romina; Ștefănescu, Ioan; Ionete, Roxana Elena; Bala, Camelia

doi:10.1016/j.foodcont.2015.10.003

Cited by 41 publications

(25 citation statements)

References 21 publications

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“…Exogenous addition of sugar and water in counterfeited wines can be detected by measuring stable isotope content (δ 13 C and δ 18 O). Furthermore, a large number of chemical parameters can be investigated such as isotopic parameters, anthocyanins, sweeteners and red dyes that are used to correct deficiencies of taste and color …”

Section: Authentication and Adulteration By Food Categorymentioning

confidence: 99%

“…Furthermore, a large number of chemical parameters can be investigated such as isotopic parameters, anthocyanins, sweeteners and red dyes that are used to correct deficiencies of taste and color. 140 The identification of adulterated and fake tequilas and correct discrimination between categories (100% blue agave and mixed) and types (white, rested and aged) can be accomplished using UV-visible spectroscopy and chemometrics. 141 Whiskey brand authenticity is usually established by determining the profile of volatile organic compounds by GC analysis.…”

Section: Alcoholic Beveragesmentioning

confidence: 99%

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Modern analytical methods for the detection of food fraud and adulteration by food category

et al. 2017

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This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.

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Section: Authentication and Adulteration By Food Categorymentioning

confidence: 99%

Section: Alcoholic Beveragesmentioning

confidence: 99%

Modern analytical methods for the detection of food fraud and adulteration by food category

et al. 2017

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“…For instance, Geana aimed at providing an accurate classification of five Romanian red wines based on variety and vintage (six harvest years), using a linear discriminant analysis [56]. Besides the organic acids profile, NMR fingerprints and isotope analysis, they managed to assign vintage membership with a percentage of 91.64% using the anthocyanins and 92.56% using anthocyanin ratios, whereas variety discrimination resulted in 95.78% using the anthocyanins and 87.82% using anthocyanin ratios [1]. Similar results on anthocyanin composition and varietal classification were also reported in Czech wines [57].…”

Section: Profiling Applicationsmentioning

confidence: 99%

“…Nowadays, food quality has become particularly important from the point of view of both the consumers and the manufacturers. Global food safety authorities have generated guidelines that are meant to regulate and assure the authentication and detection of adulteration or incorrect labelling by means of reliable analytical tools [1].…”

Section: Introductionmentioning

confidence: 99%

Polyphenol Fingerprinting Approaches in Wine Traceability and Authenticity: Assessment and Implications of Red Wines

Palade

Popa

2018

Beverages

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Like any other food/feed matrix, regardless of the employed analytical method, wine requires authentication strategies; a suitable qualitative and quantitative analysis represents the fingerprint which defines its identity. Until recently, fingerprinting approaches using liquid chromatography applications have been regarded as an effective tool for the assessment of wines employing polyphenol profiles. These profiles are of considerable importance for grapes and wines as they influence greatly the color, sensory, and nutritional quality of the final product. The authenticity and typicity characters are fundamental characteristics, which may be evaluated by the use of polyphenol fingerprinting techniques. Under these conditions, the evolution of polyphenols during the red wine elaboration and maturation processes shows a high importance at the level of the obtained fingerprints. Moreover, the environment factors (vintage, the area of origin, and variety) and the technological conditions significantly influence wine authenticity through the use of polyphenol profiles. Taking into account the complexity of the matter at hand, this review outlines the latest trends in the polyphenol fingerprinting of red wines in association with the transformations that occur during winemaking and storage.

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“…In this regard, performing a fingerprinting approach to characterize wines and to classify their characteristics according to their production regions is a necessity. Tests to confirm the origin of a specific wine are necessary, because wine production and trade have always been associated with high costs [3]. Based on this background, detailed and continuous controls are essential for maintaining the quality of wine and for identifying possible cases of fraud with respect to their geographical origin [4].…”

Section: Introductionmentioning

confidence: 99%

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

et al. 2018

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Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint's chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew's correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America. Beverages 2018, 4, 97 2 of 15Unsupervised techniques, such as principal component analysis and cluster analysis, have commonly been employed to differentiate wines [9][10][11][12][13]. However, unsupervised pattern recognition methods must not be confused with classification methods. In some cases, these techniques allow for the identification and visualization of certain patterns, while in other cases, they may be insufficient for finding patterns between food profiles and physicochemical attributes, due to the multitude of compounds and physical attributes that are present in food products [14,15].Other studies have used unsupervised techniques prior to using supervised techniques, such as linear discriminant analysis, partial least squares-discriminant analysis, and decision trees to classify wine samples [8,[16][17][18]. In addition, other advanced techniques available through current data mining methodology have been successfully used to classify wines and other food products according to their geographical origin and variety, such as support vector machines (SVM) [19]. In addition, SVM is a suitable classifier for balanced datasets [20].The SVM classification algorithm has been demonstrated to be useful for wine classification. For example, a total of 44 Polish white and red wine samples from different parts of Poland were classified [21]. The authors used principal component analysis (PCA) as an exploratory analysis and support vector machine method, with a radial basis function (RBF), to classify the data according to grape variety, geographic region, sugar content, alcohol content, type of yeast used, post-fermentation treatment, and the temperature in the fermentation process. The main results of this study of the classification of white and red wines, according to the variety of grapes used for production, showed 98...

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Verifying the red wines adulteration through isotopic and chromatographic investigations coupled with multivariate statistic interpretation of the data

Cited by 41 publications

References 21 publications

Modern analytical methods for the detection of food fraud and adulteration by food category

Modern analytical methods for the detection of food fraud and adulteration by food category

Polyphenol Fingerprinting Approaches in Wine Traceability and Authenticity: Assessment and Implications of Red Wines

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

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