APPLICATION OF VIS–NIR HYPERSPECTRAL IMAGING FOR PREDICTION OF FLAVONOIDS, ANTHOCYANINS AND SOLUBLE SOLIDS CONTENT IN TABLE GRAPES

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods. The raw spectra recorded were pre-treated with the following external procedures: Standard Normal Variate (SNV); 1st Derivative (1stDER); 2st Derivative (2stDER); and White and Black (W-B) correction. A quantitative model was developed using partial least squares regression (PLS-R) in order to find correlations between spectra information and each of the chemical references. Preliminary results showed a good correlation between each of the chemical parameters and the spectral information. The best model was obtained using 1st DER data pre-treatment, yielding the validations coefficients (P-R2) of 0.972, 0.932, 0.921 and a root mean square error of prediction (RMSEP) of 0.249, 3.619, 0.140 for titratable acidity, sugar and organic acid content, respectively. Therefore, hyperspectral systems can be a fast and non-destructive promising technology to predict the levels of titratable acidity, sugar and organic acid content in wine grapes during the ripening and at harvest time.