Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy
10.12206/j.issn.1006-0111.202007128
- VernacularTitle:硫酸羟氯喹颗粒水分近红外光谱在线定量模型的建立
- Author:
Ying KE
1
;
Zhenming ZHU
2
;
Shuoyang ZHANG
3
;
Weiqing WANG
3
;
Feng LU
3
Author Information
1. Shanghai Pharmaceuticals Holding Co., Ltd., Shanghai 200020, China.
2. Shanghai SPH Zhongxi Pharmaceutical Co., Ltd., Shanghai 201806, China.
3. Naval Medical University, Shanghai 200433, China.
- Keywords:
near infrared spectroscopy;
hydroxychloroquine sulfate particle;
water content;
online quantitative model;
drying process
- From:
Journal of Pharmaceutical Practice
2021;39(1):23-28
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish an online quantitative analysis model for moisture content assay of hydroxychloroquine sulfate particles by near infrared (NIR) spectroscopy. Methods The NIR spectra were collected in real time when the material particles were dried in the fluidized bed. Meanwhile the water content of the particles was measured with the standard moisture tester. The multiplicative signal correction (MSC) and first derivative followed by Karl Norris smoothing were used for spectra pretreatment. Two spectral range (4 935−5 336 cm−1 and 6 911−7 297 cm−1) were selected for the quantitative model with the partial least squares (PLS) regression. Results The quantitative calibration model had good correlation coefficients with Rc value=0.952 9 and Rp value=0.936 6. The root mean square error of calibration (RMSEC) was 0.408 and the root mean square error of prediction error (RMSEP) was 0.435. The ratio of standard deviation of validation set to prediction standard deviation (RPD) was 5.18. There was no significant difference between the predicted value and the reference value by t test when the established model was applied in large-scale production. Conclusion The online model established for monitoring water content has high accuracy and stability, which can be applied in industrial scale process to monitor the particle moisture in real time.
