ArticlePhytochemical analysis : PCA2022
New insights into the Van Krevelen diagram: Automated molecular formula determination from HRMS for a large chemical profiling of lichen extracts.
Article in Phytochemical analysis : PCA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Targeted Isolation of Prenylated Flavonoids fromMetabolites · 2025Article
- Assessment of Accelerated Aging Effect of Bio-Oil Fractions Utilizing Ultrahigh-Resolution Mass Spectrometry and k-Means Clustering of van Krevelen Compositional Space.Energy & fuels : an American Chemical Society journal · 2024Article
- Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes.Communications chemistry · 2023Article
- Unlocking Potentially Therapeutic Phytochemicals in Capadulla (Metabolites · 2023Article
- New insights into the Van Krevelen diagram: Automated molecular formula determination from HRMS for a large chemical profiling of lichen extracts.Phytochemical analysis : PCA · 2022Article
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7 authors.
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Abstract
introductionIn recent years, LC-MS has become the golden standard for metabolomic studies. Indeed, LC is relatively easy to couple with the soft electrospray ionization. As a consequence, many tools have been developed for the structural annotation of tandem mass spectra. However, it is sometimes difficult to do data-dependent acquisition (DDA), especially when developing new methods that stray from the classical LC-MS workflow.
objectiveAn old tool from petroleomics that has recently gained popularity in metabolomics, the Van Krevelen diagram, is adapted for an overview of the molecular diversity profile in lichens through high-resolution mass spectrometry (HRMS).
methodsA new method is benchmarked against the state-of-the-art classification tool ClassyFire using a database containing most known lichen metabolites (n ≈ 2,000). Four lichens known for their contrasted chemical composition were selected, and extractions with apolar, aprotic polar, and protic polar solvents were performed to cover a wide range of polarities. Extracts were analyzed with direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) and atmospheric solids analysis probe mass spectrometry (ASAP-MS) techniques to be compared with the chemical composition described in the literature.
resultsThe most common lichen metabolites were efficiently classified, with more than 90% of the molecules in some classes being matched with ClassyFire. Results from this method are consistent with the various extraction protocols in the present case study.
conclusionThis approach is a rapid and efficient tool to gain structural insight regarding lichen metabolites analyzed by HRMS without relying on DDA by LC-MS/MS analysis. It may notably be of use during the development phase of novel MS-based metabolomic approaches.
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