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A novel algorithm for linear multivariate calibration based on the mixed model of samples.

Xuemei Wu1, Zhiqiang Liu, Hua Li

  • 1Institute of Analytical Science, School of Chemistry and Material Science, Northwest University, Xi'an 710069, PR China; Department of Chemistry & Chemical Engineering, Xi'an University of Arts and Science, Xi'an 710065, PR China.

Analytica Chimica Acta
|October 22, 2013
PubMed
Summary

A new Mixed Model of Samples (MMS) algorithm for multivariate calibration offers improved prediction accuracy and robustness. This novel method outperforms Partial Least Squares 2 (PLS2) and shows comparable results to PLS1.

Keywords:
Lagrange multiplierMixed modelMultivariate calibrationPartial least squares

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Multivariate calibration is crucial for quantitative analysis using spectral data.
  • Existing methods like Partial Least Squares (PLS) have limitations in certain scenarios.
  • Accurate prediction and robustness against interference are key challenges.

Purpose of the Study:

  • To introduce a novel algorithm for linear multivariate calibration.
  • To evaluate the performance of the new algorithm against established methods.
  • To demonstrate the algorithm's effectiveness in prediction accuracy and robustness.

Main Methods:

  • Development of the Mixed Model of Samples (MMS) algorithm.
  • Theoretical analysis of the MMS algorithm's principles.
  • Validation using two independent datasets and comparison with PLS1 and PLS2.

Main Results:

  • The MMS algorithm achieved lower prediction errors compared to PLS2.
  • MMS demonstrated comparable prediction performance to PLS1.
  • MMS exhibited superior performance in anti-interference tests and robustness when component information was lacking.

Conclusions:

  • The MMS algorithm is a promising novel approach for linear multivariate calibration.
  • MMS offers advantages over PLS2 in terms of prediction error, anti-interference, and robustness.
  • The algorithm's mixed model basis provides a strong theoretical foundation.