000120093 001__ 120093
000120093 005__ 20240319081020.0
000120093 0247_ $$2doi$$a10.1021/acs.est.2c02853
000120093 0248_ $$2sideral$$a129968
000120093 037__ $$aART-2022-129968
000120093 041__ $$aeng
000120093 100__ $$aSong, Xue-Chao
000120093 245__ $$aPrediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products
000120093 260__ $$c2022
000120093 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120093 5203_ $$aThe use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for M + H](+) adducts and 95.0% of CCS values for M + Na](+) adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the M + H](+) and M + Na](+) adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.
000120093 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-097805-B-I00$$9info:eu-repo/grantAgreement/ES/DGA-FSE/T53-20R
000120093 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120093 590__ $$a11.4$$b2022
000120093 592__ $$a3.123$$b2022
000120093 591__ $$aENVIRONMENTAL SCIENCES$$b19 / 275 = 0.069$$c2022$$dQ1$$eT1
000120093 591__ $$aENGINEERING, ENVIRONMENTAL$$b7 / 55 = 0.127$$c2022$$dQ1$$eT1
000120093 593__ $$aChemistry (miscellaneous)$$c2022$$dQ1
000120093 593__ $$aMedicine (miscellaneous)$$c2022$$dQ1
000120093 593__ $$aEnvironmental Chemistry$$c2022$$dQ1
000120093 594__ $$a16.7$$b2022
000120093 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120093 700__ $$aDreolin, Nicola
000120093 700__ $$aCanellas, Elena
000120093 700__ $$aGoshawk, Jeff
000120093 700__ $$0(orcid)0000-0003-2685-5739$$aNerin, Cristina$$uUniversidad de Zaragoza
000120093 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000120093 773__ $$g56 (2022), 9463-9473$$pEnviron. sci. technol.$$tENVIRONMENTAL SCIENCE & TECHNOLOGY$$x0013-936X
000120093 8564_ $$s1756234$$uhttps://zaguan.unizar.es/record/120093/files/texto_completo.pdf$$yVersión publicada
000120093 8564_ $$s3017058$$uhttps://zaguan.unizar.es/record/120093/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120093 909CO $$ooai:zaguan.unizar.es:120093$$particulos$$pdriver
000120093 951__ $$a2024-03-18-16:06:50
000120093 980__ $$aARTICLE