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Headspace conditions and ingredients can affect artefactual benzene formation in beverages
Food Chemistry 293 (2019) 278–284
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Analytical Methods
Headspace conditions and ingredients can affect artefactual benzene formation in beverages
T
Eun Mi Kima, Dan Ah Kimb, Sung Won Kwonb, Yan Jina, Heesoo Leea, Seulgi Kanga, ⁎ Jeongmi Leea, a b
School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi 16419, Republic of Korea College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Benzene monitoring Artefactual formation Beverage Headspace conditions Ingredients
A headspace sampling-gas chromatography/mass spectrometry (HS-GC/MS) method using mild HS conditions (40 °C, 30 min) was established, validated in terms of specificity, linearity (1.75–87.65 ng mL−1), precision (0.3–9.1% RSD), and accuracy (81.1–117.7%); and applied for the monitoring of 900 commercial beverage samples of six different types. These mild (low-temperature) conditions were compared with 1) optimized (hightemperature) conditions and 2) a liquid-phase microextraction method involving no heat treatment. This method was desirable because a high equilibrium temperature induced artefactual benzene formation from benzoate and ascorbic acid. In a 28IV− 3 fractional factorial design, eight variables—ascorbic acid, benzoate, benzaldehyde, Cu2+, Fe2+, riboflavin, pyridoxine, and heat treatment—were tested as potential factors affecting benzene formation. All variables except Fe2+ and pyridoxine significantly affected benzene formation, both individually and interactively. The present study suggests an accurate and reliable method for benzene analysis and provides strategies to prevent unintentional benzene formation in beverages.
1. Introduction Benzene is a volatile organic compound (VOC) that has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) (Baan et al., 2009). Benzene is a food contaminant, and its maximum allowable level is strictly regulated in many countries; for example, the regulation limit for drinking water is 1–10 μg L−1 (WHO, 2011). Benzene can be unintentionally introduced into foods and beverages from external sources including contaminated environment and packaging materials (Lau & Wong, 2000; Van Poucke, Detavernier, Van Bocxlaer, Vermeylen, & Van Peteghem, 2008). It can also be formed intrinsically during food processing and storage (Casado et al., 2011). Benzoate, a common preservative, can be decarboxylated to benzene in the presence of ascorbic acid (Loch et al., 2016). Benzoate can also undergo oxidative decarboxylation in the presence of hydroxyl radicals (Medeiros Vinci et al., 2011). Production of these radicals is promoted when ascorbic acid is present at low pH or when transition metals such as copper and iron ions are present (Gardner & Lawrence, 1993). Benzene can also be produced from common food ingredients and flavor additives. For example, β-carotene and phenylalanine,
⁎
natural components of carrot juice, may lead to benzene formation during the heating process (Lachenmeier et al., 2010), and benzaldehyde, a cherry-flavoring additive, has been suggested to be a precursor of benzene in the presence of ascorbic acid (Loch et al., 2016). External conditions such as high temperatures and ultraviolet radiation can accelerate benzene formation in beverages under certain circumstances (Nyman, Wamer, Begley, Diachenko, & Perfetti, 2010). The concentrations of benzene, sodium benzoate, and ascorbic acid were significantly correlated in a recent study (Heshmati et al., 2018). More detailed studies with well-designed models are still needed to determine how various beverage components individually and collectively affect benzene formation (Medeiros Vinci et al., 2011). The benzene concentrations in beverages are determined by gas chromatography coupled to mass spectrometry (GC/MS) and is preceded by a sampling method that is either solvent extraction-based (liquid-liquid extraction and liquid-phase microextraction, LPME) (Kaykhaii, Hosseinbor, & Ghasemi, 2016; Khajeh & Zadeh, 2012; Rahmani, Kaykhaii, Ghasemi, & Tahernejad, 2015; Yang, Guo, Wang, Liang, & Liu, 2010) or headspace-based (static headspace or headspacedynamic) (Arisseto, Vicente, Furlani, Pereira, & de Figueiredo Toledo, 2013; Cao & Casey, 2008; Cao, Casey, Seaman, Tague, & Becalski, 2007;
Corresponding author. E-mail address: [email protected] (J. Lee).
https://doi.org/10.1016/j.foodchem.2019.04.089 Received 12 July 2018; Received in revised form 3 April 2019; Accepted 24 April 2019 Available online 25 April 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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from LPME.
Nyman et al., 2007). Due to its simplicity with reasonable sensitivity, headspace sampling coupled to GC/MS (HS-GC/MS) is most commonly used, especially for monitoring a large number of samples (Ju, Park, & Kwon, 2008; Techakriengkrai & Lertborwornwong, 2013; Van Poucke et al., 2008). Various equilibrium temperatures have been adopted for HS in previous benzene monitoring studies, including 70 °C (Van Poucke et al., 2008), 80 °C (Techakriengkrai & Lertborwornwong, 2013), and > 100 °C (Lachenmeier, Reusch, Sproll, Schoeberl, & Kuballa, 2008; Loch et al., 2016). Although high temperatures are generally preferred for HS to maximize the equilibrium of the analytes in the headspace (Cavalcante, de Andrade, Marins, & Oliveira, 2010), concerns have been raised about false-positive detection in HS-based methodology for benzene analysis. Indeed, artefactual benzene formation has been detected at high equilibrium temperatures (Cao et al., 2007; Ju et al., 2008; Lachenmeier et al., 2008). Ju et al. (2008) reported the false-positive determination of benzene at 80 °C in samples containing benzoate and ascorbic acid. Lachenmeier et al. (2008) employed a full-factorial experimental design to assess the effects of headspace conditions and suggested using a low headspace temperature (e.g., 50 °C) and basifying the sample pH (e.g., pH 10) to suppress artefactual benzene formation from benzoate. High sample pH has also been used in other studies (Medeiros Vinci et al., 2011; Vinci et al., 2010). The aims of the current study were to identify an analytical method that could minimize the risks of overestimation or false-positive quantification of benzene during analysis and to investigate the effects of beverage ingredients and heating on benzene formation. To this end, an HS-GC/MS method using mild (low-temperature) HS conditions was established, validated for specificity, linearity, precision, and accuracy and then applied for monitoring of 900 commercial beverage samples. Artefactual benzene formation due to heat treatment during HS was determined through comparison of the suggested HS method with two other methods: an HS method involving a high headspace temperature and an LPME method involving no heat treatment. Secondly, a systematic investigation with a fractional factorial design (FFD) was conducted on seven ingredients and different heat treatments.
2.2. Preparation of standard solutions and sample solutions Stock solutions of benzene and benzene‑d6 (internal standard; IS) were prepared in methanol at 175.3 µg mL−1 and 190.0 µg mL−1, respectively, and stored at −40 °C. Fresh working solutions were prepared every 2 h through dilution of the stock solutions in methanol. For headspace analysis, 5 mL of standard or sample solution was transferred to a 22-mL vial with a PTFE/butyl septum screw cap (Perkin-Elmer, Norwalk, CT, USA) and spiked with 19.0 ng mL−1 of IS. A series of standard solutions was prepared to contain benzene in the range of 1.75–87.7 ng mL−1 in water. 2.3. Instrumental conditions and validation of the headspace sampling-gas chromatography/mass spectrometry method The HS-GC/MS analysis was performed with a Perkin-Elmer Turbomatrix 40 headspace sampler (Perkin-Elmer) and an Agilent 7890B gas chromatograph (Santa Clara, CA, USA) equipped with a mass-selective detector (Agilent 5977A). Mass Hunter software was used for system operation and data management. A temperature of 85 °C was used for the headspace needle and transfer line. The sample was equilibrated in the oven of the headspace autosampler at 40 °C for 30 min for the mild conditions or at 75 °C for 34 min for the optimized conditions. Thereafter, 2000 µL of sample headspace was injected into the GC/MS system. For real sample analysis, a calibration curve was established with a series of freshly prepared standard solutions on each day. The injected compounds were separated on a DB-624 column (30 m × 0.32 mm I.D., 1.80 µm film thickness) supplied by J & W Scientific (Folsom, CA, USA). Ultra-pure helium (purity 99.999%) was used as the carrier gas at a flow rate of 1.0 mL min−1. The injection port was held at 200 °C and used in split mode (split ratio 10:1). The GC oven temperature was held at 40 °C for 3 min and increased to 200 °C at 10 °C min−1. The chromatogram was recorded for 19 min, and the retention time for benzene was 4.65 min. The ion source and quadrupole temperatures were set at 230 °C and 150 °C, respectively. The mass spectrometer was operated in selected ion monitoring (SIM) mode, recording mass-to-charge ratios (m/z) of 78, 77, and 51 for benzene and 84 for benzene‑d6. The established HS-GC/MS method was validated in terms of selectivity, linearity, limit of quantification (LOQ), precision, and accuracy according to AOAC guidelines. The method selectivity was assessed in six different types of beverages, based on identification of analyte peaks with m/z of 78, 77, and 51 with no interfering peaks around them. The IS peak was monitored at m/z 84. The LOQ of the analytical method was calculated from the standard deviation (σ) of the signal obtained by quintuplicate analysis of samples at 1.75 ng mL−1 and the slope (S) of the calibration curve by the following equation:
