Abstract
Acrylic acid (AA) chemical that endangers human health through contamination of water, soil, and foods. In this study, the extraction, purification, and detection of AA in various food products were established. The contamination level of AA in food products was investigated as well. Food matrices that were used for method validation were crop, fruit, vegetable, seaweed, beverage, sauce, paste, and pickled food, where the validation was confirmed through cross-checking between two different laboratories by checking the accuracy and precision. Furthermore, sample volume for analysis was optimized. Sonication and syringe filtering were all steps preparing headspace analyzer (HA) before GC-MS analysis. Linearity (R2), limit of detection (LOD), limit of quantitation (LOQ), accuracy and precision of AA, were > 0.99, 0.06–0.17 mg/L, 0.18–0.52 mg/L, 90.97–111.93% and 0.12–9.61 RSD% of intra, inter-day, respectively. White rice sample was the only one sample where AA detected (6.19 mg/L) among 102 food samples.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10068-022-01131-x.
Keywords: Acrylic acid, Food, GC-MS, Headspace analyzer, Chemical contaminant
Introduction
With the technological advances, new products are being researched and developed to be used in manufacturing various products, including food-related items, such as packaging for food. New merchandise may result in an increase in the risk of contamination in foods by residual chemicals from the products. There are possible pathways for hazardous chemicals that could get into the foods, for instance, acrylic acid monomers from the plastic package were found to be migrated to packaged food with the elevation of the temperatures (Franz and Brandsch, 2012). Furthermore, various chemicals might get into the environment, such as waterways, air, and soil (Chanda and Mehendale, 2005).
AA is an organic compound that is a colorless liquid at room temperature with a distinctive scent, which is readily used in the production of various compounds, such as plastics, coatings, and paints (Brown, 2014). As the demand for AA had gradually increased with the increase in utilization in manufacturing various products, environmental pollution by AA from wastes or leakage has become a critical concern. During AA production, the monomer could be released into the environment through polluted wastewater from factories, which was expected to be 1.2 tons per ton of AA produced (Khan et al., 2020). Leaked AA monomers in wastewater are expected to contaminate agricultural products in ways, such as irrigation or rain from the contaminated water source that modifies the nature of the soil and pollutes crops, which poses a health risk to humans through the consumption of contaminated crops (Khalid et al., 2018). Furthermore, accidents involving AA, such as AA-induced runaway polymerization that results in facility explosions or overheating (Fujita et al., 2019) and leakage of AA (Jung, 2013) could result in irreversible environmental damage. Moreover, there was special training by national fire agents and members of the factory in 2019 (Jeon, 2019), where the training was to educate and prevent leakage of AA. As the examples suggested, environmental pollution involving AA is expected to be critical, as well as to humans. Regarding the toxicity, according to the journal written by Hellwig and fellow researchers (1993), it was found that oral administration of AA to rats resulted in non-carcinogenic effects to test subjects, such as weight loss by the reduction in water and food consumption, where test results on humans were not available at the current point. There were no reports regarding AA contamination in food so far, however, there are multiple studies that suggested that AA could be presented in food. For instance, it was found that AA could be formed from the oxidation of acrolein due to the Maillard reaction or decomposition of amino acids (Youssef et al., 2004). Despite the toxicity and presence of AA in food, there was a lack of a rapid method to detect AA from food to prevent affecting the consumers.
Previous studies provided various analytical methods to detect AAs from multiple samples. For instance, ultra-performance liquid chromatography with mass spectrometry (UPLC/MS) was used to detect the AA concentration in tap water (Zhang et al., 2019). On average, most methods involve complicated and time-consuming pre-treatments on samples, such as a rotary evaporator and nitrogen evaporator, which could result in loss of AA that directly affects the rate of accuracy. In this study, gas chromatography with mass spectrometry was used to develop an analytical method to detect AA from various food products with a headspace analyzer (GC-MS/HA) by minimizing the sample pre-processing and maximizing the extraction rate of the AA from test subjects. The conditions for methods in the detection of AA were inspired by previous studies (Kim and Cho, 2014; Oostdiik et al., 2012; Zhang et al., 2017). The purpose of this study was to validate the analytical method for AA in foods and evaluate the concentration of AA in foods from the Korean domestic market. This would enable a rapid and accurate method to detect AA in foods and provide an understanding of the general level of food contamination by AA from the Korean domestic market.
Materials and methods
Materials
All reagents fell under the HPLC grade or were adequate for the analytical grade. AA (purity 99%) and N, N-dimethylformamide (DMF) (purity 99.8%) was the standard and internal standard, respectively, and were purchased from Sigma-Aldrich (Bellefonte, PA, USA). Dichloromethane (DCM) was used as a solvent and was purchased from Burdick and Jackson (Muskegon, MI, USA). Syringe filters (pore size 0.22 μm) were acquired from Advantech (Taipei, Taiwan). Clear headspace vials (23 × 75 mm for CTC PAL) for the headspace analyzer and a 20 mm crimp cap (w/3 mm thick PTFE/Sil septa, ultra-low bleed, high temperature for less or equal to 240 °C) were purchased from Advantage Moulding (Carson, CA, USA) through MLLAB FRIENDS (Gunpo, Gyeonggi-do, Korea). Blenders (Philips viva collection blender) were purchased from Philips (Amsterdam, Netherlands) through fine Labtech (Seoul, Korea).
