
Proceedings Paper
Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learningFormat | Member Price | Non-Member Price |
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Paper Abstract
Mycotoxins are toxic secondary metabolites produced by fungi. They have been demonstrated to cause various health
problems in humans, including immunosuppression and cancer. A class of mycotoxins, aflatoxins, has been studied
extensively because they have caused many deaths particularly in developing countries. Chili pepper is also prone to
aflatoxin contamination during harvesting, production and storage periods. Chemical methods to detect aflatoxins are
quite accurate but expensive and destructive in nature. Hyperspectral and multispectral imaging are becoming
increasingly important for rapid and nondestructive testing for the presence of such contaminants. We propose a compact
machine vision system based on hyperspectral imaging and machine learning for detection of aflatoxin contaminated
chili peppers. We used the difference images of consecutive spectral bands along with individual band energies to
classify chili peppers into aflatoxin contaminated and uncontaminated classes. Both UV and halogen illumination
sources were used in the experiments. The significant bands that provide better discrimination were selected based on
their neural network connection weights. Higher classification rates were achieved with fewer numbers of spectral bands.
This selection scheme was compared with an information-theoretic approach and it demonstrated robust performance
with higher classification accuracy.
Paper Details
Date Published: 2 June 2011
PDF: 12 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270F (2 June 2011); doi: 10.1117/12.883237
Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)
PDF: 12 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270F (2 June 2011); doi: 10.1117/12.883237
Show Author Affiliations
Musa Atas, Middle East Technical Univ. (Turkey)
Yasemin Yardimci, Middle East Technical Univ. (Turkey)
Yasemin Yardimci, Middle East Technical Univ. (Turkey)
Alptekin Temizel, Middle East Technical Univ. (Turkey)
Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)
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