Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data
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CitationPedersena, Troels. Karttunen, Anssi-Pekka. Korhonen, Ossi. Wu, Jian Xiong. Naelapää, Kaisa. Skibsted, Erik. Rantanen, Jukka. (2021). Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data. Journal of pharmaceutical sciences, 110 (3) , 1259-1269. 10.1016/j.xphs.2020.10.067.
Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments.
Subjectscontinuous powder blending residence time distribution (RTD) mean residence time continuously stirred tank reactor (CSTR) in series near infrared (NIR) spectroscopy process analytical technologies (PAT) principal component analysis (PCA) partial least squares discriminant analysis (PLS-DA)
Link to the original itemhttp://dx.doi.org/10.1016/j.xphs.2020.10.067
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