Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning

Musa Atas; Yasemin Yardimci; Alptekin Temizel

doi:10.1117/12.883237

2 June 2011 Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning

Musa Atas, Yasemin Yardimci, Alptekin Temizel

Proceedings Volume 8027, Sensing for Agriculture and Food Quality and Safety III; 80270F (2011) https://doi.org/10.1117/12.883237
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Musa Atas, Yasemin Yardimci, and Alptekin Temizel "Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning", Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270F (2 June 2011); https://doi.org/10.1117/12.883237

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available