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A Feasibility Study on the Evaluation of Quality Properties of Chinese Rice Wine Using Raman Spectroscopy

Wu, Zhengzong, Long, Jie, Xu, Enbo, Wang, Fang, Xu, Xueming, Jin, Zhengyu, Jiao, Aiquan
Food analytical methods 2016 v.9 no.5 pp. 1210-1219
Raman spectroscopy, algorithms, chemical compounds, chemometrics, discriminant analysis, food analysis, infrared spectroscopy, least squares, models, nondestructive methods, prediction, quality control, quantitative analysis, rapid methods, rice wines, wavelengths, wine industry
Rapid analysis of Chinese rice wine (CRW) is an important activity for quality assurance and control investigations. In recent years, due to its insensitivity to water and fewer overlapped bands, Raman spectroscopy (RS) may provide more useful qualitative and quantitative information on functional groups of various chemical compounds in CRWs than the conventional spectroscopic technique (e.g., infrared spectroscopy); there has been a growing interest in the application of RS in the qualitative and quantitative analysis in food industry. In this study, the applicability of RS hyphenated with chemometrics using different pretreated spectra was examined to develop rapid, low-cost, and non-destructive method for quantification of four enological parameters involved in CRW quality control. Partial least square (PLS) was used for building the calibration models for the four chemical parameters based on the full RS spectrum. The model was also optimized by using efficient wavelength selection algorithm, i.e., synergy interval partial least square (SiPLS) algorithm. In addition, soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA) were used as classification techniques to predict the brands (wineries) of CRW samples. The results demonstrated that compared with the PLS model using all wavelengths of RS spectra, the prediction precision of model based on the spectral variables selected by SiPLS was significantly improved with high values of the coefficient of determination (>0.90), residual predictive deviation (>3.0), and range error ratio (>10) for all of the four quality parameters. The SIMCA and LDA results, characterized by high percentages of correct classification (96.67 and 100.00 % as average value in prediction for SIMCA and LDA, respectively), showed that samples belonging to a particular brand could be correctly classified. The overall results indicated the suitability of RS combined with efficient variable selection algorithm to rapidly control the quality of CRW.