Development and Validation of a Gradient HPLC Method for Simultaneous Determination of Seven Parabens Including Heptyl Paraben in Processed Foods

Development and Validation of a Gradient HPLC Method for Simultaneous Determination of Seven Parabens Including Heptyl Paraben in Processed Foods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Gradient HPLC Method for Simultaneous Determination of Seven Parabens Including Heptyl Paraben in Processed Foods Jae-Wook Shin, Jung-Bok Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9099931/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Parabens are widely used as antimicrobial preservatives in foods, pharmaceuticals, and cosmetic products due to their effectiveness and chemical stability. However, concerns regarding their potential health effects have increased the need for reliable analytical methods for monitoring parabens in food products. In Korea, heptyl paraben has not been established as a permitted preservative in the Korean Food Additives Code. Therefore, an analytical method for heptyl paraben was first established, and subsequently incorporated into a simultaneous analytical method for commonly used parabens. In this study, a gradient high-performance liquid chromatography (HPLC) method was developed and validated for the simultaneous determination of seven parabens including methyl paraben, ethyl paraben, isopropyl paraben, propyl paraben, isobutyl paraben, butyl paraben, and heptyl paraben in processed foods. The method was validated in terms of linearity, precision, accuracy, and sensitivity. The developed method was successfully applied to several food matrices including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder. The proposed method provides a reliable analytical approach for the simultaneous qualitative and quantitative determination of multiple parabens in processed foods and can be applied for food safety monitoring. Parabens HPLC Food additives Food safety Method validation Simultaneous determination Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Parabens are widely used as antimicrobial preservatives in foods, pharmaceuticals, and cosmetic products due to their strong antimicrobial activity and chemical stability (Soni et al. 2005; Golden et al. 2005; Błędzka et al. 2014). The most commonly used parabens include methyl paraben, ethyl paraben, propyl paraben, and butyl paraben. These compounds inhibit the growth of bacteria, yeasts, and molds, thereby extending the shelf life of processed products. Despite their widespread use, increasing concerns regarding the potential health effects of parabens have led to growing interest in monitoring their occurrence in food products (Darbre and Harvey 2008; Boberg et al. 2010; Nowak et al. 2018). Several studies have suggested that parabens may exhibit endocrine-disrupting activity, which has raised public concerns about their safety. Consequently, regulatory authorities in many countries have established limits for the use of parabens as food additives and require reliable analytical methods for monitoring their levels in food products (EFSA 2004; SCCS 2013). Various analytical methods have been developed for the determination of parabens in different matrices including food, cosmetics, environmental samples, and biological samples (Guo and Kannan 2013; Liao and Kannan 2014; Liao et al. 2016). Among these techniques, high-performance liquid chromatography (HPLC) is one of the most commonly used methods because of its high sensitivity, good reproducibility, and suitability for complex food matrices (Berset and Brenneisen 2006; Konieczna et al. 2011; Sanchis et al. 2019). Other analytical techniques such as gas chromatography (GC), liquid chromatography–mass spectrometry (LC–MS), and LC–MS/MS have also been widely applied for the determination of parabens in various samples (Viñas et al. 2012; Pedrouzo et al. 2009; Ye et al. 2006). In many previous studies, analytical methods were primarily developed for the determination of four commonly used parabens, namely methyl paraben, ethyl paraben, propyl paraben, and butyl paraben (Wang et al. 2015; Gao et al. 2017; Canosa et al. 2006). However, other parabens such as isopropyl paraben, isobutyl paraben, and heptyl paraben may also occur in food matrices. These compounds have received relatively limited attention compared with the commonly studied parabens. In Korea, heptyl paraben has not been established as a permitted preservative in the Korean Food Additives Code. Therefore, monitoring the potential presence of heptyl paraben in foods requires appropriate analytical methods. To the best of our knowledge, simultaneous determination of seven parabens including heptyl paraben in food matrices has rarely been reported in previous studies. Therefore, the development of an analytical method capable of simultaneously determining multiple parabens, including less frequently studied compounds, is necessary for comprehensive food safety monitoring. Therefore, the objective of this study was to develop and validate a reliable gradient HPLC method for the simultaneous determination of seven parabens, including methyl paraben, ethyl paraben, isopropyl paraben, propyl paraben, isobutyl paraben, butyl paraben, and heptyl paraben, in processed food matrices. In addition, measurement uncertainty was evaluated according to the EURACHEM/CITAC guideline to enhance the reliability of the analytical results and to support the application of the method for routine monitoring of parabens in processed foods. 2. Materials and Methods 2.1 Chemicals and reagents Analytical standards of methyl paraben(M.P), ethyl paraben(E.P), isopropyl paraben(I.P.P), propyl paraben(P.P), isobutyl paraben(I.B.P), butyl paraben(B.P), and heptyl paraben (H.P, purity ≥ 98%) were purchased from Sigma–Aldrich (St. Louis, MO, USA). Acetonitrile and methanol (HPLC grade) were obtained from Merck (Darmstadt, Germany). Ultrapure water was produced using a Milli-Q water purification system (Millipore, Bedford, MA, USA). All other reagents used in this study were of analytical grade and used without further purification. 2.2 Classification of collected food samples Food samples including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder were purchased from local markets in Korea. A total of 102 processed food sampleswere collected, consisting of domestic products (n = 49), imported products (n = 37), and overseas direct purchase products (n = 16). All samples were transported to the laboratory and stored at 4 °C until analysis(Table 7). Prior to extraction, the samples were homogenized thoroughly to ensure sample uniformity. 2.3 Sample preparation Approximately 2 g of homogenized food sample was accurately weighed into a centrifuge tube and extracted with 10 mL of acetonitrile. The mixture was vortex-mixed for 2 min, followed by sonication for 10 min. The extract was then centrifuged at 4000 rpm for 10 min, and the resulting supernatant was filtered through a 0.45 μm membrane filter prior to HPLC analysis. The overall sample preparation procedure used in this study is illustrated in Fig. 2. 2.4 HPLC analysis Chromatographic separation was performed using a high-performance liquid chromatography (HPLC) system equipped with a photodiode array (PDA) detector. Separation of the analytes was achieved using a Kinetex C18 column (4.6 × 150 mm, 5 μm)maintained at 40 °C. The mobile phase consisted of water (A)and acetonitrile (B). Gradient elution was applied during the chromatographic run. The flow rate was set at 1.0 mL/min, and the injection volumewas 10 μL. Detection of parabens was carried out using the PDA detector at 245 nm. Under these chromatographic conditions, the seven parabens were successfully separated within a total run time of 25 min(Fig. 3). 2.5 Method validation Validation of the developed method was performed by evaluating the following parameters: linearity, matrix effect, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ). Linearity was assessed using matrix-matched calibration curves prepared by spiking blank food matrices with mixed standard solutions of seven parabens at different concentration levels. The calibration curves were constructed in the concentration range of 0.5–20 μg/mL, and satisfactory linearity was obtained for all analytes with correlation coefficients (R²) greater than 0.999. Precision and accuracy of the method were evaluated by recovery experiments using spiked food samples at three concentration levels (1, 5, and 20 μg/mL). Intra-day precision was determined by analyzing three replicate samples within a single day, while inter-day precision was evaluated over three consecutive days. Precision was expressed as relative standard deviation (RSD, %), and accuracy was expressed as recovery percentage. The limits of detection (LOD) and limits of quantification (LOQ) were calculated based on signal-to-noise ratios of 3 and 10, respectively. Alternatively, LOD and LOQ values were also estimated according to the following equations:LOD = 3.3 σ / S, LOQ = 10 σ / S. where σrepresents the standard deviation of the response and Srepresents the slope of the calibration curve. 