Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification

XU Jing; WANG Yu-tian; WU Xi-jun; ZHAO Xu

doi:10.3788/fgxb20173809.1240

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Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification

更新时间：2020-08-12

- Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification
- Chinese Journal of Luminescence Vol. 38, Issue 9, Pages: 1240-1248(2017)
- 作者机构：
  
  燕山大学河北省测试计量技术与仪器重点实验室,河北秦皇岛,066004
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China (61471312);Graduate Stud
- DOI：10.3788/fgxb20173809.1240
  CLC： TP394.1;TH691.9
- Received：23 January 2017，
  
  Revised：10 April 2017，
  
  Published Online：06 June 2017，
  
  Published：05 September 2017
- 稿件说明：
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徐婧, 王玉田, 吴希军等. 植物油荧光光谱有效信息提取及其自动分类[J]. 发光学报, 2017,38(9): 1240-1248

XU Jing, WANG Yu-tian, WU Xi-jun etc. Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification[J]. Chinese Journal of Luminescence, 2017,38(9): 1240-1248
徐婧, 王玉田, 吴希军等. 植物油荧光光谱有效信息提取及其自动分类[J]. 发光学报, 2017,38(9): 1240-1248 DOI： 10.3788/fgxb20173809.1240.

XU Jing, WANG Yu-tian, WU Xi-jun etc. Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification[J]. Chinese Journal of Luminescence, 2017,38(9): 1240-1248 DOI： 10.3788/fgxb20173809.1240.

摘要

植物油市场中出售的芝麻油、玉米油和花生油有多种品牌，不同品牌间价格差距较大，且存在假冒的现象，利用荧光光谱技术可以无损地鉴别购买油种是否为标签所标种类。主成分分析方法及平行因子方法可对这3种油种进行人工分类，但其存在类间距离相比于类内距离过小的不足，在结合传统的聚类分析方法时，会造成误分类现象。本文以提高类间距离、达到正确聚类为目标，经过比较分析，选择均值、标准差、光谱重心坐标、二阶混合中心距、相关系数、等价椭圆二倍倾角正切值、在重心激发波长处的发射光谱的偏度系数和峰度系数作为统计参数，相比于直接使用聚类方法，芝麻油分类的正确率从92.3%提高到100%，玉米油分类的正确率从75%提高到100%，花生油从57.1%提高到100%。用偏最小二乘判别分析方法验证了本文方法的合理性。本文方法可以用于植物油检测仪器的自动分类，利于市场监管及指导人们日常消费。

Abstract

There are many brands of sesame oil

corn oil and peanut oil in the vegetable oil market. There is a big difference among the price of different brands

and the phenomenon of counterfeiting is existing. Fluorescence spectroscopy can be used to identify the real label of the oil species. Principal component analysis and parallel factor method can classify manually three species of oils

but there is a problem that the distance between classes is too small compared to the in-class distance

and it is easy to cause misclassification when the traditional clustering analysis method is used. In order to improve the distance between classes to achieve the correct clustering

the mean

standard deviation

gravity coordinates of spectral center

second-order mixed center distance

correlation coefficient

equivalent elliptical double diagonal tangent

the skewness coefficient and the kurtosis coefficient of the emission spectrum at gravity excitation wavelength are selected as statistical parameters after the comparative analysis. Compared to the traditional clustering methods

the accuracy rate of sesame oil classification increased from 92.3% to 100%

and the accuracy of corn oil classification increased from 75% to 100% and the accuracy of the peanut oil classification increased from 57.1% to 100%. Finally

partial least squares discriminant analysis is used to verify the validity of the selected method. The method used here can be used for automatic classification of vegetable oil in detection equipment

which will help regulate the market and guide people's daily consumption.

关键词

Keywords

references

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