Pattern recognition SIMCA

Often the goal of a data analysis problem requites more than simple classification of samples into known categories. It is very often desirable to have a means to detect oudiers and to derive an estimate of the level of confidence in a classification result. These ate things that go beyond sttictiy nonparametric pattern recognition procedures. Also of interest is the abiUty to empirically model each category so that it is possible to make quantitative correlations and predictions with external continuous properties. As a result, a modeling and classification method called SIMCA has been developed to provide these capabihties (29—31). 

E. Saaksjarvi, M. Khaligi and P. Minkkinen, Waste water pollution modeling in the southern area of Lake Saimaa, Finland, by the simca pattern recognition method. Chemom. Intell. Lab. Systems, 7(1989) 171-180. 

Classical supervised pattern recognition methods include /( -nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA). Both... 

Soft Independent Method of Class Analogy (SIMCA), a pattern recognition technique based on principal components (25) was selected to evaluate and apply to the problems of establishing similarities among sample residue profiles. The development of a laboratory data management system to assist in the calculation and organization of results greatly enhanced the feasibility of this approach (26). 

In addition to these functions, other data base programs provided output formating and retrieval of concentration data from completed analytical reports and transfer of these data onto magnetic tape for subsequent examination by SIMCA programs. Additional features of the pattern recognition data management... 

To illustrate the problems associated with evaluating such data, we conducted several studies with Aroclor standards and mixtures of these standards in an effort to determine what information could be readily obtained with the SIMCA method of pattern recognition (30-32). The following discussion illustrates some of the features of this approach and describes how the SIMCA method works when applied to Aroclor mixtures. 

Software Availability. The SIMCA software is available in two forms, both developed by Wold (25) 1) an interactive, Fortran version which runs on Control Data Corporation (CDC) machines. The second set of programs are an interactive microcomputer version, SIMCA-3B, are available from Principal Data Components, 2505 Shepard Blvd., Columbia, MO 65201. The SIMCA-3B pattern recognition programs includes the CPLS-2 program used for PLS analysis and are available for CP/M (Digital Research, Pacific Grove, CA) and MS-DOS (Microsoft Corporation, Bellueve, WA) for 8088 or 80 86 based microcomputers. 

Pattern recognition studies on complex data from capillary gas chromatographic analyses were conducted with a series of microcomputer programs based on principal components (SIMCA-3B). Principal components sample score plots provide a means to assess sample similarity. The behavior of analytes in samples can be evaluated from variable loading plots derived from principal components calculations. A complex data set was derived from isomer specific polychlorinated biphenyl (PCBS) analyses of samples from laboratory and field studies. 

Four levels of pattern recognition have been defined by Albano (2). Levels I and II are most frequently used to determine the similarity of objects, or to characterize clusters of samples and to classify unknown objects. Level III takes advantage of the reduction of data dimensions resulting from SIMCA and seeks to establish a correlation of sample scores with independent variables... 

The SIMCA approach can be applied in all of the four levels of pattern recognition. We focus on its use to describe complex mixtures graphically, and on its utility in quality control. This approach was selected for the tasks of developing a quality control program and evaluating similarities in samples of various types. Principal components analysis has proven to be well suited for evaluating data from capillary gas chromatographic (GC) analyses (6-8). 

Dunn, III, W.J., Wold, S., and Stalling, D.L., "How SIMCA Pattern Recognition Works," Proceedings of Symposium on Chemometrics, Division of Environmental Chemistry, 188 National ACS Meeting, Philadelphia, PA, August 26-31, 1985. in press. 

Pattern recognition has been applied In many forms to various types of chemical data (1,2). In this paper the use of SIMCA pattern recognition to display data and detect outliers In different types of air pollutant analytical data Is Illustrated. Pattern recognition Is used In the sense of classification of objects Into sets with emphasis on graphical representations of data. Basic assumptions which are Implied In the use of this method are that objects In a class are similar and that the data examined are somehow related to this similarity. 

Only one class modeling method is conmonly applied to analytical data and this is the SIMCA method ( ) of pattern recognition. In this method the class structure (cluster) is approximated by a point, line, plane, or hyperplane. Distances around these geometric functions can be used to define volumes where the classes are located in variable space, and these volumes are the basis for the classification of unknowns. This method allows the development of information beyond class assignment ( ). 

The SIMCA method of pattern recognition is in a comprehensive set of programs for classification, and we have discussed how it works in this regard. Classification problems represent only a few of types of problems that can be solved with this approach. 

Supervised pattern recognition methods are used for predicting the class of unkno-wm samples given a training set of samples with known class member-sliip. Tvksmethods are discussed in Section 4.3, KNN and SIMCA,... 

Habits 5 and 6 are not described because POV is not used in this section as a predictive tool. The super ised pattern-recognition technique, SIMCA, uses PCA for class prediction and the details of Habits 5 and 6 for SIMCA are presented in Section 4.3.2.1. 

Pattern Recognition by Independent Multicategory Analysis (PRIMA) is another method retaining some characteristics of SIMCA. It has been applied to the classification of brandies... 

Current methods for supervised pattern recognition are numerous. Typical linear methods are linear discriminant analysis (LDA) based on distance calculation, soft independent modeling of class analogy (SIMCA), which emphasizes similarities within a class, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. More advanced methods are based on nonlinear techniques, such as neural networks. Parametric versus nonparametric computations is a further distinction. In parametric techniques such as LDA, statistical parameters of normal sample distribution are used in the decision rules. Such restrictions do not influence nonparametric methods such as SIMCA, which perform more efficiently on NIR data collections. 

SIMCA method relies on a pattern-recognition technique called principal component analysis (PCA). 

Polychlorobiphenyls Arochlor 1242, 2143, 1252, 1260 — Simca pattern recognition analysis performed — [304]... 

Dunn III, WJ. and Wold, S., An assessment of carcinogenicity of N-nitroso compounds by the SIMCA method of pattern recognition, J. Chem. Inf. Comput, Sci., 21, 8-13, 1981. 

Norden, B., Edlund, U., and Wold, S., Carcinogenicity of polycyclic aromatic hydrocarbons, studied by SIMCA pattern recognition, Acta Chemica Scandinavica Series B, 32, 602-608, 1978. 

Finally, feature selection is crucial to ensure a successful pattern-recognition study, since irrelevant features can introduce so much noise that a good classification of the data cannot be obtained. When these irrelevant features are removed, a clear and well-separated class structure is often found. The deletion of irrelevant variables is therefore an important goal of feature selection. For averaging techniques such as K-NN, partial least squares, or SIMCA, feature selection is vital, since signal is averaged with noise... 

The SIMCA method, first advocated by the S. Wold in tire early 1970s, is regarded by many as a form of soft modelling used in chemical pattern recognition. Although there are some differences with linear discriminant analysis as employed in traditional statistics, the distinction is not as radical as many would believe. However, SIMCA has an important role in the history of chemometrics so it is important to understand the main steps of the method. 

Dunn III, W.J. and Wold. S. SIMCA Pattern Recognition and Classification. In QSAR Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. van de Waterbeemd, H., Verlag Chemie, Weinheim, Germany, 1995. 

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