Article pubs.acs.org/IECR
Kinetic Study of Bisphenol A Migration from Low-Density Polyethylene Films into Food Simulants Yining Xia and Maria Rubino* School of Packaging, Michigan State University, East Lansing, Michigan 48824, United States S Supporting Information *
ABSTRACT: Migration testing of bisphenol A (BPA) from low-density polyethylene (LDPE) into food simulants was performed as a function of three factors: temperature, initial BPA concentration, and food simulant type. An HPLC−UV method was applied to measure the amount of BPA that migrated into the solvents. The migration process was described by Fick’s diffusion equation, and the migration parameters such as the diffusion coefficient (D) were derived from the equation. D values ranged from 10−10 to 10−8 cm2 s−1 under different migration conditions. Statistical analysis showed that the single factors had significant effects on D, but among the interaction effects only the temperature-simulant interaction was significant. The dependence of D on temperature followed an Arrhenius-type relationship, with the activation energy (Ea) ranging from 112.8 ± 1.6 kJ mol−1 to 128.9 ± 4.3 kJ mol−1 for the three food simulants. An exponential relationship was found between the diffusion coefficient and initial BPA concentration for each food simulant. BPA migration into different food simulants was influenced by the affinity between the polymer and the solvent, and better affinity may reduce the diffusion rate of BPA.
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INTRODUCTION Bisphenol A (BPA), or 2,2-bis(4-hydroxyphenyl)propane, is a chemical primarily used as a precursor in the synthesis of polycarbonate and epoxy resins for rigid containers and metal can linings, respectively. BPA is also used as an additive in various plastic materials, such as polyvinyl chloride and rubber, to improve the durability (e.g., UV resistance and heat stability) of the materials.1,2 The use of BPA is extensive, with global sales of 13.1 billion USD in 2012 and rising to an estimated 18.8 billion USD by 2019.3 However, there are increasing concerns about the migration of BPA, which may impact the environment and human health. Migration of BPA occurs when plastic materials containing BPA are in direct contact with food.4−6 Also, when the plastic materials are either recycled or discarded in the landfill, BPA migrates into the surrounding environments such as the water table and soil.7,8 BPA belongs to a group of chemicals known as endocrine disrupting compounds that interact with steroid hormone receptors of human and animals and disrupt normal endocrine functions.9 Toxicological studies showed that low dose exposure to BPA caused many adverse health effects.10 The U.S. Environmental Protection Agency (EPA) has set the oral reference dose (RfD) of BPA at 50 μg kg·bw−1 day−1 (bw = body weight). To protect human health, the estimated exposure dose of BPA based on its migration level into food should not be over the RfD value. Therefore, it is important to determine the amount of BPA migration from food packaging into food under different conditions, since food constitutes a direct route of human exposure to BPA. It is also important to understand how the migration takes place and how fast BPA migrates under different conditions. In the past decades, studies have been carried out to measure the migration of BPA from various packaging materials, including epoxy can coatings, polycarbonate (PC) bottles, and polyvinyl chloride (PVC) films.11−13 The studies usually © 2015 American Chemical Society
assessed the level of BPA migration into a specific food or food simulant but did not provide a profile of the migration process. Generally, the migration of small molecules within packaging systems can be modeled by Fick’s diffusion equations by solving parameters like diffusion coefficient (D) and the partition coefficient (KP,F) in the equations.14 Migration modeling is considered a promising method that avoids the time-consuming and expensive procedures usually involved in the conventional migration test while maintaining high accuracy. This study aimed to model BPA migration from low-density polyethylene (LDPE) into different solvents used as food simulants. LDPE is a common packaging material for food contact purposes and was selected as a model polymer because it is a simple system having weak interaction with BPA or the food simulants used in this study. Therefore, BPA migration could easily take place within this system and the migration process could be easily described by the diffusion models. A high performance liquid chromatography−ultraviolet (HPLC− UV) method was set up for the quantification of BPA migration into food simulants. Fick’s diffusion equation was applied to describe the migration process by solving the migration parameters (e.g., diffusion coefficient) in the equation through a kinetic study. The effects of various factors (temperature, initial BPA concentration, and food simulant type) and their interactions on BPA migration were also evaluated.