2.6 Measurement uncertainty Measurement uncertainty was evaluated according to the principles described in the EURACHEM/CITAC Guide: Quantifying Uncertainty in Analytical Measurement. The major sources of uncertainty considered in this study were identified and illustrated using a fishbone diagram (Fig. 4). The uncertainty sources included calibration, repeatability, sample preparation, and instrumental measurement, from which the standard uncertainty and combined uncertainty were calculated. The expanded uncertainty was obtained by multiplying the combined standard uncertainty by a coverage factor (k = 2), corresponding to a confidence level of approximately 95%. 3. Results and Discussion 3.1 Chromatographic separation The developed gradient HPLC method enabled the simultaneous separation of seven parabens within a total run time of 25 min. The retention times increased according to the hydrophobicity of the analytes due to stronger interactions with the C18 stationary phase. The chemical structures of the seven parabens analyzed in this study are presented in Fig. 1. Among the target analytes, heptyl paraben was first optimized during the method development stage and subsequently incorporated into the simultaneous analytical method together with the commonly used parabens. Under the optimized chromatographic conditions, all analytes were baseline separated with satisfactory resolution. Representative chromatograms obtained from different food matrices are shown in Fig. 2. Methyl paraben eluted first, whereas heptyl paraben exhibited the longest retention time. The gradient elution program significantly improved the separation efficiency of structurally similar compounds, particularly isopropyl/propyl parabens and isobutyl/butyl parabens, resulting in satisfactory chromatographic resolution. These results demonstrate that the developed gradient HPLC method provides efficient chromatographic separation for the simultaneous determination of multiple parabens in processed food matrices. 3.2 Linearity The linearity of the developed method was evaluated using matrix-matched calibration curvesin five representative food matrices, including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder. Calibration curves were constructed over the concentration range of 0.5–20 μg/mLfor all seven parabens. Excellent linearity was obtained for all analytes in the tested food matrices, with correlation coefficients (R²) ranging from 0.9982 to 0.9999. The use of matrix-matched calibration effectively compensated for potential matrix effects during quantitative analysis. The calibration parameters, including slope and intercept values for each analyte in different matrices, are summarized in Table 1. 3.3 Precision and accuracy of intra & Inter day The precision and accuracy of the developed method were evaluated through intra-day and inter-day validation experimentsusing spiked food samples. Intra-day precision was determined by analyzing replicate samples within the same day, whereas inter-day precision was evaluated over three consecutive days. The developed method showed good precision, with relative standard deviation (RSD) values generally below 3%for all analytes. Recovery values ranged from 93% to 114%, demonstrating satisfactory accuracy of the analytical method for different food matrices. The obtained precision and recovery values satisfied commonly accepted analytical performance criteria. The detailed results of intra-day and inter-day precision and accuracy are presented in Tables 2 and 3. 3.4 LOD & LOQ The limits of detection (LOD) and limits of quantification (LOQ) for the seven parabens were determined to evaluate the sensitivity of the developed method. The LOD values ranged from 0.01 to 0.02 μg/mL, while the LOQ values ranged from 0.02 to 0.05 μg/mL. These results indicate that the developed HPLC method provides sufficient sensitivity for the determination of parabens in processed food samples. The LOD and LOQ values for the seven parabens are summarized in Table 4. 3.5 Result of recovery in sample matrixs The accuracy of the developed method was evaluated through recovery experiments using spiked food samples at concentration levels of 1, 5, and 20 μg/mL. The recovery values obtained for the five food matrices ranged from 92.6% to 103.4%. These results demonstrate satisfactory accuracy of the developed method for the determination of parabens in various food matrices. The recovery results obtained for the five food matrices are summarized in Table 5. 3.6 Measurement uncertainty Measurement uncertainty of the developed HPLC method was evaluated according to the EURACHEM/CITAC Guide: Quantifying Uncertainty in Analytical Measurement. The expanded uncertainty was calculated using a coverage factor of k = 2, corresponding to a confidence level of approximately 95%. As summarized in Table 6, the certified concentrations of the parabens ranged from 51.44 to 123.78 mg/kg. The expanded uncertainty values were 51.44 ± 2.51 mg/kg for methyl paraben, 120.40 ± 23.27 mg/kg for ethyl paraben, 119.56 ± 22.74 mg/kg for isopropyl paraben, 118.54 ± 22.81 mg/kg for propyl paraben, 119.52 ± 23.67 mg/kg for isobutyl paraben, 120.10 ± 23.60 mg/kg for butyl paraben, and 123.78 ± 6.46 mg/kg for heptyl paraben. These results indicate that the developed analytical method provides reliable quantitative results with acceptable measurement uncertainty for the determination of parabens in processed food matrices. The relatively larger measurement uncertainty values observed for most parabens, except methyl and heptyl parabens, may be attributed to the simultaneous determination performed at a fixed detection wavelength of 245 nm rather than at the individual maximum absorbance wavelengths of each analyte. This analytical condition may result in slight differences in calibration sensitivity among the analytes, which consequently affects the estimated measurement uncertainty. 3.7 Result of samples The occurrence of seven parabens in processed food samples was investigated using the developed HPLC method. A total of 102 food samples representing six food categories were analyzed. As shown in Table X, most parabens were not detected (ND) in the analyzed samples. Among the target compounds, ethyl paraben was detected in one sample categorized as “others” at a concentration of 0.006 g/kg, while the remaining parabens were not detected in any of the analyzed samples. The detected concentration was within the regulatory limits established in the Republic of Korea, indicating that it does not pose a safety concern for consumers. Although most parabens were not detected in the analyzed samples, these results demonstrate that the developed analytical method provides sufficient sensitivity and reliability for the monitoring of parabens in processed food matrices and can be effectively applied for routine food safety surveillance. The occurrence of parabens in the analyzed food samples is summarized in Table 8. 4. Conclusions In this study, a gradient HPLC method was successfully developed and validated for the simultaneous determination of seven parabens, including methyl, ethyl, isopropyl, propyl, isobutyl, butyl, and heptyl paraben, in processed foods. The developed method demonstrated satisfactory linearity, sensitivity, precision, and accuracy across various food matrices. Measurement uncertainty was also evaluated according to the EURACHEM/CITAC guideline to improve the reliability of the analytical results. Application of the method to 102 processed food samples, including domestic products, imported products, and overseas direct purchase products, showed that most parabens were not detected. Ethyl paraben was detected in only one sample at a concentration of 0.006 g/kg, which was within the regulatory limit established in the Republic of Korea. Therefore, the developed method can be considered reliable and suitable for the simultaneous determination and monitoring of parabens in processed foods. In particular, the inclusion of heptyl paraben in the simultaneous analytical method represents a meaningful advancement that broadens the analytical scope for comprehensive monitoring of parabens in food safety management. Declarations Acknowledgements This research was supported by a grant (14162MFDS971) from the Ministry of Food and Drug Safety in South Korea. Competing interests The authors declare no competing interests. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Author Contribution J.B.K. and W.J. S conceived and designed the study. J.B.K. and W.J. S performed the experiments and analyzed the data. J.B.K. and W.J. S wrote the manuscript. All authors reviewed and approved the final manuscript. References Berset JD, Brenneisen R (2006) Determination of parabens in food by high-performance liquid chromatography. J Chromatogr A 1116:240–247. https://doi.org/10.1016/j.chroma.2006.03.064 Błędzka D, Gromadzińska J, Wąsowicz W (2014) Parabens. From environmental studies to human health. Environ Int 67:27–42. https://doi.org/10.1016/j.envint.2014.02.007 Boberg J, Taxvig C, Christiansen S, Hass U (2010) Possible endocrine disrupting effects of parabens and their metabolites. 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Linearity of parabens in food matrices Matrix STD Range (μg/mL) Analyte R 2 Slope Intercept Tomato juice 0.5~20 Methyl 0.9999 109627.9 -4150.5 Ethyl 0.9992 93299.2 1510.3 Isopropyl 0.9998 88207.9 -452.9 Propyl 0.9997 87111.9 -278.6 Isobutyl 0.9985 82844.3 -1280.7 Butyl 0.9982 82425.5 -3575.8 Heptyl 0.9993 68271.7 2765.7 Pickled cucumber 0.5~20 Methyl 0.9998 113044.1 -4637.0 Ethyl 0.9998 103062.2 -3202.1 Isopropyl 0.9998 97662.2 -2197.4 Propyl 0.9998 96378.5 -3277.5 Isobutyl 0.9998 91278.2 -2823.1 Butyl 0.9998 90851.1 -3746.9 Heptyl 0.9998 75682.1 -886.5 Mayonnaise 0.5~20 Methyl 0.9995 112905.1 2846.3 Ethyl 0.9998 104680.6 1529.0 Isopropyl 0.9992 95477.7 -216.6 Propyl 0.9987 95395.1 -2584.4 Isobutyl 0.9995 89279.2 -3086.2 Butyl 0.9996 88534.5 -2937.6 Heptyl 0.9978 72577.4 8015.6 Orange marmalade 0.5~20 Methyl 0.9999 113956.4 -8372.3 Ethyl 0.9995 105913.7 -11998.0 Isopropyl 0.9999 98271.8 -1511.4 Propyl 0.9999 97344.3 -999.5 Isobutyl 0.9999 91534.7 -1461.5 Butyl 0.9999 91252.4 8746.5 Heptyl 0.9999 76279.8 660.4 Iced tea powder 0.5~20 Methyl 0.9991 115138.9 9155.2 Ethyl 0.9996 105546.5 -12083.0 Isopropyl 0.9998 96459.2 2423.3 Propyl 0.9996 95328.8 3135.2 Isobutyl 0.9995 89681.2 893.5 Butyl 0.9995 89859.4 -331.0 Heptyl 0.9997 75425.0 4639.1 Table 2. Intra-day precision and accuracy of parabens in different food matrices Analyte Parameter Tomato juice Pickled cucumber Mayonnaise Orange marmalade Iced tea powder Range Conc. (μg/mL, n=3) 0.5~20 0.5~20 0.5~20 0.5~20 0.5~20 M.P Precision (%) 0.3–1.0 0.7–1.0 0.3–1.5 0.3–0.5 0.5–1.0 Accuracy (%) 99.7–115.3 97.5–107.3 96.8–106.4 95.6–117.5 95.6–110.3 E.P Precision (%) 0.3–1.1 0.6–1.3 0.1–0.6 0.2–0.6 0.2–0.7 Accuracy (%) 99.6–117.1 97.5–108.3 96.9–103.8 95.6–116.3 95.5–114.5 I.P.P Precision (%) 0.2–0.9 0.8–1.4 0.3–0.7 0.3–0.6 0.1–0.8 Accuracy (%) 99.5–115.0 97.5–108.6 96.2–115.9 96.3–105.3 95.4–112.5 P. P Precision (%) 0.1–1.1 0.7–1.2 0.3–1.3 0.2–0.6 0.2–1.1 Accuracy (%) 99.8–117.7 97.5–109.0 95.4–118.8 96.1–105.0 95.3–111.8 I.B.P Precision (%) 0.3–1.0 0.7–1.4 0.3–0.6 0.5–1.3 0.9–5.4 Accuracy (%) 99.6–116.8 97.4–108.1 96.2–120.5 96.1–103.6 94.3–110.0 B.P Precision (%) 0.3–0.9 0.8–1.4 0.3–0.6 0.4–3.0 1.4–13.4 Accuracy (%) 99.8–118.0 97.3–109.0 96.6–118.5 95.0–120.2 92.4–107.1 H.P Precision (%) 0.4–1.0 0.9–1.3 0.3–0.9 0.3–0.8 0.1–0.7 Accuracy (%) 99.7–118.6 97.4–109.1 92.2–99.7 95.9–112.5 95.6–113.9 Table 3. Inter-day precision and accuracy of parabens in different food matrices Analyte Parameter Tomato juice Pickled cucumber Mayonnaise Orange marmalade Iced tea powder Range Conc. (μg/mL, n=3) 0.5~20 0.5~20 0.5~20 0.5~20 0.5~20 M.P Precision (%) 2.7–10.8 2.6–6.2 2.5–6.6 5.1–7.8 2.5–9.4 Accuracy (%) 103.2–106.6 98.4–99.6 99.1–103.0 95.9–107.8 100.8–102.2 E.P Precision (%) 2.3–22.3 2.6–6.4 2.2–12.9 0.8–6.2 0.2–2.6 Accuracy (%) 93.1–106.2 98.5–100.3 98.3–102.0 96.6–109.7 95.9–111.4 I.P.P Precision (%) 2.8–12.4 2.5–6.1 0.8–22.0 0.7–5.0 6.0–16.4 Accuracy (%) 101.4–107.2 98.6–101.3 99.9–101.6 100.2–100.7 98.0–101.4 P. P Precision (%) 2.9–14.2 2.5–5.6 0.3–26.7 0.6–5.3 5.9–17.6 Accuracy (%) 102.0–106.9 98.7–102.4 100.5–103.6 98.9–100.7 94.5–101.1 I.B.P Precision (%) 2.2–13.2 2.5–5.5 0.5–17.1 3.0–5.6 0.4–13.8 Accuracy (%) 102.8–106.5 98.6–101.0 98.7–105.7 98.4–101.1 97.3–101.0 B.P Precision (%) 2.3–6.8 2.5–5.6 1.1–15.0 3.2–6.7 2.4–11.6 Accuracy (%) 103.0–110.0 98.2–102.4 98.7–105.6 99.1–104.1 95.2–100.3 H.P Precision (%) 2.4–12.7 2.4–5.1 3.1–18.9 3.3–8.7 0.2–9.1 Accuracy (%) 102.8–107.0 98.6–103.4 90.0–102.2 100.1–103.0 97.2–104.2 Table 4. Limit of detection (LOD) and limit of quantification (LOQ) of parabens Parameter M.P. E.P. I.P. P.P. I.B.P B.P. H.P. LOD (µg/mL) 0.01 0.01 0.01 0.02 0.01 0.02 0.01 LOQ (µg/mL) 0.02 0.04 0.03 0.05 0.03 0.05 0.04 Table 5. Recovery of parabens in food matrices Matrixs Spiked of Conc.(μg/mL) Recovery(%) Tomato juice 1, 5, 20 92.6-100.1 Pickled cucumber 1, 5, 20 93.6-101.4 Mayonnaise 1, 5, 20 94.1-101.7 Orange marmalade 1, 5, 20 96.1-99.8 Iced tea powder 1, 5, 20 95.9-103.4 Table 7. Classification of collected food samples according to food category Food category Included food types No. of samples * Beverages Carbonated beverage, fruit & vegetable beverage, beverage products 56 Functional beverages Ginseng beverages, herbal beverages 6 Sauces and condiments Ketchup, sauces 8 Pickled foods Pickled products 6 Fruit processed products Jam 5 Others Ginger products, miscellaneous processed foods 21 Total 102 * Samples were purchased from domestic markets (n = 49), imported products (n = 37), and overseas direct purchase products (n = 16). Table 8. Occurrence of parabens in processed food samples Category No. of samples M.P E.P I.P.P P.P I.B.P B.P H.P Beverages 56 ND ND ND ND ND ND ND Functional beverages 6 ND ND ND ND ND ND ND Sauces and condiments 8 ND ND ND ND ND ND ND Pickled foods 6 ND ND ND ND ND ND ND Fruit processed products 5 ND ND ND ND ND ND ND Others 21 ND 0.06 g/kg * (1 sample) ND ND ND ND ND Total 102 ND Detected (1) ND ND ND ND ND * Permitted limit in soy sauce: ≤ 0.25 g/kg (methyl and ethyl parabens) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9099931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616998276,"identity":"da9dd24c-5777-4583-8836-224236bf6663","order_by":0,"name":"Jae-Wook Shin","email":"","orcid":"","institution":"Korea Advanced Food Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jae-Wook","middleName":"","lastName":"Shin","suffix":""},{"id":616998277,"identity":"879a4da2-f337-4fcf-a6a9-cb0a37014306","order_by":1,"name":"Jung-Bok Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYNCCCgYGAyjTAK9CBDhDshbGNlK08E87/vDTzXmHE7eznz3A8KOGwdi8gYAWids5xtK52w4n7uzJS2DsOcZgJnOAkDW3cxjAWjYcyDFg4G1gsJEgpEP+dvrj37lzgFrOvzFg/EuMFoPbCWbSuQ1ALTdyDJiBtpgR1GJ4O8fMOudYuvGGG28MDssckzAmqEUO6LDbOTXWshvO5xg+fFNjYziDkBYoaAaTB4AhSKQGBoY6olWOglEwCkbBCAQASLRBG5A3icIAAAAASUVORK5CYII=","orcid":"","institution":"Yuhan University","correspondingAuthor":true,"prefix":"","firstName":"Jung-Bok","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2026-03-12 04:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9099931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9099931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106251173,"identity":"946ddaf2-c189-4fb7-b803-15ba06cb0e0b","added_by":"auto","created_at":"2026-04-06 17:22:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003estructure of parabens\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099931/v1/291a5299e1e2296bcc163637.jpg"},{"id":106403028,"identity":"b7105eec-cee3-45ab-8b1e-bd06f015e70c","added_by":"auto","created_at":"2026-04-08 09:13:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreparation of parabens in sample\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099931/v1/36094c47fe50545ad71178e2.jpg"},{"id":106251174,"identity":"1a5edb5c-53bd-4297-be05-be830988745e","added_by":"auto","created_at":"2026-04-06 17:22:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFood matrix chromatogram of parabens\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099931/v1/46a9cb5514709c83fca42064.jpg"},{"id":106402472,"identity":"f30572c7-fc69-4a6d-9f0f-e478db842ff3","added_by":"auto","created_at":"2026-04-08 09:12:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFish bone diagram of measurement uncertainty\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099931/v1/837ff9eb5bbc046c45e2f853.jpg"},{"id":106405708,"identity":"5e989d5e-a830-41a6-8a49-8e7df3b10df5","added_by":"auto","created_at":"2026-04-08 09:28:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1687590,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9099931/v1/6137ff60-5213-42c8-b045-1e45125d14ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Gradient HPLC Method for Simultaneous Determination of Seven Parabens Including Heptyl Paraben in Processed Foods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eParabens are widely used as antimicrobial preservatives in foods, pharmaceuticals, and cosmetic products due to their strong antimicrobial activity and chemical stability (Soni et al. 2005; Golden et al. 2005; Błędzka et al. 2014). The most commonly used parabens include methyl paraben, ethyl paraben, propyl paraben, and butyl paraben. These compounds inhibit the growth of bacteria, yeasts, and molds, thereby extending the shelf life of processed products.