Authenticity and Chemometrics of Wine and Beer

Abstract

These days, the agri-food economy is focused on the consumer demands regarding safety, quality and security of food, and especially of wine products. Authenticity of wine has been extensively examined; wine is an easily adulterated product as it has high alcohol content and low pH and also is available worldwide. For these reasons, continuous controls are required to maintain the quality of wine. Usually, volatile compounds are used to characterize varieties, minerals are used for geographical differentiation, whereas amino acids as well as phenolic compounds evaluation are used for both. There is a necessity to be developed advanced techniques for wine authentication and this is the challenge for the researchers, who currently are given a special focus. Wine adulteration for both variety and origin has been very extensive. Therefore, apart from novel experimental techniques (GC-MS, ICP-MS, 13C NMR and FT-IR among others) multivariate analysis comprising principal component analysis (PCA), discriminant analysis (DA), canonical analysis (CA) and cluster analysis (CLA) are also used. Beer is a very common alcoholic beverage. Its authenticity has always received a lot attention of the scientific literature. This resulted in numerous studies focused on the classification of beers, according to different types, brands and factories, based on the determination of characteristic compounds or chemical, physical fingerprint in beers by various analytical instruments such as vibrational spectroscopy, fluorescence, mass spectroscopy nuclear magnetic resonance and so forth. In this review, the beer authenticity and traceability studies are categorized according to their objective, from less challenging to more challenging, the determination of type and brewing process, the determination of brand identity, the determination of origin and ultimately, the determination of the style of beer (craft vs. conventional). Beer authenticity is a multivariate problem that needs advanced numerical chemometrics. The literature review identified that the number of samples and the study design need to be improved, and the quality of the chemometrics methods needs to be elevated. More work needs to be done in getting the chemometric models out of the lab and into the real world, with academia and industry working closely in larger studies and big data. The areas that are still under investigation together with other proposals are also discussed thoroughly in this review.