000117409 001__ 117409
000117409 005__ 20240319080954.0
000117409 0247_ $$2doi$$a10.1021/acs.jafc.1c06989
000117409 0248_ $$2sideral$$a128240
000117409 037__ $$aART-2022-128240
000117409 041__ $$aeng
000117409 100__ $$aSong, X. -C
000117409 245__ $$aPrediction of collision cross section values: application to non-intentionally added substance identification in food contact materials
000117409 260__ $$c2022
000117409 5060_ $$aAccess copy available to the general public$$fUnrestricted
000117409 5203_ $$aThe 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 confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed. © 2022 The Authors. Published by American Chemical Society.
000117409 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T53-20R$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-097805-B-I00
000117409 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000117409 590__ $$a6.1$$b2022
000117409 592__ $$a1.099$$b2022
000117409 591__ $$aCHEMISTRY, APPLIED$$b10 / 72 = 0.139$$c2022$$dQ1$$eT1
000117409 593__ $$aChemistry (miscellaneous)$$c2022$$dQ1
000117409 591__ $$aAGRICULTURE, MULTIDISCIPLINARY$$b6 / 58 = 0.103$$c2022$$dQ1$$eT1
000117409 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2022$$dQ1
000117409 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b22 / 142 = 0.155$$c2022$$dQ1$$eT1
000117409 594__ $$a9.7$$b2022
000117409 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000117409 700__ $$aDreolin, N.
000117409 700__ $$aDamiani, T.
000117409 700__ $$0(orcid)0000-0003-2638-9221$$aCanellas, E.$$uUniversidad de Zaragoza
000117409 700__ $$0(orcid)0000-0003-2685-5739$$aNerin, C.$$uUniversidad de Zaragoza
000117409 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000117409 773__ $$g70, 4 (2022), 1272-1281$$pJ. agric. food chem.$$tJOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY$$x0021-8561
000117409 8564_ $$s1277365$$uhttps://zaguan.unizar.es/record/117409/files/texto_completo.pdf$$yVersión publicada
000117409 8564_ $$s3148226$$uhttps://zaguan.unizar.es/record/117409/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000117409 909CO $$ooai:zaguan.unizar.es:117409$$particulos$$pdriver
000117409 951__ $$a2024-03-18-13:27:12
000117409 980__ $$aARTICLE