Identification of pummelo cultivars by using Vis/NIR spectra and pattern recognition methods |
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Authors: | Xun-lan Li Shi-lai Yi Shao-lan He Qiang Lv Rang-jin Xie Yong-qiang Zheng Lie Deng |
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Institution: | 1.Chongqing Academy of Agricultural Sciences,Chongqing,China;2.College of Horticulture and Landscape,Southwest University,Chongqing,China;3.Citrus Research Institute,Southwest University – Chinese Academy of Agricultural Sciences,Chongqing,China |
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Abstract: | 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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