期刊
FOOD CHEMISTRY
卷 317, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2020.126465
关键词
Molecular dynamics; Artificial neural network; Plasticizer; Two-component solubility parameters; Fractional free volume; Migration; PVDC
资金
- National Natural Science Foundation of China [51703114, 51873017, 51603236]
- Natural Science Foundation of Shandong Province [ZR2017BEM036]
- Key Laboratory of Rubber-plastics, Ministry of Education, Qingdao University of Science & Technology Open Fund Project [KF2017007]
- Shandong Province Higher Educational Science and Technology Program [J18KA024]
- Shandong Entry-Exit Inspection and Quarantine Bureau Inspection and Quarantine Technology Center [20173702020928]
Based on the experimental data of gas chromatography-mass spectrometry, an improved artificial neural network was first established to predict the migration of 2-ethylhexyl phthalate (DEHP) plasticizer from poly(vinylidene chloride) (PVDC) into food simulants (ie., heptane, ethanol and water). The sensitivity analysis indicated that temperature acted as a crucial factor influencing the migration values of DEHP. Then, a combined experimental and molecular dynamic (MD) simulation was performed to understand the migration kinetics and the mechanism of DEHP. Hansen solubility parameters of three component (delta(d), delta(p), delta(h)) were simplified into two-component solubility parameters (delta(vdw), delta(e)), and the tuple was successfully applied to describe the interactions between PVDC and food simulants. The MD results showed that high interaction energy and fractional free volume in PVDC/DEHP/food simulant systems accelerated the migration of DEHP. These fundamental studies would provide significant insights into the migration of environmental contaminants.
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