2. Materials and methods 2.1. Reagents, standards, samples, and equipment Analytical standards such as benzene (99.8%) and benzene‑d6 (≥99%, 99.6 atom % D; internal standard) were purchased from Sigma-Aldrich (St. Louis, MO, USA). L-Ascorbic acid (≥98%), sodium benzoate (99%), benzaldehyde (≥99%), copper (II) sulfate (≥99%), iron (II) sulfate heptahydrate (≥99%), riboflavin (97.9%), pyridoxine hydrochloride (100%), tetrachloroethylene (≥99.9%), and potassium hydroxide (≥85%) were also obtained from Sigma-Aldrich. HPLCgrade water, acetonitrile, and methanol were purchased from Burdick & Jackson (Muskegon, MI, USA). All the reagents were of analytical grade or higher and were obtained from Sigma-Aldrich unless stated. In total, 300 items of non-alcoholic beverages were collected from 10 local supermarkets in Seoul and Suwon, Korea, resulting in collection of most of the beverages marketed in Korea. For each item, three samples with the same lot number or with the same manufacturing date within the same manufacturer were analyzed. A total of 900 commercial samples belonged to one of six categories: fruit/vegetable juice (78 items), soft drinks (75 items), ginseng-containing beverages (38 items), soybean milk (38 items), fermented drinks (26 items), and others (45 items). The number of items for each category was determined in proportion to the sales and market share in Korea. A 1580 MGR (Gyrozen, Incheon, Korea) was used for centrifugation, and a water bath (Lab House, Pochen, Korea) was used to control the sample temperature when needed. A 50-µL Hamilton syringe (Reno, NV, USA) was used to measure the volume of the sedimented phase
LOQ =
σ ∗ 10 S
The calibration curve was established from seven concentration points (1.75, 4.38, 8.77, 21.91, 43.83, 65.74, and 87.65 ng mL−1). Accuracy and precision were measured in triplicate at three concentrations (1.75, 43.83, and 87.65 ng mL−1) in six types of non-alcoholic beverages. Precision was assessed as % relative standard deviation (RSD) of repeated analyses. The accuracy was estimated as % relative recovery, which was calculated as the ratio of experimental concentration to theoretical concentration of spiked sample. 2.4. Procedures and validation of liquid-phase microextraction of sample solutions Aqueous solutions containing ascorbic acid (5000 µg mL−1) and/or 279
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sodium benzoate (600 µg mL−1) were freshly made. The solutions were either instantly used or incubated at room temperature for 30 min and subjected to the following LPME method. A 5-mL sample solution was adjusted to pH 10.0 with a 10% w/w potassium hydroxide solution and spiked with 10 µL of the IS at 95.00 ng mL−1. Then, 20 µL of tetrachloroethylene (extraction solvent) was rapidly injected into the solution, and the mixture was manually shaken for 20 s (∼80 repetitions) and centrifuged at 2898g for 8 min at room temperature. The sedimented liquid phase (∼13 µL) was manually withdrawn with a Hamilton syringe, and 1 µL of it was injected into the GC/MS. The GC/ MS operation conditions including the injection port temperature and split ratio were the same as those for the HS method, except for the use of a tapered liner (4 mm i.d., Agilent) and the column temperature programming, which was as follows: held for 3 min at 40 °C, increased at 10 °C min−1 to 100 °C and then increased at 30 °C min−1 to 200 °C. A series of aqueous standard solutions of benzene was used to validate the linearity, precision, and accuracy of the established LPME method. The linearity was assessed from six concentration points (4.38, 8.77, 21.91, 43.83, 65.74, and 87.65 ng mL−1) in triplicate experiments. The precision and accuracy values were determined from quintuplicate experiments at three concentration levels (4.38, 43.83, and 87.65 ng mL−1).
3. Results and discussion 3.1. Establishment and validation of the HS-GC/MS method for benzene analysis The experimental conditions for the GC/MS analysis were established from methods in the literature (Arisseto et al., 2013; Kim, Park, & Choi, 2007; Lachenmeier et al., 2008; Van Poucke et al., 2008). The GC/MS conditions are described in the experimental section. In general, HS conditions can be optimized because various factors influence sensitivity and efficiency of a method including oven temperature, equilibration time, and vial-to-sample volume ratio (Cavalcante et al., 2010; Cheng, Liu, Mueller, & Yan, 2010). The vial-to-sample volume ratio was set to 22:5 (mL), as reasonably high peak areas of benzene could be acquired with precision on our instrumental system at this ratio (data not shown). For headspace equilibration, a high oven temperature with a long equilibration time is generally preferred because it can enhance the peak areas of analytes; however, in the present study, the lowest possible temperature was adopted in consideration of the potential risk of artefactual benzene formation during analysis. In our system, an oven temperature of 40 °C was the lowest operational temperature that allowed reproducible and reasonable efficiency when the equilibration time was maintained for no less than 30 min, consistent with the report by Ju and colleagues (Ju et al., 2008). As a result, the established HS conditions involved equilibration at 40 °C for 30 min, as described in Section 2.3. The HS-GC/MS method was validated for six different types of beverages. The method was selective for benzene in all types of beverages (Fig. S1). No significant matrix effects were observed for real samples with different matrices, given that the peak area ratios of benzene spiked in real samples versus water were close to 100% (data not shown). As a result, the method linearity was assessed with a calibration curve established from aqueous standard solutions. The method was linear in the range of 1.75–87.65 ng mL−1, with a regression equation of Y = 0.05443X + 0.03278 (r2 = 0.9986). Although the calculated LOQ value was 0.55 ng mL−1, it was not included as the starting concentration in the calibration range, because the signal-tonoise ratio was sufficiently large above 1.75 ng mL−1, with RSD ≤ 15%. The method precision and accuracy were reasonable for all the beverage types at the three tested concentration levels; the intraand inter-day precisions values were 0.34–9.82% RSD, and the accuracy values were 81.1–117.7%. The measured validation parameters are summarized in Table 1. All the validation results met the criteria required by the AOAC guidelines, supporting the validity of the current method. The quantification limit of the present method was low to moderate compared to similar HS-GC/MS methods, largely due to the low equilibrium temperature for HS (Arisseto et al., 2013; Cao & Casey, 2008). Further modifications of HS, GC or MS methods, such as coupling solid-
2.5. Experimental design and statistical analysis The experimental design was performed using Design-Expert 8 (Stat-Ease, Minneapolis, MN, USA). Response surface methodology (RSM) based on a central composite design (CCD) was used to optimize the headspace conditions to yield the highest benzene peak areas. In this design, the benzene peak area was used as the response, while the input variables were equilibrium temperature (X1), equilibration time (X2), and sample volume (X3). The benzene concentration for CCD experiments was 43.83 ng mL−1. The experimental ranges of the variables at five different levels (-1.68, −1, 0, +1, +1.68) were X1, 35–85 °C; X2, 10–40 min; and X3, 3–7 mL. As displayed in Table S1, the design matrix was composed of two blocks. Experiments were run in a random order per block, and each block included three center points. The total number of experiments was 20 (N = 2 k + 2 k + Cp; N, number of experiments; k, number of variables; Cp, number of center points). The effects of the manufacturing process and components on benzene formation were investigated through a two-level FFD (28IV− 3 ), in which the peak area ratio of benzene to the IS was used as the response. Eight factors—ascorbic acid (A), benzoate (B), benzaldehyde (C), Cu2+ (D), Fe2+ (E), riboflavin (F), pyridoxine (G), and temperature (H)—were used as variables in the FFD. Forty experiments (N = 2 k (k − 1) + Cp) were conducted in two blocks containing four center points each. The results of the CCD and FFD experiments were evaluated by analysis of variance (ANOVA) in Design-Expert 8. Student’s ttest was performed using Prism 6.0 (GraphPad Software, Inc. USA). Table 1 Precisions and accuracies of the established HS-GC/MS method. Matrix
Precision (% RSD)
Accuracy (%)
Intra-day (n = 5)
Orange Juice Mixed beverage Ginseng beverage Fermented drink Soybean milk Soft drink a b c
Inter-day (n = 3 × 3)
Lowa
Middleb
Highc
Lowa
Middleb
Highc
Lowa
Middleb
Highc
0.89 9.82 4.50 3.75 2.13 4.60
1.61 3.08 0.89 0.34 1.28 3.58
0.68 1.58 2.40 0.69 1.27 1.23
5.08 6.57 9.09 4.96 4.31 3.18
5.02 4.16 5.39 4.45 3.84 2.18
2.93 3.86 4.62 3.20 2.24 2.28
87.7 98.3 99.4 110.5 116.4 108.9
81.1 96.7 97.5 113.0 107.6 105.0
96.4 100.2 98.9 117.7 112.8 110.4
1.75 ng mL−1. 43.83 ng mL−1. 87.65 ng mL−1. 280
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In the case of ascorbic acid and benzaldehyde, their coexistence resulted in benzene formation under both HS conditions: 3.48 ± 0.57 ng mL−1 and 28.31 ± 24.81 ng mL−1 under the mild and optimized conditions, respectively, and these values were significantly different (p < 0.05). Unexpectedly, at 75 °C, a wide range of benzene levels was obtained (4.98–77.50 ng mL−1). Each sample was freshly prepared and immediately subjected to HS in all experiments, leaving no incubation time for the mixture. Furthermore, the results of the benzoate and ascorbic acid experiments were consistent. Therefore, the wide range of benzene levels formed from benzaldehyde and ascorbic acid is not likely to have resulted from imprecisely performed experiments. In the study by Loch et al., in which HS was conducted at 50 °C for 30 min, benzene formed from benzaldehyde in a temperature- and time-dependent manner, and benzene formation was expedited in the presence of ascorbic acid (Loch et al., 2016). In that study, incubation temperature and time ranged from 20 to 100 °C and 2–24 h, respectively, but it was not discernable whether benzene formed during incubation or during headspace equilibration. Our results indicate that the chemistry of benzene formation from benzaldehyde and ascorbic acid is affected by a number of factors, including temperature and time. The reaction kinetics appear to be very temperature-dependent and to differ from those of benzoate and ascorbic acid. Further detailed study will be needed to elucidate the reaction mechanism and kinetics for benzene formation from various precursors. Our results demonstrate that a high temperature for headspace equilibration could cause artefactual benzene formation in the presence of certain precursors. Therefore, the HS conditions for benzene analysis should be selected carefully. The mild HS condition of 40 °C for 30 min is suggested as a practical and reasonable method for benzene monitoring in beverages.
