Automated fragment identification for electron ionisation mass spectrometry: application to atmospheric measurements of halocarbons

• URL: http://arxiv.org/abs/2103.13807v1
• Date: Tue, 23 Mar 2021 09:35:10 GMT
• Title: Automated fragment identification for electron ionisation mass spectrometry: application to atmospheric measurements of halocarbons
• Authors: Myriam Guillevic (EMPA), Aurore Guillevic (CARAMBA), Martin Vollmer (EMPA), Paul Schlauri (EMPA), Matthias Hill (EMPA), Lukas Emmenegger (EMPA), Stefan Reimann (EMPA)
• Abstract summary: Non-target screening consists in searching a sample for all present substances, suspected or unknown. This approach has been introduced more than a decade ago in the field of water analysis, but is still very scarce for indoor and atmospheric trace gas measurements. We present data analysis tools to enable automated identification of unknown compounds measured by GC-EI-HRMS.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated identification of unknown compounds measured by GC-EI-HRMS. Results: Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method (i) does not to require the presence of the molecular ion, which is absent in $\sim$40% of EI spectra, and (ii) permits to use all measured data while giving more weight to mass/charge ratios measured with better precision. Our method has been trained and validated on >50 halocarbons and hydrocarbons with a molar masses of 30-330 g mol-1 , measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the reconstructed signal is correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound (less than six measured mass/charge) or the lack of isotopic constrain (no rare isotopocule detected). Conclusions: Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples.

Related papers

• ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering [54.80411755871931]
  Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth. Chemical QA plays a crucial role in both education and research by effectively translating complex chemical information into readily understandable format. This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. We introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data.
  arXiv  Detail & Related papers  (2024-07-24T01:46:55Z)
• Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra [0.7029155133139362]
  We show an approach for predicting the concentration of sample pollutants from messy, unprocessed Raman data using machine learning. Using standard machine learning models, the concentration of sample pollutants are predicted with more than 80 percent cross-validation accuracy from raw Raman data.
  arXiv  Detail & Related papers  (2024-01-31T21:49:40Z)
• Analyze Mass Spectrometry data with Artificial Intelligence to assist the understanding of past habitability of Mars and provide insights for future missions [0.0]
  This paper presents an application of artificial intelligence on mass spectrometry data for detecting habitability potential of ancient Mars. Although data was collected for planet Mars the same approach can be replicated for any terrestrial object of our solar system.
  arXiv  Detail & Related papers  (2023-10-18T11:14:46Z)
• Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning [73.01013149014865]
  Methane is one of the most potent greenhouse gases. Current methane emission monitoring techniques rely on approximate emission factors or self-reporting. Deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data.
  arXiv  Detail & Related papers  (2023-08-21T19:36:50Z)
• De-novo Identification of Small Molecules from Their GC-EI-MS Spectra [0.0]
  Machine learning based emphde-novo methods, which derive molecular structure directly from its mass spectrum gained attention recently. We present anovel method in this family, addressing aspecific usecase of GC-EI-MS spectra, which is particularly hard due to lack of additional information from the first stage of MS/MS experiments.
  arXiv  Detail & Related papers  (2023-04-04T08:46:00Z)
• ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning [60.02503434201552]
  This research proposes learning approximations of complex exposures from training sets of simple ones. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes.
  arXiv  Detail & Related papers  (2023-02-09T20:19:57Z)
• Combination of Raman spectroscopy and chemometrics: A review of recent studies published in the Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy Journal [0.0]
  This review considers the application of Raman spectroscopy in combination with chemometrics to study samples and their changes caused by different factors. We summarized the best strategies for creating classification models and highlighted some common drawbacks when it comes to the application of chemometrics techniques.
  arXiv  Detail & Related papers  (2022-10-18T13:08:20Z)
• Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window ($\psi$FRMV) [0.0]
  We propose a new method for automated, untargeted region of interest selection in GC-MS data. It is based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. The sensitivity of the algorithm was tested by investigating the concentration at which it can no longer pick out chromatographic regions known to contain signal.
  arXiv  Detail & Related papers  (2022-07-30T21:43:05Z)
• Exploring Chemical Space with Score-based Out-of-distribution Generation [57.15855198512551]
  We propose a score-based diffusion scheme that incorporates out-of-distribution control in the generative differential equation (SDE) Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool.
  arXiv  Detail & Related papers  (2022-06-06T06:17:11Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.