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作者机构:Univ Ghent Lab Pharmaceut Proc Analyt Technol B-9000 Ghent Belgium Univ Porto Fac Farm Dept Ciencias Quim LAQV REQUIMTE Rua Jorge Viterbo Ferreira 228 P-4050313 Porto Portugal Univ Ghent Lab Pharmaceut Technol B-9000 Ghent Belgium Univ Lisbon Fac Farm Res Inst Med iMed Lisboa Ave Prof Gama Pinto P-1649003 Lisbon Portugal
出 版 物:《JOURNAL OF PHARMACEUTICAL SCIENCES》 (药物科学杂志)
年 卷 期:2019年第108卷第1期
页 面:439-450页
核心收录:
学科分类:1007[医学-药学(可授医学、理学学位)] 0703[理学-化学] 10[医学]
基 金:FCT (Fundacao para a Ciencia e Tecnologia) POPH (Programa Operacional Potencial Humano) [SFRH/BPD/74788/2010] European Union [POCI/01/0145/FEDER/007265] National Fund (FCT/MEC, Fundacao para a Ciencia e Tecnologia) [PT2020 UDI/QUI/50006/2013] National Fund (FCT/MEC, Ministerio da Educacao e Ciencia) [PT2020 UDI/QUI/50006/2013]
主 题:multivariate statistical process monitoring principal component analysis in-process monitoring continuous manufacturing
摘 要:The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. Multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state. (C) 2019 Published by Elsevier Inc. on behalf of the American Pharmacists Association.