Selection of principal variables through a modified Gram–Schmidt process with and without supervision

In various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low-dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful for obtaining sparser but still comparable models without significant loss of information and performance. In the present paper, we propose a voting approach for sparse overall maximisation of variance analogous to PCA and a similar alternative for deriving sparse regression models influenced closely related to the PLSR method. Both cases yield pivoting strategies for a modified Gram–Schmidt process and its corresponding (partial) QRfactorisation of the underlying data matrix to manage the variable selection process. The proposed methods include score and loading plot possibilities that are acknowledged for providing efficient interpretations of the related PCA and PLS models in chemometric applications.Selection of principal variables through a modified Gram–Schmidt process with and without supervisionpublishedVersio

Unraveling Enhanced Activity, Selectivity, and Coke Resistance of Pt–Ni Bimetallic Clusters in Dry Reforming

By introducing Pt atoms into the surface of reduced hydrotalcite (HT)-derived nickel (Ni/HT) catalysts by redox reaction, we synthesized an enhanced active and stable Ni-based catalyst for methane dry reforming reaction. The bimetallic Pt–Ni catalysts can simultaneously enhance the catalyst activity, increase the H2/CO ratio by suppressing reverse water–gas shift reaction, and enhance the stability by increasing the resistance to the carbon deposition during the reaction. Kinetic study showed that 1.0Pt–12Ni reduces the activation energy for CH4 dissociation and enhances the catalytic activity of the catalyst and lowers the energy barrier for CO2 activation and promotes the formation of surface O* by CO2 adsorptive dissociation. It is beneficial to enhance the resistance to the carbon deposition and prolong its service life in the reaction process. In addition, density-functional theory calculations rationalized the higher coke resistance of Pt–Ni catalysts where CH is more favorable to be oxidized instead of cracking into surface carbon on the Pt–Ni surface, compared with Ni(111) and Pt(111). Even if a small amount of carbon deposited on the Pt–Ni surface, its oxidation process requires a lower activation barrier. Thus, it demonstrates that the bimetallic Pt–Ni catalyst has the best ability to resist carbon deposition compared with monometallic samples.publishedVersio

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

takos: R balík pro výpočty termické analýzy

Thermal analysis consists of a wide range of methodologies that can be applied to assess the composition and properties of materials. This paper describes the basic features of a new package for the R software named takos for simulating and analysing calorimetric data sets. The package can simulate data via the Sesta Berggren (SB), Johnson-Mehl-Avrami (JMA) and other common kinetic models used in solid state kinetics (power law, one dimensional diffusion, Mampel, Avrami-Erofeev, three dimensional diffusion, contracting sphere, contracting cylinder, and two-dimensional diffusion). The methodologies included in order to determine the kinetic triplet are the Avrami, Friedman, Kissinger, Ozawa, Ozawa-Flynn and Wall (OFW), Mo, Starink and Vyazovkin methodology (Vyazovkin). The package is under constant development, being improved and extended with new functionalities, as well as being continually tested on real-life data, the analyses of which are peer-reviewed during their respective publication processes.Termická analýza zahrnuje široký rozsah metodologií, které mohou být aplikovány na řadu složení a vlastností materiálů. Článek popisuje řešení pro analýzu dat získaných metodami termické analýzy

Sequential and orthogonalized PLS (SO-PLS) regression for path analysis: Order of blocks and relations between effects

This paper is about the use of the multiblock regression method sequential and orthogonalized partial least squares (SO-PLS) for path modeling. The paper is a follow up of previously published papers on the same topic and presents a number of new results for the method. First of all, the paper discusses more thoroughly the aspect of how to incorporate blocks in the models and relates this to standard concepts in the area of graphical modeling. Second, the paper defines the concept of direct and indirect effects more precisely in terms of population parameters and shows how they are related to the additional effect in SO-PLS modeling. The paper illustrates the theory by simple graphs, simulations, and a real example from process monitoring

Sequential and orthogonalized PLS (SO-PLS) regression for path analysis: Order of blocks and relations between effects

This paper is about the use of the multiblock regression method sequential and orthogonalized partial least squares (SO-PLS) for path modeling. The paper is a follow up of previously published papers on the same topic and presents a number of new results for the method. First of all, the paper discusses more thoroughly the aspect of how to incorporate blocks in the models and relates this to standard concepts in the area of graphical modeling. Second, the paper defines the concept of direct and indirect effects more precisely in terms of population parameters and shows how they are related to the additional effect in SO-PLS modeling. The paper illustrates the theory by simple graphs, simulations, and a real example from process monitoring