食品工业科技2018，Vol.39Issue(8)：205-209,5.DOI:10.13386/j.issn1002-0306.2018.08.037
核磁共振氢谱结合PCA-SVM算法分类鉴别食用植物油
Classification of edible vegetable oils based on 1H-NMR spectroscopy and PCA-SVM
摘要
Abstract
To establish a method for the classification of edible oils by 1H-NMR spectroscopy and PCA-SVM and to compare its effectiveness with that of SIMCA.First,the PCA method was used to reduce the dimensionality of independent variables.Then the first two principal components were selected as input variables of the support vector machine (SVM),based on the established PCA-SVM prediction model.The seven kinds of oils could be identified by the proposed technique.The results revealed that the value of g and c were 1.7411 and 0.3299,respectively,which were optimized by grid method.The accuracy of prediction could reach to 100％ with the PCA-SVM model,while that was only 61.90％ with SIMCA model.It was validated by results that the combination of 1H-NMR spectroscopy with PCA-SVM could achieve the classification of edible oils quickly and effectively.关键词
核磁共振氢谱/食用植物油/主成分分析-支持向量机/分类方法Key words
1H-NMR/edible oils/PCA-SVM/classification分类
轻工纺织引用本文复制引用
李玮,姜洁,杨红梅,王浩,贾婧怡..核磁共振氢谱结合PCA-SVM算法分类鉴别食用植物油[J].食品工业科技,2018,39(8):205-209,5.基金项目
北京市科技计划重大项目(D16110500210000). （D16110500210000）
