Molecular level study of hot water extracted green tea buried in soils - a proxy for labile soil organic matter

MS and NMR Analysis of Isotopically Labeled Chloramination Disinfection Byproducts: Hyperlinks and Chemical Reactions

FT-ICR MS and NMR analysis of an isotopically labeled complex mixture of water disinfection byproducts formed by chloramine disinfection of model phenolic acids is described. A new molecular formula assignment procedure using the CoreMS Python library able to assign isotopically enriched formulas is proposed. Statistical analysis of the assigned formulas showed that the number of compounds, the diversity of the mixture, and the chlorine count increase during the chloramination reaction. The complex reaction mixture was investigated as a network of reactions using PageRank and Reverse PageRank algorithms. Independent of the MS signal intensities, the PageRank algorithm calculates the formulas with the highest probability at convergence of the reaction; these were chlorinated and nitrated derivatives of the starting materials. The Reverse PageRank revealed that the most probable chemical transformations in the complex mixture were chlorination and decarboxylation. These agree with the data obtained from INADEQUATE NMR spectra and literature data, indicating that this approach could be applied to gain insight into reactions pathways taking place in complex mixtures without any prior knowledge.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data

In this paper, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.