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Multivariate calibration: applications to pharmaceutical analysis

M Forina1, M C Casolino, C de la Pezuela Martinez

  • 1Università di Genova, Dipartimento di Chimica e Tecnologie Farmaceutiche ed Alimentari, Italy. forina@anchem.unige.it

Journal of Pharmaceutical and Biomedical Analysis
|December 24, 1998
PubMed
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Multivariate calibration (MC) enhances chemical analysis by reducing prediction variance and enabling analysis in complex matrices. Novel variable selection techniques improve MC performance, particularly in drug analysis using near-infrared spectroscopy.

Area of Science:

  • Chemometrics
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Multivariate calibration (MC) is a powerful chemometrics technique.
  • MC aims to reduce prediction variance and analyze samples with minimal preparation.
  • It utilizes the entire spectral information from predictors.

Purpose of the Study:

  • To present the principles and objectives of multivariate calibration.
  • To demonstrate performance improvements in MC through variable selection.
  • To illustrate novel MC techniques for complex matrix analysis and multi-instrument calibration.

Main Methods:

  • Application of multivariate calibration (MC) principles.
  • Development and application of two novel variable selection techniques: stepwise elimination and iterative partial least squares regression (PLS) with predictor weighting.

Related Experiment Videos

  • Utilizing synthetic and real data sets, including near-infrared spectroscopy for drug analysis.

    • Main Results:

    • Demonstrated reduction in prediction variance for response variables.
    • Successful determination of chemical quantities in complex matrices with limited sample preparation.
    • Improved MC performance achieved by eliminating noisy or irrelevant predictors.
    • Application of a joint regression model for analysis across two different instruments.

    Conclusions:

    • Multivariate calibration offers significant advantages in chemical analysis, particularly for complex samples.
    • Novel variable selection strategies effectively enhance MC performance.
    • The presented techniques are applicable to real-world scenarios like drug analysis using near-infrared spectroscopy.