Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines

The feasibility of near infrared (NIR) spectroscopy for predicting reducing Sugar content during grape ripening, winemaking, and aging was assessed. NIR calibration models were developed using a set of 146 samples scanned in a quartz now cell with a 50 mm path length in the NIR region (800-1050 nm), in a fiber spectrometer system working in transmission mode. Principal component analysis (PCA), partial least squares (PLS), and multiple linear (MLR) regressions were used to interpret spectra and to develop calibrations for reducing sugar content in grape, must, and wine. The PLS model based on the full spectral range (800-1050 nm), yielded a determination coefficient (r(2)) of 0.98. a standard error of cross validation (SFCV) of 13.62 g/l and a root mean square error of cross validation (RMSECV) of 13.58 g/l. The mathematical model was tested with independent validation samples (n = 48); the resulting values for r(2), the standard error of prediction (SEP) and the root mean square error of prediction (RMSEP) for the same parameter were 0.98, 10.84, and 12.20 g/l, respectively. The loading weights of latent variables from the PLS model were used to identify sensitive wavelengths. To assess their Suitability, MLR models were built using these wavelengths. Wavelength significance was analyzed by ANOVA, and four wavelengths (909, 951, 961, and 975 nm) were selected, setting statistical significance at the 99% confidence level. The MLR model yielded acceptable results for P (0,92), SEP (19.97 g/l) and RMSEP (20.51 g/l). The results suggest that NIR spectroscopy is a promising technique for predicting reducing sugar content during grape ripening, as well as during the fermentation and aging of white and red wines. Individual fingerprint wavelengths strongly associated with reducing Sugar content could be used to enhance the efficacy of this simple, efficient and low-cost instrument. (c) 2008 Elsevier Ltd. All rights reserved.