phase microextraction to HS (Arisseto et al., 2013), applying cryofocusing in the injector liner (Van Poucke et al., 2008), and employing an isotope dilution method (Cao & Casey, 2008), could enhance the sensitivity to sub-ppb levels. Our analytical method is still useful for benzene monitoring in several ways. The quantification results can be considered reliable due to the minimized risk of false-positive detection of benzene. The sample preparation step is minimal, excluding manual operations such as sample pH adjustment (Lachenmeier et al., 2008). Most of all, this method can be readily applied to a conventional HSGC/MS platform. The compromised sensitivity in the sample introduction (HS) step could be overcome in the GC/MS step through adoption of GC/MS-MS, although this was not attempted in the present study due to lack of instrumentation. The current method would be most useful for monitoring a large number of samples for regulatory purposes. 3.2. Comparison of mild HS conditions with optimized HS conditions to examine the effects of headspace conditions on artefactual benzene formation Although the HS conditions can be optimized to maximize the equilibrium of the analytes in the headspace (Grodowska & Parczewski, 2013), these conditions can lead to artefactual benzene formation in beverages. In the present study, the HS conditions were systematically optimized for high sensitivity, and the optimized conditions were compared with mild conditions so that their effects on the artefactual benzene formation could be examined. Three variables—equilibrium temperature (X1), equilibration time (X2), and sample volume (X3)—were selected and subjected to RSM based on a CCD. The quality of the resulting model was evaluated by ANOVA, and the figures of merit are summarized in Table S2. The model, which was statistically significant (p < 0.0001), could be described by the following equation in coded units:
3.3. Comparison of the mild HS method with a non-heating LPME method to examine the effects of headspace conditions on artefactual benzene formation
Y = 75702 + 20059.13X1 + 843.88X2 + 9969.06X3
Two HS conditions were compared above to explore the effects of heat treatment on benzene formation. In fact, heat treatment is practically unavoidable during HS. An orthogonal sample preparation method involving no heat treatment for benzene analysis could corroborate the assessment results on the potential effect of heating. In this sense, a rapid sample preparation method was established based on LPME, allowing simultaneous extraction and pre-concentration without any sample heating (Asensio-Ramos, Ravelo-Pérez, González-Curbelo, & Hernández-Borges, 2011; Han & Row, 2012; Pinto, Sontag, Bernardino, & Noronha, 2010), and this method was compared with the mild HS conditions. The new LPME method described in Section 2.4, was established through the review of LPME-based studies on the analysis of benzene or related VOCs in drinking water (Demeestere, Dewulf, De Witte, & Van Langenhove, 2007; Wang, Kwok, He, & Lee, 1998). This method involved manual shaking-assisted dispersive liquid-liquid microextraction, in which the dispersive solvent was replaced by manual shaking to disperse the extraction solvent, tetrachloroethylene. An aqueous solution was prepared with either benzene or sodium benzoate (600 µg mL−1) ± ascorbic acid (5000 µg mL−1). Prior to its application to the LPME procedure, this solution was alkalized to pH 10.0 to prevent benzoate (pKa 4.08) and ascorbate (pKa 4.10) from migrating to the organic phase. Benzaldehyde could not be tested in the presence or absence of ascorbic acid by the current LPME method because it was technically impossible to selectively extract trace levels of benzene, if produced, while leaving a very high concentration of benzaldehyde in the aqueous phase. Aqueous benzene solutions were used to evaluate the enrichment factor (EF) and validation parameters of the method. The EF was calculated as the ratio of benzene concentration in the sediment phase (Csed) to initial benzene concentration (C0, 4.38 ng mL−1). The obtained
where Y is the peak area of benzene. According to the established model, the equilibrium temperature (X1) had the most significant effect on the response in a positive direction, followed by the sample volume (X3), while the equilibration time (X2) was not significant (Table S2). The resulting optimized HS conditions were used to equilibrate a 6-mL sample at 75 °C for 34 min, and the results reproduced at the optimized conditions (9.9 × 104 ± 1.8 × 103, n = 5) were very close to the predicted values (9.2 × 104 –1.2 × 105). The optimized HS conditions involved a high equilibrium temperature (75 °C) as expected, which could lead to artefactual benzene formation during equilibration (Lachenmeier et al., 2008; Loch et al., 2016). The optimized conditions were compared with mild conditions (equilibration at 40 °C for 30 min). Test samples containing sodium benzoate or benzaldehyde were prepared with and without ascorbic acid in water, as in previous studies that reported the possible formation of benzene from these compounds (Ju et al., 2008; Loch et al., 2016; Nyman et al., 2010). The tested ascorbic acid concentration, 5000 µg mL−1, was adopted from ordinary vitamin C drinks marketed in Korea, while the sodium benzoate concentration was set to 600 µg mL−1 in accordance with standards and specifications for food additives (JETRO, 2011). Benzaldehyde was tested at 75 µg mL−1, the maximum value added to non-alcoholic beverages (Burdock, 2016). The results of the comparative experiments under the two different HS conditions are summarized in Table 2. No benzene was detected under any HS conditions when each compound was solely present. The mixture of ascorbic acid and benzoate yielded no benzene under mild conditions; however, it generated benzene at 16.03 ± 0.12 ng mL−1 under the optimized conditions. These results imply that artefactual benzene was formed from the two compounds during high-temperature equilibration, consistent with previous reports (Ju et al., 2008; Loch et al., 2016; Nyman et al., 2010). 281
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Table 2 Effects of the headspace equilibrium conditions on the artefactual benzene formation during analysis. Measured benzene concentration (ng mL−1 ± SDa)
HS conditions LPME
Component(s) added to aqueous solution
None
AA
Mild (40 °C, 30 min) Optimized (75 °C, 34 min)
– – –
– – –
c
b
SB – – –
b
BA
b
– – N/Ae
SB + AA
BA + AA
– 16.0 ± 0.1 –
3.5 ± 0.6 28.3 ± 24.8d N/A
Abbreviations: AA, ascorbic acid; BA, benzaldehyde; HS, headspace; LPME, liquid-phase microextraction; SB, sodium benzoate. a n = 3 except for BA + AA, for which n = 8. b AA, 5000 µg mL−1; SB, 600 µg mL−1; BA, 75 µg mL−1. c Not detected. d Range from repeated analysis is 5.0–77.5 ng mL−1 (n = 8). Significantly different (p < 0.05) in comparison to those obtained under the mild conditions. e Not applicable to LPME.
ascorbic acid (A), sodium benzoate (B), benzaldehyde (C), Cu2+ (D), Fe2+ (E), riboflavin (F), and pyridoxine (G). The first five are known or suggested precursors or catalysts of benzene formation. Ascorbic acid is often added as a main ingredient or additive, while sodium benzoate is a common preservative, and benzaldehyde is an additive for cherry flavor. Riboflavin and pyridoxine were included based on our own monitoring results as described above. Heat treatment (H) was also included in the design because it is an important manufacturing process and is well-known to affect benzene formation. The high and low levels tested for each variable are displayed in Table 3. The high levels (+1) of ascorbic acid, sodium benzoate, and benzaldehyde were the same as those used in Section 3.2. Cu2+ and Fe2+ were set at sufficiently high concentrations of 700 µg mL−1 and 40 µg mL−1, respectively (Choi, Kang, & Kim, 2009; Gardner & Lawrence, 1993; WHO, 2005). The riboflavin and pyridoxine concentrations were set to 14 µg mL−1 and 12 µg mL−1, respectively (WHO, 2005). The temperature range for the heat treatment was 25–95 °C, and the samples were incubated at the designated temperature for 20 min before being applied to HS. Due to the large number of variables, the FFD involved an eighth-fraction factorial design with resolution IV (28IV− 3 ), resulting in 40 experiments. In this type of design, main effects and two-factor interactions are confounded with threefactor interactions and other two-factor interactions, respectively (Candioti, De Zan, Cámara, & Goicoechea, 2014). However, the interaction effects among three or more variables are usually smaller than the main effects and two-factor interaction effects (Preu, Guyot, & Petz, 1998). The evaluation results from the FFD experiments are summarized in Table S3. The magnitude and direction of the influence of the tested variables are shown in a Pareto chart, in which the horizontal axis indicates the t-value of the effects in a positive (solid bar) or negative (open bar) direction (Fig. 1). Variables above the Bonferroni limit indicate significant effects, while those below the t-limit indicate nonsignificant effects. As shown in Fig. 1(a) and Table S3, the main effects of the significant variables were found in the following order: ascorbic
EF value was 210.7 ( ± 2.1, n = 3). The method was linear in the range of 4.38–87.65 ng mL−1 with a regression equation of Y = 0.009417X + 0.0217 (r2 = 0.9986). The intra-day precision values were 5.71, 5.06, and 5.70% RSD at 4.38, 43.83, and 87.65 ng mL−1, respectively, while the intra-day accuracy values were 87.4, 96.4, and 104.7% at 4.38, 43.83, and 87.65 ng mL−1, respectively. The LOQ value was calculated to be 3.34 ng mL−1. These results support the validity of the LPME method. Samples containing sodium benzoate and/or ascorbic acid were freshly prepared and instantly applied to LPME. As a result, no benzene was detected (Table 2). In addition, samples incubated at room temperature for 30 min prior to LPME analysis exhibited no benzene. It should be noted that the same samples analyzed by the optimized HS method were found to contain 16.03 ng mL−1 of benzene, while the samples analyzed by the mild HS method exhibited no benzene (Table 2). These results indicate that artefactual benzene was indeed formed from benzoate and ascorbic acid when a high equilibrium temperature was used during HS. The two orthogonal comparative studies to assess the effects of headspace conditions confirmed that our HS-GC/MS method with mild HS conditions was desirable for benzene analysis in a large number of beverage samples, with minimal risk of false-positive detection of benzene. 3.4. Monitoring of 900 commercial beverage samples using the established headspace sampling-gas chromatography/mass spectrometry method The established method with mild HS conditions was applied to the monitoring of 900 samples (3 samples per item × 300 items) from six types of beverages (fruit juice, soft drinks, ginseng beverages, soybean milk, fermented drinks, and others). Three items (two soft drinks and one ginseng-containing beverage; nine samples in total) were found to contain benzene. Two items from soft drinks (six samples) contained benzene in the range of 2.4–2.9 ng mL−1 and 2.7–2.9 ng mL−1 for each, and the use of ascorbic acid and sodium benzoate was claimed on their label. The third item was found to contain benzene at 3.8–5.8 ng mL−1. Its label claimed the inclusion of sodium benzoate, riboflavin, and pyridoxine but not ascorbic acid. The expanded uncertainty of the analytical method, which was estimated according to Giraldez, Chaguaceda, Bujalance, and Morales (2013), was ± 0.2 ng mL−1 for the detected benzene levels (2.4–5.8 ng mL−1). The entire analysis of all the samples could be conducted using the same column without diminishing performance.
