Literature DB >> 17361713

[Construction of universal quantitative models for determination of cefoperazone sodium for injection from different manufacturers using near infrared reflectance spectroscopy].

Huan-huan Pang1, Yan-chun Feng, Chang-qin Hu, Bing-ren Xiang.   

Abstract

Universal quantitative models using NIR reflectance spectroscopy in two different kinds of sampling mode were developed for the analysis of cefoperazone sodium for injection from different manufacturers in China. The quantitative models were established using partial least squares(PLS). Nineteen batches of cefoperazone sodium for injection samples from 9 different manufacturers were predicted by the quantitative models. The root mean square errors of cross validation (RMSECV) and the root mean square errors of prediction (RMSEP) of the model in integrating sphere sampling mode were 0. 99 and 0. 98, respectively. The values of RMSECV and RMSEP of the model in fibre sampling mode were 1. 12 and 1. 17, respectively. Based on the ICH guidelines and characteristics of NIR spectra, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, and precision. The authors' study has shown that it is feasible to build a universal quantitative model in fibre sampling mode for quick analysis of pharmaceutical products from different manufacturers. As a result of its good specificity and applicability, the model could be used for quick, non-destructive prescreening of counterfeit and substandard drugs in the mobile vehicle.

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Year:  2006        PMID: 17361713

Source DB:  PubMed          Journal:  Guang Pu Xue Yu Guang Pu Fen Xi        ISSN: 1000-0593            Impact factor:   0.589


  2 in total

Review 1.  Application of near infrared spectroscopy to the analysis and fast quality assessment of traditional Chinese medicinal products.

Authors:  Chao Zhang; Jinghua Su
Journal:  Acta Pharm Sin B       Date:  2014-05-02       Impact factor: 11.413

2.  Determination of Cefoperazone Sodium in Presence of Related Impurities by Linear Support Vector Regression and Partial Least Squares Chemometric Models.

Authors:  Ibrahim A Naguib; Eglal A Abdelaleem; Hala E Zaazaa; Essraa A Hussein
Journal:  J Anal Methods Chem       Date:  2015-11-19       Impact factor: 2.193

  2 in total

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