Soft independent modelling of class analogies

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]. 

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]. 

Soft independent modeling of class analogy (SIMCA), 10 330 Soft lithography, 15 192... 

SIMCA Soft independent modeling of class analogies... 

Soft Independent Modeling of Class Analogies (SIMCA)... 

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). 

P.J. Gemperline and L.D. Webber, Raw materials testing using soft independent modeling of class analogy analysis of near infrared reflectance spectra, Anal. Chem., 61, 138-144 (1989). 

The multivariate techniques which reveal underlying factors such as principal component factor analysis (PCA), soft Independent modeling of class analogy (SIMCA), partial least squares (PLS), and cluster analysis work optimally If each measurement or parameter Is normally distributed In the measurement space. Frequency histograms should be calculated to check the normality of the data to be analyzed. Skewed distributions are often observed In atmospheric studies due to the process of mixing of plumes with ambient air. 

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. 

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

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. 

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.

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. 

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]. 

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],... 

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