Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process

In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attribute...

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Published in:Pharmaceutics Vol. 12; no. 1; p. 85
Main Authors: Hattori, Yusuke, Naganuma, Miki, Otsuka, Makoto
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 20.01.2020
MDPI
Subjects:
Accuracy
Automation
Cellulose
feed-forward control
high-shear wet granulation
Manufacturing
Moisture content
near-infrared spectroscopy
partial least squares regression
Particle size
Pharmaceuticals
Polymorphism
Process controls
process data analysis
Product quality
robust control
Spectrum analysis
tableting
Japan
Germany
high-shear wet granulation
near-infrared spectroscopy
partial least squares regression
robust control
feed-forward control
tableting
process data analysis
ISSN:1999-4923, 1999-4923
Online Access:Get full text
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Abstract In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.
AbstractList In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.
In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.
Author Hattori, Yusuke
Otsuka, Makoto
Naganuma, Miki
AuthorAffiliation Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, 1-20 Shin-machi, Nishi-Tokyo city, Tokyo 202-8585, Japan; s1443033@stu.musashino-u.ac.jp (M.N.); motsuka@musashino-u.ac.jp (M.O.)
AuthorAffiliation_xml – name: Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, 1-20 Shin-machi, Nishi-Tokyo city, Tokyo 202-8585, Japan; s1443033@stu.musashino-u.ac.jp (M.N.); motsuka@musashino-u.ac.jp (M.O.)
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crossref_primary_10_3390_pharmaceutics12100910
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Keywords high-shear wet granulation
near-infrared spectroscopy
partial least squares regression
robust control
feed-forward control
tableting
process data analysis
Language English
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  doi: 10.1007/s12247-017-9276-y
– volume: 21
  start-page: 1
  year: 2013
  ident: ref_10
  article-title: Tablet characteristics prediction by powder blending process analysis based on near infrared spectroscopy
  publication-title: J. Infrared Spectrosc.
  doi: 10.1255/jnirs.1037
SSID ssj0000331839
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Snippet In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR)...
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StartPage 85
SubjectTerms Accuracy
Automation
Cellulose
feed-forward control
high-shear wet granulation
Manufacturing
Moisture content
near-infrared spectroscopy
partial least squares regression
Particle size
Pharmaceuticals
Polymorphism
Process controls
process data analysis
Product quality
robust control
Spectrum analysis
tableting
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Title Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process
URI https://www.ncbi.nlm.nih.gov/pubmed/31968698
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