DELTA-Topology: A Science Gateway for Experimental and Computational Chemical Data Analysis using Topological Models

2021 ◽  
Author(s):  
Sudhakar Pamidighantam ◽  
Eric Coulter ◽  
Marcus Christie ◽  
Aurora Clark
Keyword(s):  
Data Analysis ◽  
Chemical Data ◽  
Science Gateway ◽  
Topological Models

scholarly journals Implications of measurement error structure on the visualization of multivariate chemical data: hazards and alternatives

2018 ◽  
Vol 96 (7) ◽  
pp. 738-748 ◽  
Author(s):  
Peter D. Wentzell ◽  
Chelsi C. Wicks ◽  
Jez W.B. Braga ◽  
Liz F. Soares ◽  
Tereza C.M. Pastore ◽  
...  
Keyword(s):  
Data Analysis ◽  
Measurement Error ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Measurement Errors ◽  
Alternative Methods ◽  
Chemical Data ◽  
Multidimensional Data ◽  
Chemical Information ◽  
Components Analysis

The analysis of multivariate chemical data is commonplace in fields ranging from metabolomics to forensic classification. Many of these studies rely on exploratory visualization methods that represent the multidimensional data in spaces of lower dimensionality, such as hierarchical cluster analysis (HCA) or principal components analysis (PCA). However, such methods rely on assumptions of independent measurement errors with uniform variance and can fail to reveal important information when these assumptions are violated, as they often are for chemical data. This work demonstrates how two alternative methods, maximum likelihood principal components analysis (MLPCA) and projection pursuit analysis (PPA), can reveal chemical information hidden from more traditional techniques. Experimental data to compare different methods consists of near-infrared (NIR) reflectance spectra from 108 samples of wood that are derived from four different species of Brazilian trees. The measurement error characteristics of the spectra are examined and it is shown that, by incorporating measurement error information into the data analysis (through MLPCA) or using alternative projection criteria (i.e., PPA), samples can be separated by species. These techniques are proposed as powerful tools for multivariate data analysis in chemistry.

scholarly journals An Integrated NMR, LC-DAD-MS, LC-QTOF Metabolomic Characterization of Sartoria hedysaroides: Correlation of Antioxidant and Enzyme Inhibitory Activity with Chemical Composition by Multivariate Data Analysis

Antioxidants ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Stefano Dall’Acqua ◽  
Stefania Sut ◽  
Kouadio Ibrahime Sinan ◽  
Gokhan Zengin ◽  
Irene Ferrarese ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Aerial Part ◽  
Methanolic Extract ◽  
Multivariate Data ◽  
Chemical Data ◽  
Acetylcholinesterase Inhibition ◽  
Biological Potential

Sartoria hedysaroides Boiss and Heldr. (Fabaceae) is an endemic plant of Turkey that has received little scientific consideration so far. In the present study, the chemical profiles of extracts from the aerial part and roots of S. hedysaroides obtained using solvents with different polarities were analyzed combining integrated NMR, LC-DAD-MSn, and LC-QTOF methods. In vitro antioxidant and enzyme inhibitory activities were evaluated, and the results were combined with chemical data using multivariate approaches. Phenolic acids, flavonoids, ellagitannins, and coumarins were identified and quantified in the extracts of aerial part and roots. Methanolic extract of S. hedysaroides aerial part showed the highest phenolic content and the highest antioxidant activity and cupric ion reducing antioxidant capacity. Dichloromethane extract of S. hedysaroides roots showed the highest inhibition of butyryl cholinesterase, while methanolic extract of S. hedysaroides aerial part was the most active tyrosinase inhibitor. Multivariate data analysis allowed us to observe a good correlation between phenolic compounds, especially caffeoylquinic derivatives and flavonoids and the antioxidant activity of extracts. Acetylcholinesterase inhibition was correlated with the presence of caffeoylquinic acids and coumarins. Overall, the present study appraised the biological potential of understudied S. hedysaroides, and provided a comprehensive approach combining metabolomic characterization of plant material and multivariate data analysis for the correlation of chemical data with results from multi-target biological assays.

Freeze-dried dispersions for automated SEM analysis of individual submicron airborne particulates

1992 ◽  
Vol 50 (1) ◽  
pp. 408-409
Author(s):  
Karen A. Katrinak ◽  
David W. Brekke ◽  
John P. Hurley
Keyword(s):  
Air Pollutants ◽  
Size Range ◽  
Sem Analysis ◽  
Individual Particle ◽  
Chemical Data ◽  
Particle Analysis ◽  
Bulk Analysis ◽  
Airborne Particulates ◽  
Freeze Dried ◽  
Individual Particle Analysis

Individual-particle analysis is well established as an alternative to bulk analysis of airborne particulates. It yields size and chemical data on a particle-by-particle basis, information that is critical in predicting the behavior of air pollutants. Individual-particle analysis is especially important for particles with diameter < 1 μm, because particles in this size range have a disproportionately large effect on atmospheric visibility and health.

Microchemical imaging in biomedical research

1992 ◽  
Vol 50 (2) ◽  
pp. 1118-1119
Author(s):  
P. Ingram
Keyword(s):  
Data Analysis ◽  
Electron Probe ◽  
The Other ◽  
X Ray ◽  
Cell Element ◽  
Physiological Information ◽  
Functional States ◽  
Elemental Maps ◽  
Electron Energy Loss ◽  
Secondary Ion

It is well established that unique physiological information can be obtained by rapidly freezing cells in various functional states and analyzing the cell element content and distribution by electron probe x-ray microanalysis. (The other techniques of microanalysis that are amenable to imaging, such as electron energy loss spectroscopy, secondary ion mass spectroscopy, particle induced x-ray emission etc., are not addressed in this tutorial.) However, the usual processes of data acquisition are labor intensive and lengthy, requiring that x-ray counts be collected from individually selected regions of each cell in question and that data analysis be performed subsequent to data collection. A judicious combination of quantitative elemental maps and static raster probes adds not only an additional overall perception of what is occurring during a particular biological manipulation or event, but substantially increases data productivity. Recent advances in microcomputer instrumentation and software have made readily feasible the acquisition and processing of digital quantitative x-ray maps of one to several cells.

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