A new strategy to iteratively update scalable universal quantitative models for the testing of azithromycin by near infrared spectroscopy

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• 作者：WenBo Zou (14807) (24807)
  YanChun Feng (24807)
  JiXiong Dong (24807)
  DanQing Song (14807)
  ChangQin Hu (14807) (24807)
  
• 关键词：near infrared spectroscopy ; universal quantitative model ; model updating ; azithromycin
• 刊名：SCIENCE CHINA Chemistry
• 出版年：2013
• 出版时间：April 2013
• 年：2013
• 卷：56
• 期：4
• 页码：533-540
• 全文大小：757KB
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• 作者单位：WenBo Zou (14807) (24807)
  YanChun Feng (24807)
  JiXiong Dong (24807)
  DanQing Song (14807)
  ChangQin Hu (14807) (24807)
  
  14807. National Institutes for Food and Drug Control, Beijing, 100050, China
  24807. Institute of Medicinal Biotechnology, Academy of Medical Science and Peking Union Medical College, Beijing, 100050, China
  
• ISSN：1869-1870

文摘

The training set of a universal near infrared (NIR) model for quantitative analysis of a drug should cover as many samples of this drug in the market as possible. Inevitably the model may fail for new products that have different excipients and production processes. In such circumstances the model should be updated. We here propose a new strategy to iteratively update a universal NIR quantitative model for azithromycin. We prove that universal quantitative models generated from this new strategy are comparably effective for azithromycin injection powders and azithromycin tablets, compared to the strategy using hierarchical clustering method which we reported previously. Furthermore, we establish the correlation coefficient r between a new sample and the training set samples can be used to decide whether or not the model should be updated.