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Updated: May 6, 2026

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Blending process modeling and control by multivariate curve resolution.

J Jaumot1, B Igne, C A Anderson

  • 1Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona, 18-26, 08034 Barcelona, Spain.

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|November 12, 2013
PubMed
Summary
This summary is machine-generated.

The Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) method effectively models pharmaceutical blending processes. This advanced technique aids in understanding blend evolution and precisely controlling the process for optimal formulation.

Keywords:
Blend trajectoriesCorrelation constraintEnd-point detectionHomogeneityMultivariate Curve ResolutionProcesses analysis

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Area of Science:

  • Pharmaceutical Sciences
  • Chemometrics
  • Process Analytical Technology (PAT)

Background:

  • Pharmaceutical blend uniformity is critical for drug efficacy and safety.
  • Traditional methods for monitoring blending processes can be time-consuming and may not capture dynamic changes.
  • The Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) offers a powerful chemometric approach for analyzing complex mixture data.

Purpose of the Study:

  • To assess the efficacy of the MCR-ALS method for modeling and controlling pharmaceutical blending processes.
  • To explore different MCR-ALS data analysis strategies tailored for blend process modeling versus blend process control.
  • To investigate the application of MCR-ALS in simultaneously analyzing multiple blending runs.

Main Methods:

  • Application of Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) for pharmaceutical blend analysis.
  • Utilizing the multiset mode of MCR-ALS to analyze multiple blending experiments concurrently.
  • Implementing natural constraints (non-negativity) for blend modeling and correlation constraints for blend control, including internal calibration.
  • Testing MCR-ALS on pharmaceutical mixtures of acetaminophen with two and four excipients.

Main Results:

  • MCR-ALS successfully modeled the evolution of individual compounds and identified physical effects during blending.
  • The method accurately detected the blend end-point in a more complex four-excipient system.
  • MCR-ALS provided insights into concentration variations and their impact on the blending process.
  • The use of correlation constraints enabled the quantification of components in their real concentration scale.

Conclusions:

  • MCR-ALS is a versatile and effective tool for both modeling and controlling pharmaceutical blending processes.
  • The multiset mode and constrained optimization variants of MCR-ALS enhance its applicability in pharmaceutical manufacturing.
  • This chemometric approach facilitates real-time monitoring and optimization of blend uniformity, contributing to improved drug product quality.