Enhancing count of Aspergillus colony in wheat based on nanoparticles modified chemo-responsive dyes combined with visible/near-infrared spectroscopy

Abstract

The current study aims to enhance the colony number for different Aspergillus species infection in for early mildewing in wheat using a combination of Visible/near-infrared spectroscopy (Vis/NIRs) and colorimetric sensor array (CSA) technology. The CSA was fabricated from nanoparticles polymer styrene acrylic and porous silica nanospheres (PSN) doped chemo-responsive dyes for the detection of volatile organic compounds (VOCs). The composition and relative content of VOCs of the infected wheat samples by Aspergillus were determined using headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS). The Vis/NIRs spectral data were preprocessed and subjected comparatively to chemometric models. The prediction model for the colony count established using synergy interval genetic algorithm partial least squares performs optimally for quantifying the three Aspergillus species. The correlation coefficients for all the established quantitative prediction models for A.glaucus, A.candidus and A.flavus in wheat were above 0.95 with a root mean square error < 0.5. The performance of the model based on the nanoparticles modified CSA was more sensitive, whereas, the sensor unit doped with PSN performed better at predicting the colonies number of Aspergillus. Based on accurate quantitative analysis of volatile markers, it would be possible to greatly improve the accuracy of early detection of wheat mildew within 95% confidence interval. The research offers enormous capability in mildewing analysis for food quality and safety control.