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EXPERIMENTAL SECTION Materials. BPA (purity >99%) was purchased from SigmaAldrich (Milwaukee, WI, USA). The solvents used for either food simulants or HPLC−UV analysis were water (HPLC
Received: Revised: Accepted: Published: 3711
January 7, 2015 March 23, 2015 March 25, 2015 March 25, 2015 DOI: 10.1021/acs.iecr.5b00088 Ind. Eng. Chem. Res. 2015, 54, 3711−3716
Article
Industrial & Engineering Chemistry Research
injection syringe at various time intervals until a steady state of migration was achieved. The solvent sample was properly diluted before injection into the HPLC system. All tests were conducted in triplicate. HPLC−UV Method. BPA determination was performed by using an Alliance 2695 HPLC instrument (Waters Co., MA, USA) equipped with an automatic sampler/injector. The chromatographic separation was run on an XBridge C18 column (150 mm × 3.0 mm, 3.5 μm) with an XBridge guard column (Waters Co.) at 25 °C. An isocratic elution was carried out with acetonitrile and water (40:60, v/v) as the mobile phase at a flow rate of 0.5 mL min−1. BPA was detected by an Alliance 2487 UV detector (Waters Co.) set at 225 nm. The injection volume was 10 μL. Standard solutions of BPA in the mobile phase with six concentrations (triplicate samples per concentration) ranging from 5 to 120 μg L−1 were used to establish the external calibration curve by plotting the analyte peak area against concentration (R2 = 0.9921). The limit of detection (LOD) was 1 μg L−1, calculated from a signal-to-noise ratio of 3. Three injections were made for each sample and standard solution. Fick’s Diffusion Models. The migration process within a packaging system involves the transfer of small molecules (e.g., additives, monomers) from a packaging film into a food product and can be expressed by the equation derived from Fick’s second law:15
grade, Mallinckrodt Baker Inc., NJ, USA), ethanol (200 proof, Decon Labs Inc., PA, USA), acetic acid (ACS grade, EMD Chemicals Inc., NJ, USA), and acetonitrile (HPLC grade, EMD Chemicals Inc.). LDPE resin (Petrothene NA960000, density of 0.92 g cm−3) was obtained from Lyondell Chemical Company (TX, USA). Equipment for the migration test included 40 mL of precleaned amber vials and slide valve caps with PTFE−silicon septa (Cole-Parmer, IL, USA), stainless steel wire, and glass beads (McMaster-Carr, IL, USA). LDPE Film Preparation. LDPE + BPA composites were prepared by melt mixing with three different levels of BPA: 0.10, 0.25, and 0.50 wt %. LDPE resin was ground in a Thomas Wiley laboratory mill (model 4, Arthur H. Thomas Co., PA, USA) equipped with a 1 mm mesh sieve and then premixed with BPA at a specific concentration in a blender. Each LDPE resin−BPA mixture (40 g) was placed in an electrically heated three-piece mixer with two roller style mixing blades (C. W. Brabender Instruments Inc., NJ, USA). The temperature was set at 150 °C in order to achieve proper viscosity for mixing, and the rotation speed was set at 50 rpm for 5 min per batch. After melt mixing, the composite was held at −4 °C for 1 h and ground again. LDPE + BPA composites prepared by melt mixing were converted to films by compression molding with a Carver Laboratory press (Carver Inc., IN, USA) at a temperature of 120 °C and a molding pressure of 10 000 pounds (5 tons) for 5 min. Film thickness was 115.6 ± 8.7 μm (n = 10), as measured with a digital micrometer. Film disks of 2 cm diameter were cut from the compressed films for use in the migration tests. The control film (LDPE without BPA) was also prepared in the same manner. Reflux Extraction. The initial BPA concentrations in the LDPE films were determined by reflux extraction. A piece of LDPE film (∼0.35 g) at each of the three BPA concentration levels, or the control film (∼0.35 g), was placed in a 250 mL round-bottom flask with 100 mL of ethanol and reflux-extracted for 60 min. A small portion of the extracting solvent was diluted, transferred to an autosampler vial, and analyzed by HPLC−UV. To ensure that all BPA was extracted, the reflux extraction was repeated. The first extraction was considered complete if 90%) in the LDPE films migrated into the food simulants at steady state. Therefore, a simplified diffusion model, eq 4, can be applied to describe the migration of BPA by solving two parameters (D and M∞) in the equation. The SSC plots for both D and M∞ (Figure 1) indicate that there is no correlation between the two parameters (different trend of each curve with time), so they can be estimated simultaneously.
