Soft independent modeling of class analog SIMCA

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

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

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). 

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. 

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... 

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. 

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. 

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