Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy

CHEN Xiujuan; CHEN Hui; WEI Hang; LIU Yan; LU Feng

doi:10.3969/j.issn.1006-0111.2018.03.004

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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 spectral signal to the LAPID spectrum as variables for Bayesian discriminant model. Results The accuracy rate of LAPIDs discrimination 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.

Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy

1. School of Pharmacy, Second Military Medical University, Shanghai, 200433, China

2. Shanghai DiaCartra Biomedical LLC, Shanghai 200070, China

3. School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China

Received Date: 2018-02-01

Rev Recd Date: 2018-03-26

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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 spectral signal to the LAPID spectrum as variables for Bayesian discriminant model. Results The accuracy rate of LAPIDs discrimination 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.

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Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy

doi: 10.3969/j.issn.1006-0111.2018.03.004

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