Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy

Guolin Shi; Bing Xu; Zhiqiang Zhang; Chan Yang; Shengyun Dai; Zhaozhou Lin; Xinyuan Shi; Jing Fu; Yanjiang Qiao

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2019.1157

Published Article

LAPSE:2019.1157

Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy

Guolin Shi, Bing Xu, Zhiqiang Zhang, Chan Yang, Shengyun Dai, Zhaozhou Lin, Xinyuan Shi, Jing Fu, Yanjiang Qiao

November 24, 2019

It is significant to analyze the blend homogeneity of cohesive powders during pharmaceutical manufacturing in order to provide the exact content of the active pharmaceutical ingredient (API) for each individual dose unit. In this paper, an online monitoring platform using an MEMS near infrared (NIR) sensor was designed to control the bin blending process of cohesive powders. The state of blend homogeneity was detected by an adaptive algorithm, which was calibration free. The online control procedures and algorithm’s parameters were fine-tuned through six pilot experiments and were successfully transferred to the industrial production. The reliability of homogeneity detection results was validated by 16 commercial scale experiments using 16 kinds of natural product powders (NPPs), respectively. Furthermore, 19 physical quality attributes of all NPPs and the excipient were fully characterized. The blending end time was used to denote the degree of difficulty of blending. The empirical relationships between variability of NPPs and the blending end time were captured by latent variable modeling. The critical material attributes (CMAs) affecting the blending process were identified as the particle shape and flowability descriptors of cohesive powders. Under the framework of quality by design (QbD) and process analytical technology (PAT), the online NIR spectroscopy together with the powder characterization facilitated a deeper understanding of the mixing process.

Record ID

LAPSE:2019.1157

Keywords

adaptive modeling algorithm, blend homogeneity, cohesive powder, near-infrared sensor, quality by design, raw material variability

Subject

Process Control

Suggested Citation

Shi G, Xu B, Zhang Z, Yang C, Dai S, Lin Z, Shi X, Fu J, Qiao Y. Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy. (2019). LAPSE:2019.1157

Author Affiliations

Shi G: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China
Xu B: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China; Beijing Key Laboratory for Production Process Control and Quality Evaluation of Tradit
Zhang Z: Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine, Beijing 100029, China; Beijing Tcmages Pharmaceutical Co., Ltd., Beijing 101301, China
Yang C: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China
Dai S: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China
Lin Z: Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine, Beijing 100029, China
Shi X: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China; Beijing Key Laboratory for Production Process Control and Quality Evaluation of Tradit
Fu J: Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine, Beijing 100029, China; Beijing Tcmages Pharmaceutical Co., Ltd., Beijing 101301, China
Qiao Y: Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China; Beijing Key Laboratory for Production Process Control and Quality Evaluation of Tradit
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Journal Name

Processes

Volume

7

Issue

9

Article Number

E568

Year

2019

Publication Date

2019-08-28