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Sequentially orthogonalized canonical partial least squares for improved multiple responses modeling in multiblock

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  • 1Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands.

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Area of Science:

  • Chemometrics
  • Multivariate Data Analysis
  • Machine Learning

Background:

  • Multiblock data analysis is common in chemometrics.
  • Existing methods like sequential orthogonalized partial least squares (SO-PLS) primarily handle single responses.
  • Canonical PLS (CPLS) offers efficient subspace extraction for multiple responses in regression and classification.

Purpose of the Study:

  • To introduce and evaluate sequential orthogonalized canonical partial least squares (SO-CPLS) for multiblock data.
  • To demonstrate SO-CPLS's effectiveness in handling multiple response variables.
  • To showcase the integration of sample meta-information for enhanced subspace extraction.

Main Methods:

  • Developed SO-CPLS by combining SO-PLS and CPLS techniques.
  • Applied SO-CPLS to multiblock data sets for both regression and classification tasks.
  • Incorporated sample meta-information (e.g., experimental design, sample classes) into the subspace extraction process.

Main Results:

  • SO-CPLS effectively models multiple responses in multiblock data.
  • The method demonstrated efficiency in extracting relevant information into fewer latent variables.
  • SO-CPLS showed improved performance compared to traditional SO-PLS, particularly when utilizing meta-information.

Conclusions:

  • SO-CPLS is a valuable extension for multiblock data modeling, supporting both regression and classification.
  • The ability to incorporate meta-information enhances the efficiency and relevance of subspace extraction.
  • SO-CPLS offers significant advantages for complex chemometric modeling tasks.