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Near-infrared spectroscopy for online quantitative monitoring of blend uniformity of hydroxychloroquine sulfate raw and auxiliary materials / 第二军医大学学报
Article em Zh | WPRIM | ID: wpr-838041
Biblioteca responsável: WPRO
ABSTRACT
Objective: To establish a quantitative analysis model for online monitoring of the blending uniformity of hydroxychloroquine sulfate raw and auxiliary materials, so as to accurately and quickly determine the blending endpoint. Methods: A mixture of excipients and hydroxychloroquine sulfate raw material was prepared with a labeling percentage of 70%-130%. The near-infrared spectrum was collected; and the standard normal variate transformation and frst derivative by Norris smoothing were used for spectra pretreating, with 8 372-9 045 cm-1, 5 616-6 058 cm 1 used as the spectral bands. A quantitative analysis model was developed using partial least squares regression. The quantitative analysis model was used to predict the labeling percentage of hydroxychloroquine sulfate in the blending process of raw and auxiliary materials, and the blending endpoint was verified by high-performance liquid chromatography (HPLC) analysis. Results: Five primary factors were used to establish the model. The root mean square error of calibration was 0.96 and the correlation coefficient of calibration set (Rc) was 0.998. The root mean square error of prediction was 0.97 and the correlation coefficient of validation set (Rp) was 0.998. The root mean square error of cross-validation was 1.56 and the correlation coefficient of cross-validation (Rcv) was 0.995. The prediction results of the near-infrared model was consistent with the results of HPLC verification. Conclusion: The near-infrared model in this study can be used for online quantitative analysis of the blending uniformity of hydroxychloroquine sulfate, and it can accurately and quickly determine the blending endpoint.
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Texto completo: 1 Índice: WPRIM Tipo de estudo: Prognostic_studies Idioma: Zh Revista: Academic Journal of Second Military Medical University Ano de publicação: 2019 Tipo de documento: Article
Texto completo: 1 Índice: WPRIM Tipo de estudo: Prognostic_studies Idioma: Zh Revista: Academic Journal of Second Military Medical University Ano de publicação: 2019 Tipo de documento: Article