Authors

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.

Abstract in Chinese

为了实现对不同成熟度欧李果进行快速、准确识别，本研究探讨基于高光谱成像技术对欧李果成熟度进行无损检测研究的方法。利用895~1700 nm范围内的高光谱成像系统采集不同成熟时期（转色期、着色期、成熟期、完熟期）的欧李果共320个样本的高光谱数据。通过对比4种预处理方法建立的PLS模型预测精度，应用CARS、SPA、RF提取特征波长，并分别建立PLS-DA和LS-SVM判别模型。结果表明，SPA-LS-SVM模型对4类成熟度样本的判别准确率最高，其校正集和预测集的判别准确率分别为85.00%和87.50%。该研究为高光谱成像技术在欧李果成熟度的快速、无损检测提供了理论参考。