Rapid Determination of Process Variables of Chinese Rice Wine Using FT-NIR Spectroscopy and Efficient Wavelengths Select
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Rapid Determination of Process Variables of Chinese Rice Wine Using FT-NIR Spectroscopy and Efficient Wavelengths Selection Methods Zhengzong Wu & Enbo Xu & Fang Wang & Jie Long & Xueming Xu Aiquan Jiao & Zhengyu Jin
Received: 29 July 2014 / Accepted: 7 October 2014 # Springer Science+Business Media New York 2014
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.
Keywords Chinese rice wine . Monitoring . FT-NIR . Variable selection . Genetic algorithm Z. Wu : E. Xu : F. Wang : J. Long : X. X. A. Jiao (*) : Z. Jin (*) The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China e-mail: [email protected] e-mail: [email protected]
Introduction Chinese rice wine (CRW), a unique alcoholic beverage in China, is one of the three most famous brewed wines (yellow wine, grape wine, and beer) in the world (Lv et al. 2013). It is very popular in China and other Asian countries with an annual consumption of more than 2 million kiloliters (Jin et al. 2013). Effective fermentation monitoring is particularly important for CRW to assure the quality and productivity of CRW products at every stage of the fermentation process. In CRW wineries, fermentation process is mainly controlled by the determination of several key parameters such as pH and total acid using wet chemistry methods. Traditional methods are precise enough; however, they usually require expensive equipments and complicated sample preparation or purification, which restrict the acquisition of real-time information (Urtubia et al. 2008). Conseq
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