Table 3 Factors and their levels of 28IV− 3 FFD. Variable
Ascorbic acid (µg mL−1) Sodium benzoate (µg mL−1) Benzaldehyde (µg mL−1) Copper (II) sulfate (µg mL−1) Iron (II) sulfate (µg mL−1) Riboflavin (µg mL−1) Pyridoxine (µg mL−1) Temperaturea (°C)
3.5. Influence of beverage ingredients and manufacturing processes on benzene formation Several studies have examined the effects of beverage ingredients on benzene formation, but none of them evaluated a large number of ingredients using an experimental design. In this study, a two-level FFD was employed as a screening design with the following compounds:
a
282
Incubation time: 20 min.
Level Low (−1)
High (+1)
0 0 0 0 0 0 0 25
5000 600 75 700 40 14 12 95
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Fig. 1. A Pareto chart displaying the individual (a) and interactive (b) effects of eight variables on benzene formation. A, ascorbic acid; B, sodium benzoate; C, benzaldehyde; D, Cu2+; E, Fe2+; F, riboflavin; G, pyridoxine; H, heat treatment. The solid line and dotted line indicate the Bonferroni limit and the t-value limit, respectively. Positive and negative effects are indicated by solid and open boxes, respectively.
acid (A) > benzaldehyde (C) > Cu2+ (D) > sodium benzoate (B) > riboflavin (F) > temperature (H). Two variables, Fe2+ (E) and pyridoxine (G), were between the Bonferroni and t-limits, indicating moderate, possibly significant effects on benzene formation. It is noteworthy that ascorbic acid, benzaldehyde, Cu2+, and sodium benzoate had positive effects (i.e., benzene formation increases with elevated levels of these additives), while riboflavin had negative effects on benzene formation. High temperature was significantly associated with elevated benzene level. These results imply that the levels of ascorbic acid, benzaldehyde, Cu2+, and sodium benzoate should be set low to minimize benzene formation in beverages. Important interactive effects among the variables were also detected in the FFD results (Fig. 1(b)). Ascorbic acid had significant interactive effects with benzaldehyde (AC), Cu2+ (AD), sodium benzoate (AB), and temperature (AH) in decreasing order. All the effects were positive, meaning that benzene formation increases if both variables change to high levels. Thus, to minimize benzene formation, these variables should not be set at high levels at the same time. While riboflavin had negative interactive effects with ascorbic acid (AF) and Cu2+ (DF), it had a positive interaction with sodium benzoate (BF). The co-presence of benzaldehyde and Cu2+ (CD), benzoate and riboflavin (BF), and
Cu2+ and Fe2+ (DE) also had significant positive effects on benzene formation. The screening experiments based on FFD provided important information on the individual and interactive effects of various ingredients that could affect benzene formation during manufacture or storage. A subsequent optimization study focused on several of the most significant variables could provide insight into more detailed and quantitative information on these variables. 4. Conclusions An analytical method with mild HS conditions was established and proven valid for linearity, LOQ, specificity, precision, and accuracy. Analysis of mixed solutions of ascorbic acid and sodium benzoate with three different methods suggests that high temperature equilibration could induce artefactual benzene formation. Therefore, our method is desirable for benzene monitoring of beverages, with minimal risk of false-positive detection. Monitoring of 900 beverage samples revealed that the presence of ascorbic acid, sodium benzoate, pyridoxine, or riboflavin might be associated with benzene formation. In the 28IV− 3 FFD experiments, ascorbic acid exhibited significant effects on benzene 283
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formation solely and with various components including benzaldehyde, Cu2+, sodium benzoate, and temperature. In summary, this study is the first comprehensive and systematic investigation of the effects of headspace conditions and ingredients on artefactual benzene formation. It not only suggests a desirable method for accurately and reliably monitoring benzene in beverages, but also provides strategies to prevent unintentional benzene formation in beverages during manufacture and/or storage.
Grodowska, K., & Parczewski, A. (2013). Simplex optimization of headspace factors for headspace gas chromatography determination of residual solvents in pharmaceutical products. Acta Poloniae Pharmaceutica, 70, 181–189. Han, D., & Row, K. H. (2012). Trends in liquid-phase microextraction, and its application to environmental and biological samples. Microchimica Acta, 176, 1–22. Heshmati, A., Ghadimi, S., Khaneghah, A. M., Barba, F. J., Lorenzo, J. M., Nazemi, F., & Fakhri, Y. (2018). Risk assessment of benzene in food samples of Iran’s market. Food and Chemical Toxicology, 114, 278–284. JETRO. (2011). Specifications and standards for foods, food additives, etc. under the food sanitation act (abstract) 2010. (pp. 133). Ju, H. K., Park, J. H., & Kwon, S. W. (2008). Evaluation of headspace-gas chromatography/mass spectrometry for the analysis of benzene in vitamin C drinks; pitfalls of headspace in benzene detection. Biomedical Chromatography, 22, 900–905. Kaykhaii, M., Hosseinbor, Z., & Ghasemi, E. (2016). Comparison of headspace-single drop microextraction and dispersive liquid-liquid microextraction for determination of benzene in juice drinks containing vitamin C. Chromatographia, 79, 781–785. Khajeh, M., & Zadeh, F. M. (2012). Response surface modeling of ultrasound-assisted dispersive liquid–liquid microextraction for determination of benzene, toluene and xylenes in water samples: Box-Behnken design. Bulletin of Environmental Contamination and Toxicology, 89, 38–43. Kim, E., Park, S., & Choi, D. (2007). Analysis of Benzene in beverages by headspace-GC/ MS. Journal of Food Hygiene and Safety, 22, 243–247. Lachenmeier, D. W., Kuballa, T., Reusch, H., Sproll, C., Kersting, M., & Alexy, U. (2010). Benzene in infant carrot juice: Further insight into formation mechanism and risk assessment including consumption data from the DONALD study. Food and Chemical Toxicology, 48, 291–297. Lachenmeier, D. W., Reusch, H., Sproll, C., Schoeberl, K., & Kuballa, T. (2008). Occurrence of benzene as a heat-induced contaminant of carrot juice for babies in a general survey of beverages. Food Additives and Contaminants, 25, 1216–1224. Lau, O.-W., & Wong, S.-K. (2000). Contamination in food from packaging material. Journal of Chromatography A, 882, 255–270. Loch, C., Reusch, H., Ruge, I., Godelmann, R., Pflaum, T., Kuballa, T., ... Lachenmeier, D. W. (2016). Benzaldehyde in cherry flavour as a precursor of benzene formation in beverages. Food Chemistry, 206, 74–77. Medeiros Vinci, R., De Meulenaer, B., Andjelkovic, M., Canfyn, M., Van Overmeire, I., & Van Loco, J. (2011). Factors influencing benzene formation from the decarboxylation of benzoate in liquid model systems. Journal of Agricultural and Food Chemistry, 59, 12975–12981. Nyman, P. J., Diachenko, G. W., Perfetti, G. A., McNeal, T. P., Hiatt, M. H., & Morehouse, K. M. (2007). Survey results of benzene in soft drinks and other beverages by headspace gas chromatography/mass spectrometry. Journal of Agricultural and Food Chemistry, 56, 571–576. Nyman, P. J., Wamer, W. G., Begley, T. H., Diachenko, G. W., & Perfetti, G. A. (2010). Evaluation of accelerated UV and thermal testing for benzene formation in beverages containing benzoate and ascorbic acid. Journal of Food Science, 75, C263–C267. Pinto, M. I., Sontag, G., Bernardino, R., & Noronha, J. (2010). Pesticides in water and the performance of the liquid-phase microextraction based techniques. A review. Microchemical Journal, 96, 225–237. Preu, M., Guyot, D., & Petz, M. (1998). Development of a gas chromatography–mass spectrometry method for the analysis of aminoglycoside antibiotics using experimental design for the optimisation of the derivatisation reactions. Journal of Chromatography A, 818, 95–108. Rahmani, M., Kaykhaii, M., Ghasemi, E., & Tahernejad, M. (2015). Application of insyringe dispersive liquid–liquid microextraction and narrow-bore tube dispersive liquid–liquid microextraction for the determination of trace amounts of BTEX in water samples. Journal of Chromatographic Science, 53, 1210–1216. Techakriengkrai, I., & Lertborwornwong, C. (2013). The analysis of benzene contaminant in Thai commercial non-alcoholic beverages by headspace gas chromatography mass spectrometry. International Food Research Journal, 20, 1883–1887. Van Poucke, C., Detavernier, C.l., Van Bocxlaer, J. F., Vermeylen, R., & Van Peteghem, C. (2008). Monitoring the benzene contents in soft drinks using headspace gas chromatography−mass spectrometry: A survey of the situation on the belgian market. Journal of Agricultural and Food Chemistry, 56, 4504–4510. Vinci, R. M., Canfyn, M., De Meulenaer, B., de Schaetzen, T., Van Overmeire, I., De Beer, J., & Van Loco, J. (2010). Determination of benzene in different food matrices by distillation and isotope dilution HS-GC/MS. Analytica Chimica Acta, 672, 124–129. Wang, Y., Kwok, Y. C., He, Y., & Lee, H. K. (1998). Application of dynamic liquid-phase microextraction to the analysis of chlorobenzenes in water by using a conventional microsyringe. Analytical Chemistry, 70, 4610–4614. WHO. (2005). Vitamin and mineral requirements in human nutrition (2nd ed.). WHO. (2011). Guidelines for drining-water quality (4th ed.). Yang, Q., Guo, Y., Wang, L., Liang, S., & Liu, X. (2010). Simultaneous determination of trace benzene and toluene in beverage by ultrasound-enhanced hollow-fiber liquidphase microextraction coupled with GC. Chromatographia, 72, 1157–1161.
