Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis
Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis
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DOI：
10.1016/j.foodchem.2015.05.001
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发表时间：
2015-12-01
期刊：
影响因子：
8.8
通讯作者：
Wang, Xuede
中科院分区：
文献类型：
--
作者：
Peng, Dan;Bi, Yanlan;Wang, Xuede
本研究采用气相色谱法（GC）建立了一种检测和定量芝麻油掺伪植物油的分级方法。首先利用支持向量机（SVM）算法建立芝麻油真伪鉴别模型。在此基础上，建立了基于支持向量机的混合油掺杂类型识别模型。最后，采用偏最小二乘法建立了芝麻油的预测模型。为了验证这种方法，通过将真实的芝麻油与五种植物油混合来制备746个样品。预测结果表明，该方法对芝麻油掺伪的检出限为5%，预测的均方根误差为1.19%~ 4.29%，为芝麻油掺伪的检测和定量提供了一种有效的方法。(C)2015爱思唯尔有限公司版权所有。
This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil. (C) 2015 Elsevier Ltd. All rights reserved.