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Related Experiment Videos

Quantitation of mixtures from two-dimensional data sets using orthonormal functions

R Jones1

  • 1Analytical Development Laboratories, Wellcome Foundation Limited, Dartford, Kent, UK.

Journal of Pharmaceutical and Biomedical Analysis
|November 1, 1995
PubMed
Summary

Orthogonal polynomials are now used for two-dimensional mixture analysis, improving component quantification by correcting interferences. This method enhances accuracy in complex data sets.

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Area of Science:

  • Multivariate data analysis
  • Chemometrics
  • Spectroscopic analysis

Background:

  • Mixture analysis is crucial for identifying and quantifying components in complex samples.
  • Traditional methods often struggle with spectral interferences between components.
  • Orthogonal polynomials offer a mathematical framework for resolving complex data.

Purpose of the Study:

  • To extend the application of orthogonal polynomials to two-dimensional (2D) data sets for mixture analysis.
  • To develop a method for accurate analyte quantification in the presence of interfering components.
  • To establish a robust approach for handling complex, multi-component mixtures.

Main Methods:

  • Construction of orthonormal two-dimensional functions using Legendre polynomials.

Related Experiment Videos

  • Fitting these functions to standard and test data sets.
  • Utilizing weighted combinations of coefficients to correct for interferences.

    • Main Results:

    • Successful application of 2D orthogonal polynomials to mixture analysis.
    • Demonstrated ability to quantify analytes accurately despite spectral interferences.
    • Validation of the method using standard and test data sets.

    Conclusions:

    • The developed method effectively extends orthogonal polynomial analysis to 2D data.
    • This approach provides accurate quantification of analytes in complex mixtures.
    • The methodology is adaptable to higher dimensional data and alternative orthogonal basis sets.