Identification of pummelo cultivars by using Vis/NIR spectra and pattern recognition methods
文献类型： 外文期刊
作者： Li, Xun-lan 1 ; Yi, Shi-lai 3 ; He, Shao-lan 3 ; Lv, Qiang 3 ; Xie, Rang-jin 3 ; Zheng, Yong-qiang 3 ; Deng, Lie 3 ;
作者机构： 1.Chongqing Acad Agr Sci, Chongqing 401329, Peoples R China
2.Southwest Univ, Coll Hort & Landscape, Chongqing 400715, Peoples R China
3.Southwest Univ, Chinese Acad Agr Sci, Citrus Res Inst, Chongqing 400712, Peoples R China
关键词： Vis/NIR spectroscopy;Pummelo;SIMCA;PLS-DA;BPNN;LS-SVM
期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）
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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.
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