Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration  

作　　者：Mahmoud Soltani Firouz Mahmoud Omid Mehrdad Babaei Mahdi Rashvand 

机构地区：[1]Department of Agricultural Machinery Engineering,Faculty of Agricultural Engineering and Technology,University of Tehran,Karaj,Iran [2]Machine Design and Mechatronics Department,Institute of Mechanics,Iranian Research Organization for Science and Technology,Tehran,Iran

出　　处：《Information Processing in Agriculture》2022年第2期233-242,共10页农业信息处理（英文）

摘　　要：Adulteration using cheap vegetable oils into expensive oils such as sesame oil is a considerable challenge in the edible oil market. To discriminate pure and adulterated sesame oilwith sunflower and canola oils (commonly used as an adulterant to the high-price oils),dielectric spectroscopy was applied in the range of 40 kHz–20 MHz. The principal component analysis (PCA) plots were able to distinguish the pure sesame oil, while it was impossible to separate the adulterated oils based on the kind of adulteration. The correlationbased feature selection (CFS) method was used to select the more relevant dielectric datawithin the spectrum and to reduce the dimensionality of the input vector belongs to theartificial neural network (ANN). The ANN classifier with topology of 19-5-4 structureshowed a perfect accuracy of 100% in detecting the authentic and the adulterated sesameoil. The regression ANN with the topology of 15-5-1, 21-8-1 and 10-11-1 were the mostrobust models in quantifying the amount of adulteration in sesame oil generated by sun-flower oil, canola oil and sunflower + canola oils, with R2Test of 1, 1 and 0.999 9, respectively.The proposed technique is a powerful and simple method to detect and quantify adulteration of sesame oil. The novelty of this research is capability of used system for authentication of adulterated sesame oil using low frequency. Furthermore, the developed systemhas a good capability for other types of sesame oil adulterations as well as to detect adulteration in other expensive edible oils.

关 键 词：ADULTERATION Artificial neural network CHEMOMETRICS Sesame oil Correlation-based feature selection Principal component analysis 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] S565[自动化与计算机技术—控制科学与工程]