Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification

L’Observatoire agricole de la biodiversité (OAB) : une pédagogie active autour d’un projet de sciences participatives

National audiencePlan de l'articleIntroductionLa vocation pédagogique des sciences participativesL’OAB : principe, objectif, importance des réseauxPrincipeObjectifsGouvernanceLe réseauLes premiers résultatsL’OAB, un outil de recherche de réponses techniques pour les agriculteursLes perspectives et voies d’amélioration du projet pour répondre aux attentes : les sites fixesConclusio

The Use of Ion Mobility Mass Spectrometry for Isomer Composition Determination Extracted from Se-Rich Yeast

peer reviewedThe isomer ratio determination of a selenium-containing metabolite produced by Se-rich yeast was performed. Electrospray Ionization and Ion Mobility Mass Spectrometry (IM-MS) were unsuccessfully used in order to resolve the isomers according to their Collisional Cross Section (CCS) difference. The isomer ratio determination of 2,3-dihydroxypropionylselenocystathionine was performed after multidimensional liquid chromatography preconcentration from a water extract of Se-rich yeast using preparative size exclusion, anion exchange and capillary reverse phase columns coupled to IM-MS. 4’-nitrobenzo-15-crown-5 ether, a Selective Shift Reagent (SSR), was added after the last chromatographic dimension in order to specifically increase the CCS of one of the isomers by the formation of a stable host-guest system with the crown-ether . Both isomers were consequently fully resolved by IM-MS and the relative ratio of the isomers was determined: 11-13% and 87-89%. The present data compared favorably with literature to support the analytical strategy despite the lack of authentic standard for method validation. In addition, computational chemistry methods were successfully applied to design the SSR and to support the experimental data