Journal of Chemometrics最新文献

Fluorescence spectroscopy has been applied for analysis of complex samples, such as food and beverages. Parallel factor analysis (PARAFAC) is a well-known decomposition method for fluorescence excitation–emission matrices (EEMs). When the complexity of the system increases, it becomes considerably more difficult to determine the optimal number of PARAFAC components, especially when the fluorophores of the system are unknown. The two commonly applied diagnostics, core consistency and split-half analysis, appear to underestimate the model complexity due to covarying components and local minima, respectively. As a more robust alternative, we propose a resampling approach with multiple initializations and submodel comparisons for estimating the optimal number of PARAFAC components in complex data.

This study addresses the complex dynamics of alcohol elimination in the human body, very important in forensic and healthcare areas. Existing models often oversimplify with the assumption of linear elimination kinetics, limiting practical application. This study presents a novel non-linear model for estimating blood alcohol concentration after multiple intakes. Initially developed for two different alcohol incorporations, it can be straightforwardly extended to the case of more intakes. Emphasising the significance of accurate parameter estimation, the research underscores the importance of precise experimental design, utilising optimal experimental design (OED) methodologies. Sensitivity analysis of model coefficients and the determination of D-optimal designs, considering correlation structures among observations, reveal a strong linear relationship between support points. This relationship can be used to obtain nearly optimal designs that are highly efficient and much easier to compute.

In this paper, we revisit the power curves in ANOVA simultaneous component analysis (ASCA) based on permutation testing and introduce the population curves derived from population parameters describing the relative effect among factors and interactions. The relative effect has important practical implications: The statistical power of a given factor depends on the design of other factors in the experiment and not only of the sample size. Thus, understanding the relative power in a specific experimental design can be extremely useful to maximize our capability of success when planning the experiment. In the paper, we derive relative and absolute population curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Both types of population curves allow us to make decisions regarding the number and nature (fixed/random) of factors, their relationships (crossed/nested), and the number of levels and replicates, among others, in an multivariate experimental design (e.g., an omics study) during the planning phase of the experiment. We illustrate both types of curves through simulation.

Many data analysis methods actually combine optimization of several criteria. In this paper, a framework is offered for categorizing such multi-criteria methods. In particular, it categorizes multiset and three-way analysis methods as well as penalized methods and combinations thereof. The framework aims to stimulate critical evaluation of methods and reflection on the purpose of methods and, by signaling gaps, to help the development of new data analysis methods.

In MCR analyses, the similarity of pairs of spectra or concentration profiles can be measured in terms of the acute angle that is enclosed by the representing vectors. Acute angles between vectors can be generalized to pairs of subspaces. So-called canonical angles, also called principal angles, measure the mutual orientation of a pair of subspaces. This work discusses how angles and canonical angles can support multivariate curve resolution analyses. A canonical angle analysis (CAA) can help to detect changes of the chemical composition during a chemical reaction in a way comparable, but different to the evolving factor analysis (EFA).

In this work, two alternative ways of analyzing three-way data with multivariate curve resolution alternating least squares (MCR-ALS) using the trilinearity constraint are described and compared. Different synthetic datasets and experimental three-way datasets covering different scenarios are analyzed, and the results obtained are compared. The two new different ways of applying the trilinearity constraint are named flexible trilinearity alignment (FTA) and shift invariant transformation (SIT). The effects of noise in the application of both types of constraints are investigated in detail. Results show that both approaches are particularly adequate for those cases like in gas chromatography and especially in liquid chromatography where the elution profiles of the same chemical component in different chromatographic runs are not totally reproducible because they are time shifted, although they preserve their shape. When strong time shifts and co-elution occur, then the “standard” trilinear model does not work, and alternative approaches should be used, such as the MCR extended bilinear model to multiset (multirun) data, or the proposed relaxation of the trilinearity constraint in the FTA and SIT methods to capture the time drift changes produced in the elution profiles of the resolved components.

A robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration-dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method.