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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Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data. / Pedersen, Troels; Karttunen, Anssi Pekka; Korhonen, Ossi; Wu, Jian Xiong; Naelapää, Kaisa; Skibsted, Erik; Rantanen, Jukka.
In: Journal of Pharmaceutical Sciences, Vol. 110, No. 3, 2021, p. 1259-1269.Research output: Contribution to journal › Journal article › peer-review
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TY - JOUR
T1 - Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data
AU - Pedersen, Troels
AU - Karttunen, Anssi Pekka
AU - Korhonen, Ossi
AU - Wu, Jian Xiong
AU - Naelapää, Kaisa
AU - Skibsted, Erik
AU - Rantanen, Jukka
N1 - Publisher Copyright: © 2020 American Pharmacists Association®
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Continuous powder blending
KW - Continuously stirred tank reactor (CSTR) in series
KW - Mean residence time
KW - Near infrared (NIR) spectroscopy
KW - Partial least squares discriminant analysis (PLS-DA)
KW - Principal component analysis (PCA)
KW - Process analytical technologies (PAT)
KW - Residence time distribution (RTD)
U2 - 10.1016/j.xphs.2020.10.067
DO - 10.1016/j.xphs.2020.10.067
M3 - Journal article
C2 - 33217424
AN - SCOPUS:85097480485
VL - 110
SP - 1259
EP - 1269
JO - Journal of Pharmaceutical Sciences
JF - Journal of Pharmaceutical Sciences
SN - 0022-3549
IS - 3
ER -
ID: 306676291