基于近红外光谱分析技术的水蜜桃产地溯源  

Traceability of honey peach origin using near infrared spectroscopy analysis techniques

作　　者：孙晓明[1] 陈小龙[1] 余向阳[1] 卞立平[1] 孙爱东[1] SUN Xiao-ming;CHEN Xiao-long;YU Xiang-yang;BIAN Li-ping;SUN Ai-dong(Institute of Food Safety and Nutrition,Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base of Ministry of Science and Technology,Nanjing 210014,China)

机构地区：[1]江苏省农业科学院农产品质量安全与营养研究所/省部共建国家重点实验室培育基地——江苏省食品质量安全重点实验室,江苏南京210014

出　　处：《江苏农业学报》2020年第2期507-512,共6页Jiangsu Journal of Agricultural Sciences

基　　金：国家重点研发计划项目(2017YFC1601000);江苏省农业科技自主创新基金项目[CX(18)3054];国家现代农业产业技术体系桃体系项目(CARS-30-5-03).

摘　　要：利用近红外光谱分析技术对来自3个省份的水蜜桃进行研究,比较主成分分析-线性判别分析(PCA-LDA)、判别偏最小二乘法(DPLS)、支持向量机(SVM)等方法对光谱数据识别的有效性差异。结果表明,SVM的准确率和召回率均高达94.47%,明显优于PCA-LDA和DPLS,更适用于水蜜桃产地溯源。In this study,honey peaches from three provinces were analyzed by near infrared spectroscopy analysis technique,and the effectiveness of principal component analysis-linear discriminant analysis(PCA-LDA),discriminant partial least squares(DPLS)and support vector machine(SVM)for spectral data recognition was compared.The results showed that the precision and recall rate of SVM were 94.47%.The SVM method was obviously better than PCA-LDA and DPLS,and it was more suitable for traceability of honey peach origin.

关 键 词：水蜜桃 产地溯源 近红外光谱 主成分分析-线性判别分析 判别偏最小二乘 支持向量机 

分 类 号：TS207.7[轻工技术与工程—食品科学]