Preparation of AA standard solutions
AA (990,000 mg/L, 99%) was diluted in DCM to get a working solution of 200 mg/L, as the concentrations of AA in samples were 5, 10, 20, 40, 80, and 100 mg/L. The concentration of the internal standard, DMF (998,000 mg/L, 99.8%), was diluted with DCM to obtain a working solution of 200 mg/L, as the concentration of DMF in each sample was set to 50 mg/L. The concentration range for AA was based on reports from International Programme On Chemical Safety – Internationally Peer-Reviewed Chemical Safety Information (IPCS-INCHEM) (International Programme on Chemical Safety, 1997) and European Chemical Agency (ECA) (2002).
Sample preparation
Food samples were selected based on AA’s properties (polar with high water solubility) and allocated into four different matrixes based on their characteristics, which were (1) crops and raw ingredients for processed foods (M1, 24 items), (2) fruits, vegetables and seaweeds that contain a high percentage of water to their weight (M2, 33 items), (3) beverages (M3, 24 items), and (4) sauce, paste and pickled food products (M4, 21 items). Samples were purchased from Korean domestic markets in five different locations, which were Seoul, Goyang-si, Yeosu, Ansung, and Incheon in the year 2020. The locations with accidents involving AA or the factories that produce AA or products with AA were selected to collect food samples.
As samples were collected, samples were labeled according to their purchased location and kept in a refrigerator under 4 °C until the extraction and purification step for the matrix-matched validation and the analysis of AA from samples.
Extraction and purification
The collected samples were treated before the matrix-matched validation and the analysis of the AA, where the detailed information was presented below. The sample preparation was based on the general standard for contaminants and toxins in food and feed (Codex Alimentarius Commission, 2019).
Crops and raw ingredients for processed foods, M1
Samples allocated in M1 were homogenized until there were no solid particles found. The blending process was carried out at least 30 min per sample to completely homogenize the sample into a paste state. The diluted 20 mL of AA and DMF, the concentration of each solution was set to 10 times the set concentration due to dilution that was occurred during the fortification and extraction process, were added into 20 g of the Homogenized sample, which was sonicated 20 min for mass transfer of AA and DMF across the sample. 10 g of Fortified samples with 50 mL of DCM was added into a 250 mL amber bottle to extract AA and DMF from the sample through ultrasonication for 20 min. After the extraction, 5 mL of extract was collected and filtered through a syringe filter. The filtered sample solutions were then diluted with DCM to 10 mL of which 6 mL was then placed in 20 mL headspace vials and sealed with metal caps, which were stored in a refrigerator at a temperature below 10 °C until to be analyzed.
Fruits, vegetables and seaweeds, M2
Non-edible parts, such as potato pills, were removed before the homogenization process. Then, the homogenization and following steps were identical to the samples from M1.
Beverages, M3
Samples were treated as mentioned above for M1 without a homogenization process.
Sauce, paste, and pickled food products, M4
Samples with no solid particles were treated the same as the M3, while samples with solid particles, such as Kimchi, were treated as mentioned in M1.
Headspace/GC-MS analysis
AA was analyzed with gas chromatograph (Agilent 7820 A GC, Agilent Technologies, Santa Clara, CA, USA) - mass spectrometer (5975 C MSD, Agilent technologies, Santa Clara, CA, USA) (GCMS) coupled with a headspace analyzer (HA) (Dani 86.50 plus, Dani instruments s.p.a, Colongno, Italy) (HA/GC-MS) with a column DB-624 (Agilent J&W, 30 m × 0.25 mm × 1.4 μm) set at 70 °C. Helium gas with a purity of 99.999% was used as the mobile phase at a flow rate of 1.3 mL/min. The oven temperature for GC was from 70 °C and stayed for 1 min, then the temperature was elevated to 260 °C with the rate of 20 °C/min and held for 5 min, which was in a total run time of 15.5 min. The temperature of the MS transfer line was set to 250 °C. EI source and analyzer were set to 230 °C and 150 °C, respectively. The target ion for AA was set to m/z 72, while qualifying ions were set to m/z 72 and m/z 55. The target ion for DMF was set to m/z 73, while qualifying ions were set to m/z 73 and m/z 44. The ratios for AA ranged from 10:6 to 10:8, while DMF ranged from 10:6 to 10:9 depending on different matrix spiked.
For the HA, the incubation time was set to 20 min, and the intensity of shaking during incubation was set to “strong”. The oven and sample loop temperatures were set to 80 °C, while the temperature for the transfer line and oven (incubation) temperature were set to 90 °C. The interval between each sample was set to 45 min. Vial pressure was set to 1.00 bar and carrier gas pressure was set to 1.50 bar. The volume of the sample for analysis was set to 10 mL to maximize the accuracy of the results. Furthermore, a blank solution (DCM) with identical volume to the samples was added in between every three samples. The operational conditions for GC-MS/HA were referenced from the journals by Zhang et al. (2017), Oostdiik et al. (2012), and Qi et al. (2019), where some modifications were made, such as the oven temperature for GC/MS and the sample amount in the headspace vial.
