Detection of Honey Adulteration using Hyperspectral Imaging

Sahameh Shafiee, Gerrit Polder, Saeid Minaei, Nasrolah Moghadam-charkari, Saskia Van Ruth, Piotr M. Kuś

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)311-314
Issue number16
Publication statusPublished - 2016

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