Song, Xuechao
Nerín de la Puerta, María Consolación Cristina (dir.) ; Canellas Aguareles, Elena Purificación (dir.)
Universidad de Zaragoza,
2022
Abstract: The 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.
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.
Abstract (other lang.):
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.
Abstract (other lang.):
Pal. clave: quimica analitica
Titulación: Programa de Doctorado en Ciencia Analítica en Química
Plan(es): Plan 487
Nota: Presentado: 23 09 2022
Nota: Tesis-Univ. Zaragoza, , 2022
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Record created 2022-11-08, last modified 2022-11-08
