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PROSAC as a selection tool for SO-PLS regression: A strategy for multi-block data fusion.

Jose A Diaz-Olivares1, Ryad Bendoula2, Wouter Saeys3

  • 1KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Campus Geel, Kleinhoefstraat 4, 2440, Geel, Belgium.

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We developed PROSAC-SO-PLS for efficient multi-block chemometric modeling. This method optimizes data pre-processing and block selection, significantly reducing prediction errors in near-infrared analysis.

Keywords:
ChemometricsData fusionMulti-blockNIRPre-processingSpectroscopy

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Area of Science:

  • Chemometrics
  • Data Science
  • Spectroscopy

Background:

  • Multi-block fusion chemometric models like SO-PLS integrate spectral data for improved sample quality prediction.
  • Pre-processing is crucial to reduce noise but selecting methods and blocks is complex with many data sources.
  • Efficiently handling pre-processing, block selection, and ordering is vital for SO-PLS model performance.

Purpose of the Study:

  • To address the challenge of efficient pre-processing, selection, and ordering of data blocks for targeted SO-PLS applications.
  • To introduce a novel methodology that automates and optimizes these complex steps.
  • To improve the accuracy and efficiency of chemometric models in handling large, multi-source spectral datasets.

Main Methods:

  • Introduction of the PROSAC-SO-PLS methodology, utilizing pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC).
  • Implementation of a stepwise forward selection strategy, aided by the Gram-Schmidt process, to prioritize data blocks.
  • Blocks are selected based on their effectiveness in minimizing prediction error, indicated by reduced prediction residuals.

Main Results:

  • PROSAC-SO-PLS successfully identified optimal pre-processed data blocks and their sequential order for SO-PLS.
  • Empirical validation on three near-infrared (NIR) datasets demonstrated consistent superiority over single-block PLS and PROSAC-only methods.
  • Significant reduction in prediction errors, with RMSEP decreasing by 5-25% for seven out of eight response variables analyzed.

Conclusions:

  • PROSAC-SO-PLS provides a versatile and efficient approach for ensemble pre-processing in NIR data modeling.
  • It simplifies the application of SO-PLS by mitigating concerns regarding pre-processing sequence and block order.
  • This methodology streamlines data pre-processing and model building, enhancing the accuracy and efficiency of chemometric analyses.