Sample volume optimization for HA
Sample volume in HA vial for analysis was optimized in this research. A standard solution that contains AA (5, 40, and 100 mg/L) and DMF (50 mg/L) was prepared by the method illustrated in the preparation of AA standard solutions. The test was carried out by preparing samples in HA vial at 2, 4, and 6 mL in triplet to analyze and obtain accuracy with precision to optimize the best volume of sample for testing.
Method validation
Representative samples from each group were selected for method validation purposes, including white rice (a fresh product that is not processed, such as boiling) for M1, cabbage (a fresh product with no treatment, such as boiling) for M2, sports drink (a commercial electrolyte drink that can be obtained from any store) for M3, and doenjang (fermented Korean bean paste with fine texture) for M4. The representative samples were selected based on market research regarding the foods occasionally consumed by a shopper.
Each sample was prepared with the method illustrated at extraction and purification for each matrix at the material and methods and analyzed with HA/GC-MS to detect AA. For validation of the analytical method, each sample was tested in triplicate, and the collected data were presented as a mean with a standard deviation. For the validation of the method, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy (%), and precision (%), each category followed the AOAC Standard Method Performance Requirements (2016). The concentration range was set to 5–100 mg/L for AA and the internal standard, DMF, was set to 50 mg/L. The internal standard method was adapted for the current research. LOD and LOQ were based on signal-to-noise ratios of 3:1 and 10:1, respectively, and were calculated to the mg/L. The LOD and LOQ were evaluated through the utilization of three sets of curves created with ratios of ion peak area for AA and DMF at each concentration (5, 10, 20, 40, 80, and 100 mg/L), where the average ion peak area ratio from each concentration was used to create the fourth curve. Y-axis intercepts and gradients of each curve were averaged to calculate LOD and LOQ. For intraday and interday analysis for accuracy and precision, triplicated samples were used for detection on one day and three consecutive days, respectively. Furthermore, accuracy was calculated with the ratio of ion peak area for AA and DMF from standard solution and spiked sample, where the ratio of ion peak area for AA and DMF from standard solution was used as a base for the calculation. For the calculation of LOD, LOQ, accuracy, and precision, the following equations were used for calculation.
where AVR y-intercept: average of y-axis intercepting points of the curves of linearity. m is the gradient of the curve of linearity
where AVR y-intercept is the average of y-axis intercepting points of the curves of linearity. m is the gradient of the curve of linearity
In this study, cross-checking of the analytical method for AA was used for the evaluation of the analysis method for AA from food samples. Cross-checking was carried out with other laboratories to confirm the accuracy of the analytical method. 3 Matrices (M1, M2, and M3) were chosen for cross-checking. Representative samples from each group mentioned in the Method validation were prepared through the preparation method that is shown in the Sample preparation, where the concentration of AA in each sample was prepared in 5, 40, and 100 mg/L with DMF (50 mg/L). Each sample was tested in triplet and accuracy was calculated with the mean concentration and spiked concentration of AA, where the calculation was made with the formula shown above.
Determination of AA from various food samples
Food samples collected from domestic markets over Korea were frozen before analysis to prevent decomposition and loss of AA. Samples were defrosted through a water bath with deionized water at room temperature and treated with the method illustrated in Extraction and purification in “Materials and methods” section. 6 mL of samples containing DMF were analyzed with GC-MS/HA to detect the AA. The collected data was then summarised, and the dilution factor was used to calculate the level of AA in tested food samples, where the dilution factor was calculated with the following equation.
where R is the ratio of area response for AA and DMF from a sample. C is the Y-axis intercept point of calibration curve from each matrix (Table 1). m is the gradient of calibration curve from each matrix (Table 1).
Table 1.
Calibration equations, linearity (R2), limit of detection (LOD), and limit of quantification (LOQ) for validation of AA
| Matrix name | Matrix code number | Calibration equation | Linearity (R2) | LOD (mg/L) | LOQ (mg/L) |
|---|---|---|---|---|---|
| Crops and raw ingredients for processed foods | M1 | y = 0.0701x + 0.4334 | 0.99 | 0.06 | 0.18 |
| Fruits, vegetables and seaweeds that contain a high percentage of water to their weight | M2 | y = 0.0576x + 0.4422 | 0.99 | 0.12 | 0.35 |
| Beverage | M3 | y = 0.0579x + 0.4305 | 0.99 | 0.17 | 0.52 |
| Sauce, paste and pickled food products | M4 | y = 0.0507x + 0.3938 | 0.99 | 0.16 | 0.49 |
All samples were acquired from the domestic local market to identify and analyze AA pollution in foods distributed domestically in Korea. Each matrix had several subgroups that allocated the collected samples. In M1, the subgroup included white rice, glutinous rice, sorghum, brown rice, potato, sweet potato, sesame seed, and perilla. In M2, it included cabbage, apple, tomato, onion, hijiki, spring onion, sea mustard, sea lettuce, daikon radish, mandarin orange, and seaweed. In M3, it included soymilk, barley tea, fruit processed beverages, black tea, toasted corn tea, carbonated beverages, coffee, and sports beverages. Lastly, in M4, the subgroup encompassed Korean soy paste, cabbage-based kimchi, young radish kimchi, leaf mustard-based kimchi, bachelor radish kimchi, Korean chili paste, and soy sauce. In total, 102 samples were tested for the presence of AA in triplicate.
