基于主成分和多类判别分析的可见-红外光谱水蜜桃品种鉴别新方法  

NEW APPROACH OF DISCRIMINATION OF VARIETIES OF JUICY PEACH BY NEAR INFRARED SPECTRA BASED ON PCA AND MDA MODEL

作　　者：李晓丽[1] 胡兴越[2] 何勇[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310029 [2]浙江大学邵逸夫医院,浙江杭州310016

出　　处：《红外与毫米波学报》2006年第6期417-420,共4页Journal of Infrared and Millimeter Waves

基　　金：国家自然科学基金(30671213);高等学校优秀青年教师教学科研奖励计划项目(02411);高等学校博士学科点专项科研基金(20040335034);浙江省重大科技攻关(2005C12029)资助项目

摘　　要：提出了一种用可见-近红外漫反射光谱技术快速鉴别水蜜桃品种的新方法.应用可见-近红外光谱仪测定三个品种水蜜桃的光谱曲线,再用主成分分析法对不同品种样本进行聚类分析,获取了水蜜桃可见-近红外光谱的特征信息,同时结合多类判别分析技术建立水蜜桃品种鉴别的模型.对经过预处理的光谱数据进行主成分分析,分析表明,以样本在第一主成分和第二主成分上的得分做出的二维散点图,对不同种类水蜜桃具有很好的聚类,能定性区分不同种类水蜜桃;经过主成分分析得到的前8个主成分的累积可信度已达94.38%,说明这8个变量能够代表绝大部分原始光谱的信息.从75个样本中随机抽取60个样本用于建立8个主成分变量的多类判别分析品种鉴别模型,余下的15个样本用于验证,准确率为100%.说明本文提出的方法具有明显的分类和鉴别作用.A new method for discrimination of varieties of juicy peach by means of visible-near infrared spectroscopy （NIRS） was developed. First, the spectral curves of three varieties juicy peaches were measured by spectrometer; the pretreated spectra data of juicy peach were analyzed through principal component analysis （PCA）. Then the diagnostic information from PCA was used as inputs of multiple discriminant analysis （MDA） for pattern recognition. The 2-dimontional plot was drawn with first and second principal components, which indicated that it was a good clustering analysis for classification varieties of juicy peach. The result of the analysis suggested that the reliabilities of first 8 principal components were more than 94.38%. 60 samples from three varieties selected randomly. Then they were used to build discriminating model. 15 unknown samples were validated by this model. The recognition rate is 100%. This model is reliable and practicable. So this study can offer a new approach to the fast discrimination of varieties of juicy peach.

关 键 词：可见-近红外光谱 水蜜桃 主成分分析 多类判别分析 鉴别 

分 类 号：S123[农业科学—农业基础科学]