Application of fuzzy algorithms in conjunction with 1H‐NMR spectroscopy to differentiate alcoholic beverages

Background Recent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of...

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Veröffentlicht in:Journal of the science of food and agriculture Jg. 103; H. 4; S. 1727 - 1735
Hauptverfasser: Pirnau, Adrian, Feher, Ioana, Sârbu, Costel, Hategan, Ariana Raluca, Guyon, Francois, Magdas, Dana Alina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 15.03.2023
John Wiley and Sons, Limited
Schlagworte:
1H‐NMR
agriculture
Alcoholic beverages
Algorithms
Artificial intelligence
Beverages
Classification
Cultivars
Differentiation
Discriminant analysis
Distillates
food fraud
Fraud
fruit spirits
Fruits
fuzzy algorithms
Geographical distribution
Magnetic resonance spectroscopy
Metabolomics
NMR
NMR spectroscopy
Nuclear magnetic resonance
nuclear magnetic resonance spectroscopy
principal component analysis
Principal components analysis
spectral analysis
Spectroscopy
Spectrum analysis
Statistical analysis
Statistics
Wine
Wines
ISSN:0022-5142, 1097-0010
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Zusammenfassung:Background Recent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of the most suitable data‐processing methods like those based on advanced statistical tools or artificial intelligence. Results The present study aims to establish a new, innovative approach for the differentiation of alcoholic beverages (wines and fruit distillates), which is able to increase the discrimination rate of the models that have been developed. A data dimensionality reduction step was applied to proton nuclear magnetic resonance (1H‐NMR) profiles. This stage consisted of the application of fuzzy principal component analysis (FPCA) prior to the development of classification models through discriminant analysis. The enhancement of the model's classification potential by the application of FPCA in comparison with principal component analysis (PCA) was discussed. Conclusion The association of 1H‐NMR spectroscopy and an appropriate statistical approach provided a very effective tool for the differentiation of alcoholic beverages. To develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1H‐NMR spectroscopic data were exploited in conjunction with fuzzy algorithms to reduce data dimensionality. The study proved the greater efficiency of using FPCA scores in comparison with those obtained through the widely applied PCA. The proposed approach enabled wines to be distinguished perfectly according to their geographical origins, cultivar, and vintage, and this could be used for wine classification. Moreover, 100% correctly classified samples were also achieved for the botanical and geographical differentiation of fruit distillates. © 2022 Society of Chemical Industry.
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ISSN:0022-5142
1097-0010
DOI:10.1002/jsfa.12402