Study on on-line near-infrared spectroscopic for moisture monitoring in a fluidized bed drying process of Guzhi Fuling Capsules
10.7501/j.issn.0253-2670.2019.22.008
- VernacularTitle: 在线近红外光谱监测桂枝茯苓胶囊流化床干燥过程水分的方法研究
- Author:
Qing WANG
1
Author Information
1. Nanjing University of Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Fluidized bed drying;
Guizhi Fuling Capsules;
Interval partial least squares method (siPLS);
Moisture;
Moving window partial least squares (mwPLS);
Multivariate correction model;
On-line near infrared spectroscopy;
Process analytical technology;
Ratio of predicton to deviation (RPD);
Real-time monitoring;
Relative standard error of prediction (RSEP);
Root mean square error of cross-validation (RMSECV)
- From:
Chinese Traditional and Herbal Drugs
2019;50(22):5429-5438
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To establish a real-time moisture monitoring model for the fluidized bed drying process of Guizhi Fuling Capsules (GFC) by using online near-infrared spectroscopy (NIRS). Methods: A total of 176 samples from 16 production batches were collected by NIRS diffuse reflection probe for modeling. The moving window average smoothing method was used for spectral preprocessing. The characteristic variables were 4 759.45—5 338.00 cm−1, 5 503.84—6 101.67 cm−1, and 8 512.25—8 809.24 cm−1, which were screened by the interval partial least squares method (siPLS) combined with the moving window partial least squares (mwPLS). The partial variable least squares (PLS) method was used to build a multivariate correction model for moisture. Results: The root mean square error of cross-validation (RMSECV) of predicted moisture was 0.243%, the ratio of predicton to deviation (RPD) was 13.384, and the relative standard error of prediction (RSEP ) was 0.270%. The reliability of the online monitoring method was continuously verified by eight production batches. The relative error of 40 samples was less than 4.7%, indicating that the PLS quantitative model prediction performance was robust and accurate. The real-time monitoring trend chart of the moisture in the drying process can accurately determine the drying end point, and the moisture content of the end sample was within the control limit. Conclusion: The quantitative model established by online NIRS combined with PLS can be applied to the on-line monitoring of moisture content in the fluidized bed drying process of production scale GFC and the prediction performance was robust and accurate.
