发光学报, 2018, 39 (9): 1310, 网络出版: 2018-09-08   

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

Optimization of Near Infrared Quantitative Model for Wine Alcohol Content Based on MC-UVE, GA Algorithm and Factor Analysis
作者单位
1 江南大学 食品科学与技术国家重点实验室, 江苏 无锡 214122
2 浙江大学 控制科学与工程学院, 浙江 杭州 310027
3 张家港出入境检验检疫局, 江苏 张家港 215600
4 江南大学 食品学院, 江苏 无锡 214122
5 食品安全国际合作联合实验室, 江苏 无锡 214122
6 江南大学 理学院, 江苏 无锡 214122
摘要
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。
Abstract
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.

王怡淼, 朱金林, 张慧, 赵建新, 顾小红, 朱华新. 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[J]. 发光学报, 2018, 39(9): 1310. WANG Yi-miao, ZHU Jin-lin, ZHANG Hui, ZHAO Jian-xin, GU Xiao-hong, ZHU Hua-xin. Optimization of Near Infrared Quantitative Model for Wine Alcohol Content Based on MC-UVE, GA Algorithm and Factor Analysis[J]. Chinese Journal of Luminescence, 2018, 39(9): 1310.

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