Introducing “Identification Probability” for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Library searches of solid-phase GC-FTIR libraries can achieve an identification probability of 1 (0.5 for chiral molecules with a non-chiral separation)
enquiries@spectrometrics.com
#MetiD #Metabolomics
pubs.acs.org/doi/full/10....

An interlaboratory study to evaluate the utility of gas chromatography‐mass spectrometry and gas chromatography‐infrared spectroscopy spectral libraries in the forensic analysis of fentanyl‐related su... Synthetic opioids such as fentanyl account for over 71,000 of the approximately 107,000 overdose deaths reported in the United States in 2021. Fentanyl remains the fourth most identified drug by stat....

Is hyphenated-solid phase FTIR with parallel MS the answer to out-of-the-box annotation to meet enhanced metabolite standards initiative level 2 #MetID? [level 1 with a curated library]
#Metabolomics
enquiries@spectrometrics.com
onlinelibrary.wiley.com/doi/abs/10.1...

I’ve been doing metabolomics since 1999 - this is a game changer for both MetID and annotation. #MetID without having to isolate the endogenous/natural metabolite.

Classical vs. Feature-Based Molecular Networking — Ometa Labs What is the difference between Classical Molecular Networking and Feature-Based Molecular Networking?

Ever wondered what's up with all the different types of molecular networking? Well, this won't answer all your questions but it might answer two of them.

www.ometalabs.net/resources/cm...

#msms #molecularnetworking #metid

Library Matching — Ometa Labs I have a mass spectrum - now how do I figure out which metabolite it represents?

Having trouble finding your perfect match? Maybe you should try cosine scores.

www.ometalabs.net/resources/ms...

#massspectrometry #msms #compoundID #metID #metabolomics

Could Spectrometrics parallel DiscovIR-GC/Agilent 7250 package be the ultimate HRAM platform for #ChemicalEcology, #BiomarkerID, #MetID and #Pharma E&L studies?
enquiries@spectrometrics.com
#TeamMassSpec #Chromatography

Bugs Insects GIF ALT: Bugs Insects GIF

If you’re attending #ISCE2024, check out Sefan Schultz’s poster on combining DFT calculations with DiscovIR-GC for accelerated metabolite ID.
#MetID #ChemicalEcology #Metabolomics

Unknown Sir Data GIF ALT: Unknown Sir Data GIF

Unambiguous #MetID requires structural elucidation of the endogenous metabolite by LC-NMR or fraction collection + NMR. LC-ddFTIR + DFT spectral prediction means you only need to analyse the standard and don’t need to isolate the endogenous metabolite!
#metabolomics
enquiries@spectrometrics.com