A new approach to aflatoxin detection in chili pepper by machine vision

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dc.contributor.authorAtaş, Musa
dc.contributor.authorÇetin, Yasemin Yardımcı
dc.contributor.authorTemizel, Alptekin
dc.date.accessioned2017-05-16T13:38:39Z
dc.date.available2017-05-16T13:38:39Z
dc.date.issued2012
dc.departmentBelirleneceken_US
dc.description.abstractAflatoxins are the toxic metabolites of Aspergillus molds, especially by Aspergillus flavus and Aspergillus parasiticus. They have been studied extensively because of being associated with various chronic and acute diseases especially immunosuppression and cancer. Aflatoxin occurrence is influenced by certain environmental conditions such as drought seasons and agronomic practices. Chili pepper may also be contaminated by aflatoxins during harvesting, production and storage. Aflatoxin detection based on chemical methods is fairly accurate. However, they are time consuming, expensive and destructive. We use hyperspectral imaging as an alternative for detection of such contaminants in a rapid and nondestructive manner. In order to classify aflatoxin contaminated chili peppers from uncontaminated ones, a compact machine vision system based on hyperspectral imaging and machine learning is proposed. In this study, both UV and Halogen excitations are used. Energy values of individual spectral bands and also difference images of consecutive spectral bands were utilized as feature vectors. Another set of features were extracted from those features by applying quantization on the histogram of the images. Significant features were selected based on proposed method of hierarchical bottleneck backward elimination (HBBE), Guyon’s SVM-RFE, classical Fisher discrimination power and Principal Component Analysis (PCA). Multi layer perceptrons (MLPs) and linear discriminant analysis (LDA) were used as the classifiers. It was observed that with the proposed features and selection methods, robust and higher classification performance was achieved with fewer numbers of spectral bands enabling the design of simpler machine vision systems.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12604/658
dc.language.isoenen_US
dc.relation.publicationcategoryUluslararası Hakemli Dergi Makalesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz#KayıtKontrol#
dc.subjectMachine vision; Aflatoxin detection; Hyperspectral imaging; Food safety; Feature extraction; Feature subset selectionen_US
dc.titleA new approach to aflatoxin detection in chili pepper by machine visionen_US
dc.typeArticleen_US
dcterms.publisherElsevier-Computers and Electronics in Agriculture

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