Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy / 药学实践杂志

ABSTRACT

Objective To propose scalable moving-window similarity combined with Bayesian for rapid discriminating low active pharmaceutical ingredient(API)signal drugs(LAPIDs).Methods The scalable moving-window similarity method was employed by setting the window size dynamically according to API′s peak width.In each window,the correlation coefficient (CC)of API′s peak spectrum signal with LAPID′s spectrum and LAPID′s spectrum with excipient′s spectrum were calculated respectively.The LAPIDs discrimination model was established by choosing windows with most contribution of the API spec-tral signal to the LAPID spectrum as variables for Bayesian discriminant model.Results The accuracy rate of LAPIDs discrim-ination model for discriminating LAPIDs was 94.7%.The accuracy rate of the model for discriminating testing samples was 95.6%.Conclusion Bayesian discrimination model based on scalable moving-window similarity and Bayesian algorithm can quickly discriminate LAPIDs.