食品科学 ›› 2017, Vol. 38 ›› Issue (16): 234-238.doi: 10.7506/spkx1002-6630-201716037

• 安全检测 • 上一篇    下一篇

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

彭丹,李晓晓,毕艳兰   

  1. (河南工业大学粮油食品学院,河南?郑州 450001)
  • 出版日期:2017-08-25 发布日期:2017-08-18
  • 基金资助:
    国家自然科学基金青年科学基金项目(31601537);国家重点攻关项目(CARS15-1-10)

Detection of Peanut Oil Adulterated with Corn Oil Based on Information Fusion of Fatty Acid Composition and Least Squares Support Vector Machine

PENG Dan, LI Xiaoxiao, BI Yanlan   

  1. (College of Food Science and Technology, Henan University of Technology, Zhengzhou 450001, China)
  • Online:2017-08-25 Published:2017-08-18

摘要: 将最小二乘支持向量机用于气相色谱分析实现对花生油掺伪玉米油的鉴别,基于油脂的全样和Sn-2位脂肪 酸组成的不同,采用主成分分析消除融合数据中信息重叠的部分,利用粒子群优化最小二乘支持向量机的参数, 对花生油的掺伪进行鉴别,识别率为100%;分别采用最小二乘支持向量机、偏最小二乘法和主成分回归对花生油 中掺入玉米油含量进行预测,结果表明基于脂肪酸融合信息的最小二乘支持向量机的预测均方根误差和相关系数R2 分别为3.452 1%和0.986 6,与偏最小二乘法和主成分回归法相比,最小二乘支持向量机具有更好的稳定性和预测精 度,同时也为食用油的真伪鉴别及掺伪情况确定提供一种新方法。

关键词: 花生油, 最小二乘支持向量机, 脂肪酸组成, 掺伪分析

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.

Key words: peanut oil, least squares support vector machine, fatty acid composition, adulteration analysis

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