Soft independent method of class analogy

Application of Soft Independent Method of Class Analogy (SIMCA) in Isomer Specific Analysis of Polychlorinated Biphenyls... 

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). 

Chemometrics, as defined by Kowalski (1), includes the application of multivariate statistical methods to the study of chemical problems. SIMCA (Soft Independent Method of Class Analogy) and other multivariate statistical methods have been used as tools in chemometric investigations. SIMCA, based on principal components, is a multivariate chemometric method that has been applied to a variety of chemical problems of varying complexity. The SIMCA-3B program is suitable for use with 8- and 16-bit microcomputers. 

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

There are many classification methods apart from linear discriminant analysis (Derde et al. [1987] Frank and Friedman [1989] Huberty [1994]). Particularly worth mentioning are the SIMCA method (Soft independent modelling of class analogies) (Wold [1976] Frank [1989]), ALLOC (Coomans et al. [1981]), UNEQ (Derde and Massart [1986]), PRIMA (Juricskay and Veress [1985] Derde and Massart [1988]), DASCO (Frank [1988]), etc. 

Nonetheless, a sub-set belonging to one class may very likely be normally distributed. In this case a PCA calculated on one class cannot work in describing data belonging to another class. In this way, the membership of data to each class can be evaluated. This aspect is used by a classification method called SIMCA (Soft Independent Modelling of Class Analogy). It is a clever exploitation of the limitations of PCA to build a classification methodology [20]. 

Distance-based methods possess a superior discriminating power and allow highly similar compounds (e.g. substances with different particle sizes or purity grades, products from different manufacturers) to be distinguished. One other choice for classification purposes is the residual variance, which is a variant of soft independent modeling of class analogy (SIMCA). 

Nevertheless, in most of the electronic tongue applications found in the literature, classification techniques like linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) have been used in place of more appropriate class-modeling methods. Moreover, in the few cases in which a class-modeling technique such as soft independent modeling of class analogy (SIMCA) is applied, attention is frequently focused only on its classification performance (e.g., correct classification rate). Use of such a restricted focus considerably underutilizes the significant characteristics of the class-modeling approach. 

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. 

Currently, several linear and nonlinear multivariate classification methods exist the choice implies the evaluation of discriminatory power against the ability to interpret the meaning of class differences. In this respect, Soft Independent Modeling of Class Analogy (SIMCA ... 

NIR spectroscopy was utilized by Aldridge and coworkers86 to determine, in a rapid manner, the polymorphic quality of a solid drug substance. Two computational methods, Mahalonobis distance and soft independent modeling of class analogy (SIMCA) residual variance, were used to distinguish between acceptable and unacceptable samples. The authors not only determined that the Mahalonobis distance classification yielded the best results, they addressed one of the key implementation issues regarding NIR as a PAT tool. 

Supervised learning methods - multivariate analysis of variance and discriminant analysis (MVDA) - k nearest neighbors (kNN) - linear learning machine (LLM) - BAYES classification - soft independent modeling of class analogy (SIMCA) - UNEQ classification Quantitative demarcation of a priori classes, relationships between class properties and variables... 

When data are high dimensional, the approach of the previous section can no longer be applied because the MCD becomes uncomputable. In the previous example (Section 6.8.1.3), this was solved by applying a dimension-reduction procedure (PC A) on the whole set of observations. Instead, one can also apply a PC A method on each group separately. This is the idea behind the SIMCA method (soft independent modeling of class analogy) [77],... 

Disjoint principal components modelling [266] and SIMCA (soft independent modelling of class analogy) [261,262,267] are examples of PCR wherein principal components models are developed for individual groups of responses within a data set. For these methods, classification is based on quality of fit of an unknown response pattern to the model developed for a given analyte [268-270]. This approach differs from standard PCR, where principal components are derived from the data matrix as a whole. 

The most popular classification methods are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA), K th Nearest Neighbours (KNN), classification tree methods (such as CART), Soft-Independent Modeling of Class Analogy (SIMCA), potential function classifiers (PFC), Nearest Mean Classifier (NMC) and Weighted Nearest Mean Classifier (WNMC). Moreover, several classification methods can be found among the artificial neural networks. 

It often occurs that active compounds cannot be well separated from inactive ones using linear models such as PLS or LDA. This may be because the active compounds cluster together in an area of property space and they are surrounded by inactive compounds. Such data are called embedded or asymmetric data. Several methods have been developed to treat such data sets, the best known is the SIMCA algorithm. The SIMCA (soft independent modelling of class analogy) method is a tool for pattern... 

Then the next step consists on application of multivariate statistical methods to find key features involving molecules, descriptors and anticancer activity. The methods include principal component analysis (PCA), hiererchical cluster analysis (HCA), K-nearest neighbor method (KNN), soft independent modeling of class analogy method (SIMCA) and stepwise discriminant analysis (SDA). The analyses were performed on a data matrix with dimension 25 lines (molecules) x 1700 columns (descriptors), not shown for convenience. For a further study of the methodology apphed there are standard books available such as (Varmuza FUzmoser, 2009) and (Manly, 2004). 

If the membership of objects to particular clusters is known in advance, the methods of supervised pattern recognition can be used. In this section, the following methods are explained linear learning machine (LLM), discriminant analysis, A -NN, the soft independent modeling of class analogies (SIMCA) method, and Support Vector Machines (SVMs). 

For classification, one may use a method called Soft Independent Modeling of Class Analogies in which one PCA model is constructed for each group of samples, but a range of other methods are also available. 

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