2020
DOI: 10.1016/j.foodchem.2020.126465
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Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network

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Cited by 42 publications
(29 citation statements)
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“…Therefore, in the COMPASS force field, the three-component Hansen solubility parameter is converted into a two-component solubility parameter (δ vdW and δ ele ). It can be expressed by the following formula [39,40]:…”
Section: Dynamic Mechanical Properties Of Nbr Compositesmentioning
confidence: 99%
“…Therefore, in the COMPASS force field, the three-component Hansen solubility parameter is converted into a two-component solubility parameter (δ vdW and δ ele ). It can be expressed by the following formula [39,40]:…”
Section: Dynamic Mechanical Properties Of Nbr Compositesmentioning
confidence: 99%
“…The migration process is the unintended release of additives and degradation compounds from polymers into food. The study of the migration process can be done through: (i) food simulants, in accordance with the European Committee for Standardization [ 76 ]; (ii) predictive mathematical models [ 77 ]; and (iii) molecular dynamics simulation approaches, based on classical or quantum physics theories [ 78 ]. Migration is a mass-transfer process characterized by the rate (release kinetics) and the level of migration (thermodynamic equilibrium) that are determined by the properties of the polymer (crystallinity, crosslinking degree, etc.…”
Section: Release Kinetics Of Active Agents and Their Functionalitiesmentioning
confidence: 99%
“…RSM involves less time and labor than any other tools, but it cannot be applied for data estimation beyond the investigated conditions (Lee, Yusof, Hamid, & Baharin, 2006; Mohebbi, Shahidi, Fathi, Ehtiati, & Noshad, 2011). Hence, artificial neural network (ANN) emerged as an alternative computational tool for modeling of nonlinear multivariate regression problems, which predicts the responses with respects to the trained data (Wang, Song, Liu, Wu, & Thu, 2020). By learning from different examples, ANN can recognize patterns from a sequence of input–output parameters, without any previous knowledge about the nature and interactions of these parameters.…”
Section: Introductionmentioning
confidence: 99%