Early detection of wheat Aspergillus infection based on nanocomposite colorimetric sensor and multivariable models

Abstract

This work presents a novel work for the early detection of Aspergillus infected wheat by using porous silica nanospheres (PSN) to fabricated nanocomposite colorimetric sensors and multivariable models. The volatile organic compounds (VOCs) of three Aspergillus (Aspergillus glaucus, Aspergillus candida, and Aspergillus flavus) were investigated at different mildew stages. The synthesized nano-porous silica was employed to modify the colorimetric sensor. And then, the nanocomposite colorimetric sensor was used to analyze the VOCs metabolized by the infected wheat with different mildew degrees. The result shows that the nanocomposite colorimetric sensor could improve the chemical response of Aspergillus infected wheat in the initial stage (3–4lgCFU/g colonies) and mild mildew (4–5lgCFU/g colonies), and these infected wheat samples could be accurately distinguished. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to establish the identification model of infected wheat samples, it shows that 100% of infected wheat samples were correctly identified. Based on the achieved results, this work demonstrated that nanocomposite colorimetric sensor modified by PSN was an effective way for early detection of different Aspergillus infections in wheat.