The Increasing of Discrimination Accuracy of Waxed Apples Based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis

Abstract

To increase the classification accuracy and stability of the prediction model, an approach to evaluate the quality of samples’ hyperspectral image is needed. The spectral correlation analysis of each pixel was used to determine quality of the sample’s hyperspectral image in this study. 400 hyperspectral image ROIs were extracted from 20 apples (10 apples with waxed and the other 10 apples without any waxed) and the data were separated into 300 as train set and 100 as test set randomly. The experimental group data were evaluated by the spectral correlation analysis, and only qualified data were used for model training. The control group data were all used for modeling training. The least squares support vector machine (LS-SVM) model were used to establish the classification model between the hyperspectral image and waxed situation. The prediction result showed the classification accuracy were 94% and 86% when the low-quality sample data for training were filtered by spectral correlation analysis. By evaluating the quality of the hyperspectral image measured, more reliable prediction results can be obtained, which can make the noninvasive discrimination of food safety come to the practice application sooner.