000119640 001__ 119640
000119640 005__ 20221108132412.0
000119640 037__ $$aTESIS-2022-248
000119640 041__ $$aeng
000119640 1001_ $$aSong, Xuechao
000119640 24500 $$aApplication of Ion Mobility-High Resolution Mass Spectrometry and In Silico Tools for Identifying Non-Volatile Substances in Food Contact Material
000119640 260__ $$aZaragoza$$bUniversidad de Zaragoza, Prensas de la Universidad$$c2022
000119640 300__ $$a317
000119640 4900_ $$aTesis de la Universidad de Zaragoza$$v2022-184$$x2254-7606
000119640 500__ $$aPresentado: 23 09 2022
000119640 502__ $$aTesis-Univ. Zaragoza, , 2022$$bZaragoza, Universidad de Zaragoza$$c2022
000119640 506__ $$aby-nc$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc/3.0/es
000119640 520__ $$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 food contact materials (FCMs). 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 FCMs is the lack of published empirical CCS values, thus, this thesis firstly established a CCS database for extractables and leachables from FCMs. On the other hand, many chemicals in FCMs don't have commercial standards, their experimental CCS values cannot be obtained, in this case, machine learning approaches were used to build the models to predict the CCS values for chemicals in FCMs. 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.<br />Besides the CCS values, the retention time (RT) is also very important for the unknown identifications. therefore, we also developed a prediction model to generate the predicted RT values. Based on the in-silico RT and CCS prediction models, a workflow to identify nonvolatile migrates from FCMs was proposed using liquid chromatography-ion mobility-high-resolution mass spectrometry. This workflow was evaluated by screening the chemicals that migrated from polyamide spatulas, we found that the predicted RT and CCS values can reduce the number of candidates and increase the confidence of identification in targeted and suspect screening analysis. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.<br />
000119640 520__ $$a<br />
000119640 521__ $$97075$$aPrograma de Doctorado en Ciencia Analítica en Química
000119640 6531_ $$aquimica analitica
000119640 700__ $$aNerín de la Puerta, María Consolación Cristina $$edir.
000119640 700__ $$aCanellas Aguareles, Elena Purificación $$edir.
000119640 7102_ $$aUniversidad de Zaragoza$$b
000119640 830__ $$9487
000119640 8560_ $$fcdeurop@unizar.es
000119640 8564_ $$s9990344$$uhttps://zaguan.unizar.es/record/119640/files/TESIS-2022-248.pdf$$zTexto completo (eng)
000119640 909CO $$ooai:zaguan.unizar.es:119640$$pdriver
000119640 909co $$ptesis
000119640 9102_ $$a$$b
000119640 980__ $$aTESIS