Results and discussion
GC-MS/HA chromatogram of AA and DMF
The chromatogram of the standard, AA, and the internal standard, DMF, were identified, where their retention times for each peak of AA and DMF were acquired. The retention times for AA and DMF were 3.253 and 3.939 min, respectively, where the chromatogram of the AA and DMF is illustrated in Fig. 1. Furthermore, the mass spectrum pattern of AA in Fig. 2 A that was acquired from the research was compared with the mass spectrum pattern provided by NIST Standard Reference Database 69: NIST Chemistry WebBook, where the patterns were found to be identical to each other. The findings showed that it was successful in the analysis of AA and DMF with the method illustrated in this research.
Fig. 1.
GC-MS chromatogram of AA and DMF at concentrations of 80 mg/L and 50 mg/L, respectively
Fig. 2.
Full scan mass spectra before the dissociation of AA standard (A) and AA in white rice sample from Yeosu (B)
Validation of the analytical method for AA
Tables 2 and 3 show the results of validation for the method in detection of AA (R2, LOD, LOQ, accuracy, precision). The calibration curve for each matrix was acquired with the data given from each matrix containing AA and DMF at a concentration of 5-100 mg/L and 50 mg/L, respectively. From the calibration curves, the linearity (R2) of each curve was obtained, which was higher than 0.99. This indicated that all curves were suitable for estimating the concentration of AA in samples from each matrix. According to Table 1, the obtained LOD data for all four matrixes ranged from 0.06 to 0.17 mg/L. Furthermore, the LOQ was found to be 0.18–0.52 mg/L (Table 2). The accuracy and precision were obtained through analysis of the triplicated samples at each concentration (5–100 mg/L) in a day and three consecutive days. According to Table 2, the accuracy of four different matrixes ranged between 90.97 and 111.93%. The precision for all matrixes was 0.12–9.61%. Based on the regulation standards provided by AOAC (2016), the R2 (> 0.99), accuracy (80–120%), precision (< 15%), LOD (< 0.2 mg/L), and LOQ (< 0.6 mg/L), were appropriate. Furthermore, according to the data shown in Supplementary material 2, the accuracy and precision calculated from the data of the ratio of chromatogram peak area for AA (5, 40, and 100 mg/L) and DMF were found to be fell within the acceptable range (80–120% for accuracy and < 15% for precision) (AOAC, 2016).
Table 2.
Accuracy and precision for validation of AA
| Matrix code number | Concentration (mg/L) | Intraday (n = 3) | Interday (n = 9) | ||
|---|---|---|---|---|---|
| Accuracy (%) | Precision (%) | Accuracy (%) | Precision (%) | ||
| 1 | 5 | 98.00 | 1.85 | 101.48 | 4.29 |
| 10 | 97.76 | 3.38 | 99.87 | 0.74 | |
| 20 | 100.84 | 0.73 | 102.70 | 3.91 | |
| 40 | 100.64 | 0.68 | 100.61 | 1.22 | |
| 80 | 98.58 | 2.36 | 90.97 | 7.96 | |
| 100 | 101.98 | 2.00 | 94.60 | 5.33 | |
| 2 | 5 | 98.86 | 1.16 | 98.74 | 1.98 |
| 10 | 98.40 | 3.37 | 100.95 | 1.38 | |
| 20 | 100.10 | 0.23 | 100.94 | 1.04 | |
| 40 | 100.19 | 1.20 | 99.87 | 0.71 | |
| 80 | 98.85 | 2.69 | 100.46 | 0.83 | |
| 100 | 97.59 | 2.15 | 97.71 | 1.60 | |
| 3 | 5 | 99.91 | 0.12 | 101.74 | 4.16 |
| 10 | 98.38 | 3.90 | 111.93 | 9.61 | |
| 20 | 100.41 | 0.76 | 103.61 | 3.46 | |
| 40 | 100.78 | 1.29 | 102.86 | 2.85 | |
| 80 | 98.87 | 2.28 | 98.36 | 1.76 | |
| 100 | 101.32 | 1.63 | 103.25 | 2.62 | |
| 4 | 5 | 98.86 | 1.16 | 98.74 | 1.98 |
| 10 | 98.40 | 3.37 | 100.95 | 1.38 | |
| 20 | 100.10 | 0.23 | 100.94 | 1.04 | |
| 40 | 100.19 | 1.20 | 99.87 | 0.71 | |
| 80 | 98.85 | 2.69 | 100.46 | 0.83 | |
| 100 | 97.59 | 2.15 | 97.71 | 1.60 | |
aPrecision (%) means % relative standard deviation
Table 3.