Conflict of interest None. Acknowledgement This research was supported by a grant (17162MFDS037) from the Ministry of Food and Drug Safety, Korea, in 2017. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.04.089. References Arisseto, A. P., Vicente, E., Furlani, R. P. Z., Pereira, A. L. D., & de Figueiredo Toledo, M. C. (2013). Development of a headspace-solid phase microextraction-gas chromatography/mass spectrometry (HS-SPME-GC/MS) method for the determination of benzene in soft drinks. Food Analytical Methods, 6, 1379–1387. Asensio-Ramos, M., Ravelo-Pérez, L. M., González-Curbelo, M.Á., & Hernández-Borges, J. (2011). Liquid phase microextraction applications in food analysis. Journal of Chromatography A, 1218, 7415–7437. Baan, R., Grosse, Y., Straif, K., Secretan, B., El Ghissassi, F., Bouvard, V., ... Galichet, L. (2009). A review of human carcinogens—Part F: Chemical agents and related occupations. The Lancet Oncology, 10, 1143–1144. Burdock, G. A. (2016). Fenaroli’s handbook of flavor ingredients. CRC Press. Candioti, L. V., De Zan, M. M., Cámara, M. S., & Goicoechea, H. C. (2014). Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta, 124, 123–138. Cao, X.-L., & Casey, V. (2008). Improved method for the determination of benzene in soft drinks at sub-ppb levels. Food Additives and Contaminants, 25, 401–405. Cao, X.-L., Casey, V., Seaman, S., Tague, B., & Becalski, A. (2007). Determination of benzene in soft drinks and other beverages by isotope dilution headspace gas chromatography/mass spectrometry. Journal of AOAC International, 90, 479–484. Casado, F. J., Sánchez, A. H., De Castro, A., Rejano, L., Beato, V.c. M., & Montano, A. (2011). Fermented vegetables containing benzoic and ascorbic acids as additives: Benzene formation during storage and impact of additives on quality parameters. Journal of Agricultural and Food Chemistry, 59, 2403–2409. Cavalcante, R. M., de Andrade, M. V., Marins, R. V., & Oliveira, L. D. (2010). Development of a headspace-gas chromatography (HS-GC-PID-FID) method for the determination of VOCs in environmental aqueous matrices: Optimization, verification and elimination of matrix effect and VOC distribution on the Fortaleza Coast, Brazil. Microchemical Journal, 96, 337–343. Cheng, C., Liu, S. R., Mueller, B. J., & Yan, Z. M. (2010). A generic static headspace gas chromatography method for determination of residual solvents in drug substance. Journal of Chromatography A, 1217, 6413–6421. Choi, M.-K., Kang, M.-H., & Kim, M.-H. (2009). The analysis of copper, selenium, and molybdenum contents in frequently consumed foods and an estimation of their daily intake in Korean adults. Biological Trace Element Research, 128, 104–117. Demeestere, K., Dewulf, J., De Witte, B., & Van Langenhove, H. (2007). Sample preparation for the analysis of volatile organic compounds in air and water matrices. Journal of Chromatography A, 1153, 130–144. Gardner, L. K., & Lawrence, G. D. (1993). Benzene production from decarboxylation of benzoic acid in the presence of ascorbic acid and a transition-metal catalyst. Journal of Agricultural and Food Chemistry, 41, 693–695. Giraldez, I., Chaguaceda, E., Bujalance, M., & Morales, E. (2013). Determination of five booster biocides in seawater by stir bar sorptive extraction-thermal desorption-gas chromatography-mass spectrometry. Journal of Chromatography A, 1271, 17–26.
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Analytical Methods
Headspace conditions and ingredients can affect artefactual benzene formation in beverages
T
Eun Mi Kima, Dan Ah Kimb, Sung Won Kwonb, Yan Jina, Heesoo Leea, Seulgi Kanga, ⁎ Jeongmi Leea, a b
School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi 16419, Republic of Korea College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Benzene monitoring Artefactual formation Beverage Headspace conditions Ingredients
A headspace sampling-gas chromatography/mass spectrometry (HS-GC/MS) method using mild HS conditions (40 °C, 30 min) was established, validated in terms of specificity, linearity (1.75–87.65 ng mL−1), precision (0.3–9.1% RSD), and accuracy (81.1–117.7%); and applied for the monitoring of 900 commercial beverage samples of six different types. These mild (low-temperature) conditions were compared with 1) optimized (hightemperature) conditions and 2) a liquid-phase microextraction method involving no heat treatment. This method was desirable because a high equilibrium temperature induced artefactual benzene formation from benzoate and ascorbic acid. In a 28IV− 3 fractional factorial design, eight variables—ascorbic acid, benzoate, benzaldehyde, Cu2+, Fe2+, riboflavin, pyridoxine, and heat treatment—were tested as potential factors affecting benzene formation. All variables except Fe2+ and pyridoxine significantly affected benzene formation, both individually and interactively. The present study suggests an accurate and reliable method for benzene analysis and provides strategies to prevent unintentional benzene formation in beverages.
1. Introduction Benzene is a volatile organic compound (VOC) that has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) (Baan et al., 2009). Benzene is a food contaminant, and its maximum allowable level is strictly regulated in many countries; for example, the regulation limit for drinking water is 1–10 μg L−1 (WHO, 2011). Benzene can be unintentionally introduced into foods and beverages from external sources including contaminated environment and packaging materials (Lau & Wong, 2000; Van Poucke, Detavernier, Van Bocxlaer, Vermeylen, & Van Peteghem, 2008). It can also be formed intrinsically during food processing and storage (Casado et al., 2011). Benzoate, a common preservative, can be decarboxylated to benzene in the presence of ascorbic acid (Loch et al., 2016). Benzoate can also undergo oxidative decarboxylation in the presence of hydroxyl radicals (Medeiros Vinci et al., 2011). Production of these radicals is promoted when ascorbic acid is present at low pH or when transition metals such as copper and iron ions are present (Gardner & Lawrence, 1993). Benzene can also be produced from common food ingredients and flavor additives. For example, β-carotene and phenylalanine,
⁎
natural components of carrot juice, may lead to benzene formation during the heating process (Lachenmeier et al., 2010), and benzaldehyde, a cherry-flavoring additive, has been suggested to be a precursor of benzene in the presence of ascorbic acid (Loch et al., 2016). External conditions such as high temperatures and ultraviolet radiation can accelerate benzene formation in beverages under certain circumstances (Nyman, Wamer, Begley, Diachenko, & Perfetti, 2010). The concentrations of benzene, sodium benzoate, and ascorbic acid were significantly correlated in a recent study (Heshmati et al., 2018). More detailed studies with well-designed models are still needed to determine how various beverage components individually and collectively affect benzene formation (Medeiros Vinci et al., 2011). The benzene concentrations in beverages are determined by gas chromatography coupled to mass spectrometry (GC/MS) and is preceded by a sampling method that is either solvent extraction-based (liquid-liquid extraction and liquid-phase microextraction, LPME) (Kaykhaii, Hosseinbor, & Ghasemi, 2016; Khajeh & Zadeh, 2012; Rahmani, Kaykhaii, Ghasemi, & Tahernejad, 2015; Yang, Guo, Wang, Liang, & Liu, 2010) or headspace-based (static headspace or headspacedynamic) (Arisseto, Vicente, Furlani, Pereira, & de Figueiredo Toledo, 2013; Cao & Casey, 2008; Cao, Casey, Seaman, Tague, & Becalski, 2007;
Corresponding author. E-mail address: [email protected] (J. Lee).
https://doi.org/10.1016/j.foodchem.2019.04.089 Received 12 July 2018; Received in revised form 3 April 2019; Accepted 24 April 2019 Available online 25 April 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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from LPME.
