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

王怡淼; 朱金林; 张慧; 赵建新; 顾小红; 朱华新

doi:10.3788/fgxb20183909.1310

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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

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 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
- 发光学报 2018年39卷第9期页码：1310-1316
- 作者机构：
  
  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. 江南大学食品学院,江苏无锡,214122
  3. 浙江大学控制科学与工程学院, 浙江杭州 310027
  4. 张家港出入境检验检疫局, 江苏张家港,215600
  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
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20183909.1310
  中图分类号： TN219
- 纸质出版日期：2018-9-5，
  
  网络出版日期：2018-5-9，
  
  收稿日期：2018-2-28，
  
  修回日期：2018-4-13，
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王怡淼, 朱金林, 张慧等. 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): 1310-1316

WANG Yi-miao, ZHU Jin-lin, ZHANG Hui etc. 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): 1310-1316
王怡淼, 朱金林, 张慧等. 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): 1310-1316 DOI： 10.3788/fgxb20183909.1310.

WANG Yi-miao, ZHU Jin-lin, ZHANG Hui etc. 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): 1310-1316 DOI： 10.3788/fgxb20183909.1310.

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（

）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

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.

关键词

近红外光谱葡萄酒遗传算法蒙特卡罗无信息变量消除因子分析

Keywords

near-infrared spectroscopywinegenetic algorithmMonte-Carlo uninformative variable eliminationfactor analysis

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