ArticleJournal of the American Society for Mass Spectrometry2017
Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data.
Article in Journal of the American Society for Mass Spectrometry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Metabolomics identifies plasma biomarkers of localized radiation injury.Scientific reports · 2025Article
- Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites.Animals : an open access journal from MDPI · 2024Review
- Discovering New Natural Products Using Metabolomics-Based Approaches.Advances in experimental medicine and biology · 2023Article
- HPLC-(Q)-TOF-MS-Based Study of Plasma Metabolic Profile Differences Associated with Age in Pediatric Population Using an Animal Model.Metabolites · 2022Article
- Application of Liquid Chromatography Coupled to Mass Spectrometry in Quality Assessment of Dietary Supplements-A Case Study of Tryptophan Supplements: Release Assay, Targeted and Untargeted Studies.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation.iScience · 2022Article
- Data Processing of Product Ion Spectra: Quality Improvement by Averaging Multiple Similar Spectra of Small Molecules.Mass spectrometry (Tokyo, Japan) · 2022Article
- Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Possibilities and Perspectives.Advances in experimental medicine and biology · 2021Article
- Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research.Journal of proteome research · 2021Article
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- Metabolomes and Lipidomes of the Infective Stages of the Gastrointestinal nematodes,Metabolites · 2020Article
- Reproducible molecular networking of untargeted mass spectrometry data using GNPS.Nature protocols · 2020Article
- Metabolomic study of soft corals from the Colombian Caribbean: PSYCHE andScientific reports · 2020Article
- Choosing an Optimal Sample Preparation inMetabolites · 2019Article
- Screening 89 Pesticides in Fishery Drugs by Ultrahigh Performance Liquid Chromatography Tandem Quadrupole-Orbitrap Mass Spectrometer.Molecules (Basel, Switzerland) · 2019Article
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1 author.
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Abstract
High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.
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