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International Journal of Analytical Chemistry
Volume 2015, Article ID 704940, 9 pages
Research Article

Rapid Characterization of Tanshinone Extract Powder by Near Infrared Spectroscopy

1Beijing University of Chinese Medicine, Beijing 100029, China
2The Key Laboratory of TCM Information Engineering of State Administration of Traditional Chinese Medicine, Beijing 100029, China

Received 28 December 2014; Revised 4 March 2015; Accepted 8 March 2015

Academic Editor: Peter A. Tanner

Copyright © 2015 Gan Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Chemical and physical quality attributes of herbal extract powders play an important role in the research and development of Chinese medicine preparations. The active pharmaceutical ingredients have a direct impact on the herbal extract’s efficacy, while the physical properties of raw material affect the pharmaceutical manufacturing process and the final products’ quality. In this study, tanshinone extract powders from Salvia miltiorrhiza which are widely used for the treatment of cardiovascular diseases in the clinic are taken as the research object. Both the chemical information and physical information of tanshinone extract powders are analyzed by near infrared (NIR) spectroscopy. The partial least squares (PLS) and least square support vector machine (LS-SVM) models are investigated to build the relationship between NIR spectra and reference values. PLS models performed well for the content of crytotanshinone, tanshinone IIA, the moisture, and average median particle size, while, for specific surface area and tapped density, the LS-SVM models performed better than the PLS models. Results demonstrated NIR to be a valid and fast process analytical technology tool to simultaneously determine multiple quality attributes of herbal extract powders and indicated that there existed some nonlinear relationship between NIR spectra and physical quality attributes.