光谱学与光谱分析 |
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Feasibility of Using Ethanol Liquor NIRS Model to Predict Ethanol in Vinous Ferment Liquid |
GENG Zhao-xi,SUN Qian,TIAN Lei,HAN Dong-hai* |
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China |
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Abstract The robust NIRS model must be developed by the representative samples and precise chemical values, taking much of work. To reduce the calibration work, the present paper explored the NIRS model developed using ethanol liquor to predict ethanol of the wine samples. The authors used the gene arithmetic (GA) method to select the calibration region(2 245-2 320 nm)which has relatively high correlation with the consistency of ethanol in ethanol liquor and has little interfere by other components in wine. To remove the systematic error between the calibration set of ethanol liquor and the prediction set of turbid vinous ferment liquid, according to the method of slope/bias, the authors selected 21 samples in prediction set which can represent the range of consistency of vinous ferment liquid to revise the ethanol model in order to predict the remaining wine samples well. After the calculation, the authors obtained the bias and the slope to be 0.523 3 and 0.980 8, respectively. Then we predicted the other turbid samples of wine using the ethanol liquor model after being revised by the slope/bias method. And the prediction model for the ethanol of turbid samples was developed, with r, RPD and RSD for the prediction model for ethanol of samples being 0.99%, 11.71% and 3.11%, respectively, indicating that the ethanol liquor model is robust and can serve as the model of vinous ferment liquid to detect the ethanol of the wine. So this method can largely reduce the calibration work during the NIR calibration process, and has the practical feasibility and application value.
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Received: 2008-05-28
Accepted: 2008-09-08
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Corresponding Authors:
HAN Dong-hai
E-mail: caundt@cau.edu.cn
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[1] Weeks S. Australian Grape-Grower and Wine Maker, 2005, 19: 21. [2] Garcia-Jares C M, Medina B. Fresenius Journal of Analysis Chemistry, 1997, 357(1): 86. [3] Manley M, Zyl A Van, Wolf E E H. South African Journal of Enology and Viticulture, 2001, 22(2): 93. [4] Gishen M, Dambergs R G, Kambouris A, et al. Proceeding of the 10th International Conference on Near Infrared Spectroscopy, 2002. 187. [5] Cozzolino D, Kwiatkowski M J, Parker M, et al. Food Chemistry, 2003, 80: 7703. [6] Cozzolino D, Kwiatkowski M J, Parker M. Analytica Chimica Acta, 2004, 513: 73. [7] Urbano-Cuadrado M, Luque de Castro M D, Pérez-Juan P M, et al. Analytica Chimica Acta, 2004, 527: 81. [8] LU Jia-hui, TENG Li-rong, JIANG Fu-ming, et al(逯家辉,滕利荣,蒋富明,等). Journal of Jilin University(Science Edition)(吉林大学学报·理学版), 2002, 41(2): 245. [9] QIU Fang-ping, LU Jia-hui, TENG Li-rong, et al(邱芳萍,逯家辉,滕利荣,等). Journal of Northeast Normal University(Natural Science Edition)(东北师范大学报·自然科学版), 2002, 34(4): 50. [10] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍,袁洪福,徐广通,等). Modern Analysis Technique of Near-Infrared Spectrum(First, ed.)(现代近红外光谱分析技术·第1版). Beijing: China Petrochemical Press(北京:中国石化出版社),2000. 69, 167. [11] HU Bin, CHEN Da, SU Qing-de(胡斌, 陈达, 苏庆德). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(7): 1049.
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