Quality inspection of nectarine based on hyperspectral imaging technology

Abstract

ABSTRACT In this paper, the quality detection of nectarines based on hyperspectral imaging technology is proposed. The external quality indexes consist of the intact, cracked, rust, dysmorphic and dark damaged, while the internal quality index is composed of the soluble solid content (SSC). Firstly, 480 nectarine samples (160 intact and 320 defective nectarines) with the similar shape and size are selected. Secondly, 5 spectral principal components and 6 texture values are acquired in the spectral range of 420–1000 nm based on the indexes of external and internal quality. Finally, the methods of Partial Least Squares (PLS), Least Squares Support Vector Machine(LS-SVM) and Extreme Learning Machine (ELM) are used to establish the external quality discrimination models and internal quality prediction models, respectively. As a result, accuracies of 89.73%, 94.45% and 88.62% are obtained in the identification of the external quality. SSC is predicted with determination coefficients of 0.8540, 0.8747, 0.8146, and the root mean squared errors of 0.9849, 0.9101, 1.0732. The results obtained indicate the great potential of the LS-SVM model to predict and discriminate the inner and outer quality of nectarines.