Detection of Early Bruises in Honey Peaches Using Shortwave Infrared Hyperspectral Imaging

Abstract

Honey peaches can bruise during harvesting, handling, storage, transportation, and distribution. In this study, the spectral range used was 400–1100 nm, and we extracted the RGB and HSI color space characteristics of the images. After principal component analysis (PCA) of the original data, the gray histogram features of the PC1 images were extracted. Partial least squares qualitative discriminant analysis (PLS-DA) and extreme learning machine (ELM) discriminant models were established. Among the 38 color features, the PLS-DA and ELM models had a high rate of misclassification, and the best classification accuracy was 74.29%. When extracting the spectral information of the bruised sample to build the model, the highest classification accuracy was 92.86% for the 176 characteristic wavelength points of the full band. In contrast, only 40 wavelength bands were used after selecting the genetic algorithm’s valid information. The classification accuracy of the PLS-DA model was 100%, which is because the softening and browning of the peach was not apparent after early bruising. However, the changes in the tissue’s thermal properties caused by internal defects are expressed in the internal spectrum. Therefore, the shortwave NIR hyperspectral imaging technique’s spectral information can detect the early bruising of peaches.