Application of multispectral imaging combined with machine learning methods for rapid and non-destructive detection of zearalenone (ZEN) in maize

Abstract

Maize is inevitably contaminated by zearalenone (ZEN) that will cause serious harm to human beings. In this study, multispectral imaging (MSI) technology combined with different machine learning methods were used to detect ZEN content in maize. The wavelengths that were most related to ZEN content in maize could be selected by genetic algorithm with back-propagation neural network (GA-BPNN). Our results showed that ZEN contamination level could be detected with the accuracy of 93.33 % by GA-BPNN method. In addition, for quantitative prediction of ZEN content GA-BPNN algorithm was the best method with the correlation coefficient (Rp), the root means square error (RMSEP), residual predictive deviation (RPD) and bias achieved to 0.95, 3.66 μg/kg, 5.39 and 1.55 μg/kg, respectively in prediction set. It can be concluded that multispectral imaging combined with machine learning was applicable for rapid measurement of ZEN content in maize.