Table of Contents Author Guidelines Submit a Manuscript
Journal of Analytical Methods in Chemistry
Volume 2015 (2015), Article ID 583841, 7 pages
Research Article

Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set

Zhisheng Wu,1,2,3 Min Du,4 Xinyuan Shi,1,2,3 Bing Xu,1,2,3 and Yanjiang Qiao1,2,3

1Beijing University of Chinese Medicine, Beijing 100102, China
2Key Laboratory of TCM-Information Engineering of State Administration of TCM, Beijing 100102, China
3Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing 100102, China
4World Federation of Chinese Medicine Societies, Beijing 100101, China

Received 25 June 2014; Accepted 4 September 2014

Academic Editor: Peter Spearman

Copyright © 2015 Zhisheng Wu 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.


This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficient , and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, , and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.