Introduction

Nowadays, organic solvents are extensively used in various processes because of their unique characters. Benzene, a well-known organic solvent, is applied to synthesize new material in different processes such as petrochemical, paint, adhesive process, rubber, and plastic1. Breathing and skin contact are regarded as the major paths of exposure to benzene2. The carcinogenicity of benzene (class I carcinogen) has been confirmed by the US Environmental Protection Agency (EPA) and International Agency for Research on Cancer (IARC)3,4. Many adverse health problems may be created through benzene intake. The effect of benzene on the hematopoietic organ is one of the main adverse health effects that has long been considered. Suppression of blood factors such as hemoglobin and white blood cells has been reported in people exposed to benzene5,6. Moreover, long-term exposure may suppress the immune system and reduce the number of lymphocytes7.

There are two ways to accurately determine the level of exposure to pollutants: air sampling and biological monitoring. Benzene could enter the body via inhalation, gastrointestinal system and skin contact8. Therefore, biological monitoring is necessary for precise determination of real exposure to benzene. During metabolism process, benzene is metabolized to different ring-opened and hydroxylated compounds such as trans, trans-muconic acid (t,t-MA), phenol, catechol and S-phenylmercapturic acid9,10. Phenol and catechol metabolites were associated with high concentration of benzene exposure (higher than 10 ppm). However, in low levels of benzene (lower than 1 ppm), no association was found between urinary levels of phenol and catechol compounds11. It has been found that there is a good correlation between urinary t,t-MA and concentration of benzene in air and blood in low levels of benzene exposure12,13. Furthermore, the American Conference of Governmental Industrial Hygienists (ACGIH) has introduced t,t-MA as a urinary biomarker of benzene exposure14.

Oxidation is the first step of benzene metabolism which produces benzene oxide. Under the influence of the metabolism processes, benzene oxide undergoes various fates. As an alternative way during the metabolism process, benzene ring may be opened in the benzene oxide or oxepin step via the reactive intermediate muconaldehyde. By oxidizing the trans,trans-mucondialdehyde, t,t-MA is produced15.

Although monitoring of biological media is an acceptable way to accurately measure the real exposure to the compounds, it is always complicated, laborious, time consuming and costly process16. The complexity of the biological fluids and also low amounts of the metabolites are the major barriers for analyzing the biomarkers in theses media. Sample preparation is a good solution to conquer these problems.

Up to now, various modern, high-throughput, miniaturized sample preparation techniques coupled with analytical instruments have been introduced for trace monitoring of t,t-MA in urine samples17,18,19. Due to low organic solvent consumption and high preconcentration factor, dispersive liquid–liquid microextraction (DLLME) and dispersive solid-phase microextraction (SPME) methods are among the most popular methods. The SMPE procedure requires preparation of the solid sorbent and subsequently analysis of the prepared sorbent by analytical instrument, which is time consuming and costly. The DLLME procedures are easy, rapid, and also require no extra characterization20,21. However, the use of toxic solvents (solvents used as extractant and disperser) is the main drawback of this method. Halogenated hydrocarbons are an example of these solvents, which are toxic and heavier than water22,23. As an attempt to solve these limitations, DLLME using a floating organic droplet (DLLME-SFOD) has been introduced with remarkable advantages over conventional extraction methods. The use of less toxic organic solvent, use of low organic solvent, operator safety, rapid extraction, and also environmentally friendly are some advantages of the proposed sample preparation compared to conventional DLLME24. In DLLME-SFOD technique, the density of the applied organic solvent is often lower than water, which help to be floated in the sample surface and simply collected from the sample media25. In this work, for the first time, a new DLLME-SFOD technique was optimized for the trace monitoring of t,t-MA from urine specimens. The applied organic solvent during solidification procedure has good consistency for the extraction of t,t-MA with excellent recovery which was comparable with routinely applied SPE procedure in samples collected from real exposed workers with benzene. Another important point in the current work is using response surface methodology for optimization of the solidified floating organic droplet microextraction in a complex sample such as urine. Here, we tried to show the importance of multivariate-optimization (instead of one-at-a-time process), to handle a multi-factor method such as SFOD especially for a complex real media. We think the obtained wonderful results can be related to this precise optimization and considering all factors and between-factors interactions.

