用二维相关近红外谱和多维主成分分析判别掺杂牛奶

杨仁杰; 刘蓉; 杨延荣; 张伟玉

doi:10.3788/OPE.20142209.2352

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用二维相关近红外谱和多维主成分分析判别掺杂牛奶

现代应用光学 | 更新时间：2020-08-13

- 用二维相关近红外谱和多维主成分分析判别掺杂牛奶
- Classification of adulterated milk by two-dimensional correlation near-infrared spectroscopy and multi-way principal component analysis
- 光学精密工程 2014年22卷第9期页码：2352-2358
- 作者机构：
  
  1. 天津大学精密测试技术及仪器国家重点实验室天津,300072
  2. 天津农学院工程技术系天津,300384
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.31201359，No.81471698）();天津市自然科学基金资助项目（No.13JCYBJC25700）()
- DOI：10.3788/OPE.20142209.2352
  中图分类号： O657.33
- 收稿日期：2013-11-05，
  
  修回日期：2013-12-31，
  
  纸质出版日期：2014-09-25
- 稿件说明：
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杨仁杰, 刘蓉, 杨延荣等. 用二维相关近红外谱和多维主成分分析判别掺杂牛奶[J]. 光学精密工程, 2014,22(9): 2352-2358

YANG Ren-jie, LIU Rong, YANG Yan-rong etc. Classification of adulterated milk by two-dimensional correlation near-infrared spectroscopy and multi-way principal component analysis[J]. Editorial Office of Optics and Precision Engineering, 2014,22(9): 2352-2358
杨仁杰, 刘蓉, 杨延荣等. 用二维相关近红外谱和多维主成分分析判别掺杂牛奶[J]. 光学精密工程, 2014,22(9): 2352-2358 DOI： 10.3788/OPE.20142209.2352.

YANG Ren-jie, LIU Rong, YANG Yan-rong etc. Classification of adulterated milk by two-dimensional correlation near-infrared spectroscopy and multi-way principal component analysis[J]. Editorial Office of Optics and Precision Engineering, 2014,22(9): 2352-2358 DOI： 10.3788/OPE.20142209.2352.

摘要

为了有效地提取牛奶中微量的掺杂物特征信息，提出了基于二维相关近红外光谱多维主成分分析（MPCA）和最小二乘支持向量机（LS-SVM）判别牛奶掺杂物的方法。首先，采集纯牛奶、掺杂尿素牛奶和掺杂三聚氰胺牛奶的一维近红外谱，并对其进行相关计算，构建各样品的二维相关近红外谱。然后，采用多维主成分分析法分析二维相关谱矩阵，压缩数据，提取相关谱的得分矩阵。最后，将提取的得分矩阵输入最小二乘支持向量机，分别建立掺杂尿素牛奶、掺杂三聚氰胺牛奶及两种掺杂牛奶与纯牛奶的LS-SVM判别模型。用所建模型对测试集未知样品进行了判别，结果显示其判别正确率分别为92.3%，96.2%，92.3%。研究结果表明：所提出的方法不仅有效提取了牛奶中掺杂物的特征信息，而且缩短了建模所需时间，取得了较好的判别效果。

Abstract

To extract effectively characteristic information of adulterants in milk

the classification models for adulterated milk were established using two-dimensional(2D) correlation near-infrared spectra combining a Multi-way Principal Component Analysis(MPCA) with Least Square Support Vector Machines(LS-SVM). First

one-dimensional near-infrared spectra of pure milk and adulterated milk samples were collected and the synchronous 2D correlation spectra of all samples were calculated. Then

the MPCA was used to reduce dimension by extracting score matrix of 2D correlation data set. Finally

LS-SVM models for urea-tainted milk

melamine-tainted milk

and the above two kinds of adulterated milk were constructed by using score matrix extracted from 2D correlation spectra as the input variables. These models were used to discriminate the known samples in the test set and the results show that the classification accuracy rates of unknown samples are 92.3%

96.2%

92.3%

respectively. It demonstrates that the proposed method not only extracts effectively feature information of adulterants in milk

but also reduces the input dimension of LS-SVM and computational time. It realizes a better classification of adulterated milk and pure milk.

关键词

Keywords

references

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红外光谱结合核隐变量正交投影法判别掺杂牛奶