Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data

Research output: Contribution to journalJournal articlepeer-review

Standard

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 journalJournal articlepeer-review

Harvard

Pedersen, T, Karttunen, AP, Korhonen, O, Wu, JX, Naelapää, K, Skibsted, E & Rantanen, J 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, vol. 110, no. 3, pp. 1259-1269. https://doi.org/10.1016/j.xphs.2020.10.067

APA

Pedersen, T., Karttunen, A. P., Korhonen, O., Wu, J. X., Naelapää, K., Skibsted, E., & Rantanen, J. (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. https://doi.org/10.1016/j.xphs.2020.10.067

Vancouver

Pedersen T, Karttunen AP, Korhonen O, Wu JX, Naelapää K, Skibsted E et al. 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. 2021;110(3):1259-1269. https://doi.org/10.1016/j.xphs.2020.10.067

Author

Pedersen, Troels ; Karttunen, Anssi Pekka ; Korhonen, Ossi ; Wu, Jian Xiong ; Naelapää, Kaisa ; Skibsted, Erik ; Rantanen, Jukka. / Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data. In: Journal of Pharmaceutical Sciences. 2021 ; Vol. 110, No. 3. pp. 1259-1269.

Bibtex

@article{14867b0535014b6195a7371170d0aa84,
title = "Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data",
abstract = "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.",
keywords = "Continuous powder blending, Continuously stirred tank reactor (CSTR) in series, Mean residence time, Near infrared (NIR) spectroscopy, Partial least squares discriminant analysis (PLS-DA), Principal component analysis (PCA), Process analytical technologies (PAT), Residence time distribution (RTD)",
author = "Troels Pedersen and Karttunen, {Anssi Pekka} and Ossi Korhonen and Wu, {Jian Xiong} and Kaisa Naelap{\"a}{\"a} and Erik Skibsted and Jukka Rantanen",
note = "Publisher Copyright: {\textcopyright} 2020 American Pharmacists Association{\textregistered}",
year = "2021",
doi = "10.1016/j.xphs.2020.10.067",
language = "English",
volume = "110",
pages = "1259--1269",
journal = "Journal of Pharmaceutical Sciences",
issn = "0022-3549",
publisher = "Elsevier",
number = "3",

}

RIS

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