Nyman et al., 2007). Due to its simplicity with reasonable sensitivity, headspace sampling coupled to GC/MS (HS-GC/MS) is most commonly used, especially for monitoring a large number of samples (Ju, Park, & Kwon, 2008; Techakriengkrai & Lertborwornwong, 2013; Van Poucke et al., 2008). Various equilibrium temperatures have been adopted for HS in previous benzene monitoring studies, including 70 °C (Van Poucke et al., 2008), 80 °C (Techakriengkrai & Lertborwornwong, 2013), and > 100 °C (Lachenmeier, Reusch, Sproll, Schoeberl, & Kuballa, 2008; Loch et al., 2016). Although high temperatures are generally preferred for HS to maximize the equilibrium of the analytes in the headspace (Cavalcante, de Andrade, Marins, & Oliveira, 2010), concerns have been raised about false-positive detection in HS-based methodology for benzene analysis. Indeed, artefactual benzene formation has been detected at high equilibrium temperatures (Cao et al., 2007; Ju et al., 2008; Lachenmeier et al., 2008). Ju et al. (2008) reported the false-positive determination of benzene at 80 °C in samples containing benzoate and ascorbic acid. Lachenmeier et al. (2008) employed a full-factorial experimental design to assess the effects of headspace conditions and suggested using a low headspace temperature (e.g., 50 °C) and basifying the sample pH (e.g., pH 10) to suppress artefactual benzene formation from benzoate. High sample pH has also been used in other studies (Medeiros Vinci et al., 2011; Vinci et al., 2010). The aims of the current study were to identify an analytical method that could minimize the risks of overestimation or false-positive quantification of benzene during analysis and to investigate the effects of beverage ingredients and heating on benzene formation. To this end, an HS-GC/MS method using mild (low-temperature) HS conditions was established, validated for specificity, linearity, precision, and accuracy and then applied for monitoring of 900 commercial beverage samples. Artefactual benzene formation due to heat treatment during HS was determined through comparison of the suggested HS method with two other methods: an HS method involving a high headspace temperature and an LPME method involving no heat treatment. Secondly, a systematic investigation with a fractional factorial design (FFD) was conducted on seven ingredients and different heat treatments.
2.2. Preparation of standard solutions and sample solutions Stock solutions of benzene and benzene‑d6 (internal standard; IS) were prepared in methanol at 175.3 µg mL−1 and 190.0 µg mL−1, respectively, and stored at −40 °C. Fresh working solutions were prepared every 2 h through dilution of the stock solutions in methanol. For headspace analysis, 5 mL of standard or sample solution was transferred to a 22-mL vial with a PTFE/butyl septum screw cap (Perkin-Elmer, Norwalk, CT, USA) and spiked with 19.0 ng mL−1 of IS. A series of standard solutions was prepared to contain benzene in the range of 1.75–87.7 ng mL−1 in water. 2.3. Instrumental conditions and validation of the headspace sampling-gas chromatography/mass spectrometry method The HS-GC/MS analysis was performed with a Perkin-Elmer Turbomatrix 40 headspace sampler (Perkin-Elmer) and an Agilent 7890B gas chromatograph (Santa Clara, CA, USA) equipped with a mass-selective detector (Agilent 5977A). Mass Hunter software was used for system operation and data management. A temperature of 85 °C was used for the headspace needle and transfer line. The sample was equilibrated in the oven of the headspace autosampler at 40 °C for 30 min for the mild conditions or at 75 °C for 34 min for the optimized conditions. Thereafter, 2000 µL of sample headspace was injected into the GC/MS system. For real sample analysis, a calibration curve was established with a series of freshly prepared standard solutions on each day. The injected compounds were separated on a DB-624 column (30 m × 0.32 mm I.D., 1.80 µm film thickness) supplied by J & W Scientific (Folsom, CA, USA). Ultra-pure helium (purity 99.999%) was used as the carrier gas at a flow rate of 1.0 mL min−1. The injection port was held at 200 °C and used in split mode (split ratio 10:1). The GC oven temperature was held at 40 °C for 3 min and increased to 200 °C at 10 °C min−1. The chromatogram was recorded for 19 min, and the retention time for benzene was 4.65 min. The ion source and quadrupole temperatures were set at 230 °C and 150 °C, respectively. The mass spectrometer was operated in selected ion monitoring (SIM) mode, recording mass-to-charge ratios (m/z) of 78, 77, and 51 for benzene and 84 for benzene‑d6. The established HS-GC/MS method was validated in terms of selectivity, linearity, limit of quantification (LOQ), precision, and accuracy according to AOAC guidelines. The method selectivity was assessed in six different types of beverages, based on identification of analyte peaks with m/z of 78, 77, and 51 with no interfering peaks around them. The IS peak was monitored at m/z 84. The LOQ of the analytical method was calculated from the standard deviation (σ) of the signal obtained by quintuplicate analysis of samples at 1.75 ng mL−1 and the slope (S) of the calibration curve by the following equation:
2. Materials and methods 2.1. Reagents, standards, samples, and equipment Analytical standards such as benzene (99.8%) and benzene‑d6 (≥99%, 99.6 atom % D; internal standard) were purchased from Sigma-Aldrich (St. Louis, MO, USA). L-Ascorbic acid (≥98%), sodium benzoate (99%), benzaldehyde (≥99%), copper (II) sulfate (≥99%), iron (II) sulfate heptahydrate (≥99%), riboflavin (97.9%), pyridoxine hydrochloride (100%), tetrachloroethylene (≥99.9%), and potassium hydroxide (≥85%) were also obtained from Sigma-Aldrich. HPLCgrade water, acetonitrile, and methanol were purchased from Burdick & Jackson (Muskegon, MI, USA). All the reagents were of analytical grade or higher and were obtained from Sigma-Aldrich unless stated. In total, 300 items of non-alcoholic beverages were collected from 10 local supermarkets in Seoul and Suwon, Korea, resulting in collection of most of the beverages marketed in Korea. For each item, three samples with the same lot number or with the same manufacturing date within the same manufacturer were analyzed. A total of 900 commercial samples belonged to one of six categories: fruit/vegetable juice (78 items), soft drinks (75 items), ginseng-containing beverages (38 items), soybean milk (38 items), fermented drinks (26 items), and others (45 items). The number of items for each category was determined in proportion to the sales and market share in Korea. A 1580 MGR (Gyrozen, Incheon, Korea) was used for centrifugation, and a water bath (Lab House, Pochen, Korea) was used to control the sample temperature when needed. A 50-µL Hamilton syringe (Reno, NV, USA) was used to measure the volume of the sedimented phase
LOQ =
σ ∗ 10 S
The calibration curve was established from seven concentration points (1.75, 4.38, 8.77, 21.91, 43.83, 65.74, and 87.65 ng mL−1). Accuracy and precision were measured in triplicate at three concentrations (1.75, 43.83, and 87.65 ng mL−1) in six types of non-alcoholic beverages. Precision was assessed as % relative standard deviation (RSD) of repeated analyses. The accuracy was estimated as % relative recovery, which was calculated as the ratio of experimental concentration to theoretical concentration of spiked sample. 2.4. Procedures and validation of liquid-phase microextraction of sample solutions Aqueous solutions containing ascorbic acid (5000 µg mL−1) and/or 279
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sodium benzoate (600 µg mL−1) were freshly made. The solutions were either instantly used or incubated at room temperature for 30 min and subjected to the following LPME method. A 5-mL sample solution was adjusted to pH 10.0 with a 10% w/w potassium hydroxide solution and spiked with 10 µL of the IS at 95.00 ng mL−1. Then, 20 µL of tetrachloroethylene (extraction solvent) was rapidly injected into the solution, and the mixture was manually shaken for 20 s (∼80 repetitions) and centrifuged at 2898g for 8 min at room temperature. The sedimented liquid phase (∼13 µL) was manually withdrawn with a Hamilton syringe, and 1 µL of it was injected into the GC/MS. The GC/ MS operation conditions including the injection port temperature and split ratio were the same as those for the HS method, except for the use of a tapered liner (4 mm i.d., Agilent) and the column temperature programming, which was as follows: held for 3 min at 40 °C, increased at 10 °C min−1 to 100 °C and then increased at 30 °C min−1 to 200 °C. A series of aqueous standard solutions of benzene was used to validate the linearity, precision, and accuracy of the established LPME method. The linearity was assessed from six concentration points (4.38, 8.77, 21.91, 43.83, 65.74, and 87.65 ng mL−1) in triplicate experiments. The precision and accuracy values were determined from quintuplicate experiments at three concentration levels (4.38, 43.83, and 87.65 ng mL−1).
