Partial Least Square Analysis for University Student Satisfaction

Partial Least Square Discriminant Analysis for bankruptcy prediction

Partial Least Square and Parallel Factor Analysis Methods Applied for Spectrophotometric Determination of Cefixime in Pharmaceutical Formulations and Biological Fluid

In this study, the direct determination of cefixime as an anti-bacterial agent, in pharmaceutical formulations, urine and human blood plasma was conducted based on spectrophotometric measurements using parallel factor analysis (PARAFAC) and partial least squares (PLS). The calibration set was composed of fourteen solutions in the range of 0.50- 9.00 µg mL-1. PLS models were calculated at each p...

Partial Least Square and Parallel Factor Analysis Methods Applied for Spectrophotometric Determination of Cefixime in Pharmaceutical Formulations and Biological Fluid

In this study, the direct determination of cefixime as an anti-bacterial agent, in pharmaceutical formulations, urine and human blood plasma was conducted based on spectrophotometric measurements using parallel factor analysis (PARAFAC) and partial least squares (PLS). The calibration set was composed of fourteen solutions in the range of 0.50- 9.00 µg mL-1. PLS models were calculated at each p...

Partial least squares methods: partial least squares correlation and partial least square regression.

Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the ap...

Application of Partial Least Square Regression in Uncertainty Analysis

The aim of this work is to show how partial least squares (PLS) regression when combined with two other techniques Karhunen-Loeve (KL) expansion and Markov chain Monte Carlo (MCMC) can be efficient and effective at addressing parameter uncertainties that affect the predictive ability of a model for critical applications such as monitoring and control. We introduce a combination of PLS regressio...