This paper addresses the use of temperature measurements to estimate product compositions in distillation columns. A simple linear multivariate calibration procedure based on steady-state data is used, which requires minimal modeling effort. It is found that these principal-component-regression (PCR) and partial-least-squares (PLS) estimators perform well, even for multicomponent mixtures, pres...

Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is...

PURPOSE To propose a more accurate method to predict the soft tissue changes after orthognathic surgery. PATIENTS AND METHODS The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordi...

partial least square and principal component regression methods were applied to various mixtures of Allura Red and Brilliant Blue to determine the concentrations. Colorants, at the same time, were analyzed with UV-spectrophotometry in chemical separation. The obtained experimental data have been evaluated by chemometric methods as Partial Least Squares (PLS) and Principle Component Regressi...

PLS univariate regression is a model linking a dependent variable y to a set X= {x1; : : : ; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi6cant explanatory variables to include in PLS regression and to ...

In this work we find out how PLS algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had until now been totally unexplored, allowed us to devise a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. Mots-clès: Analyse des données data mining, Problèmes inverses et sparsité

Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the 2nite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. c © 2003 Elsevier B.V. ...

Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functio...

a method for the simultaneous spectrophotometric determination of nickel, cobalt and copper based on theformation of their complexes with 2-(2-thiazolylazo)-p-cresol (tac) in micellar media of triton x-100 isproposed. the absorbance spectra were recorded in the range of 500 to 800 nm. the linear concentrationrange for nickel, cobalt and copper in solution calibration sets were 0.05–1.80, 0.10–6...

Non-invasive blood glucose measurement has attracted much attention in recent years. Near-infrared (NIR) spectroscopy is currently used for frequent detection of blood glucose levels. The dynamic spectrum (DS) method, which can eliminate the individual discrepancy, is the development of ordinary NIR spectroscopy. Classic Least Squares (CLS) is usually employed to analyze the spectra. In this wo...