Abstract
Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g−1 of grape seed, expressed as gallic acid equivalents and 4.85 mg g−1 of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished.
Highlights
There is a high variability of phenolic compounds in grapes (Vitis vinifera L.)
Samples were classified according to their extractability of phenolic compounds
The model classifies correctly the 83.3% of the samples in leave-one-out cross-validation and the 76.9% of the samples in external validation
Summary
There is a high variability of phenolic compounds in grapes (Vitis vinifera L.). These compounds can be found in the whole berry (skin, pulp and seeds) and in the fermentation stage, they become part of the wine [1]. Phenolic acids (benzoic or hidroxycinnamic acids) are found in grape seeds [4] These phenolic compounds play an important role in the sensory characteristics of wine. 4-dimethyl-aminocinnamaldehyde (DMACA) [7] methods can be applied in order to obtain the extractable or total content of total phenols and flavanols, respectively (being total phenols the totality of phenolic compounds present in grape seeds, i.e., phenolic acids, flavanols, flavonols, etc.). These kind of traditional methods for the control of parameters of interest in grapes are being replaced by non-destructive and green chemistry methods. The developed methods have been applied to all samples with the exception of spectral outliers and the obtained distributions of the reference parameters have been evaluated in the samples
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