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Three independent methodologies were investigated to achieve the differentiation of red grapes from different grape varieties (Garnacha, Graciano, Mazuelo and Tempranillo) collected from five vineyards located in the D.O.Ca. Rioja. Anthocyanin chromatographic analysis, color image analysis and near infrared hyperspectral imaging were carried out for the grapes. Then, a Stepwise Linear Discriminant Analysis (SLDA) was developed for each data set in order to discriminate grapes according to their grape variety. As a result, using anthocyanin profile, color image analysis and near infrared hyperspectral imaging respectively, 88%, 54% and 100% of the samples were correctly classified in the internal validation process and 86%, 52% and 86% were correctly classified in the leave-one-out cross-validation process

Chemical composition of seeds changes during grape ripening and this affects the sensory properties of wine. In order to control the features of wines, the condition of seeds is becoming an important factor for deciding the moment of harvesting by winemakers. Sensory analysis is not easy to carry out and chemical analysis needs lengthy procedures, reagents, and it is destructive and time-consuming. In the present work, near infrared hyperspectral imaging has been used to determine flavanols in seeds of red (cv. Tempranillo) and white (cv. Zalema) grapes (Vitis vinifera L.). As reference measurements, the flavanol content was estimated using the p-dimethylaminocinnamaldehyde (DMACA) method. Not only total flavanol content was evaluated but also the quantity of flavanols that would be extracted into the wine during winemaking. A like-wine model solution was used for this purpose. Calibrations were performed by partial least squares regression and they provide coefficients of determination R2=0.73 for total flavanol content and R2=0.85 for predicting flavanols extracted with model solution. Values up to R2=0.88 were reached when cultivars were considered individually.