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Proceedings Paper

Detecting peanuts inoculated with toxigenic and atoxienic Aspergillus flavus strains with fluorescence hyperspectral imagery
Author(s): Fuguo Xing; Haibo Yao; Zuzana Hruska; Russell Kincaid; Fengle Zhu; Robert L. Brown; Deepak Bhatnagar; Yang Liu
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Paper Abstract

Aflatoxin contamination in peanut products has been an important and long-standing problem around the world. Produced mainly by Aspergillus flavus and Aspergillus parasiticus, aflatoxins are the most toxic and carcinogenic compounds among toxins. This study investigated the application of fluorescence visible near-infrared (VNIR) hyperspectral images to assess the spectral difference between peanut kernels inoculated with toxigenic and atoxigenic inocula of A. flavus and healthy kernels. Peanut kernels were inoculated with NRRL3357, a toxigenic strain of A. flavus, and AF36, an atoxigenic strain of A. flavus, respectively. Fluorescence hyperspectral images under ultraviolet (UV) excitation were recorded on peanut kernels with and without skin. Contaminated kernels exhibited different fluorescence features compared with healthy kernels. For the kernels without skin, the inoculated kernels had a fluorescence peaks shifted to longer wavelengths with lower intensity than healthy kernels. In addition, the fluorescence intensity of peanuts without skin was higher than that of peanuts with skin (10 times). The fluorescence spectra of kernels with skin are significantly different from that of the control group (p<0.001). Furthermore, the fluorescence intensity of the toxigenic, AF3357 peanuts with skin was lower than that of the atoxigenic AF36 group. Discriminate analysis showed that the inoculation group can be separated from the controls with 100% accuracy. However, the two inoculation groups (AF3357 vis AF36) can be separated with only ~80% accuracy. This study demonstrated the potential of fluorescence hyperspectral imaging techniques for screening of peanut kernels contaminated with A. flavus, which could potentially lead to the production of rapid and non-destructive scanning-based detection technology for the peanut industry.

Paper Details

Date Published: 1 May 2017
PDF: 8 pages
Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 102170I (1 May 2017); doi: 10.1117/12.2262801
Show Author Affiliations
Fuguo Xing, Mississippi State Univ. (United States)
Chinese Academy of Agricultural Sciences (China)
USDA-ARS (United States)
Haibo Yao, Mississippi State Univ. (United States)
Zuzana Hruska, Mississippi State Univ. (United States)
Russell Kincaid, Mississippi State Univ. (United States)
Fengle Zhu, Mississippi State Univ. (United States)
Robert L. Brown, USDA-ARS (United States)
Deepak Bhatnagar, USDA-ARS (United States)
Yang Liu, Chinese Academy of Agricultural Sciences (China)

Published in SPIE Proceedings Vol. 10217:
Sensing for Agriculture and Food Quality and Safety IX
Moon S. Kim; Kuanglin Chao; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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