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Rationale for using multiple regression analysis with complex interferences.

M H Kroll1, R Chesler

  • 1Clinical Pathology Department, National Institutes of Health, Bethesda, Maryland.

European Journal of Clinical Chemistry and Clinical Biochemistry : Journal of the Forum of European Clinical Chemistry Societies
|July 1, 1992
PubMed
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This study introduces graph theory to model complex interferences in analytical chemistry. It establishes a theoretical basis for using multiple regression analysis to understand interference mechanisms, improving analytical accuracy.

Area of Science:

  • Analytical Chemistry
  • Chemical Analysis
  • Instrumental Analysis

Background:

  • Non-specificities and interferences in analytical measurements can be complex, involving both the analyte and other interfering substances.
  • These are termed analyte-dependent and multi-interferent interferences.
  • Multiple regression analysis is useful for analyzing these interferences, but lacks a clear theoretical foundation for studying their underlying mechanisms.

Purpose of the Study:

  • To establish a theoretical foundation for using multiple regression analysis to study the basic mechanisms of analyte-dependent and multi-interferent interferences.
  • To demonstrate how graph theory can model these interference mechanisms and interactions.

Main Methods:

  • Utilized graph theory to model the basic mechanisms and interactions of interferences.

Related Experiment Videos

  • Approximated the relationship between analyte, interferents, and instrument response using a polynomial of order three.
  • Associated partial derivatives from the polynomial model with coefficients in multiple regression analysis.

    • Main Results:

    • Demonstrated that partial derivatives in the polynomial model correspond to different interactions identified through graph theory.
    • Showed that these partial derivatives can be linked to multiple regression coefficients when variable values are multiplied.
    • Established a method to retain or discard coefficients based on statistical significance, aiding in mechanism elucidation.

    Conclusions:

    • Graph theory provides a framework for visualizing and modeling complex interference mechanisms.
    • The study provides a theoretical basis for applying multiple regression analysis to understand interference phenomena.
    • This approach allows for the systematic assembly of interaction components to establish the interference mechanism.