Identification of pummelo cultivars by using Vis/NIR spectra and pattern recognition methods

文献类型： 外文期刊

第一作者： Li, Xun-lan

作者： Li, Xun-lan;Li, Xun-lan;Li, Xun-lan;Yi, Shi-lai;He, Shao-lan;Lv, Qiang;Xie, Rang-jin;Zheng, Yong-qiang;Deng, Lie

作者机构：

关键词： Vis/NIR spectroscopy;Pummelo;SIMCA;PLS-DA;BPNN;LS-SVM

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.4； 五年影响因子：6.1 ）

ISSN：

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收录情况： SCI

摘要： Vis/NIR spectroscopy was used in combination with pattern recognition methods to identify cultivars of pummelo (Citrus grandis (L.) Osbeck). A total of 240 leaf samples, 60 for each of the four cultivars were analyzed by Vis/NIR spectroscopy. Soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLS-DA), back propagation neural network (BPNN) and least squares support vector machine (LS-SVM) were applied to the spectral data. The first 8 principal components extracted by principal component analysis were used as inputs in building the BPNN and the LS-SVM models. The results showed that a 97.92 % of discrimination accuracy was achieved for both the BPNN and the LS-SVM models when used to identify samples of the validation set, indicating that the performance of the two models was acceptable. Comparatively, the results of the PLS-DA and the SIMCA models were unacceptable because they had lower discrimination accuracy. The overall results demonstrated that use of Vis/NIR spectroscopy coupled with the use of BPNN and LS-SVM could achieve an accurate identification of pummelo cultivars.

分类号： S

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