支持向量机在苹果分类的近红外光谱模型中的应用  

Application of support vector machine to apple classification with near-infrared spectroscopy

作　　者：赵杰文[1] 呼怀平[1] 邹小波[1] 

机构地区：[1]江苏大学食品与生物工程学院,镇江212013

出　　处：《农业工程学报》2007年第4期149-152,共4页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家高技术"863"项目(2002AA248051);国家自然科学基金项目(30370813);教育部博士点基金(20040299009)

摘　　要：建立了一套苹果近红外光谱采集装置来减少因苹果的部位差异性而造成的试验误差。采用一种新的机器学习算法——支持向量机(SVM)建立不同产地、不同品种苹果的近红外光谱分类模型。通过选定RBF函数作为核函数,并确定合适的光谱预处理方法和核函数中惩罚系数C、正则化系数γ,使得所建立的不同品种苹果分类模型的回判识别率和预测识别率均达到100%,不同产地苹果分类模型的回判识别率为87%,预测识别率为100%,与传统的判别分析法相比其预测识别精度提高5%左右。结果表明,支持向量机可以建立高精度的苹果近红外光谱分类模型。An apple NIR Spectroscopy acquisition device was developed to diminish experimental errors in apple clasification. To improve and simplify the prediction model of classification, a new machine learning method called Support Vector Machine （SVM） was used to build near infrared （NIR） spectrum classification models for apples from different production areas and of different varieties. By choosing RBF as the core function, the suitable preprocessing method, penalty coefficient C and normal coefficient y, for the model were determined. The classification accuracies for training set and test set of the SVM model for different apple varieties were both 100%, while those of the apples from origin areas were 87% and 100%, respectively. Compared with the discrimination analysis model, the SVM models＇ accuracy increased by about 5%. The results show that SVM has a perfect performance in establishing the NIR models for apple classification.

关 键 词：支持向量机 近红外光谱 苹果 分类 

分 类 号：O433.1[机械工程—光学工程] S661.1[理学—光学]