Abstract：In order to improve the technical level of the rapid detection of liquor fermented grains, in this paper, use near infrared spectroscopy technology to quantitative analysis moisture, starch, acidity and alcohol of liquor fermented grains. Using CARS, iPLS and no information variable elimination method (UVE), realize the characteristics of spectral band selection. And use the multiple scattering correction (MSC), derivative and standard normal variable transformation (SNV) pretreatment method to optimize the models. Establish models of quantitative analysis of fermented grains by PLS, and in order to select the best modeling method, using R2, RMSEP and optimal number of main factors to evaluate models. The results showed that the band selection is vital to optimize the model and CARS is the best optimization of the most significant effect. The calculation results showed that R2 of moisture，starch，acidity and alcohol were 0.885, 0.915, 0.951, 0.885 respectively and RMSEP of moisture，starch，acidity and alcohol were 0.630, 0.519, 0.228, 0.234 respectively. After optimization, the model prediction effect is good, the models can satisfy the requirement of the rapid detection of liquor fermented grains, which has certain reference value in the practical.
熊雅婷，李宗朋，王 健*，张 英，王淑军，尹建军，宋全厚. 近红外光谱波段优化在白酒酒醅成分分析中的应用[J]. 光谱学与光谱分析, 2016, 36(01): 84-90.
XIONG Ya-ting, LI Zong-peng, WANG Jian*, ZHANG Ying, WANG Shu-jun, YIN Jian-jun, SONG Quan-hou. The Near Infrared Spectral Bands Optimal Selection in the Application of Liquor Fermented Grains Composition Analysis . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(01): 84-90.
