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Related Experiment Videos

Analyte identification in multivariate calibration.

G Jones1, D M Rocke

  • 1Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand.

Biometrics
|June 21, 2001
PubMed
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This study introduces a new discrimination statistic to identify unknown samples in calibration experiments. This method is crucial for environmental monitoring, especially when multiple pollutants cross-react in assays.

Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Statistics

Background:

  • Calibration experiments estimate relationships between sample covariates and observed responses to infer unknown sample information.
  • Covariate information can include nominal (e.g., chemical identity) and real-valued (e.g., concentration) variables.
  • Accurate identification of sample identity is critical when calibrating relationships separately for each candidate identity.

Purpose of the Study:

  • To propose a novel discrimination statistic for correctly determining the identity of unknown samples in calibration analyses.
  • To derive the asymptotic distribution of the proposed discrimination statistic.
  • To address challenges in environmental monitoring involving multiple cross-reactive pollutants.

Main Methods:

Related Experiment Videos

  • Development of a discrimination statistic for sample identification in calibration.
  • Derivation of the asymptotic distribution for the proposed statistic.
  • Application to environmental monitoring scenarios with multiple immunoassays.

    • Main Results:

    • A statistically sound method for identifying unknown samples based on their responses.
    • The asymptotic distribution provides a basis for statistical inference and hypothesis testing.
    • Demonstration of utility in a four-antibody assay for simultaneous triazine herbicide monitoring.

    Conclusions:

    • The proposed discrimination statistic effectively aids in identifying unknown samples when multiple candidate identities exist.
    • This statistical approach is valuable for environmental monitoring applications, particularly with complex cross-reactive assay data.
    • The method facilitates accurate identification and quantitation of pollutants in water samples.