Content of AA in various foods collected from the Korean domestic markets
| Matrix name | Locations of collection | Sample name (sample no.) | Levels of AA (mg/kg) | Average levels of AA (mg/kg) |
|---|---|---|---|---|
| Crops and raw ingredients for processed foods | Yeosu | White rice A (1) | 6.12 | 6.20 ± 0.08 |
| 6.19 | ||||
| 6.28 | ||||
| Incheon | White rice B (2), Glutenous rice B (5), Sorghum A (7), Brown rice B (11), Potato C (15), Sesame seed A (19), Perilla seed A (22) | ND | ND | |
| Ansung | White rice C (3), Glutenous rice C (6), Sorghum C (9), Brown rice A (10), Potato A (13), Sweet potato A (16), Sesame seed B (20), Perilla seed C (24) | ND | ND | |
| Seoul | Glutenous rice A (4), Sorghum B (8), Brown rice C (12), Potato B (14), Sweet potato B (17), Sesame seed C (21), Perilla seed B (23) | ND | ND | |
| Yeosu | Sweet potato C (18) | ND | ND | |
| Fruits, vegetables and seaweeds that contain a high percentage of water to their weight | Incheon | Cabbage A (25), Onion A (34), Hijiki A (37), Spring onion C (42), Sea mustard B (44), Sea lettuce A (46), Sea lettuce B (47), Daikon radish B (50), Mandarin orange B (53), Seaweed B (56) | ND | ND |
| Ansung | Cabbage C (27), Apple C (30), Tomato C (33), Onion C (36), Spring onion B (41), Sea lettuce C (48), Mandarin orange A (53), | ND | ND | |
| Seoul | Cabbage B (26), Apple B (29), Tomato B (32), Onion B (35), Hijiki C (39), Spring onion A (40), Daikon radish A (50), Daikon radish C (51), Seaweed A (55) | ND | ND | |
| Yeosu | Apple A (28), Tomato A (31), Hijiki B (38), Sea mustard A (43), Sea mustard C (45), Mandarin orange C (54), Seaweed C (57) | ND | ND | |
| Beverage | Goyang-si | Soymilk A (58), Soymilk B (59), Soymilk C (60), Barely tea A (61), Barely tea B (62), Barely tea C (63), Fruit processed beverage A (64), Fruit processed beverage B (65), Fruit processed beverage C (66), Black tea A (67), Black tea B (68), Black tea C (69), Toasted corn tea A (70), Toasted corn tea B (71), Toasted corn tea C (72), Carbonated beverage A (73), Carbonated beverage B (74), Carbonated beverage C (75), Coffee A (76), Coffee B (77), Coffee C (78), Sports drink A (79), Sports drink B (80), Sports drink C (81) | ND | ND |
| Sauce, paste and pickled food products | Goyang-si | Korean soy paste A (82), Korean soy paste B (83), Korean soy paste C (84), Cabbage kimchi A (85), Cabbage kimchi B (86), Cabbage kimchi C (87), Young radish kimchi A (88), Young radish kimchi B (89), Young radish kimchi C (90), Leaf mustard kimchi A (91), Leaf mustard kimchi B (92), Leaf mustard kimchi C (93), Bachelor radish kimchi A (94), Bachelor radish kimchi B (95), Bachelor radish kimchi C (96), Korean chilli paste A (97), Korean chilli paste B (98), Korean chilli paste C (99), Soy sauce A (100), Soy sauce B (101), Soy sauce C (102) | ND | ND |
Casella et al. (2006) analyzed AA by liquid chromatography coupled with pulsed electrochemical detection using water as a solvent giving a lower LOD compared to our research. Despite the LOD value, the method validated in this research allowed more rapid analysis with high accuracy. According to Lai et al. (2013), residual AA was detected from solid and liquid acrylic resins with GC-MS using the microwave for extraction, which showed LOQ in the range of 1–10 mg/L for liquid and 3–50 mg/L for solid resin. This was a lower range than the LOQ acquired from analytical methods in this research. The method that was validated in this research lowered the risk of AA loss by minimizing the steps for the prior treatment in the AA analysis.
With help of HA, it became easier to extract and analyze various chemicals from a variety of samples. In this study, the temperatures for the oven, sample loop, and transfer line were set lower than the boiling point of AA (141 °C). Despite the conditions, the accuracy of the AA analysis was found to be in the range of 80–120% (Table 2). This shows that the AA was successfully extracted from the samples. Moreover, the volume of samples was optimized through testing with samples at different volumes. In this research, samples of 2, 4, and 6 mL were tested to conclude the best sample volume by checking the accuracy with standard deviation. According to Supplementary material 1, it is shown that the accuracy of analyzing AA was found to be lower than the acceptable range (80–120%) for the sample volumes of 2 and 4 mL. On the other hand, the result of the analyzing sample with a volume of 6 mL showed data that meet the criteria given by AOAC (80–120% for accuracy and < 15% for precision). The data regarding the accuracy from Table 2 and the example of the extraction for the chemical compounds with lower boiling temperatures, validates that the HA analytical method for AA from food samples was successful.
