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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #318951

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

item WANG, WEI - China Agriculture University
item Ni, Xinzhi
item Lawrence, Kurt
item Yoon, Seung-Chul
item Heitschmidt, Gerald - Jerry
item Feldner, Peggy

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2015
Publication Date: 6/6/2015
Citation: Wang, W., Ni, X., Lawrence, K.C., Yoon, S.C., Heitschmidt, G.W., Feldner, P.W. 2015. Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging. Journal of Food Engineering. Volume 166, Pages 182–192 (2015).

Interpretive Summary: Maize kernels are susceptible to aflatoxins produced by Aspergillus species of fungi. Especially, Aflatoxin B1 (AFB1) in maize kernels is the most toxic type of aflatoxin and a carcinogen. Rapid and non-destructive detection of AFB1 in individual maize kernels is important for food safety because it enables early detection and removal of contaminated kernels from the food chain. A study was conducted to develop a near-infrared hyperspectral imaging technique in the wavelength range between 1,000 and 2,500 nm to rapidly and non-destructively detect maize kernels contaminated with AFB1. The results of the study demonstrated the feasibility of near-infrared hyperspectral imaging to detect AFB1 in single maize kernels with classification accuracy from 80% up to 96% and to predict spatial distributions of AFB1 in each imaged kernel.

Technical 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.