基于介电频谱技术的甜瓜品种无损检测  

Nondestructive testing of muskmelons varieties based on dielectric spectrum technology

作　　者：王转卫[1] 赵春江[2] 商亮[1] 孔繁荣[1] 翁小凤 

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]国家农业信息化工程技术研究中心,北京100097

出　　处：《农业工程学报》2017年第9期290-295,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家科技支撑计划(2015BAD19B03);陕西省农业科技攻关项目(2016NY170)联合资助

摘　　要：研究应用介电频谱技术实现对甜瓜的无损、快速及准确分类。以陕西杨凌某4家大棚外形相似的"红阎良"、"新早蜜"、"208"及"玛瑙"等4类成熟甜瓜为研究对象,采用矢量网络分析仪测量共246个样品在20 MHz^4 500 MHz的介电频谱。用Kennard-Stone方法划分校正集与验证集,分别建立支持向量机(support vector machine,SVM)和极限学习机(extremelearning machine,ELM)种类判别模型,并比较全频谱(full frequencies,FF)、连续投影算法(successive projectionalgorithm,SPA)和主成分分析(principal componentanalysis,PCA)等不同预处理方法对模型精度的影响。结果表明:1)所建6个判别模型验证集总正确率均大于96%,均可用于甜瓜种类的判别。2)对比3种预处理方法,FF完好地保留了样品的原始信息,2种判别模型的验证集总正确率都达到了100%,但由于存在干扰信息导致模型稳定性不好;PCA方法选择能代表原谱信息99.99%的前10个主成分信息用来建模,能有效简化模型,但验证集每个模型均有误判,两种判别模型总正确率分别为96.72%及98.36%;SPA从202个变量中提取17个特征变量参与建模,验证模型整体稳定性较其他两种好,总正确率分别达到96.72%和100%。3)综合考虑判别模型的验证集总正确率及模型稳定性,SPA-ELM模型判别效果最好,验证集总正确率达到100%,更适用于基于介电频谱的甜瓜种类判别。因此,基于甜瓜的介电频谱,通过支持向量机和极限学习机方法可以成功区分甜瓜种类,为甜瓜的无损检测及分类研究提供了一种新方法。To classify muskmelons quickly and accurately based on dielectric spectroscopy, dielectric properties of 4 kinds of melons （a total of 246） were measured with network analyzer over the frequency range from 20 to 4 500 MHz. The samples were selected from 4 different greenhouses in Yangling, Shaanxi Province, which had similar shape and size, and had no injury and disease. All samples were divided into calibration set and validation set with a ratio of about 3：1 based on Kennard-Stone method. Methods of support vector machine （SVM） and extreme learning machine （ELM） were applied to establish discriminative models of muskmelons. We chose 2 different variable selecting methods as pre-processing methods before modeling. One method was principal component analysis （PCA） for data dimension reduction, and the other was successive projections algorithm （SPA） for characteristic variables selecting. The model validating effects after the processing of PCA and SPA were used to compare with that with no pre-processing; besides, directly modeling with full frequencies （FF） spectra data was also adopted. The results were shown as below： 1） All discriminative models under FF, PCA and SPA methods could be used for classifying muskmelons. The total correct rate of each validation set reached over 96%, and the ELM modeling method was better than SVM method as a whole. 2） The models based on the FF method retained all original information of the frequency spectra data, so it had the highest validation correct rate, up to 100%. But its stability and reliability were not good enough because of the existing interference information. Under the PCA method, the accumulating contribution rate of the former 10 principal components extracted from all variables approached to 99.99%, which well reflected original information while simplifying the model in some degree and improved performance of models, however, the results were not very stable and the total correct rate of 2 models was much lower than others, up

关 键 词：介电特性 支持向量机 模型 甜瓜 极限学习机 分类 

分 类 号：O657[理学—分析化学]