\u003c/p\u003e \u003cp\u003eDespite their widespread use, increasing concerns regarding the potential health effects of parabens have led to growing interest in monitoring their occurrence in food products (Darbre and Harvey 2008; Boberg et al. 2010; Nowak et al. 2018). Several studies have suggested that parabens may exhibit endocrine-disrupting activity, which has raised public concerns about their safety. Consequently, regulatory authorities in many countries have established limits for the use of parabens as food additives and require reliable analytical methods for monitoring their levels in food products (EFSA 2004; SCCS 2013).\u003c/p\u003e \u003cp\u003eVarious analytical methods have been developed for the determination of parabens in different matrices including food, cosmetics, environmental samples, and biological samples (Guo and Kannan 2013; Liao and Kannan 2014; Liao et al. 2016). Among these techniques, high-performance liquid chromatography (HPLC) is one of the most commonly used methods because of its high sensitivity, good reproducibility, and suitability for complex food matrices (Berset and Brenneisen 2006; Konieczna et al. 2011; Sanchis et al. 2019). Other analytical techniques such as gas chromatography (GC), liquid chromatography\u0026ndash;mass spectrometry (LC\u0026ndash;MS), and LC\u0026ndash;MS/MS have also been widely applied for the determination of parabens in various samples (Vi\u0026ntilde;as et al. 2012; Pedrouzo et al. 2009; Ye et al. 2006).\u003c/p\u003e \u003cp\u003eIn many previous studies, analytical methods were primarily developed for the determination of four commonly used parabens, namely methyl paraben, ethyl paraben, propyl paraben, and butyl paraben (Wang et al. 2015; Gao et al. 2017; Canosa et al. 2006). However, other parabens such as isopropyl paraben, isobutyl paraben, and heptyl paraben may also occur in food matrices. These compounds have received relatively limited attention compared with the commonly studied parabens.\u003c/p\u003e \u003cp\u003eIn Korea, heptyl paraben has not been established as a permitted preservative in the Korean Food Additives Code. Therefore, monitoring the potential presence of heptyl paraben in foods requires appropriate analytical methods. To the best of our knowledge, simultaneous determination of seven parabens including heptyl paraben in food matrices has rarely been reported in previous studies. Therefore, the development of an analytical method capable of simultaneously determining multiple parabens, including less frequently studied compounds, is necessary for comprehensive food safety monitoring.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this study was to develop and validate a reliable gradient HPLC method for the simultaneous determination of seven parabens, including methyl paraben, ethyl paraben, isopropyl paraben, propyl paraben, isobutyl paraben, butyl paraben, and heptyl paraben, in processed food matrices. In addition, measurement uncertainty was evaluated according to the EURACHEM/CITAC guideline to enhance the reliability of the analytical results and to support the application of the method for routine monitoring of parabens in processed foods.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Chemicals and reagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalytical standards of methyl paraben(M.P), ethyl paraben(E.P), isopropyl paraben(I.P.P), propyl paraben(P.P), isobutyl paraben(I.B.P), butyl paraben(B.P), and heptyl paraben (H.P, purity ≥ 98%) were purchased from Sigma–Aldrich (St. Louis, MO, USA). Acetonitrile and methanol (HPLC grade) were obtained from Merck (Darmstadt, Germany). Ultrapure water was produced using a Milli-Q water purification system (Millipore, Bedford, MA, USA). All other reagents used in this study were of analytical grade and used without further purification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Classification of collected food samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFood samples including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder were purchased from local markets in Korea. A total of 102 processed food sampleswere collected, consisting of domestic products (n = 49), imported products (n = 37), and overseas direct purchase products (n = 16). All samples were transported to the laboratory and stored at 4 °C until analysis(Table 7). Prior to extraction, the samples were homogenized thoroughly to ensure sample uniformity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sample preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 2 g of homogenized food sample was accurately weighed into a centrifuge tube and extracted with 10 mL of acetonitrile. The mixture was vortex-mixed for 2 min, followed by sonication for 10 min. The extract was then centrifuged at 4000 rpm for 10 min, and the resulting supernatant was filtered through a 0.45 μm membrane filter prior to HPLC analysis. The overall sample preparation procedure used in this study is illustrated in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 HPLC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChromatographic separation was performed using a high-performance liquid chromatography (HPLC) system equipped with a photodiode array (PDA) detector. Separation of the analytes was achieved using a Kinetex C18 column (4.6 × 150 mm, 5 μm)maintained at 40 °C. The mobile phase consisted of water (A)and acetonitrile (B). Gradient elution was applied during the chromatographic run. The flow rate was set at 1.0 mL/min, and the injection volumewas 10 μL. Detection of parabens was carried out using the PDA detector at 245 nm. Under these chromatographic conditions, the seven parabens were successfully separated within a total run time of 25 min(Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Method validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValidation of the developed method was performed by evaluating the following parameters: linearity, matrix effect, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ). Linearity was assessed using matrix-matched calibration curves prepared by spiking blank food matrices with mixed standard solutions of seven parabens at different concentration levels. The calibration curves were constructed in the concentration range of 0.5–20 μg/mL, and satisfactory linearity was obtained for all analytes with correlation coefficients (R²) greater than 0.999. Precision and accuracy of the method were evaluated by recovery experiments using spiked food samples at three concentration levels (1, 5, and 20 μg/mL). Intra-day precision was determined by analyzing three replicate samples within a single day, while inter-day precision was evaluated over three consecutive days. Precision was expressed as relative standard deviation (RSD, %), and accuracy was expressed as recovery percentage. The limits of detection (LOD) and limits of quantification (LOQ) were calculated based on signal-to-noise ratios of 3 and 10, respectively. Alternatively, LOD and LOQ values were also estimated according to the following equations:LOD = 3.3 σ / S, LOQ = 10 σ / S. where σrepresents the standard deviation of the response and Srepresents the slope of the calibration curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Measurement uncertainty\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasurement uncertainty was evaluated according to the principles described in the EURACHEM/CITAC Guide: Quantifying Uncertainty in Analytical Measurement. The major sources of uncertainty considered in this study were identified and illustrated using a fishbone diagram (Fig. 4). The uncertainty sources included calibration, repeatability, sample preparation, and instrumental measurement, from which the standard uncertainty and combined uncertainty were calculated. The expanded uncertainty was obtained by multiplying the combined standard uncertainty by a coverage factor (k = 2), corresponding to a confidence level of approximately 95%.