Statistical models partial least squares

Quantitative structure-activity/pharmacokinetic relationships (QSAR/ QSPKR) for a series of synthesized DHPs and pyridines as Pgp (type I (100) II (101)) inhibitors was generated by 3D molecular modelling using SYBYL and KowWin programs. A multivariate statistical technique, partial least square (PLS) regression, was applied to derive a QSAR model for Pgp inhibition and QSPKR models. Cross-validation using the leave-one-out method was performed to evaluate the predictive performance of models. For Pgp reversal, the model obtained by PLS could account for most of the variation in Pgp inhibition (R2 = 0.76) with fair predictive performance (Q2 = 0.62). Nine structurally related 1,4-DHPs drugs were used for QSPKR analysis. The models could explain the majority of the variation in clearance (R2 = 0.90), and cross-validation confirmed the prediction ability (Q2 = 0.69) [ 129]. 

As a multivariate statistical method, partial least square (PLS) is of particular interest in the QSAR field [3]. PLS can analyze data with strongly collinear, noisy, and numerous X variables, while simultaneously modeling several response variables Y. PLS can also provide several prediction regions and diagnostic plots as statistical measures. Using such an approach, QSAR scientists can extract the patterns embedded in the structure-activity data. 

A system to carry out a nondestructive punctual measurement of salt content in meat samples using impedance spectroscopy was designed by Masot et al. The choice of the electrodes and their configuration in EIS experiments is one of the most critical points. The designed system includes a concentric needle electrode that is introduced into the food sample. Collected impedance data were then statistically processed. Partial least squares (PLS) was applied to a set of samples in order to obtain a model. Studies were carried... 

Partial least squares regression (PLS) is more important in chemometrics than in other fields of applied statistics (see Partial Least Squares Projections to Latent Structures (PLS) in Chemistry). PLS can be considered as an alternative method to PCR and LDA. The aim of data interpretation is to build a linear model for the prediction of a response y from the independent variables (regressors, features)x],X2- - Xp as given in equation (27) ... 

For many applications, quantitative band shape analysis is difficult to apply. Bands may be numerous or may overlap, the optical transmission properties of the film or host matrix may distort features, and features may be indistinct. If one can prepare samples of known properties and collect the FTIR spectra, then it is possible to produce a calibration matrix that can be used to assist in predicting these properties in unknown samples. Statistical, chemometric techniques, such as PLS (partial least-squares) and PCR (principle components of regression), may be applied to this matrix. Chemometric methods permit much larger segments of the spectra to be comprehended in developing an analysis model than is usually the case for simple band shape analyses. 

H. Wold, Soft modelling by latent variables the non-linear iterative partial least squares (NIPALS) algorithm. In Perspectives in Probability and Statistics, J. Gani (Ed.). Academic Press, London, 1975, pp. 117-142. 

A total of 185 emission lines for both major and trace elements were attributed from each LIBS broadband spectrum. Then background-corrected, summed, and normalized intensities were calculated for 18 selected emission lines and 153 emission line ratios were generated. Finally, the summed intensities and ratios were used as input variables to multivariate statistical chemometric models. A total of 3100 spectra were used to generate Partial Least Squares Discriminant Analysis (PLS-DA) models and test sets. 

Statistical Receptor Models Solved by Partial Least Squares... 

In the past few years, PLS, a multiblock, multivariate regression model solved by partial least squares found its application in various fields of chemistry (1-7). This method can be viewed as an extension and generalization of other commonly used multivariate statistical techniques, like regression solved by least squares and principal component analysis. PLS has several advantages over the ordinary least squares solution therefore, it becomes more and more popular in solving regression models in chemical problems. 

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