in food at the beginning of migration; (d) no obvious swelling of the film caused by food. Parameter Estimation. The experimental data collected in the migration test were used to estimate the parameters in Fick’s diffusion equation. To a certain extent, the migration parameters provide some physical meaning of the migration process: for example, the diffusion coefficient describes the velocity of additives traveled in the polymer. MATLAB (version 7.11.0, The MathWorks, Inc., MA, USA) was implemented to solve the migration parameters by using the nlinfit function (Supporting Information, Appendixes S1 and S2). The migration curve was automatically fitted to the experimental data until the best fit was achieved. The conventional migration study usually focuses on the estimation of only the diffusion coefficient. However, it is more interesting to involve more parameters (e.g., α and M∞) in the estimation that are also critical to the migration study. When multiple parameters are included in modeling, it is necessary to assess whether those parameters can be estimated simultaneously with reliability and accuracy. For this purpose, a sensitivity coefficient method18 was adopted to determine the correlation among the estimated parameters, and the scaled sensitivity coefficient (SSC or X′) was plotted vs time to compare the parameters on the same scale. The sensitivity analysis was also done with MATLAB (Supporting Information, Appendix S3). Statistical Analysis. A 33 full factorial design was adopted for the migration test considering three factors: temperature, initial BPA concentration, and food simulant type, each at three levels. Food simulant type was considered to be a categorical variable, and temperature and initial BPA concentration were continuous variables under each category. The diffusion coefficient, D, as an indicator of the migration velocity, was the response variable. To investigate the effect of the three factors and their interactions on D, a general linear model was used:19
Figure 1. Scaled sensitivity coefficients of the two parameters in eq 4 describing the migration of BPA (initial concentration of 1.42 mg g−1) from LDPE into ethanol at 40 °C. Initial guesses for the two parameters were D = 0.55 × 10−10 cm2 s−1 and M∞ = 11.8 mg L−1.
Y = β0 + β1X1 + β2X 2 + β3X3 + β12X1X 2 + β23X 2X3 + β13X1X3 + β123X1X 2X3
(5)
There were seven effects involved, including three main effects (effect of each single factor), three two-way interactions, and one three-way interaction. A normal distribution of the response variable was assumed for the use of the model. To meet this assumption, natural log transformation on D values was carried out without changing the nature of the interaction term. Three-way analysis of variance (ANOVA) was conducted with SAS software (version 9.2, SAS Institute, NC, USA) to determine whether each of the effects was significant (P < 0.05) on D.
D and M∞ values derived from eq 4 at different migration conditions are listed in Table 1 and Table 2, respectively. D values were in the range of 10−10−10−8 cm2 s−1 with a small variance (standard error below 5%) for most migration conditions. The variance was caused by the variable film thicknesses and the variation in initial BPA concentrations. A good estimation of the maximum amount of BPA migration could be achieved through modeling, as the M∞ values obtained from the diffusion equation were close to the values determined from the experiment at each migration condition. A good fit of the applied equation to the experimental data was found at all migration conditions. An example is shown in Figure 2; the migration curve fitted well to the measured migration points within a wide range. Effects of Factors on the Diffusion Coefficient. The effects of the various factors (temperature, initial BPA concentration, and food simulant type) and their interactions on D were evaluated from eq 5. The ANOVA results (Supporting Information, Table S1) showed that all of the main effects on D were significant (P < 0.05). For the interaction effects, the temperature−simulant interaction affected the diffusion coefficient significantly (P < 0.05),
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RESULTS AND DISCUSSION Properties of LDPE Films. Initial BPA concentrations in the LDPE films were determined to be 0.41 ± 0.01, 1.42 ± 0.08, and 2.66 ± 0.14 mg g−1, corresponding to 0.041 ± 0.001, 0.142 ± 0.008, and 0.266 ± 0.014 wt % of the films, respectively. When compared to the amount of BPA added before film processing (corresponding to 0.10, 0.25, and 0.50 wt % of the films), around 50% of BPA was lost after film processing. This could be due to the thermal degradation of BPA under the heat and high pressure during film processing. The Tm values of LDPE films without BPA and with BPA were determined by DSC (DSC curves are shown in Supporting 3713
DOI: 10.1021/acs.iecr.5b00088 Ind. Eng. Chem. Res. 2015, 54, 3711−3716
Article
Industrial & Engineering Chemistry Research Table 1. Diffusion Coefficient (D) Values Derived from Equation 4 at Different Conditions of BPA Migrationa D × 10−10 (cm2 s−1) food simulant
−1
BPA concn (mg g )
water
0.41 1.42 2.66 0.41 1.42 2.66 0.41 1.42 2.66
3% acetic acid
95% ethanol
a
40 °C
60 °C
80 °C
± ± ± ± ± ± ± ± ±
39.24 ± 2.26 26.12 ± 1.87 7.11 ± 0.34 31.06 ± 3.08 24.52 ± 1.76 8.23 ± 0.36 23.99 ± 2.49 16.00 ± 0.68 6.38 ± 0.18
300.33 ± 28.45 179.76 ± 11.01 85.69 ± 4.93 319.71 ± 32.56 170.54 ± 11.94 91.42 ± 5.71 286.95 ± 27.21 179.45 ± 8.35 81.37 ± 4.87
1.85 1.16 0.57 2.55 1.21 0.66 1.26 0.55 0.30
0.22 0.03 0.04 0.22 0.03 0.03 0.07 0.02 0.01
D values are expressed as the mean ± standard error, n = 3.