In many studies, an internal standard calibration method was used for validation of the method in the detection of various chemical compounds. For higher accuracy, many methods used radioactive isotopes of the target compound as an internal standard, which possesses identical chemical properties that could enhance accuracy. In this study, DMF was used to validate the analytical method for AA. According to Fig. 1, the AA and DMF peaks were successfully separated from each other, showing no interference in the detection of AA. In addition, DMF was used as an internal standard to detect AA instead of other organic acids (Kim and Cho, 2014), which suggests that DMF could be utilized as an internal standard in the detection of AA with GC/MS. The response given by the internal standard must be relatively stable to provide a higher R2 value, as the concentration of the internal standard in each sample was fixed at 50 mg/L. As shown in Table 1, the data of linearity, R2, for each matrix was higher than 0.99, which shows a relationship between the ratio of ion peak area AA and DMF against the concentration of AA despite using a non-radioactive isotope as an internal standard. This supports using DMF as an alternative internal standard to radioactive isotopes, such as AA-d4, to detect AA in food samples. This could help as AA radioactive isotopes are pricey, which could be a burden for the research teamer. Utilization of non-radioactive internal standards would be beneficial in terms of experimental cost and ease in reagent preparation for analysis.
Location selection for sample collection in domestic markets
Samples from each subgroup were purchased from three different locations in Korea according to the geological characteristics of their grown native habitat. As mentioned earlier, foods and food ingredients that were harvested from the ocean were mainly purchased from the seaside cities of Incheon and Yeosu, which have a large food-related domestic market based on wild-caught and farm-raised seafood. In addition, there are multiple companies manufacturing products, such as lenses, using AA. There were several accidental cases involving AA, such as a 2013 incident in Incheon in which a factory released roughly one tone of AA into the air resulting in the evacuation of local citizens (Jung, 2013). We chose Yeosu because it has the largest factory in Korea that produces AA for retail and merchandise with AA in the domestic and international markets. Ansung was selected because there was AA leakage in the factory here, which caused the evacuation of workers and people near the factory (Yoon, 2019). Seoul, the capital city of Korea, and Go yang-Si were selected as they have a high population density. Furthermore, various markets located in both cities sell harvested food ingredients and processed food products from all over Korea, which could provide a general understanding of AA contamination in foods in the current domestic food market network.
Contamination in foods by AA
The result of analyzing the AA concentration from 102 food samples from the Korean domestic market is shown in Table 3. Most of the samples from M1 were free of AA contamination, while the white rice sample collected from the domestic market at Yeosu had a mean concentration of 6.19 ± 0.08 mg/L, where the presence of the AA was confirmed through comparison of the mass spectrums of AA standard (Fig. 2(A)), and AA from the white rice sample (Fig. 2(B)), which showed high similarity pattern with each other. Moreover, the selected ion mass spectra of AA (m/z 55 and 72) and DMF (m/z 44 and 73), standards and the ones in the white rice sample from Yeosu, showed identical ion ratios and patterns to the full scan mass spectra. This occurrence might have been caused by the migration of AA monomers from the food packaging, where it was reported by Franz and Brandsch (2012) that AA monomers tend to be migrated from food packaging with an elevation of the temperature. However, it requires further research to prove that the presence of AA in white rice from Yeosu was purely affected by the migration of monomers. Moreover, further tests would be required for the presence of AA in soil, where the white rice sample had grown to understand the source of AA. Therefore, it would require further tests on soils and packaging to figure out the source of the contamination of AA. On the other hand, there was no AA detected in samples from M2, M3, or M4 (Table 3).
In this study, the level of AA was analyzed in multiple samples containing a high percentage of water to their weight. This was because AA is highly soluble in water (Akyildiz and Michielsen, 2013), which excluded samples with high fat or high protein concentrations, such as beef. In addition, the detoxification of AA through the action of the propionate catabolism involving the β-oxidation pathway metabolized 80% of orally exposed AA (40–150 mg/L) into CO2 in 24 h (Black et al., 1995). This suggests it is unlikely to detect AA in meat products, as AA would be digested before the product went to market. This supports the relatively high concentration range for method validation, as ingestion of a low concentration of AA would be not hazardous to humans and it would be unlikely to find a low concentration of AA from food samples. According to a report by Alberta Environment (Alberta Government, 2002), AA could be degraded in multiple pathways. For instance, AA was degraded through a reaction with ozone and hydroxyl radicals in the air and microbial degradation in the soil and water. In addition, AA in the water could be irradiated with UV light. Despite microbial degradation and UV irradiation, AA could remain in water and soil, due to low vapor pressure. The presence of H2 could limit the UV irradiation of AA. This suggests that the identification and eradication of AA from the soil and water may prevent the impacts on food and consumers (Fig. 3).
Fig. 3.
