A MATLAB toolbox for class modeling using one-class partial least squares (OCPLS) classifiers

Lu Xu, Mohammad Goodarzi, Wei Shi, Chen Bo Cai, Jian Hui Jiang

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

One-class classifiers are widely used to solve the classification problems where control or class modeling of a target class is necessary, e.g., untargeted analysis of food adulterations and frauds, tracing the origins of a food with Protected Denomination of Origin, fault diagnosis, etc. Recently, one-class partial least squares (OCPLS) has been developed and demonstrated to be a useful technique for class modeling. For analysis of nonlinear and outlier-contaminated data, nonlinear and robust OCPLS algorithms are required. This paper describes a free MATLAB toolbox for class modeling using OCPLS classifiers. The toolbox includes ordinary, nonlinear and robust OCPLS methods. The nonlinear algorithm is based on the Gaussian radial basis function (GRBF), and the robust algorithm is based on the partial robust M-regression (PRM). The usage of the toolbox is demonstrated by analysis of a real data set.

Original languageEnglish (US)
Pages (from-to)58-63
Number of pages6
JournalChemometrics and Intelligent Laboratory Systems
Volume139
DOIs
StatePublished - Dec 15 2014

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Keywords

  • Class modeling
  • Fault diagnosis
  • MATLAB toolbox
  • Nonlinear and robust algorithms
  • One-class partial least squares (OCPLS) classifiers

ASJC Scopus subject areas

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

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