Artificial Neural Network Prediction and Mechanism Analysis for

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Article Cite This: Ind. Eng. Chem. Res. 2019, 58, 11093−11100

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Artificial Neural Network Prediction and Mechanism Analysis for Migration of Environmental Contaminant Cyclic Organosiloxane Oligomer from Silicone Rubber Aung Myat Thu,† Meng Song,‡ Sizhu Wu,§ Anbang Sheng,∥ Xinghao Chen,† and Xiujuan Wang*,†

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†

Key Laboratory of Rubber-Plastics, Ministry of Education/Shandong Provincial Key Laboratory of Rubber-plastics, Qingdao University of Science & Technology, Qingdao 266042, P.R. China ‡ School of Materials and Chemical Engineering, Zhongyuan University of Technology, Zhengzhou 450007, P.R. China § State Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, P.R. China ∥ Qingdao Metro Group Co., Ltd, Qingdao 266000, P.R. China ABSTRACT: Migration testing of cyclic organosiloxane oligomer molecules from silicone rubber into food simulants was performed as a function of three factors: temperature, time, and food simulant type (i.e., n-hexane, n-heptane, and ethyl alcohol). A back-propagation artificial neural network (BP-ANN) was proposed to study the migration properties with an average prediction accuracy of 0.997. The analysis of the ANN indicated that high-temperature and n-hexane environments accelerate the migration of cyclic organosiloxane oligomer molecules from silicone rubber. A molecular dynamics simulation was employed to illustrate the migration mechanism at the molecular level. The results indicated that the high-temperature and n-hexane condition led to a high interaction energy, fractional free volume, and diffusion coefficient and a low difference solubility parameter of the system, which accelerate the molecules transfer process and threaten the safety of the materials. These fundamental studies provide a comprehensive understanding of the migration of cyclic organosiloxane oligomer contaminants and guidance for the failure prediction of materials. temperature, storage time, food simulant type, etc.8 A previous study9 showed that the environment affected the amount of migration in polyvinyl chloride (PVC) gaskets. The polymer material structures change with the effects of temperature, time, and the application environment. Migrating substances have become subject to controls and regulations.10 Therefore, it is significantly important to study the effects of different factors on migration. It is commonly known that research on environmental contaminants in polymer materials is crucial for safety evaluations. Some sensitive, efficient, and environmentally friendly analytical technologies have successfully been used in the analysis of environmental contaminants in materials. For example, gas chromatography-mass spectrometry (GC-MS) is widely applied to identify contaminates in materials.11,12 However, there are only a few studies on contaminates in silicone rubber teats. Analyses of the volatile substances in silicone rubber products using a thermal desorption/cold trap injector on a GC-MS indicated that cyclic organosiloxane oligomers were the main migrating substances and that

1. INTRODUCTION Because of the excellent elasticity, antifouling ability, and chemical inertness, silicon rubber (Q) products, such as bakeware, tableware, feeding bottle teats, etc., have achieved a significant market share and are currently marketed as userfriendly and inexpensive alternatives to traditional metal products.1−3 According to European Regulation 1935/2004/ EC, food contact materials (FCM) must be sufficiently inert to preclude compounds being transferred into foodstuffs in quantities large enough to endanger human health.4 However, the production and application environments of silicon rubber are very complex. Various newly formed chemical depolymerization substances and/or residual additives in raw materials, such as cyclic organosiloxane oligomer dodecamethylcyclohexasiloxane (D6) molecules, may exist in the products and migrate into foodstuffs to potentially cause safety problems.5 Previous study recommended the proper usage of molds to restrict the migration of contaminants into different foodstuffs.6 The migration process of environmental contaminants from polymer materials into food simulants follows two steps. First, migration takes place via a diffusion process within the polymer, according to Fick’s Second Law. Second, migration takes place into food simulants, and the transport mechanism depends on the physical properties of this phase.7 Therefore, the migration process depends on several factors, such as, the © 2019 American Chemical Society

Received: Revised: Accepted: Published: 11093

March 9, 2019 June 4, 2019 June 5, 2019 June 5, 2019 DOI: 10.1021/acs.iecr.9b01320 Ind. Eng. Chem. Res. 2019, 58, 11093−11100

