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Analyzing the Methods to Remove Artifacts Encountered in the Development of a NIR Quantitative Model for Powder Medicines | OMICS International | Abstract
ISSN: 2155-9872

Journal of Analytical & Bioanalytical Techniques
Open Access

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Research Article

Analyzing the Methods to Remove Artifacts Encountered in the Development of a NIR Quantitative Model for Powder Medicines

Zhen Ni1,2, Yan-Chun Feng1 and Chang-Qin Hu1*

1National Institute for the Control of Pharmaceutical and Biological Products, Beijing 100050, PR China

2Tibet Institute for the Control of food and Pharmaceutical Products, Lhasa 850000, PR China

*Corresponding Author:
Dr. Chang-Qin Hu
National Key Technologies R&D Program
Foundation of China, China
Tel: +86-10-67095308
Fax: +86-10-65115148
E-mail: hucq@nicpbp.org.cn

Received date: October 10, 2010; Accepted date: December 23, 2010; Published date: December 29, 2010

Citation: Ni Z, Feng YC, Hu CQ (2010) Analyzing the Methods to Remove Artifacts Encountered in the Development of a NIR Quantitative Model for Powder Medicines. J Anal Bioanal Tech 1:114 doi: 10.4172/2155-9872.1000114

Copyright: © 2010 Ni Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The wrapper composition, particle size and crystallinity of powder drugs all affect their NIR spectra. To remove these effects, one must apply proper spectral preprocessing methods and good algorithms before developing a NIR quantitative model. Though different spectral preprocessing methods possess different functions aimed at removing different effects, we have found that use of the first derivative+vector normalization (FD+VN) removes the effects of packaging bottle and particle size in the NIR spectra. The effect of crystallinity cannot be removed with spectral preprocessing methods, but it can be reduced by choosing a proper calibration set, choosing a specific principal component, and applying partial least squares fitting.

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