Nondestructive Characterization of Citrus Fruit by near-Infrared Diffuse Reflectance Spectroscopy (NIRDRS) with Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA)

文献类型： 外文期刊

第一作者： Dong, Yiqing

作者： Dong, Yiqing;Li, Pao;Jiang, Liwen;Liu, Xia;Shan, Yang;Li, Pao

作者机构：

关键词： Fisher linear discriminant analysis (FLDA); near-infrared diffuse reflectance spectroscopy (NIRDRS); nondestructive analysis; orange; principal component analysis (PCA)

期刊名称：ANALYTICAL LETTERS （ 影响因子：2.267； 五年影响因子：1.823 ）

ISSN： 0003-2719

年卷期： 2022 年 55 卷 16 期

页码：

收录情况： SCI

摘要： The nondestructive characterization of citrus varieties (Egyptian sweet orange, Lane Late navel orange, Australian orange, and Blood orange) was developed based on near-infrared diffuse reflectance spectroscopy (NIRDRS) together with principal component analysis (PCA) and Fisher linear discriminant analysis (FLDA). An experiment for the penetration of NIRDRS into the peel was designed and the effects of different spectral acquisition points were investigated. Pretreatments were used to eliminate the spectral interferences. As an unsupervised pattern recognition method, PCA was used to establish the characterization models. Furthermore, supervised pattern recognition based on PCA and FLDA was employed to enhance the accuracy. The results of the penetration experiments show that near-infrared light enters the citrus peel and is able to characterize the internal composition. Even with the optimized spectral pretreatment, accurate characterization of citrus varieties was not achieved by PCA. However, the accurate characterization of citrus varieties was provided by PCA-FLDA. The accuracies of four spectral acquisition points are 95%, while the characterization accuracies of six spectral acquisition points are 100% combined with optimized spectral pretreatment. Therefore, NIRDRS with PCA-FLDA is suitable for the rapid and nondestructive characterization of citrus varieties.

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