Experimental

Reagents and solutions

t,t-MA (analytical standard) was obtained from Sigma–Aldrich (Darmstadt, Germany). 1-undecanol (99%), 1-dodecanol (99%), sodium hydroxide, sodium chloride, hydrochloric acid (HCl), acetic acid, and methanol (HPLC grade) were all provided from Merck Company (Darmstadt, Germany). Doubled distilled water was obtained from a milli-Q system (Bedford, MA, USA). Standard solutions of t,t-MA (1000 mg L−1) were prepared individually using methanol and double-distilled water at 1:4 ratio. The required working standards were daily prepared from the stock solution. The prepared stock solutions were kept at refrigerator and were freshly prepared after 10 days.

Instrumentation

Chromatographic analyses were done by a HPLC system (HPLC, Knauer,Smartline system 1000, Berlin, Germany) combined with an ultraviolet detector (Knauer, 2000). The separation of analyte was performed via a C18 analytical column (Knauer, Eurospher 100–5 C18, 150 mm × 4.6 mm). A mixture of acetic acid 1%-methanol (70/30 v/v) with a flow rate of 1.0 ml/min was used as the mobile phase. The wavelength of UV detector was set at 274 nm. The injection was done manually using a 20µL stainless steel loop. A Metrohm 827 pH-meter (Metrohm, Switzer-land) was used for measurement of pH. Moreover, Hettich EBA 20 centrifuge was used for the separation of organic solvent from sample solution.

DLLME-SFO procedure

Figure 1 presents the schematic diagram of DLLME-SFOD procedure. In the proposed extraction method, a 10 mL of aqueous sample solution consist of 1.0 μg mL−1 of t,t-MA was poured into a 15-mL screw-cap glass centrifuge tube. pH of the solution was regulated in definite value (A) by stepwise addition of hydrochloric acid and sodium hydroxide solution. Then, the salt amount of the solution was adjusted using required percent (w/v) of NaCl (D,%). In the next step, a mixed solution of 1-undecanol, as extractant solvent (B, µL) and the needed volume of methanol, as dispersive solvent, (C, µL) was quickly introduced into the sample solution and the resultant mixture was agitated for 30 s using a vortex mixer. In this step, a cloudy suspension was created because of the dispersion of tiny droplets of 1-undecanol in the sample solution and subsequently, the extraction of analyte into 1-undecanol occurred quickly. The formed emulsion was then centrifuged for 5.0 min at 4000 rpm. After centrifugation, the tiny droplets of extractant solvent 1-undecanol containing analyte were floated at the top of the sample solution due to the difference between the density of 1-undecanol and aqueous solution. The glass tube was then put into cold-water immersion for 5 min until the solidification of organic solvent occurred. Finally, the solidified organic droplet was put into a HPLC vial and brought to lab temperature. In lab temperature, the solidified droplet was quickly melted and then analyzed by the HPLC–UV.

Figure 1
figure 1

A schematic diagram of the utilized DLLME-SDOD procedure.

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Real sample collection

Metabolite-free urine specimen was obtained from a healthy male in the analytical lab. Moreover, five end-shift urine samples from exposed workers of a petrochemical company were taken for monitoring of t,t-MA. All collected urine samples were stored at – 20 °C, prior to analysis. Sampling urine sample and human participation was done in compliance with the relevant regulations and the ethical principles outlined in the Helsinki’s declaration. This study was approved by the Ethics Committee of the Shiraz University of Medical Sciences.

Sample treatment

Before analysis, both blank (samples taken from unexposed healthy male) and real samples (samples taken from workers who occupationally expose to benzene) were prepared according to the following method; 10 mL of each sample was transferred to a conical centrifuged tube and centrifuged for 10 min at 4000 rpm. The supernatant liquid was then filtered via a 0.45 μm filter and 5 mL of the obtained solution was poured to a vial. For reducing the effect of matrix, the urine sample was diluted to 10 mL using double distilled water. The obtained solution was then pre-concentrated using DLLME-SFOD procedure and finally analyzed by HPLC–UV.

