文章基本信息

标题：Detection of Honey Adulteration using Hyperspectral Imaging
本地全文：下载
作者：Sahameh Shafiee ; Gerrit Polder ; Saeid Minaei 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：16
页码：311-314
DOI：10.1016/j.ifacol.2016.10.057
语种：English
出版社：Elsevier
摘要：This study investigates the application of hyperspectral imaging system and data mining based classifiers for honey adulteration detection. Hyperspectral images from pure and adulterated samples were captured in using a VIS-NIR hyperspectral camera (400 – 1000 nm). After preprocessing the images, five different data mining based techniques, including artificial neural network (ANN), support vector machine (SVM), Linear discriminant analysis (LDA), Fisher and Parzen classifiers were applied for supervised image classification. Classifier test results show the highest classification accuracy of 95% for ANN classifier. Other classifiers including SVM with radial basis kernel function (92%), LDA (90%), Fisher (89 %), and Parzen with 84% correct classification rate also showed acceptable results. This research shows the capability of hyperspectral imaging for honey authentication.
关键词：Honey adulterationHyperspectral imagingArtificial neural networkSupport vector machineLinear discriminant classifier