Software Description A MATLAB toolbox for class modeling using one-class partial least squares (OCPLS) classifiers Lu Xu a, ⁎, Mohammad Goodarzi b , Wei Shi a , Chen-Bo Cai c, ⁎, Jian-Hui Jiang d, ⁎ a College of Material and Chemical Engineering, Tongren University, Tongren, 554300, Guizhou, PR China b Department of Biosystems, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, B-3001, Leuven, Belgium c College of Chemistry and Life Science, Chuxiong Normal University, Chuxiong 675000, PR China d State Key Laboratory of Chemo/BioSensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China abstract article info Article history: Received 25 June 2014 Received in revised form 12 September 2014 Accepted 16 September 2014 Available online 23 September 2014 Keywords: MATLAB toolbox Class modeling One-class partial least squares (OCPLS) classifiers Nonlinear and robust algorithms Fault diagnosis 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. © 2014 Elsevier B.V. All rights reserved. 1. Introduction It was recognized by one of the founders of chemometrics, Kowalski, and Bender [1], that “whenever something must be learned from ob- jects (elements, compounds, and mixtures) and a chemical/physical theory has not been sufficiently developed, pattern recognition may provide a solution.” The above viewpoint has been fully proved by various applications of pattern recognition techniques in order to un- derstand complex objects in chemistry [2–4]. Besides the commonly used multi-class classification or discriminant analysis (DA) techniques, recently the so-called one-class classifiers [5–7] or class modeling tech- niques (CMTs) [8–16] have attracted much attention and the difference between DA and CMTs has been discussed by some authors [7,13–15]. While DA aims at classifying two or more predefined classes [17], CMTs are especially useful when it is necessary to define or model the range of a target class. Some typical problems which require the use of CMTs include untargeted detection of food adulterations or frauds, trac- ing the geographical origins of protected denomination of origin (PDO) foods [18,19], fault diagnosis, etc. Some usually used CMTs may include the following: (1) soft inde- pendent modeling of class analogy (SIMCA) [8] using principal compo- nent analysis (PCA); (2) unequal dispersed classes (UNEQ) [9,10] based on the hypothesis of multivariate normal distribution and the Hotelling's T 2 test; (3) potential functions methods [20] by estimation of the multivariate probability distribution; and (4) those based on artificial neural networks (ANNs) and support vector machines (SVMs) [5,21,22]. The most popular CMTs are SIMCA and PCA-related techniques [23–25], which is especially useful in chemometrics by extracting a few of primary and informative components or latent variables (LVs). Partial least squares (PLS), as one of the cornerstones of chemometrics, has been widely used to solve both regression and clas- sification problems. The rationale of PLS-DA has been demonstrated by the relationship among PLS, canonical correlation analysis (CCA) and linear discriminant analysis (LDA) [26]. Recently, one-class partial least squares (OCPLS) or PLS class model (PLSCM) [27] has been pro- posed and demonstrated to be an effective tool for class modeling. Unlike SIMCA, whose components explain most of the data variances, OCPLS components consider simultaneously the explained variances and the compactness of the target class. Moreover, OCPLS can be performed as a special PLS regression and works in the framework of Multivariate Calibration. Practical data analysis sometimes encounters nonlinear and outlier- contaminated data sets, which would cause bias or even breakdown in estimation of OCPLS parameters. Therefore, it is necessary to develop nonlinear and robust algorithms for OCPLS. This paper describes a free MATLAB toolbox for OCPLS, including the ordinary linear, nonlinear Gaussian radial basis function (RBF) OCPLS (GRBF-OCPLS) and robust Chemometrics and Intelligent Laboratory Systems 139 (2014) 58–63 ⁎ Corresponding authors. Tel.: +86 856 5222556; fax: +86 856 5230977. E-mail addresses: lxchemo@163.com (L. Xu), ccp66516@163.com (C.-B. Cai), jianhuijiang@hnu.edu.cn (J.-H. Jiang). http://dx.doi.org/10.1016/j.chemolab.2014.09.005 0169-7439/© 2014 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Chemometrics and Intelligent Laboratory Systems journal homepage: www.elsevier.com/locate/chemolab