3. Results and discussion 3.1. Establishment and validation of the HS-GC/MS method for benzene analysis The experimental conditions for the GC/MS analysis were established from methods in the literature (Arisseto et al., 2013; Kim, Park, & Choi, 2007; Lachenmeier et al., 2008; Van Poucke et al., 2008). The GC/MS conditions are described in the experimental section. In general, HS conditions can be optimized because various factors influence sensitivity and efficiency of a method including oven temperature, equilibration time, and vial-to-sample volume ratio (Cavalcante et al., 2010; Cheng, Liu, Mueller, & Yan, 2010). The vial-to-sample volume ratio was set to 22:5 (mL), as reasonably high peak areas of benzene could be acquired with precision on our instrumental system at this ratio (data not shown). For headspace equilibration, a high oven temperature with a long equilibration time is generally preferred because it can enhance the peak areas of analytes; however, in the present study, the lowest possible temperature was adopted in consideration of the potential risk of artefactual benzene formation during analysis. In our system, an oven temperature of 40 °C was the lowest operational temperature that allowed reproducible and reasonable efficiency when the equilibration time was maintained for no less than 30 min, consistent with the report by Ju and colleagues (Ju et al., 2008). As a result, the established HS conditions involved equilibration at 40 °C for 30 min, as described in Section 2.3. The HS-GC/MS method was validated for six different types of beverages. The method was selective for benzene in all types of beverages (Fig. S1). No significant matrix effects were observed for real samples with different matrices, given that the peak area ratios of benzene spiked in real samples versus water were close to 100% (data not shown). As a result, the method linearity was assessed with a calibration curve established from aqueous standard solutions. The method was linear in the range of 1.75–87.65 ng mL−1, with a regression equation of Y = 0.05443X + 0.03278 (r2 = 0.9986). Although the calculated LOQ value was 0.55 ng mL−1, it was not included as the starting concentration in the calibration range, because the signal-tonoise ratio was sufficiently large above 1.75 ng mL−1, with RSD ≤ 15%. The method precision and accuracy were reasonable for all the beverage types at the three tested concentration levels; the intraand inter-day precisions values were 0.34–9.82% RSD, and the accuracy values were 81.1–117.7%. The measured validation parameters are summarized in Table 1. All the validation results met the criteria required by the AOAC guidelines, supporting the validity of the current method. The quantification limit of the present method was low to moderate compared to similar HS-GC/MS methods, largely due to the low equilibrium temperature for HS (Arisseto et al., 2013; Cao & Casey, 2008). Further modifications of HS, GC or MS methods, such as coupling solid-
2.5. Experimental design and statistical analysis The experimental design was performed using Design-Expert 8 (Stat-Ease, Minneapolis, MN, USA). Response surface methodology (RSM) based on a central composite design (CCD) was used to optimize the headspace conditions to yield the highest benzene peak areas. In this design, the benzene peak area was used as the response, while the input variables were equilibrium temperature (X1), equilibration time (X2), and sample volume (X3). The benzene concentration for CCD experiments was 43.83 ng mL−1. The experimental ranges of the variables at five different levels (-1.68, −1, 0, +1, +1.68) were X1, 35–85 °C; X2, 10–40 min; and X3, 3–7 mL. As displayed in Table S1, the design matrix was composed of two blocks. Experiments were run in a random order per block, and each block included three center points. The total number of experiments was 20 (N = 2 k + 2 k + Cp; N, number of experiments; k, number of variables; Cp, number of center points). The effects of the manufacturing process and components on benzene formation were investigated through a two-level FFD (28IV− 3 ), in which the peak area ratio of benzene to the IS was used as the response. Eight factors—ascorbic acid (A), benzoate (B), benzaldehyde (C), Cu2+ (D), Fe2+ (E), riboflavin (F), pyridoxine (G), and temperature (H)—were used as variables in the FFD. Forty experiments (N = 2 k (k − 1) + Cp) were conducted in two blocks containing four center points each. The results of the CCD and FFD experiments were evaluated by analysis of variance (ANOVA) in Design-Expert 8. Student’s ttest was performed using Prism 6.0 (GraphPad Software, Inc. USA). Table 1 Precisions and accuracies of the established HS-GC/MS method. Matrix
Precision (% RSD)
Accuracy (%)
Intra-day (n = 5)
Orange Juice Mixed beverage Ginseng beverage Fermented drink Soybean milk Soft drink a b c
Inter-day (n = 3 × 3)
Lowa
Middleb
Highc
Lowa
Middleb
Highc
Lowa
Middleb
Highc
0.89 9.82 4.50 3.75 2.13 4.60
1.61 3.08 0.89 0.34 1.28 3.58
0.68 1.58 2.40 0.69 1.27 1.23
5.08 6.57 9.09 4.96 4.31 3.18
5.02 4.16 5.39 4.45 3.84 2.18
2.93 3.86 4.62 3.20 2.24 2.28
87.7 98.3 99.4 110.5 116.4 108.9
81.1 96.7 97.5 113.0 107.6 105.0
96.4 100.2 98.9 117.7 112.8 110.4
1.75 ng mL−1. 43.83 ng mL−1. 87.65 ng mL−1. 280
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In the case of ascorbic acid and benzaldehyde, their coexistence resulted in benzene formation under both HS conditions: 3.48 ± 0.57 ng mL−1 and 28.31 ± 24.81 ng mL−1 under the mild and optimized conditions, respectively, and these values were significantly different (p < 0.05). Unexpectedly, at 75 °C, a wide range of benzene levels was obtained (4.98–77.50 ng mL−1). Each sample was freshly prepared and immediately subjected to HS in all experiments, leaving no incubation time for the mixture. Furthermore, the results of the benzoate and ascorbic acid experiments were consistent. Therefore, the wide range of benzene levels formed from benzaldehyde and ascorbic acid is not likely to have resulted from imprecisely performed experiments. In the study by Loch et al., in which HS was conducted at 50 °C for 30 min, benzene formed from benzaldehyde in a temperature- and time-dependent manner, and benzene formation was expedited in the presence of ascorbic acid (Loch et al., 2016). In that study, incubation temperature and time ranged from 20 to 100 °C and 2–24 h, respectively, but it was not discernable whether benzene formed during incubation or during headspace equilibration. Our results indicate that the chemistry of benzene formation from benzaldehyde and ascorbic acid is affected by a number of factors, including temperature and time. The reaction kinetics appear to be very temperature-dependent and to differ from those of benzoate and ascorbic acid. Further detailed study will be needed to elucidate the reaction mechanism and kinetics for benzene formation from various precursors. Our results demonstrate that a high temperature for headspace equilibration could cause artefactual benzene formation in the presence of certain precursors. Therefore, the HS conditions for benzene analysis should be selected carefully. The mild HS condition of 40 °C for 30 min is suggested as a practical and reasonable method for benzene monitoring in beverages.
phase microextraction to HS (Arisseto et al., 2013), applying cryofocusing in the injector liner (Van Poucke et al., 2008), and employing an isotope dilution method (Cao & Casey, 2008), could enhance the sensitivity to sub-ppb levels. Our analytical method is still useful for benzene monitoring in several ways. The quantification results can be considered reliable due to the minimized risk of false-positive detection of benzene. The sample preparation step is minimal, excluding manual operations such as sample pH adjustment (Lachenmeier et al., 2008). Most of all, this method can be readily applied to a conventional HSGC/MS platform. The compromised sensitivity in the sample introduction (HS) step could be overcome in the GC/MS step through adoption of GC/MS-MS, although this was not attempted in the present study due to lack of instrumentation. The current method would be most useful for monitoring a large number of samples for regulatory purposes. 3.2. Comparison of mild HS conditions with optimized HS conditions to examine the effects of headspace conditions on artefactual benzene formation Although the HS conditions can be optimized to maximize the equilibrium of the analytes in the headspace (Grodowska & Parczewski, 2013), these conditions can lead to artefactual benzene formation in beverages. In the present study, the HS conditions were systematically optimized for high sensitivity, and the optimized conditions were compared with mild conditions so that their effects on the artefactual benzene formation could be examined. Three variables—equilibrium temperature (X1), equilibration time (X2), and sample volume (X3)—were selected and subjected to RSM based on a CCD. The quality of the resulting model was evaluated by ANOVA, and the figures of merit are summarized in Table S2. The model, which was statistically significant (p < 0.0001), could be described by the following equation in coded units:
3.3. Comparison of the mild HS method with a non-heating LPME method to examine the effects of headspace conditions on artefactual benzene formation
Y = 75702 + 20059.13X1 + 843.88X2 + 9969.06X3
Two HS conditions were compared above to explore the effects of heat treatment on benzene formation. In fact, heat treatment is practically unavoidable during HS. An orthogonal sample preparation method involving no heat treatment for benzene analysis could corroborate the assessment results on the potential effect of heating. In this sense, a rapid sample preparation method was established based on LPME, allowing simultaneous extraction and pre-concentration without any sample heating (Asensio-Ramos, Ravelo-Pérez, González-Curbelo, & Hernández-Borges, 2011; Han & Row, 2012; Pinto, Sontag, Bernardino, & Noronha, 2010), and this method was compared with the mild HS conditions. The new LPME method described in Section 2.4, was established through the review of LPME-based studies on the analysis of benzene or related VOCs in drinking water (Demeestere, Dewulf, De Witte, & Van Langenhove, 2007; Wang, Kwok, He, & Lee, 1998). This method involved manual shaking-assisted dispersive liquid-liquid microextraction, in which the dispersive solvent was replaced by manual shaking to disperse the extraction solvent, tetrachloroethylene. An aqueous solution was prepared with either benzene or sodium benzoate (600 µg mL−1) ± ascorbic acid (5000 µg mL−1). Prior to its application to the LPME procedure, this solution was alkalized to pH 10.0 to prevent benzoate (pKa 4.08) and ascorbate (pKa 4.10) from migrating to the organic phase. Benzaldehyde could not be tested in the presence or absence of ascorbic acid by the current LPME method because it was technically impossible to selectively extract trace levels of benzene, if produced, while leaving a very high concentration of benzaldehyde in the aqueous phase. Aqueous benzene solutions were used to evaluate the enrichment factor (EF) and validation parameters of the method. The EF was calculated as the ratio of benzene concentration in the sediment phase (Csed) to initial benzene concentration (C0, 4.38 ng mL−1). The obtained
where Y is the peak area of benzene. According to the established model, the equilibrium temperature (X1) had the most significant effect on the response in a positive direction, followed by the sample volume (X3), while the equilibration time (X2) was not significant (Table S2). The resulting optimized HS conditions were used to equilibrate a 6-mL sample at 75 °C for 34 min, and the results reproduced at the optimized conditions (9.9 × 104 ± 1.8 × 103, n = 5) were very close to the predicted values (9.2 × 104 –1.2 × 105). The optimized HS conditions involved a high equilibrium temperature (75 °C) as expected, which could lead to artefactual benzene formation during equilibration (Lachenmeier et al., 2008; Loch et al., 2016). The optimized conditions were compared with mild conditions (equilibration at 40 °C for 30 min). Test samples containing sodium benzoate or benzaldehyde were prepared with and without ascorbic acid in water, as in previous studies that reported the possible formation of benzene from these compounds (Ju et al., 2008; Loch et al., 2016; Nyman et al., 2010). The tested ascorbic acid concentration, 5000 µg mL−1, was adopted from ordinary vitamin C drinks marketed in Korea, while the sodium benzoate concentration was set to 600 µg mL−1 in accordance with standards and specifications for food additives (JETRO, 2011). Benzaldehyde was tested at 75 µg mL−1, the maximum value added to non-alcoholic beverages (Burdock, 2016). The results of the comparative experiments under the two different HS conditions are summarized in Table 2. No benzene was detected under any HS conditions when each compound was solely present. The mixture of ascorbic acid and benzoate yielded no benzene under mild conditions; however, it generated benzene at 16.03 ± 0.12 ng mL−1 under the optimized conditions. These results imply that artefactual benzene was formed from the two compounds during high-temperature equilibration, consistent with previous reports (Ju et al., 2008; Loch et al., 2016; Nyman et al., 2010). 281
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Table 2 Effects of the headspace equilibrium conditions on the artefactual benzene formation during analysis. Measured benzene concentration (ng mL−1 ± SDa)
HS conditions LPME
Component(s) added to aqueous solution
None
AA
Mild (40 °C, 30 min) Optimized (75 °C, 34 min)
– – –
– – –
c
b
SB – – –
b
BA
b
– – N/Ae
SB + AA
BA + AA
– 16.0 ± 0.1 –
3.5 ± 0.6 28.3 ± 24.8d N/A
Abbreviations: AA, ascorbic acid; BA, benzaldehyde; HS, headspace; LPME, liquid-phase microextraction; SB, sodium benzoate. a n = 3 except for BA + AA, for which n = 8. b AA, 5000 µg mL−1; SB, 600 µg mL−1; BA, 75 µg mL−1. c Not detected. d Range from repeated analysis is 5.0–77.5 ng mL−1 (n = 8). Significantly different (p < 0.05) in comparison to those obtained under the mild conditions. e Not applicable to LPME.
