Abstract
Volatile organic compounds (VOCs) are an important indicator for fungal-infected wheat identification. This work proposes a novel approach for toxigenic Aspergillus flavus infected wheat identification through characteristic VOCs analyzed by nano-composite colorimetric sensors. Nanoparticles of poly styrene-co-acrylic acid (PSA), porous silica nanoparticles (PSN), and metal–organic framework (MOF) were combined with boron dipyrromethene (BODIPY) to fabricate nano-composite colorimetric sensors. The combination mechanisms for nanoparticles and the information extracted from nano-colorimetric sensors by digital images were analyzed in the current work. Furthermore, linear discriminant analysis (LDA) and k-nearest neighbor (KNN) were used comparatively to analyze the data from images, and toxigenic Aspergillus flavus infected wheat samples could be 100.00% correctly identified when using the optimal KNN model. This research contributes to the practical analysis of VOCs and the detection of toxigenic Aspergillus flavus infected wheat.
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