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Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

  • Wei Wang*
  • , Xinzhi Ni
  • , Kurt C. Lawrence
  • , Seung Chul Yoon
  • , Gerald W. Heitschmidt
  • , Peggy Feldner
  • *Corresponding author for this work
  • China Agricultural University
  • United States Department of Agriculture

Research output: Contribution to journalArticlepeer-review

Abstract

The feasibility of detecting Aflatoxin B1 (AFB1) in single maize kernel inoculated with Aspergillus flavus conidia in the field, as well as its spatial distribution in the kernels, was assessed using near-infrared hyperspectral imaging (HSI) technique. Firstly, an image mask was applied to a pixel-based image mosaic to remove background and shading. Secondly, bad lines in spectra imaging caused by inherent defects of Mercury Cadmium Telluride (MCT) detector were removed through an interactive analysis based on principal component analysis (PCA). Then a PCA procedure was carried out again on the cleaned image, key wavelengths such as 1729 and 2344 nm were shown clearly from the loading line plot of the seventh principal component (PC7). And the pixel of AFB1 extracted from the 5-dimensional scatter plot space formed by five principal components (PCs) from PC4 to PC8 (especially PC7 and PC5) were taken as the input of the spectral angle mapper (SAM) classifier, accuracies of the three varieties of kernels reached 96.15%, 80%, and 82.61% respectively if kernels containing either high (≥100 ppb) or low (<10 ppb) levels of aflatoxin. A slightly better test result could be got if the kernels placed with different germ orientation. Finally, the repeatability was verified using the fourth variety of kernels.

Original languageEnglish
Pages (from-to)182-192
Number of pages11
JournalJournal of Food Engineering
Volume166
DOIs
StatePublished - 11 Jun 2015
Externally publishedYes

Keywords

  • Aflatoxin B (AFB)
  • Hyperspectral imaging (HSI)
  • Maize
  • Principal component analysis (PCA)
  • Score image
  • Spectral angle mapper (SAM) classifier

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