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Chromatographic separation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe developed gradient HPLC method enabled the simultaneous separation of seven parabens within a total run time of 25 min. The retention times increased according to the hydrophobicity of the analytes due to stronger interactions with the C18 stationary phase. The chemical structures of the seven parabens analyzed in this study are presented in Fig. 1. Among the target analytes, heptyl paraben was first optimized during the method development stage and subsequently incorporated into the simultaneous analytical method together with the commonly used parabens. Under the optimized chromatographic conditions, all analytes were baseline separated with satisfactory resolution. Representative chromatograms obtained from different food matrices are shown in Fig. 2. Methyl paraben eluted first, whereas heptyl paraben exhibited the longest retention time. The gradient elution program significantly improved the separation efficiency of structurally similar compounds, particularly isopropyl/propyl parabens and isobutyl/butyl parabens, resulting in satisfactory chromatographic resolution. These results demonstrate that the developed gradient HPLC method provides efficient chromatographic separation for the simultaneous determination of multiple parabens in processed food matrices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Linearity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe linearity of the developed method was evaluated using matrix-matched calibration curvesin five representative food matrices, including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder. Calibration curves were constructed over the concentration range of 0.5–20 μg/mLfor all seven parabens. Excellent linearity was obtained for all analytes in the tested food matrices, with correlation coefficients (R²) ranging from 0.9982 to 0.9999. The use of matrix-matched calibration effectively compensated for potential matrix effects during quantitative analysis. The calibration parameters, including slope and intercept values for each analyte in different matrices, are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 \u0026nbsp;Precision and accuracy of intra \u0026amp; Inter day\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe precision and accuracy of the developed method were evaluated through intra-day and inter-day validation experimentsusing spiked food samples. Intra-day precision was determined by analyzing replicate samples within the same day, whereas inter-day precision was evaluated over three consecutive days. The developed method showed good precision, with relative standard deviation (RSD) values generally below 3%for all analytes. Recovery values ranged from 93% to 114%, demonstrating satisfactory accuracy of the analytical method for different food matrices. The obtained precision and recovery values satisfied commonly accepted analytical performance criteria. The detailed results of intra-day and inter-day precision and accuracy are presented in Tables 2 and 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 \u0026nbsp;LOD \u0026amp; LOQ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe limits of detection (LOD) and limits of quantification (LOQ) for the seven parabens were determined to evaluate the sensitivity of the developed method. The LOD values ranged from 0.01 to 0.02 μg/mL, while the LOQ values ranged from 0.02 to 0.05 μg/mL. These results indicate that the developed HPLC method provides sufficient sensitivity for the determination of parabens in processed food samples. The LOD and LOQ values for the seven parabens are summarized in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 \u0026nbsp;Result of recovery in sample matrixs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe accuracy of the developed method was evaluated through recovery experiments using spiked food samples at concentration levels of 1, 5, and 20 μg/mL. The recovery values obtained for the five food matrices ranged from 92.6% to 103.4%. These results demonstrate satisfactory accuracy of the developed method for the determination of parabens in various food matrices. The recovery results obtained for the five food matrices are summarized in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 \u0026nbsp;Measurement uncertainty\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasurement uncertainty of the developed HPLC method was evaluated according to the EURACHEM/CITAC Guide: Quantifying Uncertainty in Analytical Measurement. The expanded uncertainty was calculated using a coverage factor of k = 2, corresponding to a confidence level of approximately 95%. As summarized in Table 6, the certified concentrations of the parabens ranged from 51.44 to 123.78 mg/kg. The expanded uncertainty values were 51.44 ± 2.51 mg/kg for methyl paraben, 120.40 ± 23.27 mg/kg for ethyl paraben, 119.56 ± 22.74 mg/kg for isopropyl paraben, 118.54 ± 22.81 mg/kg for propyl paraben, 119.52 ± 23.67 mg/kg for isobutyl paraben, 120.10 ± 23.60 mg/kg for butyl paraben, and 123.78 ± 6.46 mg/kg for heptyl paraben. These results indicate that the developed analytical method provides reliable quantitative results with acceptable measurement uncertainty for the determination of parabens in processed food matrices. The relatively larger measurement uncertainty values observed for most parabens, except methyl and heptyl parabens, may be attributed to the simultaneous determination performed at a fixed detection wavelength of 245 nm rather than at the individual maximum absorbance wavelengths of each analyte. This analytical condition may result in slight differences in calibration sensitivity among the analytes, which consequently affects the estimated measurement uncertainty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 \u0026nbsp;Result of samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe occurrence of seven parabens in processed food samples was investigated using the developed HPLC method. A total of 102 food samples representing six food categories were analyzed. As shown in Table X, most parabens were not detected (ND) in the analyzed samples. Among the target compounds, ethyl paraben was detected in one sample categorized as “others” at a concentration of 0.006 g/kg, while the remaining parabens were not detected in any of the analyzed samples. The detected concentration was within the regulatory limits established in the Republic of Korea, indicating that it does not pose a safety concern for consumers. Although most parabens were not detected in the analyzed samples, these results demonstrate that the developed analytical method provides sufficient sensitivity and reliability for the monitoring of parabens in processed food matrices and can be effectively applied for routine food safety surveillance. The occurrence of parabens in the analyzed food samples is summarized in Table 8.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this study, a gradient HPLC method was successfully developed and validated for the simultaneous determination of seven parabens, including methyl, ethyl, isopropyl, propyl, isobutyl, butyl, and heptyl paraben, in processed foods. The developed method demonstrated satisfactory linearity, sensitivity, precision, and accuracy across various food matrices. Measurement uncertainty was also evaluated according to the EURACHEM/CITAC guideline to improve the reliability of the analytical results.\u003c/p\u003e\n\u003cp\u003eApplication of the method to 102 processed food samples, including domestic products, imported products, and overseas direct purchase products, showed that most parabens were not detected. Ethyl paraben was detected in only one sample at a concentration of 0.006 g/kg, which was within the regulatory limit established in the Republic of Korea. Therefore, the developed method can be considered reliable and suitable for the simultaneous determination and monitoring of parabens in processed foods. In particular, the inclusion of heptyl paraben in the simultaneous analytical method represents a meaningful advancement that broadens the analytical scope for comprehensive monitoring of parabens in food safety management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant (14162MFDS971) from the Ministry of Food and Drug Safety in South Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.B.K. and W.J. S conceived and designed the study. J.B.K. and W.J. S performed the experiments and analyzed the data. J.B.K. and W.J. S wrote the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerset JD, Brenneisen R (2006) Determination of parabens in food by high-performance liquid chromatography. J Chromatogr A 1116:240\u0026ndash;247. https://doi.org/10.1016/j.chroma.2006.03.064\u003c/li\u003e\n\u003cli\u003eBłędzka D, Gromadzińska J, Wąsowicz W (2014) Parabens. From environmental studies to human health. Environ Int 67:27\u0026ndash;42. https://doi.org/10.1016/j.envint.2014.02.007\u003c/li\u003e\n\u003cli\u003eBoberg J, Taxvig C, Christiansen S, Hass U (2010) Possible endocrine disrupting effects of parabens and their metabolites. Reprod Toxicol 30:301\u0026ndash;312. https://doi.org/10.1016/j.reprotox.2010.03.011\u003c/li\u003e\n\u003cli\u003eCanosa P, Rodr\u0026iacute;guez I, Rub\u0026iacute; E, Cela R (2006) Determination of parabens in food samples by liquid chromatography\u0026ndash;mass spectrometry. J Chromatogr A 1135:167\u0026ndash;173. https://doi.org/10.1016 /j.chroma.2006.09.059\u003c/li\u003e\n\u003cli\u003eDarbre PD, Harvey PW (2008) Paraben esters: review of recent studies of endocrine toxicity, absorption, esterase and human exposure, and discussion of potential human health risks. J Appl Toxicol 28:561\u0026ndash;578. https://doi.org/10.1002/jat.1358\u003c/li\u003e\n\u003cli\u003eEFSA (2004) Opinion of the Scientific Panel on Food Additives, Flavourings, Processing Aids and Materials in Contact with Food on parabens. 