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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Acknowledgments
This research was supported by Program of China (863 Program) (Project No. 2012AA101904), International Science and Technology Cooperation program of China (Project No. 2013DFA11470), International Science & Technology Cooperation Program of Chongqing (Project No. CSTC2011gjhz80001), National Key Technology R&D Program (Project No. 2012BAD35B08-3), The national sparking plan project (Project No. 2012GA8110017).
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Li, Xl., Yi, Sl., He, Sl. et al. Identification of pummelo cultivars by using Vis/NIR spectra and pattern recognition methods. Precision Agric 17, 365–374 (2016). https://doi.org/10.1007/s11119-015-9426-5
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DOI: https://doi.org/10.1007/s11119-015-9426-5