DISCRIMINATION OF CERASUS HUMILIS FRUIT MATURITY BASED ON HYPERSPECTRAL IMAGING TECHNOLOGY

Abstract

In order to realize the rapid and accurate identification of different maturity of Cerasus humilis fruit, this study explored the nondestructive testing method of Cerasus Humilis fruit maturity based on hyperspectral imaging technology. The hyperspectral data of 320 samples of Cerasus humilis fruit were collected by using a hyperspectral imaging system in the range of 895~1700 nm. By comparing the prediction accuracy of the partial least squares (PLS) model established by four preprocessing methods, the competitive adaptive reweighted algorithm (CARS), successive projection algorithm (SPA), and random frog (RF) were used to extract characteristic wavelengths, and partial least squares-discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) discriminant models were established. The results showed that the SPA-LS-SVM model had the highest discrimination accuracy for the four types of maturity samples, and the discrimination accuracy of the correction set and prediction set were 85.00% and 87.50%, respectively. This study provides a theoretical reference for the rapid and nondestructive testing of the maturity of Cerasus Humilis fruit by hyperspectral imaging technology.