Rapid Determination of Process Variables of Chinese Rice Wine Using FT-NIR Spectroscopy and Efficient Wavelengths Selection Methods

Abstract

There is a growing need for the effective fermentation monitoring during the manufacture of wine due to the rapid pace of change in the wine industry. In this study, Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics was applied to monitor time-related changes during Chinese rice wine (CRW) fermentation. Various wavelength selection methods and support vector machine (SVM) algorithm were used to improve the performances of partial least squares (PLS) models. In total, ten different calibration models were established. It was observed that the performances of models based on wavelength variables selected by variable selection methods were much better than those based on the full spectrum. In addition, nonlinear models outperformed linear models in prediction of fermentation parameters. After systemically comparing and discussing, it was found that for both ethanol and total acid, genetic algorithm-support vector machine (GA-SVM) models obtained the best result with excellent prediction accuracy. The correlation coefficients (R2 (pre)), root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) for the prediction set were 0.94, 3.02 g/L, and 8.7 for ethanol and 0.97, 0.10 g/L, and 6.1 for total acid, respectively. The results of this study demonstrated that FT-NIR could monitor and control CRW fermentation process rapidly and efficiently with efficient variable selection algorithms and nonlinear regression tool.