High-precision recognition of wheat mildew degree based on colorimetric sensor technique combined with multivariate analysis

Abstract To achieve in-situ non-destructive monitoring of grain mildew degree and ensure food safety, this study took wheat as the object and carried out high-precision qualitative identification of wheat mildew degree based on colorimetric sensor technique. The gas chromatography-mass spectrometry (GC–MS) technique was used to analyze the volatile components of wheat samples with different levels of mold, and to determine the components and contents of indicative volatile substances. Accordingly, we choose 12 kinds of color materials which are sensitive to specific color reaction to prepare a set of colorimetric sensors. The odor information of wheat samples with different degrees of mildew was captured using the colorimetric sensor and display it in imaging. The principal component analysis (PCA) was performed on the color feature components of the preprocessed sensor difference image to achieve compression of sensor image data and feature reduction. Different linear (KNN; LDA) and non-linear (ELM; SVM) chemometric methods were used to create a high-quality qualitative identification models for wheat mildew based on sensor image features, and in the process of model calibration, the best parameters and the quantity of principal components (PCs) of the model are determined by the five-fold cross-validation method. Based on final results, the SVM identification model achieved a 100% correct identification rate for independent samples. The results of this study show that it is viable to monitor wheat mildew degree with high precision by using the colorimetric sensor technology with strong specificity combined with appropriate stoichiometry.