食品科学2017，Vol.38Issue(16)：234-238,5.DOI:10.7506/spkx1002-6630-201716037
最小二乘支持向量机和脂肪酸融合信息应用于花生油掺伪玉米油检测
Detection of Peanut Oil Adulterated with Corn Oil Based on Information Fusion of Fatty Acid Composition and Least Squares Support Vector Machine
摘要
Abstract
This study aimed to develop a new hybrid method to detect and quantify adulterated peanut oil based on the compositions of total fatty acids and Sn-2 position fatty acids determined by gas chromatography (GC).Firstly,the information on total and Sn-2 position fatty acids was fused together by principal component analysis (PCA) to reduce the data dimension.Then,a least squares support vector machine (LS-SVM)-based model,whose parameters were optimized by particle swarm optimization (PSO),was established to discriminate between authentic and adulterated peanut oil with a 100％ recognition rate.Besides,a partial least square model and a principal component regression model were constructed to predict the level of adulteration in the mixed oils.To validate the effectiveness of these methods,a set of samples was prepared by mixing peanut oil with corn oil.Experimental results showed that the LS-SVM method a higher prediction accuracy with a root-mean-square error and a correlation coefficient of 3.452 1％ and 0.986 6,respectively,indicating that this method is a potentially valuable tool in the detection of adulterated oils.关键词
花生油/最小二乘支持向量机/脂肪酸组成/掺伪分析Key words
peanut oil/least squares support vector machine/fatty acid composition/adulteration analysis分类
轻工纺织引用本文复制引用
彭丹,李晓晓,毕艳兰..最小二乘支持向量机和脂肪酸融合信息应用于花生油掺伪玉米油检测[J].食品科学,2017,38(16):234-238,5.基金项目
国家自然科学基金青年科学基金项目(31601537) （31601537）
国家重点攻关项目(CARS15-1-10) （CARS15-1-10）
