PLS-discriminant analysis

The VolSurf method was used to produce molecular descriptors, and PLS discriminant analysis (DA) was applied. The statistical model showed two significant latent variables after cross-validation. The 2D PLS score model offers a discrimination between the permeable and less permeable compounds. When the spectrum color is active (Fig. 17.2), red points refer to high permeability, whereas blue points indicate low permeability. There is a region in the central part of the plot with both red and blue compounds. In this region, and in between the two continuous lines, the permeability prediction is less reliable. The permeability model... 

In this example, we apply D-PLS (PLS discriminant analysis, see Section 5.2.2) for the recognition of a chemical substructure from low-resolution mass spectral data. This type of classification problems stood at the beginning of the use of multivariate data analysis methods in chemistry (see Section 1.3). 

Chemometrics in Process Analytical Technology (PAT) 395 12.3.3.6 PLS-Discriminant Analysis (PLS-DA)... 

A number of PLS variants have been deployed, for instance, for developing nonlinear models and for predicting together several response variables (PLS-2). Furthermore, when category indices are taken as response variables, PLS may work as a classification method which is usually called PLS discriminant analysis (PLS-DA). 

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. 

Y is constructed so that there are as many columns as there are groups and let each column represent one group. The objects belonging to the jth group get a 1 in the jth column and 0 in the others (Figure 6.9). This design is usually referred to as PLS discriminant analysis [23] or analysis of variance like PLS design [24]. 

After converting the VD data (Lkg ) into -log[VD] values, PLS discriminant analysis was used to build the statistical model. Two significant latent variables emerged from the PLS discriminant analysis statistics give an = 0.61, = 0.53,... 

BBB permeability is predicted from a PLS discriminant analysis model [18]. 

Nouwen, J., Lindgren, R, Hansen, B. and Karcher, W. (1997). Classification of Environmentally Occurring Chemicals Using Structural Fragments and PLS Discriminant Analysis. Environ. Sci.TechnoL, 31,2313-2318. 

PCA is a least square method and therefore its results depend on data scaling. The initial variance of a column variable partly determines its importance in the model. In order to avoid the problem of over- or under-representation of variables, column variables are scaled to unit variance before analysis. The column average is then subtracted from each variable, which, from a statistical point of view, corresponds to moving the multivariate system to the center of the data, which becomes the starting point of the mathematical analysis. The same auto-scaUng and centering procedures are applied in PLS discriminant analysis. 

Classification of environmentally occurring chemicals using structural fragments and PLS discriminant analysis. Environ. Sci. Technol, 31, 2313-2318. 

Nouwen, J. Lindgren, F. Hansen, B. Karcher, W Verhaar, H. J. M. Hermens, J. L. M. Classification of environmentally occurring chemicals using structural fragments and PLS discriminant analysis. Environ. Sci. Technol. 1997, 31, 2313-2318. 

C. Mello, D. Ribeiro, F. Novaes and R. J. Poppi, Rapid differentiation among bacteria that cause gastroenteritis by use of low-resolution Raman spectroscopy and PLS discriminant analysis. Analytical Bioanalytical Chemistry, 2005, 383(4), 701-706. 

With many and collinear X variables it is natural to use PLS instead of regression for the model estimation. This gives PLS discriminant analysis (PLS-DA). With PLS-DA it is easier to use G instead of C7 - 1 columns in the Y dummy matrix, since the rank deficiency is automatically taken care of in PLS. Projecting new observations onto the discriminant plane gives predicted values of all the Y columns, thus predicting the class of these observations. 

PLS discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), multiway N-PLS-DA... 

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