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Outlier detection for the Generalized Rank Annihilation Method in HPLC-DAD analysis.

Joan Ferré1, Enric Comas

  • 1Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Marcel·lí Domingo s/n, Campus Sescelades, 43007 Tarragona, Spain. joan.ferre@urv.cat

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The Generalized Rank Annihilation Method (GRAM) helps quantify coeluting analytes in chromatography. New plots detect outliers caused by peak shape and retention time variations, ensuring accurate quantification.

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Spectroscopy

Background:

  • Second-order calibration methods like GRAM are crucial for quantifying analytes that coelute with interferences in chromatography.
  • Accurate quantification relies on analyte peaks in standards and test samples having identical shapes and retention times (trilinear structure).
  • Deviations in peak shape or retention time can lead to outliers and incorrect predictions, which are not always detectable by standard GRAM output checks.

Purpose of the Study:

  • To introduce novel graphical methods for detecting outliers in GRAM-based chromatographic quantification.
  • To address the limitations of existing GRAM checks in identifying peak shape and retention time variations.
  • To improve the reliability and accuracy of analyte quantification when dealing with complex sample matrices.

Main Methods:

  • Development and application of graphical plots to compare elution profiles recovered by GRAM.
  • Utilizing GRAM-derived elution profiles and spectra to define interference vector spaces.
  • Projecting measured peaks onto orthogonal spaces and checking for proportionality using singular vectors or orthogonal signal versus net sensitivity plots.

Main Results:

  • Demonstrated the effectiveness of the proposed plots in identifying outliers caused by variations in peak alignment and shape.
  • Validated the graphical methods using simulated data, confirming their ability to detect deviations from trilinearity.
  • Successfully applied the methods to quantify 4-nitrophenol in river water samples using liquid chromatography/UV-Vis detection, showcasing real-world applicability.

Conclusions:

  • The presented graphical methods provide a reliable means to detect outliers in GRAM analyses that arise from peak shape and retention time mismatches.
  • These diagnostic tools enhance the robustness of GRAM for accurate analyte quantification in chromatography, especially in the presence of interferences.
  • The study offers practical solutions for improving data quality and interpretation in complex chromatographic analyses.