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Wu Z.,Beijing University of Chinese Medicine | Wu Z.,Key Laboratory of TCM Information Engineering | Wu Z.,Beijing Key Laboratory for Basic and Development Research on Chinese Medicine | Du M.,World Federation of Chinese Medicine Societies | And 9 more authors.
Journal of Analytical Methods in Chemistry

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 R P = 0.9221, 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, R P = 0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set. © 2015 Zhisheng Wu et al. Source

Zhou L.,Beijing University of Chinese Medicine | Zhou L.,Key Laboratory of TCM Information Engineering | Zhou L.,Beijing Key Laboratory for Basic and Development Research on Chinese Medicine | Xu M.,Beijing University of Chinese Medicine | And 11 more authors.
Drug Testing and Analysis

Near-infrared chemical imaging (NIR-CI) is an emerging technology that combines traditional near-infrared spectroscopy with chemical imaging. Therefore, NIR-CI can extract spectral information from pharmaceutical products and simultaneously visualize the spatial distribution of chemical components. The rapid and non-destructive features of NIR-CI make it an attractive process analytical technology (PAT) for identifying and monitoring critical control parameters during the pharmaceutical manufacturing process. This review mainly focuses on the pharmaceutical applications of NIR-CI in each unit operation during the manufacturing processes, from the Western solid dosage forms to the Chinese materia medica preparations. Finally, future applications of chemical imaging in the pharmaceutical industry are discussed. © 2016 John Wiley & Sons, Ltd. Source

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