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Volume 36 Issue 3
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Article Navigation >  Journal of Pharmaceutical Practice and Service  > 2018  >  36(3): 210-214

XIN Bo, WAN Lili, WANG Jing, LIU Jinbian, HUO Yan, GUO Cheng. Cisatracurium assay in human plasma by LC-MS[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(2): 148-151. doi: 10.3969/j.issn.1006-0111.201909001
Citation: CHEN Xiujuan, CHEN Hui, WEI Hang, LIU Yan, LU Feng. Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy[J]. Journal of Pharmaceutical Practice and Service, 2018, 36(3): 210-214. doi: 10.3969/j.issn.1006-0111.2018.03.004
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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
  • 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
  • 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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    [11] 陶思嘉,李梦华,李景明,等. 红外光谱及Bayes信息融合技术的葡萄酒鉴别研究[J]. 分析化学,2014, 42(2):215-220.
    [12] 周扬,吕进,戴曙光,等. 基于贝叶斯决策的近红外光谱药片分类方法[J]. 分析化学, 2013, 41(2):293-296.
    [13] 张微. 朴素贝叶斯分类模型在中红外光谱测原料奶掺假中的应用研究[J]. 食品工程, 2011(1):49-53.
    [14] Cebeci Maltas D, Kwok K, Wang P, et al. Rapid classification of pharmaceutical ingredients with Raman spectroscopy using compressive detection strategy with PLS-DA multivariate filters[J]. J Pharm Biomed Anal, 2013, 80:63-68.
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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
  • 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

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.

XIN Bo, WAN Lili, WANG Jing, LIU Jinbian, HUO Yan, GUO Cheng. Cisatracurium assay in human plasma by LC-MS[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(2): 148-151. doi: 10.3969/j.issn.1006-0111.201909001
Citation: CHEN Xiujuan, CHEN Hui, WEI Hang, LIU Yan, LU Feng. Drug analysis based on scalable moving-window similarity and Bayesian method by Raman spectroscopy[J]. Journal of Pharmaceutical Practice and Service, 2018, 36(3): 210-214. doi: 10.3969/j.issn.1006-0111.2018.03.004
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