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Industrial & Engineering Chemistry Research

parameter of silicone rubber were comprehensively investigated using the MD simulation. This work aimed at proposing a BP-ANN model to predict and investigate the migration of the environmental contaminant D6 from silicone rubber into simulants. The failure conditions of silicone rubber materials can be predicted by studying the migration of the cyclic organosiloxane oligomer. On the basis of the results of the BP-ANN model, the combined experimental-theoretical research provides insight into the connections between the macroscopic migration properties and the microscopic migration mechanism in silicone rubber during different engineering applications.

dodecamethylcyclohexasiloxane (D6) had higher migration risks than did the others.13 In addition, volatile cyclic organosiloxane oligomers, such as octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), and D6, have been identified as environmental contaminants because of their endocrine-disruptive, bioaccumulative, and toxic properties.14 In accordance with European Union regulations, the transfer quantities of FCM constituents to food stimulants should not exceed 10 mg/dm2 of food contact surface. Therefore, research of the migration of cyclic organosiloxane oligomers from silicone rubber into food simulants should be a focus of future work. At present, there is growing pressure within the food industry to improve material quality. Migration testing is an important way to evaluate the safety of materials, but it is laborious, expensive, and imprecise and can be avoided by mathematically determining the migration values. Three kinds of regression methods for assessing the migration from silicone molds confirmed that multivariate modeling can be adopted to predict the migration values from silicone rubber molds.15 In recent decades, more attention has been paid to artificial neural network (ANN) modeling in scientific and engineering applications.16 Previous study compared the prediction accuracy between ANNs and equations of state (EOS), and the results showed that ANN modeling was an effective method with a higher accuracy.17 ANN models had better accuracies compared to statistical models in predicting from unseen patterns.18 Since ANNs are assumed to have considerable advantages over traditional statistical techniques, lately, ANNs have been successfully used in the material technology field. An ANN was used to predict the shelf lives of moisture-sensitive products and to optimize product packaging.19,20 The present study is the first attempt to propose a back-propagation (BP) ANN model to quantitatively predict and understand the impact of complex usage environments on the migration of the cyclic organosiloxane oligomer from silicon rubber. Research on macroscopic migration properties and microcosmic migration mechanisms could not be feasibly measured by experimental methods. As is well-known, molecular simulation technology has become the third study method in parallel with experiments and theories in the field of materials.21−23 Molecular simulation can be adopted to understand the structure−property relationships of materials and has been widely used in studying the fractional free volume (FFV), binding energy, mean square displacement (MSD), cohesive energy density (CED), solubility parameter (δ), migration behavior, and the penetrant diffusion coefficient (D) of small molecules in polymer materials.24−26 Therefore, compared with experimental methods, molecular simulation techniques display more advantages in studying the relationships between structure and migration properties for silicone rubber materials at the molecular level. Motivated and inspired by the aforementioned studies, an ANN was proposed to predict the cyclic organosiloxane oligomer migration from silicone rubber into its surroundings. Two typical food simulants (i.e., n-hexane and n-heptane) were used to simulate an oily surrounding environment, while an ethyl alcohol food simulant was used to simulate an alcoholcontaining surrounding environment of silicone rubber. Moreover, a molecular dynamics (MD) simulation was adopted to inquiry the migration mechanisms. The binding energy, FFV, MSD, diffusion coefficient, and solubility

2. MATERIAL AND METHODS 2.1. Computational Details. 2.1.1. ANN Modeling. ANNs can process input information into the corresponding output prediction. BP-ANN is a single-hidden-layer feedforward neural network trained by an error back-propagation algorithm (Figure 1). BP-ANN has strong nonlinear mapping,

Figure 1. Structure of BP-ANN.

