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Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics

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Abstract

Shaoxing rice wine (also called Shaoxing wine) is the most well-known Chinese rice wine in China. The common fraudulent practice in the commercialization of Chinese rice wine is to sell wines from different geographical origins under the denomination of Shaoxing rice wine. In this study, the use of near-infrared (NIR) spectroscopy combined with chemometrics as a rapid tool for the discrimination of Chinese rice wine from three geographical origins (“Fujian”, “non-Shaoxing”, “Shaoxing”) has been preliminarily investigated. NIR spectra were collected in transmission mode in the wavelength range of 800–2,500 nm. Discriminant models were developed by principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least-squares analysis (DPLS). The chemical properties of Chinese rice wine were also investigated to find out the difference between samples from three varied origins. The results showed that good classification could be obtained after spectral pre-treatment. The percentage of samples correctly classified by both DA and DPLS methods in calibration and validation set was 97.2% and 100%, respectively. The results demonstrated that NIR could be used as a simple and rapid technique to distinguish Shaoxing wines from non-Shaoxing wines and Fujian wines. To further validate the ability of NIR spectroscopy, more samples should be incorporated to build a more robust model.

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Acknowledgments

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (No.30825027) and the Research Fund for the Doctoral Program of Higher Education (No. 20070335027).

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Correspondence to Yibin Ying.

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Shen, F., Yang, D., Ying, Y. et al. Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics. Food Bioprocess Technol 5, 786–795 (2012). https://doi.org/10.1007/s11947-010-0347-z

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