Experimental design

Different parameters including the solution pH, salt amount, type and volume of the disperser and extractant solvents, and extraction time affect the efficiency of the microextraction techniques. Therefore, an essential step in extraction techniques is determination of the optimum conditions of experiments. Experimental design methodology is an effective way for optimizing of the effective parameters on the extraction process. Saving time, enhancing the efficiency of the process, investigation of the interactions between parameters, and decreasing the errors with the minimum numbers of runs are some advantages of the experimental design method26–28. Response surface methodology (RSM) have been extensively utilized to link polynomial models with experimental data using different approaches, including central composite design (CCD). In this study, a CCD with five variables of pH (A), extractant solvent volume (B, μL), disperser solvent volume (C, μL), salt amount (D w/v,%)), and extraction time (min) (E) at five boundary levels (− α, − 1, 0, + 1, + α) was used (Table 1). The total runs number (N) (\({\text{N}} = \frac{1}{2}\) 2 K + 2 K + N0, which, k is introduced as the number of variables and N0 is the number of central points) was calculated to be 32 runs with central and axial points (α = 2) (Table S1, Supplementary information). The results obtained from the CCD were fitted to the following polynomial equation:

$$Y = \mathop \sum \limits_{i = 1}^{n} b_{i} x_{i} + \mathop \sum \limits_{i = 1}^{n} b_{ii} x_{i}^{2} + \mathop \sum \limits_{i < j}^{n} b_{ij} x_{i} x_{j} + \varepsilon$$
(1)

where Y is a predicted response, n is the number of parameters, xi, xj are independent variables in coded units ,b0, bi, bii, bij are the regression coefficients, and \(\varepsilon\) is the residual error29,30. As denoted previously, we had five independent variables (i = 1,2,3,4,5) which is shown by A,B,C,D, E as code names. Subsequently, the analysis of variance (ANOVA) was used to determine the effects of linear, quadratic and interaction regression coefficient using Design Expert 7.1.3 software (Stat-Ease Inc., Minneapolis, Minnesota, USA). The adaptability between polynomial equation and response was evaluated using determination coefficient (R2). The significance of the polynomial equation terms was analyzed statistically by computing F value at P < 0.1. Because of high numerical values of peak area, and to have ordered results and plots, the peak areas was normalized using the maximum (highest peak area) and the normalized responses was utilized for further modeling, as shown in Table S1 (Supplementary information).

Table 1 The matrix of central composite design and responses.
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Results and discussion

In this study, the DLLME-SFOD followed by HPLC–UV was optimized for the extraction and determination of t,t-MA from urine samples.

Selection of the extractant and disperser solvents

In the DLLME-SFOD method, the choice of the suitable extractant and disperser solvents is critical to attain maximum extraction output. The extractant solvent was selected regarding some requirements such as high affinity to the analyte, low density (in compared to water), immiscible with water, high extraction output for target compound, low toxicity, compatibility with HPLC chromatographic separation, inexpensive, and low volatility. Among the mentioned requirements, density parameter is one of the most important parameters compared to others due to easy collection of the extractant solvent. Considering the above-mentioned requirements, some solvents such as 1-undecanol, 1-dodecanol and 2-dodecanol, as the most popular solvents, were candidate as the extractant solvents. Herein, because of practical ease, lowest cost and availability, 1-undecanol and 1-dodecanol, which had been introduced as the most efficient extractant solvent in previous studies31,32 were tested. The results showed that 1-undecanol was the best solvent and selected for additional examinations. Miscibility in both aqueous media and extractant solvent is the only requirement for selecting disperser solvent. In this work, acetone, acetonitrile and methanol were tested and methanol was chosen as the disperser solvent because of its ability to form a suspension with fine droplets in both media and significant potential for procedure in compare with other solvents such as acetonitrile and acetone during pre-tests.

RSM-CCD

To prevent the impact of uncontrolled variables, the order of the experimental runs, shown in supplementary material, was done randomly in the CCD matrix. According to the outputs of ANOVA analysis on the collected data, the quadratic polynomial model was suitably fitted to the obtained data. Then to provide a refined model and to remove the factors or interaction terms with not-significant P-value (> 0.1) a backward elimination variable selection was done33 and the final findings as following equation:

$$\begin{aligned} {\text{Peak}}\;{\text{Area}} = & {364}.{27} - {32}.{24} \times {\text{A}} + 0.{38} \times {\text{B}} - 0.{53} \times {\text{C}} - {3}.{34} \times {\text{D}} \\ & \quad {-}{149}.{28} \times {\text{E}} + 0.0{4} \times {\text{AC}} + {7}.{39} \times {\text{AE}} + 0.0{2} \times {\text{CD}} + 0.{1}0 \times {\text{CE}} + {16}.0{6} \times {\text{E}}^{{2}} . \\ \end{aligned}$$
(2)

To evaluate the significance of the above multiple linear regression (MLR) model, Fisher’s statistical test (F-test) was used33,34. The F-value of the above-developed model was 27.23 and was higher than the critical F-value in required degree of freedom and shows the significance of that the model which is shown in Eq. (2). Another important criterion for confirming the validity of such MLR models, obtained from the table of experimental design, is a non-significant ‘Lack of Fit (LOF). F-value’ of the LOF in the current model was 0.669 and confirms that LOF was not significant, and represents the absence of pure error in the suggested MLR model.