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Food Chem 127:723\u0026ndash;728. https://doi.org/10.1016/j.foodchem.2011.01.042\u003c/li\u003e\n\u003cli\u003eLiao C, Kannan K (2014) Occurrence of parabens in foodstuffs from China and their implications for human dietary exposure. Environ Sci Technol 48:391\u0026ndash;398. https://doi.org/10.1021/es4044658\u003c/li\u003e\n\u003cli\u003eLiao C, Liu F, Guo Y (2016) Occurrence of parabens in food samples and dietary exposure assessment. Environ Sci Technol 50:2016\u0026ndash;2024. https://doi.org/10.1021/acs.est.5b05547\u003c/li\u003e\n\u003cli\u003eNowak K, Ratajczak-Wrona W, G\u0026oacute;rska M, Jabłońska E (2018) Parabens and their effects on the endocrine system. Molecules 23:164. https://doi.org/10.3390/molecules23071640\u003c/li\u003e\n\u003cli\u003ePedrouzo M, Borrull F, Pocurull E, Marc\u0026eacute; RM (2009) Determination of parabens in food samples by liquid chromatography\u0026ndash;tandem mass spectrometry. J Chromatogr A 1216:6995\u0026ndash;7002. https://doi.org/10.1016/j.chroma.2009.07.064\u003c/li\u003e\n\u003cli\u003eSanchis Y, Yus\u0026agrave; V, Coscoll\u0026agrave; C (2019) Determination of parabens in food samples by LC-MS/MS. Food Anal Methods 12:1458\u0026ndash;1467. https://doi.org/10.1007/s12161-019-01528-4\u003c/li\u003e\n\u003cli\u003eSCCS (2013) Scientific Committee on Consumer Safety: Opinion on parabens. SCCS/1514/13.\u003c/li\u003e\n\u003cli\u003eSoni MG, Taylor SL, Greenberg NA, Burdock GA (2005) Evaluation of the health aspects of methyl paraben: a review of the published literature. Food Chem Toxicol 43:985\u0026ndash;1015. https://doi.org/10.1016/j.fct.2005.01.020\u003c/li\u003e\n\u003cli\u003eVi\u0026ntilde;as P, Campillo N, Mart\u0026iacute;nez-Castillo N, Hern\u0026aacute;ndez-C\u0026oacute;rdoba M (2012) Determination of parabens in food samples by liquid chromatography\u0026ndash;mass spectrometry. Anal Bioanal Chem 402:263\u0026ndash;272. https://doi.org/10.1007/s00216-011-5397-6\u003c/li\u003e\n\u003cli\u003eWang X, Li Y, Chen B, Zhang J (2015) Simultaneous determination of parabens in foods by high-performance liquid chromatography. Food Control 50:204\u0026ndash;210. https://doi.org/10.1016/ j.foodcont.2014.09.010\u003c/li\u003e\n\u003cli\u003eYe X, Bishop AM, Reidy JA, Needham LL, Calafat AM (2006) Automated on-line column-switching HPLC-MS method for the determination of parabens in human urine. Anal Chim Acta 562:38\u0026ndash;44. https://doi.org/10.1016/j.aca.2006.01.025\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLinearity of parabens in food matrices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTD Range (\u0026mu;g/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTomato juice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e109627.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-4150.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEthyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9992\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e93299.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1510.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsopropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e88207.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-452.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9997\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e87111.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-278.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsobutyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9985\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e82844.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1280.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eButyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9982\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e82425.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3575.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHeptyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9993\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e68271.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2765.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePickled cucumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e113044.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-4637.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEthyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e103062.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3202.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsopropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e97662.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-2197.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e96378.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3277.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsobutyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e91278.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-2823.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eButyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e90851.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3746.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHeptyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e75682.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-886.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMayonnaise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e112905.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2846.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEthyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e104680.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1529.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsopropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9992\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e95477.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-216.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e95395.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-2584.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsobutyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e89279.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3086.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eButyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9996\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e88534.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-2937.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHeptyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9978\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e72577.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8015.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrange marmalade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e113956.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-8372.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEthyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e105913.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-11998.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsopropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e98271.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1511.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e97344.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-999.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsobutyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e91534.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1461.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eButyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e91252.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8746.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHeptyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e76279.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e660.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIced tea powder\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e115138.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9155.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEthyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9996\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e105546.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-12083.