self-learing, and generalization capabilities.27 One neural unit (also called a node) collects information provided by other neural units to which it is connected through weighted connections, working as synapses. These synaptic weights multiply the input information. Each of these units is a simplified model of a biological neuron and can transform its input into an output response. This transformation follows two steps. First, the activation of the unit is calculated as the weighted sum of the inputs. Second, this activation is transformed into a response by using a transfer function.28 The values of the synaptic weights are trained and adjusted by reducing the error between the ANN output and the target. A multiple-inputs and single-output unit equation is as follows:29 ÄÅ n ÉÑ ÅÅ ÑÑ ÅÅ Ñ output jk = f ÅÅ∑ (Wj − 1, i × input j − 1, i) − θji ÑÑÑ ÅÅ ÑÑ ÅÇ i = 1 ÑÖ (1) where input j−1,i is the ith input variable from the (j−1)th layer; Wj−1,i is the weight of the input j − 1,i; θji is the ith threshold in the jth layer; f is an activation function; and output j,k is the kth output unit in the jth layer. Since no first principle model is available to describe cyclic organosiloxane oligomer migration behavior, the framework of BP-ANN was emplyed to use as a prediction estimator and an analytical expression. Three service conditions (temperature, time, and food simulant type) were taken as input variables, and the migration value of cyclic organosiloxane oligomer D6 was taken as the output variable. 2.1.2. Molecular Simulation. On the basis of a previous simulation method,30 a few minor changes were implemented to construct and equilibrate Q/D6/food simulant packing 11094

DOI: 10.1021/acs.iecr.9b01320 Ind. Eng. Chem. Res. 2019, 58, 11093−11100

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Industrial & Engineering Chemistry Research models and are discussed as follows. The theoretical calculations adopted the condensed-phase optimized molecular potentials force field (COMPASS).31 An Andersen barostat and Nose thermoset were adopted to control the temperature and pressure of the packing models, respectively.32,33 The van der Waals forces were calculated by the Lennard-Jones function with a cut off value of 12.5 Å. The electrostatic forces were computed using the Ewald summation method with an accuracy of 0.001 kcal mol−1.34,35 The effect of silicone rubber models with different numbers of repeat units on the solubility parameter (δ) was studied to choose a sufficiently large model size. The solubility parameter can be obtained from a common statistical approach based on the concept that δ can be decomposed into contributions from each functional group in the polymer. The effect of the number of repeat units on δ was studied (Figure 2). The results indicated that the

Figure 3. Entire process of constructing three types of periodic boundary cells (n-hexane is the food simulant in this example, and the gray, blue, yellow, and magenta represent C, H, Si, and O atoms, respectively).

simulated values of density (ρ) and δ reasonably agree with the experimental values or reference36 with the relative errors of 3.0% and 2.0%, respectively. Therefore, the established silicone rubber packing models can be compared with actual silicone rubber materials. 2.2. Experimental Section. 2.2.1. Materials and Sample. Silicone molds provided by Dongjue Silicone Co., LTD (Nanjing, China) were submitted to migration tests. The structure of the silicone molds was dimethyl silicone rubber. Dodecamethylcyclohexasiloxane and an internal standard of ndodecane (>99.5%) were purchased from Aladdin (Shanghai, China). The three food simulants used were n-hexane, nheptane, and ethyl alcohol (absolute, min. 99.8%) from Merck (Darmstadt, Germany). Silicone rubber was not swollen by the food simulant before the migration of D6 occurred. 2.2.2. GC-MS Analysis. The migration test was as follows. First, the silicone rubber was cut into 3 × 3 mm pieces. Second, every piece was put into a different food simulant, and three temperatures (i.e., 293, 313, and 333 K) were set for the same type of food simulants. Then, the supernatant was extracted and filtered. Finally, the migration of D6 from the silicone rubber into the different simulants was measured by GC-MS. In accordance with European Union regulations, three typical types of food simulants, n-hexane, n-heptane, and ethyl alcohol, were selected to carry out the migration experiment. A Trace 1310 gas chromatograph together with an ISQ mass-selective detector (Thermo Fisher, America) and a TR1MS capillary column (30 m × 0.25 mm i.d. × 0.25 mm film thickness; Thermo Fisher, America) were adopted. The column temperature for GC-MS was programmed to run from 60 °C (held for 3 min) to 300 °C at a rate of 10 °C/min (held for 13 min). The scanning ranged from 45 aum to 900 aum. Thermo Xcalibur software was adopted to collect and process the data.