ascorbic acid (A), sodium benzoate (B), benzaldehyde (C), Cu2+ (D), Fe2+ (E), riboflavin (F), and pyridoxine (G). The first five are known or suggested precursors or catalysts of benzene formation. Ascorbic acid is often added as a main ingredient or additive, while sodium benzoate is a common preservative, and benzaldehyde is an additive for cherry flavor. Riboflavin and pyridoxine were included based on our own monitoring results as described above. Heat treatment (H) was also included in the design because it is an important manufacturing process and is well-known to affect benzene formation. The high and low levels tested for each variable are displayed in Table 3. The high levels (+1) of ascorbic acid, sodium benzoate, and benzaldehyde were the same as those used in Section 3.2. Cu2+ and Fe2+ were set at sufficiently high concentrations of 700 µg mL−1 and 40 µg mL−1, respectively (Choi, Kang, & Kim, 2009; Gardner & Lawrence, 1993; WHO, 2005). The riboflavin and pyridoxine concentrations were set to 14 µg mL−1 and 12 µg mL−1, respectively (WHO, 2005). The temperature range for the heat treatment was 25–95 °C, and the samples were incubated at the designated temperature for 20 min before being applied to HS. Due to the large number of variables, the FFD involved an eighth-fraction factorial design with resolution IV (28IV− 3 ), resulting in 40 experiments. In this type of design, main effects and two-factor interactions are confounded with threefactor interactions and other two-factor interactions, respectively (Candioti, De Zan, Cámara, & Goicoechea, 2014). However, the interaction effects among three or more variables are usually smaller than the main effects and two-factor interaction effects (Preu, Guyot, & Petz, 1998). The evaluation results from the FFD experiments are summarized in Table S3. The magnitude and direction of the influence of the tested variables are shown in a Pareto chart, in which the horizontal axis indicates the t-value of the effects in a positive (solid bar) or negative (open bar) direction (Fig. 1). Variables above the Bonferroni limit indicate significant effects, while those below the t-limit indicate nonsignificant effects. As shown in Fig. 1(a) and Table S3, the main effects of the significant variables were found in the following order: ascorbic
EF value was 210.7 ( ± 2.1, n = 3). The method was linear in the range of 4.38–87.65 ng mL−1 with a regression equation of Y = 0.009417X + 0.0217 (r2 = 0.9986). The intra-day precision values were 5.71, 5.06, and 5.70% RSD at 4.38, 43.83, and 87.65 ng mL−1, respectively, while the intra-day accuracy values were 87.4, 96.4, and 104.7% at 4.38, 43.83, and 87.65 ng mL−1, respectively. The LOQ value was calculated to be 3.34 ng mL−1. These results support the validity of the LPME method. Samples containing sodium benzoate and/or ascorbic acid were freshly prepared and instantly applied to LPME. As a result, no benzene was detected (Table 2). In addition, samples incubated at room temperature for 30 min prior to LPME analysis exhibited no benzene. It should be noted that the same samples analyzed by the optimized HS method were found to contain 16.03 ng mL−1 of benzene, while the samples analyzed by the mild HS method exhibited no benzene (Table 2). These results indicate that artefactual benzene was indeed formed from benzoate and ascorbic acid when a high equilibrium temperature was used during HS. The two orthogonal comparative studies to assess the effects of headspace conditions confirmed that our HS-GC/MS method with mild HS conditions was desirable for benzene analysis in a large number of beverage samples, with minimal risk of false-positive detection of benzene. 3.4. Monitoring of 900 commercial beverage samples using the established headspace sampling-gas chromatography/mass spectrometry method The established method with mild HS conditions was applied to the monitoring of 900 samples (3 samples per item × 300 items) from six types of beverages (fruit juice, soft drinks, ginseng beverages, soybean milk, fermented drinks, and others). Three items (two soft drinks and one ginseng-containing beverage; nine samples in total) were found to contain benzene. Two items from soft drinks (six samples) contained benzene in the range of 2.4–2.9 ng mL−1 and 2.7–2.9 ng mL−1 for each, and the use of ascorbic acid and sodium benzoate was claimed on their label. The third item was found to contain benzene at 3.8–5.8 ng mL−1. Its label claimed the inclusion of sodium benzoate, riboflavin, and pyridoxine but not ascorbic acid. The expanded uncertainty of the analytical method, which was estimated according to Giraldez, Chaguaceda, Bujalance, and Morales (2013), was ± 0.2 ng mL−1 for the detected benzene levels (2.4–5.8 ng mL−1). The entire analysis of all the samples could be conducted using the same column without diminishing performance.
Table 3 Factors and their levels of 28IV− 3 FFD. Variable
Ascorbic acid (µg mL−1) Sodium benzoate (µg mL−1) Benzaldehyde (µg mL−1) Copper (II) sulfate (µg mL−1) Iron (II) sulfate (µg mL−1) Riboflavin (µg mL−1) Pyridoxine (µg mL−1) Temperaturea (°C)
3.5. Influence of beverage ingredients and manufacturing processes on benzene formation Several studies have examined the effects of beverage ingredients on benzene formation, but none of them evaluated a large number of ingredients using an experimental design. In this study, a two-level FFD was employed as a screening design with the following compounds:
a
282
Incubation time: 20 min.
Level Low (−1)
High (+1)
0 0 0 0 0 0 0 25
5000 600 75 700 40 14 12 95
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Fig. 1. A Pareto chart displaying the individual (a) and interactive (b) effects of eight variables on benzene formation. A, ascorbic acid; B, sodium benzoate; C, benzaldehyde; D, Cu2+; E, Fe2+; F, riboflavin; G, pyridoxine; H, heat treatment. The solid line and dotted line indicate the Bonferroni limit and the t-value limit, respectively. Positive and negative effects are indicated by solid and open boxes, respectively.
acid (A) > benzaldehyde (C) > Cu2+ (D) > sodium benzoate (B) > riboflavin (F) > temperature (H). Two variables, Fe2+ (E) and pyridoxine (G), were between the Bonferroni and t-limits, indicating moderate, possibly significant effects on benzene formation. It is noteworthy that ascorbic acid, benzaldehyde, Cu2+, and sodium benzoate had positive effects (i.e., benzene formation increases with elevated levels of these additives), while riboflavin had negative effects on benzene formation. High temperature was significantly associated with elevated benzene level. These results imply that the levels of ascorbic acid, benzaldehyde, Cu2+, and sodium benzoate should be set low to minimize benzene formation in beverages. Important interactive effects among the variables were also detected in the FFD results (Fig. 1(b)). Ascorbic acid had significant interactive effects with benzaldehyde (AC), Cu2+ (AD), sodium benzoate (AB), and temperature (AH) in decreasing order. All the effects were positive, meaning that benzene formation increases if both variables change to high levels. Thus, to minimize benzene formation, these variables should not be set at high levels at the same time. While riboflavin had negative interactive effects with ascorbic acid (AF) and Cu2+ (DF), it had a positive interaction with sodium benzoate (BF). The co-presence of benzaldehyde and Cu2+ (CD), benzoate and riboflavin (BF), and
Cu2+ and Fe2+ (DE) also had significant positive effects on benzene formation. The screening experiments based on FFD provided important information on the individual and interactive effects of various ingredients that could affect benzene formation during manufacture or storage. A subsequent optimization study focused on several of the most significant variables could provide insight into more detailed and quantitative information on these variables. 4. Conclusions An analytical method with mild HS conditions was established and proven valid for linearity, LOQ, specificity, precision, and accuracy. Analysis of mixed solutions of ascorbic acid and sodium benzoate with three different methods suggests that high temperature equilibration could induce artefactual benzene formation. Therefore, our method is desirable for benzene monitoring of beverages, with minimal risk of false-positive detection. Monitoring of 900 beverage samples revealed that the presence of ascorbic acid, sodium benzoate, pyridoxine, or riboflavin might be associated with benzene formation. In the 28IV− 3 FFD experiments, ascorbic acid exhibited significant effects on benzene 283
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formation solely and with various components including benzaldehyde, Cu2+, sodium benzoate, and temperature. In summary, this study is the first comprehensive and systematic investigation of the effects of headspace conditions and ingredients on artefactual benzene formation. It not only suggests a desirable method for accurately and reliably monitoring benzene in beverages, but also provides strategies to prevent unintentional benzene formation in beverages during manufacture and/or storage.
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