Grapes are rich in phenolic compounds, being important for human health with anti-inflammatory, antiatherosclerotic, antimutagenic, anticarcinogenic, antibacterial, antiviral, and antimicrobial activity. The winemaking of the grapes generates significant amounts of waste. These wastes contain bioactive compounds in their biomass that can be used as a source of food improvement or as a source of nutrition supplementation. This study looks at the content of bioactive compounds, the polyphenolic profile, and the antioxidant activity in different white and red grape pomaces. The investigation of bioactive characteristics (total polyphenols, total flavonoids, catechins, tannins, and antioxidant activity) was carried out by UV-Vis spectrophotometric methods, while the individual polyphenolic composition was investigated by target and screening UHPLC-HRMS/MS analysis. Principal components (PCA) and the heat maps analysis allows the discrimination between the grape pomace resulted from white grape cultivars (Muscat Ottonel and Tamaioasa Romaneasca) and red grape pomaces (Cabernet Sauvignon, Merlot, Feteasca Neagra, Burgund Mare, Pinot Nore), with the identification of the specific phenolic compounds for each grape pomace type.

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.