Figure 2. Relationship between the solubility parameters of silicone rubber and the number of repeat units of molecular chain.

simulated δ value is close to the experimental value [δ = 14.8 (J cm−3)1/2] when the number of repeat units is 30.36 Three chains in the packing models were built to ensure sufficient mobility of the polymer chains and manageable simulation time.37 The process of constructing periodic boundary cells was as follows (Figure 3). Initially, silicone rubber molecular repeating units with 30 monomer units were built, and amorphous cells of silicone rubber were constructed. Next, 8 food simulant molecules and 8 D6 molecules were added to the amorphous cells. Simulation procedures were carried out to ensure that each periodic boundary cell is in equilibrium. First, smart minimizer method was employed to minimize the periodic boundary cells by 2,000,000 steps of energy minimization. Second, an NVE (with a constant number of atoms, a constant volume, and constant energy) ensemble was conducted to anneal the minimized cells from 300 to 500 K for 5 annealing cycles. Then, an NPT (with a constant number of atoms, a constant pressure, and a constant temperature) and an NVT (with a constant number of atoms, a constant volume and a constant temperature) ensemble dynamic simulations were conducted to further relax the annealed cells. Finally, the equalized packing models were adopted to study the physical parameters of the system. Three replicates of the systems were built. The parameters of the silicone rubber model after equilibration were summarized (Table 1). The standard deviations (σ) for the values of density and solubility parameter were also calculated. The results showed that the 11095

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Industrial & Engineering Chemistry Research Table 1. Parameters of the Simulated Silicone Rubber Model after Equilibration at 293 K packing model

ρsim (g cm−3)

ρexp (g cm−3)

σρ

δsim (J cm−3)1/2

δexp36 (J cm−3)1/2

σδ

cell edge (Å)

silicone rubber

1.03

0.98

0.02

14.5

14.8

0.16

31.6

Figure 4. Trained MSE of BP-ANN with respect to epochs.

Figure 5. Comparison between measured and predicted migration values of D6.

3. RESULTS AND DISCUSSION 3.1. ANN Modeling Results. On the basis of the migration experiments in section 2.2.2, a BP-ANN model was constructed. The temperature, time, and food simulant type were set as the input variables, and the migration of D6 was set as the output variable. Two learning methods, vanilla gradient descent and gradient descent with momentum, were adopted to train the weights and bias of the network. The values of the gradients were computed using the BFGS quasiNewton algorithm, Levenberg−Marquardt algorithm, and conjugate gradient algorithm with Polka-Ribiére updates.38 The total of 170 samples was equally and randomly divided into training and predicting data sets. The hidden layer neuron parameter was determined by the sample size. Since the selection of a network structure and its parameters is usually done by trial and error,39 the training results of the ANN with seven hidden layer neurons were studied (Figure 4). It can be seen that when the number of hidden layer neurons was seven, the network global error was 0.000455, which was less than error goal (0.001), after only 1000 iterations. The experimental and predicted migration values of D6 (the values in Figure 5 are normalized with reference to the input and output of BP) from silicone rubber into the three food simulants were compared. The multiple correlation coefficients (MCC) between the predicted and experimental migration values were studied. The results indicated that the proposed BP-ANN model can be used to predict the migration values of

D6 with the accuracy of 0.997. On the basis of the training MSE and predicted MCC results, the designed ANN is capable of modeling and predicting the migration properties of D6 from silicone rubber into food simulants. 3.2. Migration Prediction. The predicted evolutions of migration values of D6 versus migration temperature and time were studied (Figure 6). The validity of the predicted values is demonstrated by their good agreement with experimental values. The migration values of D6 vary markedly with time in the first 100 min, which suggests that the effect of time is more prominent at the beginning. The migration values of D6 increase with temperature, which suggests that high temperature leads to accelerated migration of D6 molecules into food simulants. More interestingly, the migration value of D6 from silicone rubber into n-hexane is significantly higher than those into n-heptane and ethyl alcohol. The migration values of cyclic siloxanes were adopted to judge the failure of materials.40 Therefore, the established BP-ANN can be used to predict the failure conditions of silicone rubber. It can be asserted that high temperature and n-hexane environment tend to induce the failure of silicone rubber materials. 3.3. Intermolecular Interaction. The interaction in the Q/D6/food simulant system was computed from the equilibrium configuration of trajectory files at the end of the MD simulation.41 The interaction energy (Einter) between the Q-D6 system and the D6-food simulant system was determined as follows: 11096

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Industrial & Engineering Chemistry Research FFV = 1 −

Vo Vs

(3)

where V0 = 1.3Vw is the occupied volume, Vs is the specific volume, and Vw is the van der Waals surface without using Bondi’s group contribution method. The Connolly volume morphology of Q/D6/simulant systems was simulated (Figure 7). Inefficient polymer chain packing leads to the high free

