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Position: Home > Articles > Non-Destructive Identification of Different Egg Varieties Based on Dielectric Properties FOOD SCIENCE 2017,38 (6) 282-286

基于介电特性的鸡蛋品种无损鉴别

作  者：
孙俊;刘彬;毛罕平;武小红;高洪燕;杨宁
单  位：
江苏大学江苏省现代农业装备与技术重点实验室;江苏大学电气信息工程学院
关键词：
介电特性;鸡蛋;品种;无损检测;支持向量机
摘  要：
为更合理有效实现鸡蛋品种分类,研究一种介电特性无损鉴别鸡蛋品种方法。本实验以4组不同品种鸡蛋(江苏镇江洋鸡蛋、江苏镇江草鸡蛋、安徽老南沟草鸡蛋、江苏东台草鸡蛋)为研究对象,采用平行极板法测量4组鸡蛋在10~200 k Hz条件下的介电特性参数,并利用支持向量机(support vector machine,SVM)算法建立鸡蛋品种鉴别分类检测模型。研究不同核函数(线性核函数、多项式核函数、RBF核函数和Sigmoid核函数)、不同参数寻优算法(网格搜索法、遗传算法和粒子群算法)选择对分类模型准确率的影响。结果表明,以线性核函数为SVM核函数、粒子群算法为SVM参数寻优算法时,建立的鸡蛋品种SVM分类模型的性能最优,其训练集正确率为95.83%,测试集正确率为95.83%。利用介电特性无损检测技术结合SVM算法,取得了很好的分类效果,为鸡蛋品种鉴别提供了一种新的快速有效的方法。
译  名：
Non-Destructive Identification of Different Egg Varieties Based on Dielectric Properties
作  者：
SUN Jun;LIU Bin;MAO Hanping;WU Xiaohong;GAO Hongyan;YANG Ning;School of Electrical and Information Engineering, Jiangsu University;Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University;
关键词：
dielectric properties;;egg;;variety;;non-destructive testing;;support vector machine(SVM)
摘  要：
For more reasonable and effective classification of eggs, a method for non-destructive identification of egg varieties based on dielectric properties was developed. In this experiment, four groups of eggs(caged eggs from Zhenjiang, Jiangsu province, and free-range eggs from Zhenjiang, Jiangsu province, from Laonangou, Anhui province, and from Dongtai, Jiangsu province) were measured for dielectric properties in the frequency range of 10–200 k Hz by the parallel plate method. A classification model for egg varieties by the support vector machine(SVM) algorithm was established. The effects of different kernel functions(linear, polynomial, RBF, and Sigmoid) and different parameter optimization algorithms(grid search, genetic algorithm, and particle swarm optimization) on the accuracy rate of the classification model were analyzed. The results showed that the performance of the SVM classification model based on linear kernel function and particle swarm optimization was the best, giving a prediction accuracy of 95.83% and 95.83% for the training and test sets, respectively. The non-destructive testing technology based on SVM algorithm using dielectric properties achieved good classification results. This study has provided a new effective method for the identification of egg varieties.

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