Partial least squares and compositional data: problems and alternatives

John Hinkle, William Rayens

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


It is still widely unknown in chemometrics that the statistical analysis of compositional data requires fundamentally different tools than a similar analysis of unconstrained data. This article examines the problems that potentially occur when one performs a partial least squares (PLS) analysis on compositional data and suggests logcontrast partial least squares (LCPLS) as an alternative.

Original languageEnglish
Pages (from-to)159-172
Number of pages14
JournalChemometrics and Intelligent Laboratory Systems
Issue number1
StatePublished - Nov 1995

Bibliographical note

Funding Information:
During the course of this research Professor Rayens was supported by NSF grant ATM-9108177.


  • Compositional data
  • Partial least squares

ASJC Scopus subject areas

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


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