SIMCA Soft Independent Modeling

SIMCA soft independent modeling of class analogy... 

As explained in Section 33.2.1, one can prefer to consider each class separately and to perform outlier tests to decide whether a new object belongs to a certain class or not. The earliest approaches, introduced in chemometrics, were called SIMCA (soft independent modelling of class analogy) [27] and UNEQ [28]. 

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]. 

SIMCA Soft independent modeling of class analogies... 

SIMCA (Soft Independent Modelling of Class Analogy) is the first modelling... 

Figure 10 SIMCA analysis of the scans shown in Figure 5. (A) SICMA model for Avicel PH 101, (B) SIMCA model for Lactose, (C) SIMCA model for Mg Stearate and (D) SIMCA model for di-tab. Abbreviation SIMCA, Soft Independent Modelling of Class Analogies.

Another use of PCA in multivariate characterization is the formulation of a class model. If there are several classes of subjects in a study, a PC model can be made of each class with surrounding tolerance volumes. New subjects are assigned to a class if it is inside the tolerance volume of this class. This simple but efficient classification scheme is called SIMCA (soft independent modelling of class analogy) and it is described in detail elsewhere [17, 18]. 

Principal component analysis is central to many of the more popular multivariate data analysis methods in chemistry. For example, a classification method based on principal component analysis called SIMCA [69, 70] is by the far the most popular method for describing the class structure of a data set. In SIMCA (soft independent modeling by class analogy), a separate principal component analysis is performed on each class in the data set, and a sufficient number of principal components are retained to account for most of the variation within each class. The number of principal components retained for each class is usually determined directly from the data by a method called cross validation [71] and is often different for each class model. 

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. 

RBF-ANN Radial Basis Function- SIMCA Soft-Independent Modeling... 

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... 

SIMCA Soft Independent Modeling of Class Analogy (SIMCA) [13,14]... 

Acoustic emission power spectra are similar in many respects to optical spectra and are amenable to chemometric processing (multivariate analysis). Principal component analysis, partial least squares (PLS), neural networks, and qualitative techniques such as SIMCA (soft independent modeling of class analogy a pattern recognition technique) have been employed... 

The literature of multivariate classification shows that several types of methods have found utility in application to chemical problems. Excellent discussions of the major methods can be found in Strouf ° and Tou and Gon-zalez. The most frequently used methods include parametric approaches involving linear and quadratic discriminant analysis based on the Bayesian approach,nonparametric linear discriminant development methods,and those methods based on principal components analysis such as SIMCA (Soft Independent Modeling by Class Analogy). 

SIMCA Soft independent modelling of class UV-C UV wavelength range 200-280 nm... 

SIMCA Soft Independent Modeling of Class Analogy... 

Fig. 1. Pattern recognition methods. ANN, artificial neural networks BP ANN, back-propagation ANN CA, cluster analysis CART, classification and regression trees (recursive partitioning) CCA, canonical correlation analysis CVA, canonical variate analysis kNN, -nearest neighbor methods LDA, linear discriminant analysis PCA, principal component analysis PLS DA, partial least squares regression discriminant analysis SIMCA, soft independent modeling of class analogy SOM, self-organizing maps.

Mass spectrometry and chemometric methods cover very diverse fields Different origin of enzymes can be disclosed with LC-MS and multivariate analysis [45], Pyrolysis mass spectrometry and chemometrics have been applied for quality control of paints [46] and food analysis [47], Olive oils can be classified by analyzing volatile organic hydrocarbons (of benzene type) with headspace-mass spectrometry and CA as well as PC A [48], Differentiation and classification of wines can similarly be solved with headspace-mass spectrometry using unsupervised and supervised principal component analyses (SIMCA = soft independent modeling of class analogy) [49], Early prediction of wheat quality is possible using mass spectrometry and multivariate data analysis [50],... 

When some of the classes are not tight, often due to a lack of homogeneity and similarity in these non-tight classes, the discriminant analysis does not work. Then other approaches, such as SIMCA (soft independent modeling of class analogy) have to be used, where a PC or PLS model is developed for each tight class, and new observations are classified according to their nearness in X space to these class models. This is often called asymmetric classification. ... 

SIMCA soft independent modelling of class analogy—Supervised... 

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