基于MC-UVE、GA算法及因子分析对葡萄酒酒精度近红外定量模型的优化研究

对葡萄酒酒精度偏最小二乘（Partial least squares，PLS）回归模型进行优化研究。使用近红外光谱仪采集葡萄酒样本的光谱数据，用于建立酒精度定量模型，实现在线快速检测。通过蒙特卡罗无信息变量消除（Monte Carlo uninformative variable elimination，MC-UVE）和遗传算法（Genetic algorithm，GA）进行变量选择，基于被选择的变量分别进行PLS和因子分析（Factor analysis，FA），建立回归模型。结果表明，MC-UVE-GA-FAR模型预测集相关系数（R²）为0.946，预测均方根误差（Root mean square error of prediction，RMSEP）为0.215，效果优于MC-UVE-GA-PLS模型。与基于全范围光谱所建PLS回归模型相比，模型效果有所提升，而且模型所选变量个数仅为6，极大地简化了模型。MC-UVE和GA算法与FA分析结合可以实现模型的优化。

Optimization of Near Infrared Quantitative Model for Wine Alcohol Content Based on MC-UVE,GA Algorithm and Factor Analysis

The optimization of the PLS regression model of wine alcohol content was studied. The near-infrared spectroscopy was used to collect the spectral data of the wine samples and the data were used to establish the quantitative model of alcohol to achieve rapid on-line detection. PLS regression model and FA model were established based on the selected variables, chosen by MC-UVE and GA. The results show that the MC-UVE-GA-FAR model, which yielded R² of 0.946 and RMSEP of 0.215, is superior to the MV-UVE-GA-PLS model. In comparison of the performance of the full-spectra PLS regression model, the model based on the selected wave numbers is much better, and 6 variables in total are selected, which greatly simplifies the model. The study indicates the MC-UVE, GA and FA can optimize the model.