{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Song XC"],"funding":["China Scholarship Council","European Social Fund","Ministerio de Ciencia, Innovaci?n y Universidades"],"pagination":["1272-1281"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8815070"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["70(4)"],"pubmed_abstract":["The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confide"],"journal":["Journal of agricultural and food chemistry"],"pubmed_title":["Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials."],"pmcid":["PMC8815070"],"funding_grant_id":["RTI2018-097805-B-100","201806780031","T53-20R"],"pubmed_authors":["Dreolin N","Canellas E","Nerin C","Damiani T","Song XC"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials.","description":"The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confide","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-04T19:57:33.748Z","creation":"2025-04-04T19:57:33.748Z"},"accession":"S-EPMC8815070","cross_references":{"pubmed":["35041428"],"doi":["10.1021/acs.jafc.1c06989"]}}