Total ion chromatogram (TIC) of white rice sample from Yeosu
There was no AA detected in M2, M3, and M4 samples. This suggests that food ingredients from the ocean are generally safe from AA. Despite these results, investigations of AA in ocean food should continue. Sverdrup et al. (2001) claimed that AA caused critical toxic affection on marine algae, such as Selenastrum campriconrnutum with a concentration lower than 1 mg/L. This suggests that future research should examine a diverse array of samples, harvested from the ocean, such as marine algae.
A water-soluble poly AA, a widely used macromolecule in the manufacturing of paints, cosmetics, and pharmaceuticals that are produced by polymerization of AA, exists in wastewater and could be deposited in the soil (Wiśniewska and Nowicki, 2019). It may be taken up by plants grown in contaminated soil, which could then be consumed by humans. The uptake of AA could irreversibly impact the plants. According to Hay and Url (1954), growth retardation was observed in tomatoes, radishes, sunflowers, and soybeans with AA at a concentration of 0.05–0.4%. This suggests that detecting the presence of AA in vegetables to trace and prevent contamination, which could impact the domestic food market.
The results of M3 and M4 samples support the idea that processed foods from the Korean domestic market are currently not in danger of AA contamination. Further research might be required involving diverse types of processed foods to confirm the presence of AA. According to Ubaoji and Orji (2016), heating during the preparation of food products could result in the emergence of 3-carbon molecules, which include AA. Frying converts lipids into AA through thermal-induced oxidation. Acrylamide and AA tend to be converted by certain identical organic compounds, such as asparagine and pyruvic acid. This shows the danger of AA consumption, which stresses the importance of rapid and accurate analytical methods to detect residual AA in food. The method validated in this research could improve the food safety of foods that could protect the health of shoppers.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
This research was supported by a grant (20162MFDS037) from the Ministry of Food and Drug Safety in 2021.
Declarations
Conflict of interest
The authors declare no conflict of interests.
Research involving human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Yoon-Seong Kim, Email: biobkim1996@gmail.com.
Yong-Yeon Kim, Email: kimyy613@naver.com.
Hee-Jeong Hwang, Email: piatop@hanmail.net.
Han-Seung Shin, Email: spartan@dongguk.edu.
References
- Akyildiz HI, Michielsen S. Improving water solubility of poly(AA-co-styrene) copolymers by adding styrene sulfonic acid as a termonomer. Journal of Applied Polymer Science. 2013;129(4):2208–2215. doi: 10.1002/app.38933. [DOI] [Google Scholar]
- Alberta Government. Assessment report on AA for developing an ambient air quality guideline. Alberta Environment, Edmonton, Canada (2002)
- AOAC International. Guidelines for standard method performance requirements. Appendix F. Association of Official Analytical Chemist, Rockville, MD, USA (2016)
- Bao Q, Fu K, Ren Q, Zhong Y, Qian D. Accuracy Profiles for Analyzing Residual Solvents in Textiles by GC-MS. Journal of Chromatographic Science. 2017;55(9):882–890. doi: 10.1093/chromsci/bmx052. [DOI] [PubMed] [Google Scholar]
- Black KA, Beskitt JL, Finch L, Tallant MJ, Udinsky JR, Frantz SW. Disposition and metabolism of AA in C3H mice and Fischer 344 rats after oral or cutaneous administration. Journal of Toxicology and Environmental Health Sciences. 1995;45(3):291–311. doi: 10.1080/15287399509531997. [DOI] [PubMed] [Google Scholar]
- Brown D. Acrylic Acid. In: Encyclopedia of Toxicology, pp. 74–75 (2014)
- Casella IG, Pierri M, Contursi M. Determination of acrylamide and acrylic acid by isocratic liquid chromatography with pulsed electrochemical detection. Journal of Chromatography A. 2006;1107(1–2):198–203. doi: 10.1016/j.chroma.2005.12.076. [DOI] [PubMed] [Google Scholar]
- Codex Alimentarius Commission. GENERAL STANDARD FOR CONTAMINANTS AND TOXINS IN FOOD AND FEED. CXS 193–1995: 1–66 (2019)
- Chanda S, Mehendale MH. Acrylic acid. In: Encyclopedia of Toxicology, pp. 45–46 (2005)
- European Chemical Agency. Acrylic acid—Summary Risk Assessment Report. Institute for Health and Consumer Protection European Chemicals Bureau Special Publication 1.01.71 (2002)
- Franz R, Brandsch R. Migradtion of Acrylic Monomers from Methacrylate Polymers – Establishing Parameters for Migradtion Modelling. Packaging Technology and Science. 2012;26(8):435–451. doi: 10.1002/pts.1995. [DOI] [Google Scholar]
- Fujita M, Izato Y, Iizuka Y, Miyake A. Thermal hazard evaluation of runaway polymerization of AA. Process Safety and Environmental Protection. 2019;129:339–347. doi: 10.1016/j.psep.2019.08.003. [DOI] [Google Scholar]
- Hay RE, Uhl DP. Response of Certain Plant Species to an AA Derivative. Botanical Gazette. 1954;116(2):193–195. doi: 10.1086/335860. [DOI] [Google Scholar]
- Hellwig J, Deckardt K, Freisberg KO. Subchronic and chronic studies of the effects of oral administration of acrylic acid to rats. Food and Chemical Toxicology. 1993;31(1):1–18. doi: 10.1016/0278-6915(93)90172-U. [DOI] [PubMed] [Google Scholar]
- International Programme on Chemical Safety (INCHEM). Environmental Health Criteria 191-AA. United Nations Environment Programme Internation Labour Oragnisation-World Health Organisation (1997)
- Jeon J. Training was organized to prevent accidents at Yeosu industrial complex (2019). Available from: http://www.enewstoday.co.kr/news/articleView.html?idxno=1350789. Accessed Jan. 31, 2022.