Figure 7. Free volume morphology of silicone rubber and D6 molecules in food simulants of (a) n-hexane, (b) heptane, and (c) ethyl alcohol.

volumes, which is represented by the blue regions. The gray regions represent the occupied volumes. The free volume is the highest when silicone rubber is in contact with n-hexane, while the free volume is the lowest when silicone rubber is in contact with ethyl alcohol. This may be because of the swelling of the silicone rubber materials. Swelling generally leads to volume expansion.43 In this study, the silicone rubber with n-hexane system has the most severe swelling effect. Swelling changes the morphology and structure of the polymer network, leading to an increase of the free volume. In accordance with the free volume theory, two criteria govern whether migrating molecules are mobile in polymer materials. First, the polymer materials should supply sufficient free volume for the migration molecules to diffuse. Second, to enter the free volume, the migrating molecules should have enough energy to overcome the attraction from surrounding molecules.44 The FFV values of the Q/D6/simulant systems were calculated (Figure 8). The FFV increases with increasing temperature because high temperatures increase the activity of polymer chains and lead to the expansion of the Q/D6/food simulant system. Thereby, more D6 molecules will be accommodated in the vacated space at high temperatures. Moreover, Q/D6/n-hexane exhibits the highest FFV among the three systems, indicating that D6 has the maximum space to migrate. The computed FFV results explain the ANN prediction results in section 3.2. 3.5. Migration of D6. As is well-known, the compatibility between simulants and materials affects the service life of the materials. The solubility parameter can be defined by the following equation.45

Figure 6. Predicted migration values of D6 vs temperature and time (circles denote experimental values).

Einter = EQ ‐ D6 − ED6 ‐ L

(2)

where EQ, ED6, and EL are the energies of silicone rubber, D6, and the food simulant. EQ‑D6 is the interaction energy of the QD6 system, and ED6‑L is the interaction energy of the D6-food simulant system. Three independent packing models were adopted to calculate the average energy value. The interaction energy analyses of the Q/D6/food simulant system (Table 2) showed that Q/D6/n-hexane exhibits the highest negative value among the three systems, indicating that the system has the strongest intermolecular interactions, which is favorable for D6 molecular migration. The Q/D6/n-hexane mixture has a higher negative interaction energy than do the Q/D6/n-heptane and Q/D6/ethyl alcohol mixtures. The study of the interaction energy explained the migration behavior of D6 in the three different simulants. 3.4. Fractional Free Volume in Q/D6/Simulant System. The fractional free volume (FFV) is widely used to characterize the efficiency of chain packing in a system. A common empirical equation is described as follows:42

CED =

δ=

Ecoh = V

ΔH vap − RT V

(4)

Table 2. Interaction Energies of Q/D6/Food Simulant System at 293 K Q/D6/food simulant

EQ‑D6 (kcal mol−1)

ED6‑L (kcalq mol−1)

Einter (kcal·mol−1)

Q/D6/n-hexane Q/D6/n-heptane Q/D6/ethyl alcohol

−4538.6 ± 0.39 −4538.6 ± 0.39 −4538.6 ± 0.39

−489.5 ± 0.22 −499.2 ± 0.16 −500.7 ± 0.11

−4049.1 −4039.4 −4037.9

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where ri(0) is the initial position of atom i in the selected D6 molecule, ri(t) is the position of atom i after a time t, |ri(t) − ri(0)| represents the displacement of this atom during time t, and the brackets < > represent the average square of the displacement for all of the selected atoms. The MSD value of D6 molecules in silicone rubber/nhexane system is higher than other systems (Figure 9).

Figure 8. Fractional free volume of Q/D6/food simulant system under different temperatures.

where CED is the cohesive energy density, Ecoh is the cohesive energy, V is the molar volume, ΔHvap represents the enthalpy of vaporization, R represents the perfect gas constant, and T represents the absolute temperature. The absolute value of the difference in the value of δ between material A and B (Δδ = |δA − δB|) reflects the compatibility of materials A and B. Materials A and B are compatible when Δδ is less than 2.05 (J/cm3) 1/2, while materials A and B are incompatible, when Δδ is greater than 10.02 (J/cm3) 1/2.46 The solubility parameters of silicone rubber and the simulants were calculated using MD simulations (Table 3).

Figure 9. MSD curves of D6 in different silicone rubber/food simulant systems.

