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 language | English (US) |
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Pages (from-to) | 58-63 |
Number of pages | 6 |
Journal | Chemometrics and Intelligent Laboratory Systems |
Volume | 139 |
DOIs | |
State | Published - Dec 15 2014 |
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
- Process Chemistry and Technology
- Spectroscopy
- Computer Science Applications