Prediction of tannin profile in grape (Vitis vinifera L.) skins during berry maturation using a rapid mechanical puncture approach.

Tannin structure and composition are variable during grape maturation, and crucially determine perceived astringency, body structure and aging capacity of red wines. This study investigated the evolution of condensed tannins (CTs) in grape skins as maturation progressed and the feasibility of using a rapid mechanical puncture approach for assessing the CTs profile. The results showed that the mean degree of polymerization (mDP), molecular mass (MM), and proportions of (-)-epigallocatechin in extension subunits (EGC_ext) and (-)-epicatechin-3-O-gallate in terminal subunits (ECG_term) of skins increased during grape maturation, while CTs content and the proportion of (-)-epicatechin-3-O-gallate in extension subunits decreased. The predictive models built by random forest for CTs content based on skin weight, mDP, MM_subunit, EGC_ext, and ECG_term obtained good results with high squared correlation coefficients of prediction and calibration (R2_P > 0.85 and R2_C ≈ 0.95). In addition, the classifications of CTs characteristics obtained from ripe and unripe samples were observed in different principal component spaces. This study indicated that the mechanical properties were useful for predicting skin CTs profile, estimating tannin maturity stages, and providing information for optimal harvesting and winemaking protocols.

Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging.

Phenolics contents in wine grapes are key indicators for assessing ripeness. Near-infrared hyperspectral images during ripening have been explored to achieve an effective method for predicting phenolics contents. Principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) models were built, respectively. The results show that SVR behaves globally better than PLSR and PCR, except in predicting tannins content of seeds. For the best prediction results, the squared correlation coefficient and root mean square error reached 0.8960 and 0.1069g/L (+)-catechin equivalents (CE), respectively, for tannins in skins, 0.9065 and 0.1776 (g/L CE) for total iron-reactive phenolics (TIRP) in skins, 0.8789 and 0.1442 (g/L M3G) for anthocyanins in skins, 0.9243 and 0.2401 (g/L CE) for tannins in seeds, and 0.8790 and 0.5190 (g/L CE) for TIRP in seeds. Our results indicated that NIR hyperspectral imaging has good prospects for evaluation of phenolics in wine grapes.