Monitoring of fermentation process parameters of Chinese rice wine using attenuated total reflectance mid-infrared spectroscopy

There is a growing need for the effective fermentation monitoring during the manufacture of wine due to the rapid pace of change in the industry. In this study, the potential of attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy to monitor time-related changes during Chinese rice wine (CRW) fermentation was investigated. Interval partial least-squares (i-PLS) and support vector machine (SVM) were used to improve the performances of partial least-squares (PLS) models. In total, four different calibration models, namely PLS, i-PLS, SVM and interval support vector machine (i-SVM), were established. It was observed that the performances of models based on the efficient spectra intervals selected by i-PLS were much better than those based on the full spectrum. In addition, nonlinear models outperformed linear models in predicting fermentation parameters. After systemically comparison and discussion, it was found that i-SVM model gave the best result with excellent prediction accuracy. The correlation coefficients (R2 (pre)), root mean square error (RMSEP (%)) and the residual predictive deviation (RPD) for the prediction set were 0.96, 6.92 and 14.34 for total sugar, 0.97, 3.32 and 12.64 for ethanol, 0.93, 3.24 and 9.3 for total acid and 0.95, 6.33 and 8.46 for amino nitrogen, respectively. The results demonstrated that ATR-MIR combined with efficient variable selection algorithm and nonlinear regression tool as a rapid method to monitor and control CRW fermentation process was feasible.