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Pattern recognition used to investigate multivariate data in analytical chemistry.

P C Jurs

    Science (New York, N.Y.)
    |June 6, 1986
    PubMed
    Summary

    Pattern recognition methods help analyze complex chemical data. These multivariate techniques, including clustering and modeling, are applied to problems like classifying genetic conditions and simulating NMR spectra.

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    Area of Science:

    • Analytical Chemistry
    • Chemometrics
    • Multivariate Data Analysis

    Background:

    • Analytical chemistry frequently generates complex multivariate data.
    • Interpreting this data requires advanced statistical and computational methods.
    • Pattern recognition offers a powerful framework for such interpretations.

    Purpose of the Study:

    • To demonstrate the utility of pattern recognition and multivariate methods in analytical chemistry.
    • To showcase diverse applications of these techniques.
    • To present results from recent case studies.

    Main Methods:

    • Application of pattern recognition techniques.
    • Use of multivariate methods such as mapping, display, discriminant development, and clustering.
    • Development of linear model equations for spectral simulation.

    Main Results:

    • Successful classification of subjects into normal or cystic fibrosis heterozygote groups.
    • Accurate simulation of carbon-13 nuclear magnetic resonance spectra using linear models.
    • Demonstration of broad applicability across various chemical problems.

    Conclusions:

    • Pattern recognition and multivariate methods are effective tools for analytical chemistry data interpretation.
    • These methods provide valuable insights and predictive capabilities.
    • The presented examples highlight the practical success of these approaches.

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