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsopropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e96459.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2423.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePropyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9996\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e95328.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3135.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIsobutyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e89681.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e893.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eButyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e89859.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-331.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHeptyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.9997\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e75425.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4639.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIntra-day precision and accuracy of parabens in different food matrices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTomato juice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePickled cucumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMayonnaise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrange marmalade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIced tea powder\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003eRange\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConc. (\u0026mu;g/mL, n=3)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.7\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.5\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.7\u0026ndash;115.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.5\u0026ndash;107.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.8\u0026ndash;106.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.6\u0026ndash;117.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.6\u0026ndash;110.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.6\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.6\u0026ndash;117.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.5\u0026ndash;108.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.9\u0026ndash;103.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.6\u0026ndash;116.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.5\u0026ndash;114.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.P.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.8\u0026ndash;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1\u0026ndash;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.5\u0026ndash;115.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.5\u0026ndash;108.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.2\u0026ndash;115.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.3\u0026ndash;105.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.4\u0026ndash;112.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP. P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1\u0026ndash;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.7\u0026ndash;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.8\u0026ndash;117.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.5\u0026ndash;109.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.4\u0026ndash;118.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.1\u0026ndash;105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.3\u0026ndash;111.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.B.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.7\u0026ndash;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.5\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.9\u0026ndash;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.6\u0026ndash;116.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.4\u0026ndash;108.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.2\u0026ndash;120.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.1\u0026ndash;103.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e94.3\u0026ndash;110.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.8\u0026ndash;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.4\u0026ndash;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.4\u0026ndash;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.8\u0026ndash;118.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.3\u0026ndash;109.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.6\u0026ndash;118.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.0\u0026ndash;120.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e92.4\u0026ndash;107.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.4\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.9\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1\u0026ndash;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.7\u0026ndash;118.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.4\u0026ndash;109.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e92.2\u0026ndash;99.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.9\u0026ndash;112.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.6\u0026ndash;113.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInter-day precision and accuracy of parabens in different food matrices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTomato juice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePickled cucumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMayonnaise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrange marmalade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIced tea powder\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003eRange\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConc. (\u0026mu;g/mL, n=3)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5~20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.7\u0026ndash;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.6\u0026ndash;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.1\u0026ndash;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e103.2\u0026ndash;106.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.4\u0026ndash;99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.1\u0026ndash;103.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.9\u0026ndash;107.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e100.8\u0026ndash;102.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.3\u0026ndash;22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.6\u0026ndash;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.2\u0026ndash;12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.8\u0026ndash;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e93.1\u0026ndash;106.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.5\u0026ndash;100.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.3\u0026ndash;102.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e96.6\u0026ndash;109.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.9\u0026ndash;111.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.P.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.8\u0026ndash;12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.8\u0026ndash;22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.7\u0026ndash;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.0\u0026ndash;16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e101.4\u0026ndash;107.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.6\u0026ndash;101.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.9\u0026ndash;101.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e100.2\u0026ndash;100.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.0\u0026ndash;101.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP. P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.9\u0026ndash;14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.3\u0026ndash;26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.6\u0026ndash;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.9\u0026ndash;17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e102.0\u0026ndash;106.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.7\u0026ndash;102.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e100.5\u0026ndash;103.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.9\u0026ndash;100.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e94.5\u0026ndash;101.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.B.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.2\u0026ndash;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.5\u0026ndash;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.0\u0026ndash;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.4\u0026ndash;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e102.8\u0026ndash;106.