Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy
MA Xiao2, YUAN Hong-fu1*, SONG Chun-feng1, HU Ai-qin1, LI Xiao-yu1, ZHAO Zhong2, LI Xiu-qin3, GUO Zhen3, ZHU Zhi-qiang1
1. College of Information Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China 2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China 3. National Institute of Metrology, Beijing 100029, China
Abstract：A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model，PLS-DA model，PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.
马 啸2，袁洪福1*，宋春风1，胡爱琴1，李效玉1，赵 众2，李秀琴3，国 振3，朱志强1 . 衰减全反射红外光谱法快速分类和识别多种食用油的方法 [J]. 光谱学与光谱分析, 2015, 35(07): 1879-1884.
MA Xiao2, YUAN Hong-fu1*, SONG Chun-feng1, HU Ai-qin1, LI Xiao-yu1, ZHAO Zhong2, LI Xiu-qin3, GUO Zhen3, ZHU Zhi-qiang1. Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(07): 1879-1884.
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