Abstract
Visible and short-wave near-infrared (Vis-SWNIR) spectroscopy was investigated to differentiate species of panax, including American Panax quinquefoliumI, Chinese Panax quinquefolium, and Chinese Panax ginseng. Principal component analysis (PCA) was applied before least-square support vector machine (LS-SVM) modeling, and the vast points of the spectral data (376–1,025 nm) were effectively reduced. PCA-LS-SVM differentiated species with 100% correct classification rate for the tested samples. In addition, effective wavelengths were selected according to modeling power, discrimination power, regression coefficients, loading weights, and genetic algorithms, respectively. The optimal and simplified LS-SVM model with 100% correct classification rate was achieved using the effective wavelengths selected by genetic algorithms. The results showed that Vis-SWNIR spectroscopy technique can be applied as a high accuracy and fast way for the qualitative discrimination of herb species.
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Acknowledgements
This study was supported by the National Science and Technology Support Program of China (2006BAD10A09, 2006BAD10A0711), 863 National High-Tech Research and Development Plan (2007AA10Z210), Natural Science Foundation of China (Project No: 30671213), Science and Technology Department of Zhejiang Province (Project No. 2005C12029). National Special Public Sector Research of Agriculture (200803037) and Science and Technology Department of Ningbo (Project No. 2007C10034).
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Xiaojing Chen and Di Wu contribute equally to this paper.
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Chen, X., Wu, D., He, Y. et al. Nondestructive Differentiation of Panax Species Using Visible and Shortwave Near-Infrared Spectroscopy. Food Bioprocess Technol 4, 753–761 (2011). https://doi.org/10.1007/s11947-009-0199-6
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DOI: https://doi.org/10.1007/s11947-009-0199-6