Jan Gerretzen

Warping methods for spectroscopic and chromatographic signal alignment: A tutorial

Warping methods are an important class of methods that can correct for misalignments in (a.o.) chemical measurements. Their use in preprocessing of chromatographic, spectroscopic and spectrometric data has grown rapidly over the last decade. This tutorial review aims to give a critical introduction to the most important warping methods, the place of warping in preprocessing and current views on the related matters of reference selection, optimization, and evaluation. Some pitfalls in warping, notably for liquid chromatography-mass spectrometry (LC-MS) data and similar, will be discussed. Examples will be given of the application of a number of freely available warping methods to a nuclear magnetic resonance (NMR) spectroscopic dataset and a chromatographic dataset. As part of the Supporting Information, we provide a number of programming scripts in Matlab and R, allowing the reader to work the extended examples in detail and to reproduce the figures in this paper.

Weight randomization test for the selection of the number of components in PLS models

The selection of the optimal number of components remains a difficult but essential task in partial least squares (PLS). Randomization tests have the advantage of being automatic and they make use of the entire dataset, in contrary with the widely used cross‐validation approaches. Partial least squares modeling may include component(s) with a large amount of irrelevant data variation, and this might affect the model, depending on the assigned y‐loading (which is the regression coefficient in the latent domain). This has recently been indicated by us in the basic sequence framework with respect to the underlying theory of the PLS algorithm and presented to the chemometrics society. We will show in this work that this irrelevant data variation is the root cause of the difficulty in current methods for selecting the optimal number of components. For randomization tests, PLS models with nonsignificant components may result in false positive tests because of the incorrect assumption that “the components enter the model in a natural order”.

Boosting model performance and interpretation by entangling preprocessing selection and variable selection

The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of the user. In this work, the approach is illustrated by using PLS as model and PPRV-FCAM (Predictive Property Ranked Variable using Final Complexity Adapted Models) for variable selection.