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Achieving the analytical second-order advantage with non-bilinear second-order data.

Fabricio A Chiappini1, Fabiana Gutierrez1, Hector C Goicoechea1

  • 1Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe, S3000ZAA, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290, CABA, C1425FQB, Argentina.

Analytica Chimica Acta
|September 24, 2021
PubMed
Summary

This study evaluates non-bilinear second-order calibration models for analytical chemistry. While no single method works universally, non-bilinear rank annihilation (NBRA), U-PLS-RBL, and MCR-ALS show potential for specific data types.

Keywords:
Analyte selectivityMultivariate curve resolution alternating least-squaresNon-bilinear rank annihilationNon-bilinear second-order dataSecond-order advantageUnfolded partial least-squares regression with residual bilinearization

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Area of Science:

  • Analytical Chemistry
  • Chemometrics

Background:

  • Multi-way calibration using second-order data is crucial in analytical applications.
  • Classical chemometric models often assume low-rank bilinearity, which is not always met by instrumental data.
  • Non-bilinear data present challenges and opportunities for novel second-order calibration methods.

Purpose of the Study:

  • To systematically assess the analytical performance of three second-order models for non-bilinear data.
  • To investigate the applicability of non-bilinear rank annihilation (NBRA), unfolded partial least-squares with residual bilinearization (U-PLS-RBL), and multivariate curve resolution - alternating least-squares (MCR-ALS).
  • To understand the factors influencing the prediction capacity of these models with non-bilinear data.

Main Methods:

  • Utilized simulated and experimental non-bilinear second-order data sets.
  • Included one analyte and one interferent in the data.
  • Systematically evaluated NBRA, U-PLS-RBL, and MCR-ALS models.

Main Results:

  • No single method universally models all types of non-bilinear second-order data.
  • Each evaluated method (NBRA, U-PLS-RBL, MCR-ALS) demonstrated successful predictions under specific circumstances.
  • Prediction accuracy was significantly impacted by instrumental noise, response matrix rank, and analyte signal selectivity.

Conclusions:

  • The choice of the best method for non-bilinear second-order data depends on the specific data characteristics.
  • Further exploration of comprehensive modeling techniques for non-bilinear data is warranted.
  • Understanding data properties is key to achieving the second-order advantage in complex analytical scenarios.