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Rapid Assessment of Deoxynivalenol Content in Barley Using Hyperspectral imaging

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2021 ASABE Annual International Virtual Meeting  2100348.(doi:10.13031/aim.202100348)
Authors:   Wen-Hao Su
Keywords:   Hyperspectral imaging, Deoxynivalenol, Food safety, Feature variable selection, Machine learning.

Abstract. As a mycotoxin produced by the causal pathogen Fusarium graminearum, Deoxynivalenol (DON) poses serious health risks to both humans and livestock. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. In this study, the feasibility of using hyperspectral imaging (382–1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels was explored. It was found that partial least square discriminant analysis (PLSDA) was able to discriminate kernels into separate classes corresponding to their DON levels. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). CARS-ISSPA-PLSDA discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels with high accuracy. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

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