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《Chinese Journal of Luminescence》 2017-09
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Effective Information Extraction of Vegetable Oil Fluorescence Spectrum and Its Automatic Classification

XU Jing;WANG Yu-tian;WU Xi-jun;ZHAO Xu;Measurement Technology and Instrumention Key Laboratory of Hebei Province,Yanshan University;  
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 discriminantanalysis 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.
【Fund】: 国家自然科学基金(61471312);; 河北省研究生创新项目(2016SJBS021);; 河北省自然科学基金(F2015203072 C2014203212)资助项目~~
【CateGory Index】: O433;TS227
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