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.6\u0026ndash;101.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.7\u0026ndash;105.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.4\u0026ndash;101.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.3\u0026ndash;101.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.3\u0026ndash;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.5\u0026ndash;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.1\u0026ndash;15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.2\u0026ndash;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.4\u0026ndash;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e103.0\u0026ndash;110.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.2\u0026ndash;102.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.7\u0026ndash;105.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e99.1\u0026ndash;104.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95.2\u0026ndash;100.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ePrecision (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.4\u0026ndash;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.4\u0026ndash;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.1\u0026ndash;18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.3\u0026ndash;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2\u0026ndash;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e102.8\u0026ndash;107.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e98.6\u0026ndash;103.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e90.0\u0026ndash;102.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e100.1\u0026ndash;103.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e97.2\u0026ndash;104.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Limit of detection (LOD) and limit of quantification (LOQ) of parabens\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eE.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eI.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eI.B.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eB.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eH.P.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLOD (\u0026micro;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLOQ (\u0026micro;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Recovery of parabens in food matrices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatrixs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpiked of Conc.(\u0026mu;g/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecovery(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eTomato juice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1, 5, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e92.6-100.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003ePickled cucumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1, 5, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e93.6-101.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eMayonnaise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1, 5, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e94.1-101.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eOrange marmalade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1, 5, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e96.1-99.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eIced tea powder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1, 5, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e95.9-103.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cimg 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\" width=\"502\" height=\"550\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Classification of collected food samples according to food category\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncluded food types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of samples\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eBeverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eCarbonated beverage, fruit \u0026amp; vegetable beverage, beverage products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eFunctional beverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eGinseng beverages, herbal beverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eSauces and condiments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eKetchup, sauces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003ePickled foods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003ePickled products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eFruit processed products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eJam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eGinger products, miscellaneous processed foods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Samples were purchased from domestic markets (n = 49), imported products (n = 37), and overseas direct purchase products (n = 16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. Occurrence of parabens in processed food samples\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.P.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI.B.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH.P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBeverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eFunctional beverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eSauces and condiments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003ePickled foods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eFruit processed products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.06 g/kg\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(1 sample)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eDetected (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Permitted limit in soy sauce: \u0026le; 0.25 g/kg (methyl and ethyl parabens)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Parabens, HPLC, Food additives, Food safety, Method validation, Simultaneous determination","lastPublishedDoi":"10.21203/rs.3.rs-9099931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9099931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParabens are widely used as antimicrobial preservatives in foods, pharmaceuticals, and cosmetic products due to their effectiveness and chemical stability. However, concerns regarding their potential health effects have increased the need for reliable analytical methods for monitoring parabens in food products. In Korea, heptyl paraben has not been established as a permitted preservative in the Korean Food Additives Code. Therefore, an analytical method for heptyl paraben was first established, and subsequently incorporated into a simultaneous analytical method for commonly used parabens. In this study, a gradient high-performance liquid chromatography (HPLC) method was developed and validated for the simultaneous determination of seven parabens including methyl paraben, ethyl paraben, isopropyl paraben, propyl paraben, isobutyl paraben, butyl paraben, and heptyl paraben in processed foods. The method was validated in terms of linearity, precision, accuracy, and sensitivity. The developed method was successfully applied to several food matrices including tomato juice, pickled cucumber, mayonnaise, orange marmalade, and iced tea powder. The proposed method provides a reliable analytical approach for the simultaneous qualitative and quantitative determination of multiple parabens in processed foods and can be applied for food safety monitoring.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Gradient HPLC Method for Simultaneous Determination of Seven Parabens Including Heptyl Paraben in Processed Foods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 17:22:31","doi":"10.21203/rs.3.rs-9099931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T16:08:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T17:58:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142846817491193567971473221024577230441","date":"2026-04-18T07:55:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255721739787319520392961246921206525028","date":"2026-04-10T06:10:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T04:26:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T12:34:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T12:33:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2026-03-12T04:16:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff02ad9b-c3e3-495f-93a9-501e9f94c51b","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T17:22:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 17:22:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9099931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9099931","identity":"rs-9099931","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}