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DISCRIMINATION OF MEDICINE RADIX ASTRAGALI FROM DIFFERENT GEOGRAPHIC ORIGINS USING MULTIPLE SPECTROSCOPIES COMBINED WITH DATA FUSION METHODS

Abstract

Raman spectra and ultraviolet-visible absorption spectra of four different geographic origins of radix astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet-visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet-visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data.

About the Authors

H. -Y. Wang
School of Management Science and Engineering, Nanjing University of Finance and Economics; Jiangsu Province Institute of Quality and Safety Engineering
Russian Federation


C. . Song
School of Management Science and Engineering, Nanjing University of Finance and Economics
Russian Federation


M. . Sha
School of Management Science and Engineering, Nanjing University of Finance and Economics; Jiangsu Province Institute of Quality and Safety Engineering
Russian Federation


J. . Liu
School of Management Science and Engineering, Nanjing University of Finance and Economics; Jiangsu Province Institute of Quality and Safety Engineering; Jiangsu Yi Pu Heng Technology Company Limited
Russian Federation


L. -P. Li
Jiangsu Province Institute of Quality and Safety Engineering
Russian Federation


Z. -Y. Zhang
School of Management Science and Engineering, Nanjing University of Finance and Economics; Jiangsu Province Institute of Quality and Safety Engineering
Russian Federation


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Review

For citations:


Wang H.-., Song C., Sha M., Liu J., Li L.-., Zhang Z.-. DISCRIMINATION OF MEDICINE RADIX ASTRAGALI FROM DIFFERENT GEOGRAPHIC ORIGINS USING MULTIPLE SPECTROSCOPIES COMBINED WITH DATA FUSION METHODS. Zhurnal Prikladnoii Spektroskopii. 2018;85(2):299-304. (In Russ.)

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ISSN 0514-7506 (Print)