Furthermore, the higher temperature the higher the MSD values of D6 molecules. The MSD results indicated that the D6 molecules are much more mobile in the silicone rubber/nhexane system at higher temperatures. To quantitatively describe the mobility of D6 in different silicone rubber/food simulant system, the Einstein equation is introduced to calculate the diffusion coefficient (D) of the D6 molecules.48

Table 3. Solubility Parameters for Silicone Rubber and Food Simulants at 293 K packing model

δsim (J cm−3)1/2

δexp36 (J cm−3)1/2

Δδsim = (δliquid − δQ) (J cm−3)1/2

silicone rubber N-hexane N-heptane ethyl alcohol

14.5 ± 0.02

14.8

−

15.1 ± 0.07 15.9 ± 0.05 26 ± 0.08

14.9 15.3 26.5

0.6 1.4 11.5

D = lim

t →∞

(6)

Their diffusion coefficients were calculated (Figure 10). The mobility of D6 in Q/hexane under high temperatures is the

The good agreement between the simulated and experimental values demonstrated that the MD simulation results are reliable. The value of Δδ between silicone rubber and n-hexane is lower than the value between silicone rubber and n-heptane and the value between silicone rubber and ethyl alcohol, suggesting that silicone rubber should be degraded more easily in the presence of n-hexane and, therefore, significantly migrate into it. Similarly, the enhanced migration described in a previous study indicated that the degradation products of silicone rubber contained D6 molecules.13 The calculated results of the solubility parameters confirmed the migration experiment results indicating that when silicone rubber contacted n-hexane, the migration value of D6 was the highest. This work also indicates that the polarity of food simulants is crucial in determining the migration properties of D6 molecules. Each average solubility parameter value was calculated from three independent packing models. Furthermore, the dynamic process of molecular migration can be observed visually by MD simulations. The mean squared displacement (MSD) can be described as follows:47 MSD = |ri(t ) − ri(0)|2

1 MSD ⟨|ri(0)−ri(t )|2 ⟩ = 6t 6t

Figure 10. Diffusion coefficients of D6 in different silicone rubber/ food simulant systems.

highest, followed by D6 in Q/heptane, and D6 in Q/ethyl alcohol has the lowest mobility. This is because high temperatures and n-hexane contact lead to a high negative binding energy, FFV, and MSD, and a low value of Δδ, which facilitates the migration of D6. The molecular simulation results are in good agreement with the experimental results

