A New Multivariate Classification and Identification Method of Spectroscopy
WU Yan-xian2, SONG Chun-feng1, 4, YUAN Hong-fu1, 4*, ZHAO Zhong2, TIAN Ling-ling3, YAN Yu-jiang3, TIAN Wen-liang5, WANG Li5
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. Beijing Wool and Linen Fabric Quality Supervision and Inspection Station, Beijing 100085, China
4. Key Laboratory of Carbon Fiber, Beijing 100029, China
5. Inner Mongolia Fibre Inspection Bureau, Huhhot 010000, China
Abstract：In the SIMCA, the parameters of PCA model and F test are used to construct T2 and Q for classification, and Euclidean distance is used to determine the range of sample distribution of the model. Since the range which is defined by Euclidean distance is a circle in the plane of T2 vs Q, the boundary of actual samples which distributes in some directions and irregular space cannot be presented accurately. Besides, SIMCA is still inaccurate for classification and identification in theory. Therefore, a new multivariate classification and identification method was proposed using Mahalanobis Distance instead of Euclidean distance in this paper. Experiments of infrared spectra of blending edible oils and near infrared spectra of animal furs were designed to compare the performance of the new method and SIMCA. The recognition rates of the new method and SIMCA for three kinds of furs are 85.5% and 75%, respectively. The recognition rates of the new method and SIMCA for two classes of blending edible oils are 65% and 55%, respectively. It has shown that the new method is superior to SIMCA in the performance of discriminating the different materials with a small difference in their chemical composition.
吴妍娴，宋春风，袁洪福，赵 众，田玲玲，闫玉疆，田文亮，王 莉. 一种新型光谱多元分析模式识别方法[J]. 光谱学与光谱分析, 2017, 37(08): 2493-2499.
WU Yan-xian, SONG Chun-feng, YUAN Hong-fu, ZHAO Zhong, TIAN Ling-ling, YAN Yu-jiang, TIAN Wen-liang, WANG Li. A New Multivariate Classification and Identification Method of Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(08): 2493-2499.
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