Abstract
Aflatoxin contamination in peanut kernels seriously harms the health of humans and causes significant economic losses. Rapid and accurate detection of aflatoxin is necessary to minimize its contamination. However, current detection methods are time-consuming, expensive and destructive to samples. Therefore, short-wave infrared (SWIR) hyperspectral imaging coupled with multivariate statistical analysis was used to investigate the spatio-temporal distribution patterns of aflatoxin, and quantitatively detect the aflatoxin B1 (AFB1) and total aflatoxin in peanut kernels. In addition, Aspergillus flavus contamination was identified to prevent the production of aflatoxin. The result of validation set demonstrated that SWIR hyperspectral imaging could predict the contents of the AFB1 and total aflatoxin accurately, with residual prediction deviation values of 2.7959 and 2.7274, and limits of detection of 29.3722 and 45.7429μg/kg, respectively. This study presents a novel method for the quantitative detection of aflatoxin and offers an early warning system for its potential application.
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