- Jung S. One tone of AA release into the air… one injured from the accident (2013) Available from:. https://news.kbs.co.kr/news/view.do?ncd=2689971. Accessed Nov. 09, 2021.
- Jung D. Trunk fallen over… hazardous chemical compound released into the environment. Available from: http://www.najunews.kr/news/articleView.html?idxno=211534. Accessed Jan. 31, 2022.
- Khalid S, Shahid M, Natasha, Bibi I, Sarwar T, Sha AH, Niazi NK. A Review of Environmental Contamination and Health Risk Assessment of Wastewater Use for Crop Irrigation with a Focus on Low and High-Income Countries. International Journal of Environmental Research and Public Health. 2018;15(5):895. doi: 10.3390/ijerph15050895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Khan H, Iram, Gul K, Ara B, Khan A, Ali N, Bilal M. Adsorptive removal of AA from the aqueous environment using raw and chemically modified alumina: Batch adsorption, kinetic, equilibrium and thermodynamic studies. Journal of Environmental Chemical Engineering. 2020;8(4):1–11. doi: 10.1016/j.jece.2020.103927. [DOI] [Google Scholar]
- Kim BH, Cho HS. Gas Chromatographic Method for the Analysis of Organic Acids in the Bio-Catalytic Conversion Process. Journal of Chromatographic Science. 2014;53(6):849–853. doi: 10.1093/chromsci/bmu148. [DOI] [PubMed] [Google Scholar]
- Lai Y, Lin R, Cai L, Ge X, Huang CC. Determination of 9 residual acrylic monomers in acrylic resins by gas chromatography-mass spectrometry coupled with microwave assisted extraction. Chinese Journal of Chromatography. 2013;30(1):21–26. doi: 10.3724/SP.J.1123.2011.09011. [DOI] [PubMed] [Google Scholar]
- Oostdiik J, Lynam K, Jiang R. Trace Analysis of Volatile Organic Acids with the Agilent J&W DB-624UI GC Column. Agilent Technologies Inc. (2012)
- Qi Y, Ma C, Wan M, Liu Z, Zheng L, Jin F, Jin M, She Y, Wang J, Wang S. Multiresidue Determination of Six Pesticide Adjuvants in Characteristic Minor Crops Using QuEChERS Method and Gas Chromatography-Mass Spectrometry. ChemistrySelect. 2019;4(1):66–70. doi: 10.1002/slct.201803328. [DOI] [Google Scholar]
- Qiu Y, Li G, Zhang Q, Tan C, Xiang D. Study on migration of two acrylate monomers in plastic food contact materials. IOP Conference Series: Earth And Environmental Science 657: 1–5 (2021)
- Sverdrup LE, Källqvist T, Kelley AE, Fürst CS, Hagen SB. Comparative toxicity of AA to marine and freshwater microalgae and the significance for environmental effects assessments. Chemosphere. 2001;45(4–5):653–658. doi: 10.1016/S0045-6535(01)00044-3. [DOI] [PubMed] [Google Scholar]
- Ubaoji KI, Orji V. A review on a review on acrylamide in foods: Sources and implications to health. Journal of African Studies. 6(1): 1–17 (2016)
- Wiśniewska M, Nowicki P. Simultaneous removal of lead(II) ions and poly(AA) macromolecules from liquid phase using of biocarbons obtained from corncob and peanut shell precursors. Journal of Molecular Liquids. 2019;296:1–14. doi: 10.1016/j.molliq.2019.111806. [DOI] [Google Scholar]
- Yoon S. Incident involving release of AA, what is the AA?. Available from: http://www.polinews.co.kr/news/article.html?no=393190. Accessed Nov. 09, 2021.
- Youssef MM, Abou-Gharbia HA, Abou-Bakr HA. Acrylamide in food: An overview. Alexandria Journal of Food Science and Technology. 1(1): 1–22 (2004)
- Zhang SX, Chai XS, Jiang R. Accurate determination of residual AA in superabsorbent polymer of hygiene products by headspace gas chromatography. Journal of Chromatography A. 2017;1485:20–23. doi: 10.1016/j.chroma.2017.01.023. [DOI] [PubMed] [Google Scholar]
- Zhang H, Wei J, Huo ZL. UPLC-MS/MS method for the determination of AA in tap water. Journal of Liquid Chromatography & Related Technologies. 2019;43(5–6):164–171. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.