期刊
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
卷 140, 期 -, 页码 130-136出版社
ELSEVIER
DOI: 10.1016/j.jpba.2017.02.053
关键词
Polymorphism; Fusidic acid; Diffuse reflectance FTIR; Diffuse reflectance FT-NIR; Raman; Partial least square
资金
- National Found for Fostering Talents of Basic Science [J1310032]
Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multi-variate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. (C) 2017 Elsevier B.V. All rights reserved.
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