Soft independent modeling by class analogy SIMCA

Various classification approaches have been reported to be used successfully in conjunction with fragment descriptors for building classification SAR models the Linear Discriminant Analysis (LDA), the Partial Least Square Discriminant Analysis (PLS-DA), Soft Independent Modeling by Class Analogy (SIMCA), Artificial Neural Networks (ANN), ° Support Vector... 

The main classification methods for drug development are discriminant analysis (DA), possibly based on principal components (PLS-DA) and soft independent models for class analogy (SIMCA). SIMCA is based only on PCA analysis one PCA model is created for each class, and distances between objects and the projection space of PCA models are evaluated. PLS-DA is for example applied for the prediction of adverse effects by nonsteroidal anti-... 

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. 

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. 

Another approach is based on the combination of molecular interaction fields using the 3D-QSAR technique CoMFA and soft independent modeling of class analogy (SIMCA) [33], Predictions were made for h % ranges by using the data sets from Refs [19, 27], with about 60% correctly classified. 

Classification of the citrus oils was possible by using Soft Independent Modeling of Class Analogy (SIMCA). This type of algorithm is designed to compare new samples against previously-analyzed sets. Another ability if SIMCA is the determination if a sample does not belong to any predefined class. 

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). 

In another paper by Lodder et al. [124], the qualification of a number of tablet characteristics was performed in a comparative study of two classification algorithms soft independent modeling of class analogies (SIMCA) and the quantile-BEAST. The study involved qualitative classification of tablet hardness, moisture content, dissolution rate, and degradant concentration. 

Such a diagnosis is obtained best using supervised or trained algorithms. A number of such predictive algorithms exist, some very similar to that of the principles of PCA (i.e. soft independent modeling of class analogy, SIMCA), linear discriminant analysis (LDA) to more compUcated classifiers such as support vector machines (SVMs), which are based on separating spectral classes by complicated, multidimensional separation planes [30]. At the LSpD, ANNs have been used [52-54] for supervised prediction of class memberships. 

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]. 

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],... 

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... 

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],... 

One of the most popular pattern recognition methods in chemistry is SIMCA, an acronym for soft/simple independent modeling of class analogy. The central idea is to represent each class of objects by a separate principal component model. Because a probability can be estimated for belonging to a certain class and because outliers can be detected the method is called soft. Classification methods such as discriminant analysis are called hard if they give a categorical answer about the class membership. 

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