A sequential algorithm for multiblock orthogonal projections to latent structures

Bradley Worley, Robert Powers

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Methods of multiblock bilinear factorizations have increased in popularity in chemistry and biology as recent increases in the availability of information-rich spectroscopic platforms have made collecting multiple spectroscopic observations per sample a practicable possibility. Of the existing multiblock methods, consensus PCA (CPCA-W) and multiblock PLS (MB-PLS) have been shown to bear desirable qualities for multivariate modeling, most notably their computability from single-block PCA and PLS factorizations. While MB-PLS is a powerful extension to the nonlinear iterative partial least squares (NIPALS) framework, it still spreads predictive information across multiple components when response-uncorrelated variation exists in the data. The OnPLS extension to O2PLS provides a means of simultaneously extracting predictive and uncorrelated variation from a set of matrices, but is more suited to unsupervised data discovery than regression. We describe the union of NIPALS MB-PLS with an orthogonal signal correction (OSC) filter, called MB-OPLS, and illustrate its equivalence to single-block OPLS for regression and discriminant analysis.

Original languageEnglish (US)
Pages (from-to)33-39
Number of pages7
JournalChemometrics and Intelligent Laboratory Systems
StatePublished - Dec 15 2015


  • CPCA-W
  • MB-PLS
  • Multiblock data
  • OnPLS

ASJC Scopus subject areas

  • Analytical Chemistry
  • Software
  • Computer Science Applications
  • Process Chemistry and Technology
  • Spectroscopy


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