Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be
used for predictive and descriptive modelling as well as for discriminative variable selection …

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze
metabolomics datasets (indeed, it is the most well-known tool to perform classification and …

Partial least squares discriminant analysis (PLS‐DA) has been available for nearly 20 years
yet is poorly understood by most users. By simple examples, it is shown graphically and …

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells
and biological fluids, free of observational biases inherent to more focused studies of …

Metabolomics experiments usually result in a large quantity of data. Univariate and
multivariate analysis techniques are routinely used to extract relevant information from the …

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is
described. Methods are illustrated using simulated case studies, and 4 experimental case …

This review focuses on recent and potential advances in chemometric methods in relation to
data processing in metabolomics, especially for data generated from mass spectrometric …

Classifying groups of individuals based on their metabolic profile is one of the main topics in
metabolomics research. Due to the low number of individuals compared to the large number …

Metabolomics is the discipline where endogenous and exogenous metabolites are
assessed, identified and quantified in different biological samples. Metabolites are crucial …

We provide an overview of latent variable methods used in pharmaceutics and integrated
with advanced characterization techniques such as vibrational spectroscopy. The basics of …