Table 2. Predicted Equilibrium Concentration (M∞) Values Derived from Equation 4 at Different Conditions of BPA Migrationa M∞ (mg L−1) food simulant water
3% acetic acid
95% ethanol
BPA concn (mg g−1)
40 °C
60 °C
80 °C
0.41 1.42 2.66 0.41 1.42 2.66 0.41 1.42 2.66
3.77 ± 0.15 (3.47) 10.87 ± 0.06 (10.80) 20.78 ± 0.33 (20.36) 3.48 ± 0.09 (3.40) 11.65 ± 0.06 (11.66) 20.07 ± 0.25 (19.94) 3.63 ± 0.06 (3.51) 11.75 ± 0.09 (11.60) 20.33 ± 0.22 (19.52)
2.70 ± 0.04 (2.65) 10.98 ± 0.21 (10.76) 30.13 ± 0.43 (27.95) 3.51 ± 0.11 (3.13) 11.17 ± 0.22 (10.85) 29.56 ± 0.37 (28.14) 3.59 ± 0.11 (3.48) 11.52 ± 0.14 (11.15) 25.67 ± 0.20 (24.91)
3.63 ± 0.10 (3.48) 11.29 ± 0.17 (11.04) 22.78 ± 0.37 (21.80) 3.75 ± 0.12 (3.56) 12.60 ± 0.26 (11.99) 23.72 ± 0.40 (22.80) 3.75 ± 0.11 (3.58) 12.09 ± 0.13 (11.84) 22.14 ± 0.38 (21.16)
M∞ values are expressed as the mean ± standard error. The values in the parentheses represent the mean value of BPA concentration in the food simulant determined from the last sampling point, which is also considered as the experimental equilibrium concentration.
a
dependence of the diffusion coefficient can be expressed in an exponential form, and an approximately linear relationship (R2 > 0.95) was obtained after natural log transformation on the D values (Figure 3). The transport properties of the polymer phase varied with different food simulants. The D values obtained were higher in water and 3% acetic acid than in 95% ethanol at 40 and 60 °C (Table 1). This phenomenon may be attributed to the affinity between LDPE and the food simulant. According to regular solution theory, the affinity between the polymer and the solvent can be quantified by the Hansen solubility parameter distance, Ra, expressed as
while no other two-way and three-way interaction effects were significant. In rubbery polymers (above glass transition temperature), the relationship between the diffusion coefficient and the temperature can be described by the Arrhenius equation:21 ⎛ E ⎞ D = D0 exp⎜ − a ⎟ ⎝ RT ⎠
(6)
where Ea represents the activation energy of diffusion in the polymer, R is the gas constant (8.314 J mol−1 K−1), and T is the absolute temperature. A linear relationship was obtained by plotting ln(D) as a function of inverse temperature (R2 > 0.99). Activation energies of BPA migration at three initial BPA concentrations were calculated to be 116.2 ± 1.2 kJ mol−1 for water, 112.8 ± 1.6 kJ mol−1 for 3% acetic acid, and 128.9 ± 4.3 kJ mol−1 for 95% ethanol. For each food simulant, similar Ea values were obtained at different initial BPA concentrations, as indicated by the small standard deviation of each Ea value. The reason could be the small amount of BPA (
