基于高光谱技术和化学计量学的苹果身份识别方法研究

摘要：本研究旨在利用高光谱技术和化学计量学方法，对来自四个产区的十个品种的苹果进行精准识别，以提高苹果鉴 别的准确性。研究采集了苹果样品的理化数据和光谱图像，并对光谱数据进行了预处理和特征选择。随后，建立了K近 邻算法（（ KNN）和支持向量机-径向基核函数（（ SVM-RBF）模型。针对基于产地信息的苹果品种识别，研究分别对同产地 和不同产地的情况进行了分析。在同产地建模中，KNN模型表现最佳，建模集和预测集的准确率、F1分数和召回率均达 到100%。PLS-DA模型的预测结果略逊于KNN，而SVM建模效果最差。对于不同产地苹果的预测，PLS-DA模型表现 最优，建模集和预测集的准确率、F1分数和召回率均超过98%。在基于品种信息的苹果产地识别方面，研究分别对同品 种和不同品种的情况进行了探讨。对于同品种苹果的产地识别，SD-PLS-DA模型效果最佳，建模集的准确率、F1分数和 召回率分别为97.38%、99.59%和99.60%，预测集的相应指标分别为99.61%、97.35%和97.84%。对于不同品种的苹果， KNN 和 PLS-DA 模型均表现良好，其中 KNN 模型优于 PLS-DA 模型，建模集和预测集的所有指标均达到 100%。研究 结果表明，本研究中采用的品种和产地识别模型能够有效实现苹果的精准鉴别，具有较高的识别准确性。

关键词：高光谱技术；化学计量学方法；苹果鉴别；K近邻算法；偏最小二乘判别分析

期刊：Infrared Physics and Technology

DOI：https://doi.org/10.1016/j.infrared.2023.104996

设备：GaiaSorter

Research on apple identity recognition method based on hyperspectral technology and chemometrics method

Abstract：This study focuses on using hyperspectral technology and chemometric methods to identify apples from four production areas and ten varieties to improve the accuracy of apple identiffcation. Physical and chemical data and hyperspectral images of apple samples were collected for preprocessing and feature selection of spectral data. In the next step, K-nearest neighbour algorithm (KNN) and the support vector machine-radial basis kernel function (SVM-RBF) models were established. Regarding the identiffcation of apple varieties based on origin information, research was conducted for both the same and different origins. For the same origin modeling, the best results were obtained from the KNN model, with accuracy, F1-score, and recall all reaching 100% for both the modeling and prediction sets. The PLS-DA prediction results were slightly inferior to KNN, and SVM modeling produced the worst results. The PLS-DA model produced the best results for predicting different apple origins, with accuracy, F1- score, and recall all achieving over 98% for both the modeling and prediction sets. Regarding the identiffcation of apple origin based on variety information, research was conducted for both the same and different varieties. The best model for identifying the origin of apples with the same variety was SD-PLS-DA, with accuracy, F1-score, and recall of 97.38%, 99.59%, and 99.60% for the modeling set, and 99.61%, 97.35%, and 97.84% for the prediction set. For different varieties, both the KNN and PLS-DA models were effective, with the KNN model outperforming the PLS-DA model, and all indicators reaching 100% for both the modeling and prediction sets. The results indicated that the use of variety and origin identiffcation models in this study was effective for apple identiffcation, and can accurately identify apples.

Keywords：Hyperspectral; Chemometrics Method; Apple identity; KNN; PLS-DA