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(3) He, S.; Hu, J.; Zhang, C.; Wang, J.; Chen, L.; Bian, X.; Lin, J.; Du, X. Performance improvement in nano-alumina filled silicone rubber composites by using vinyl tri-methoxysilane. Polym. Test. 2018, 67, 295. (4) Regulation (EC) No 1935/2004/EC of the European Parliament and of the Council of 27 October 2004. Official Journal of the European Union C Series; 2004, 338, 4. (5) Helling, R.; Kutschbach, K.; Simat, T. J. Migration behaviour of silicone moulds in contact with different foodstuffs. Food Addit. Contam., Part A 2010, 27, 396. (6) Zhang, K.; Wong, J.; Begley, T.; Hayward, D.; Limm, W. Determination of siloxanes in silicone products and potential migration to milk, formula and liquid simulants. Food Addit. Contam., Part A 2012, 29, 1311. (7) Coltro, L.; Afonso da Costa, P.; Pitta, J. B.; Favaro Pereza, M. A.; Aparecida de Arujo, V.; Rodrigues, R. Migration of conventional and new plasticizers from PVC films into food simulants: a comparative study. Food Control 2014, 44, 118. (8) Goulas, A. E.; Salpea, E.; Kontominas, M. G. Di(2-ethylhexyl) adipatemigration from PVC cling film into packaged sea bream (Sparusaurata) andrainbow trout (Oncorhynchus mykiss) fillets: kinetic study and control ofcompliance with EU specifications. Eur. Food Res. Technol. 2008, 226, 915. (9) Fankhauser-Noti, A.; Grob, K. Migration of plasticizers from PVC gaskets of lids for glass jars into oily foods: amount of gasket material in food contact, proportion of plasticizer migrating into food and compliance testing by simulation. Trends Food Sci. Technol. 2006, 17, 105. (10) Garcia, C. V.; Shin, G. H.; Kim, J. T. Metal oxide-based nanocomposites in food packaging: applications, migration, and regulations. Trends Food Sci. Technol. 2018, 82, 21. (11) Reid, L. M.; O’Donnell, C. P.; Downey, G. Recent technological advances for the determination of food authenticity. Trends Food Sci. Technol. 2006, 17, 344. (12) Hertz, H. S.; Hites, R. A.; Biemann, K. Identification of mass spectra by computer-searching a file of known spectra. Anal. Chem. 1971, 43, 681. (13) Lund, K. H.; Petersen, J. H. Safety of food contact silicone rubber: liberation of volatile compounds from soothers and teats. Eur. Food Res. Technol. 2002, 214, 429. (14) Alaee, M.; Wang, D. G.; Gouin, T. Cyclic volatile methyl siloxanes in the environment. Chemosphere 2013, 93, 709. (15) Hong, W. J.; Jia, H.; Liu, C.; Zhang, Z.; Sun, Y.; Li, Y. F. Distribution, source, fate and bioaccumulation of methyl siloxanes in marine environment. Environ. Pollut. 2014, 191, 175. (16) Ziegel, E. R. The elements of statistical learning. Technometrics 2003, 45, 267. (17) Normandin, A.; Grandjean, B. P. A.; Thibault, J. PVT data analysis using neural network models. Ind. Eng. Chem. Res. 1993, 32, 970. (18) Gosukona, R.; Mahapatra, A. K.; Liu, X. L.; Kannan, G. Application of artificial neural network to predict Escherichia coliO157:H7 inactivation on beef surfaces. Food Control 2015, 47, 606. (19) Siripatrawan, U.; Jantawat, P. Artificial neural network approach tosimultaneously predict shelf life of two varieties of packaged rice snacks. Int. J. Food Sci. Technol. 2009, 44, 42. (20) Zheng, H.; Jiang, L.; Lou, H.; Hu, Y.; Kong, X.; Lu, H. Application of artificial neural network (ANN) and partial leastsquares regression (PLSR) to predict the changes of anthocyanins, ascorbic acid, Total phenols, flavonoids, and antioxidant activity during storage of red bayberry juice based on fractal analysis and red, green, and blue (RGB) intensity values. J. Agric. Food Chem. 2011, 59, 592. (21) Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. Gromacs 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4, 435. (22) Liu, Z. F.; Liu, Y. J.; Zeng, G. M.; Shao, B. B.; Chen, M.; Li, Z. G.; Jiang, Y. L.; Liu, Y.; Zhang, Y.; Zhong, H. Application of molecular

indicating that higher temperatures facilitate the migration of D6 molecules. On the basis of the combination of the experimental and simulation results, it can be concluded that silicone rubber easily undergoes failure when it comes into contact with n-hexane under high temperatures. These two factors mentioned in the free volume theory explain the increase of the migration value at higher temperatures.

4. CONLUSIONS This study investigated the migration behavior and mechanism of D6 molecules from silicone rubber into different food simulants. The established BP-ANN model indicated that the migration values of D6 increase with the increase of the temperature. Moreover, the results of BP-ANN indicated that the migration of D6 is remarkable when silicone rubber comes into contact with n-hexane. The MD simulations were adopted to systematically study the effects of temperature, time, and the food simulant type on the migration properties. The results showed that high temperatures and n-hexane contact lead to the increase of the interaction energy, fractional free volume, mean square displacement, and diffusion coefficient and the decrease of the difference in solubility parameter, which favor D6 migration. Our results demonstrated that MD simulations provide microscopic insights for determining the migration mechanisms. Furthermore, computer simulation technology will play a critical role in the safety prediction and structural design of food materials.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: 86-0532-84023536. E-mail: [email protected]. ORCID

Sizhu Wu: 0000-0001-7863-2954 Xiujuan Wang: 0000-0003-4007-797X Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grants 51703114, 51873017, and 51603236), the Natural Science Foundation of Shandong Province (Grant ZR2017BEM036), the Key Laboratory of Rubber-plastics, Ministry of Education, Qingdao University of Science & Technology Open Fund Project (Grant KF2017007), and Support of Shandong Entry-Exit Inspection and Quarantine Bureau Inspection and Quarantine Technology Center (Grant 20173702020928).

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DOI: 10.1021/acs.iecr.9b01320 Ind. Eng. Chem. Res. 2019, 58, 11093−11100