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

Comparison of the digestion of caseins and whey proteins in equine, bovine, caprine and human milks by human gastrointestinal enzymes

The aim of this study was to compare the digestion of milk proteins from different species using an in vitro gastrointestinal model. Raw and heated milks from bovine, caprine, human and equine species were digested by human digestive enzymes. Digestion was performed in two 30-min sequential steps by digestive juices from the stomach (pH 2.5/37 °C) and from the duodenum (pH 8.0/37 °C). The degradation patterns of the milk proteins were visualized by SDS-PAGE and quantified using the ImageQuant program. Caseins in the equine milk were rapidly digested by the gastric juice in contrast to the caseins from the other species. During the subsequent digestion by the duodenal juice most of the caseins from all species were degraded within 5 min, and within 30 min only traces of caseins were detected. The mean casein micellar size varied between species in the range of 146.0–311.5 nm (equine > caprine > bovine > human). The α-lactalbumin from all species appeared to be very resistant to both gastric and duodenal digestions. A similar trend was shown for β-lactoglobulin from bovine and caprine milks, of which ~ 60% intact protein remained, while only 25% remained intact in equine milk after total digestion. Equine milk contained a high amount of lysozyme, of which 60% remained intact in the present study. In heated milks from all species, only α-lactalbumin degradation increased approximately 12–20% in comparison to the raw milk. This study shows that equine milk with fast digestible proteins could be considered as a replacement for bovine milk in the diet of people with special needs, such as infants and the elderly

How would citizens react to official advice in a nuclear emergency? Insights from research in three European countries

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