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Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.

Ana F T Silva1, Mafalda Cruz Sarraguça2, Paulo R Ribeiro3

  • 1LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; Laboratory of Pharmaceutical Process Analytical Technology, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.

International Journal of Pharmaceutics
|February 1, 2017
PubMed
Summary

Orthogonal partial least squares regression (OPLS) offers superior fault detection in batch processes compared to traditional partial least squares (PLS). OPLS enhances process monitoring by improving sensitivity and specificity, identifying disturbances missed by PLS.

Keywords:
Batch statistical process controlCocrystallizationNear infrared spectroscopyOrthogonal partial least squaresPartial least squares

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Area of Science:

  • Chemometrics
  • Process Analytical Technology (PAT)
  • Chemical Engineering

Background:

  • Partial Least Squares (PLS) regression is widely used in multivariate Batch Statistical Process Control (BSPC).
  • Orthogonal Partial Least Squares (OPLS) regression offers improved generalization and is theoretically advantageous for estimating nominal trajectories in BSPC.
  • The application of OPLS in BSPC, particularly for fault detection, remains less explored compared to its use in regression.

Purpose of the Study:

  • To propose and evaluate an OPLS-based approach for BSPC of a cocrystallization process.
  • To compare the fault detection performance of OPLS-based BSPC with a traditional PLS-based BSPC method.
  • To assess the ability of OPLS to detect process disturbances more effectively than PLS.

Main Methods:

  • Implementation of an OPLS model for estimating nominal trajectories in a cocrystallization process.
  • On-line monitoring of the cocrystallization process using near-infrared (NIR) spectroscopy.
  • Comparison of fault detection performance using OPLS and PLS models on batches with imposed disturbances.
  • Evaluation of model performance based on sensitivity and specificity metrics.

Main Results:

  • OPLS-based BSPC demonstrated superior fault detection performance compared to PLS-based BSPC in most tested situations.
  • OPLS exhibited higher sensitivity and specificity in identifying process disturbances.
  • Certain process disturbances were detected exclusively by the OPLS approach, highlighting its enhanced detection capabilities.

Conclusions:

  • OPLS is a more effective method than PLS for multivariate BSPC, particularly for fault detection in chemical processes like cocrystallization.
  • The OPLS approach provides better process monitoring by accurately capturing nominal trajectories and filtering out uncorrelated variations.
  • OPLS enhances the reliability of process control by reducing false positives and negatives, leading to improved detection of abnormal situations.