Moreover, to evaluate the overall fitness and predictive potential of the model, the squared regression coefficients, including the calibration R2 (R2cal), adjusted R2 (R2adj), and R2 of prediction (R2pred) were calculated35.As denoted in Table 2, R2cal shows that the suggested CCD model covers 92.8% of data. Based on literature, the amount of R2adj was bigger than 0.8, which confirms its goodness of fit36. Besides, the R2pred (= 0.807) was consistent with the R2adj (= 0.894). The closeness of R2pred and R2adj with a difference lower than 0.2 is an indicator for the very good prediction ability. Another statistical metrics for measuring the Signal to Noise(S/N) ratio is the adequate precision. As it is well known in literature, a ratio greater than 4 indicates an acceptable precision36. According to the results shown in Table 2, the value of 18.54 for adequate precision indicates a suitable S/N. All of the above-mentioned metrics are indicators of suitable correlation between the included factors and interaction terms in Eq. (2) with the peak area as the response value of the extraction recovery of t,t-MA using DLLME-SFOD. To show the goodness of fit in the propped model, the plot of predicted peak area versus the obtained experimental values is represented in Fig. 2a. One of the criteria for showing the applicability domain of a multiple linear regression model is laying the residual value (difference of actual and prediction response) between the accepted range of ± 3σ37. As shown in Fig. 2b, the studentized residual of the obtained model had a narrow range within ± 2σ which shows the reliability of model. On the other hand, the residual value of all the runs are scattered randomly in both side of zero line which confirms the absence of systematic error37.

Table 2 Analysis of variance (ANOVA), model statistics summery and quality of the quadratic model for the extraction of t,t-MA.
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Figure 2
figure 2

The internally studentized residuals vs. the performed runs (a); and the predicted value vs. actual response (b).

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Optimization of effective variables using CCD

According to which was shown in Eq. (2), some interaction terms was entered in the multiparameter model, including between-factors-interactions (such as AC, AE, CD and CE) and self-interactions (E2). The three-dimensional (3D) response surface curves were applied to present the mixed effect of factors in between-factors-interaction terms, which can provide useful information to find optimal values of the independent parameters. These curves provide important information about maximum response and possible interactions between two independent variables. Figure 3a illustrates the simultaneous effect of pH of sample and disperser solvent volume on the response (peak area). In all extraction techniques in which analytes are basic or acidic, pH is an important parameter for extraction. To survey the influence of pH of sample solution on extraction of t,t-MA, various designed experiments were performed. As indicated in Fig. 3a, the maximal response achieved in the pH of 3.0, and the peak area increased by decreasing the pH and disperser solvent volume. At pH values greater than 3.0, the response is decreased because of the fact that the ratios of the ionic to molecular forms of the target molecule can be influenced by pH value of sample solutions38. Therefore, acidification of the sample is essential to achieve the highest extraction of t,t- MA. With increasing the pH, t,t-MA (pKa = 3.87) is converted to negative ionized species, which could reduce the agglomeration of the tiny droplets of 1-undecanol containing the migrated t,t-MA to create the floating phase. In order to assay the effect of the volume of disperser solvent, different examinations were designed using several volumes of methanol in the ranges of 100–300 μL. The highest response level (peak area) was obtained using 100 μL of methanol. It is because of highly solubility of 1-undecanol in methanol that generate small droplets of extractant solvent and increase the extraction output. To investigate the effect of the volume of extractant solvent, various tests were conducted using different volumes of 1-undecanol (20–60 μL). The optimization process obtained 49.0 µL of 1-undecanol as the best value of highest extraction efficiency. Therefore, this value was used to extract t,t-MA from the spiked or real samples.

Figure 3
figure 3

three dimensional response surface plots of between-factor interaction terms (volume of disperser-pH (a), time–pH (b), salt- volume of disperse (c) and time-volume of disperser (d) on the extraction of t,t-MA.

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The variation in extraction time of t,t-MA versus pH (Fig. 3b) shows that maximum signal of analyte was obtained after 2.0 min of extraction. The time interval between the injection of the optimum values of extraction and disperser solvents to the sample solution and centrifugation is defined as the extraction time39. The impact of extraction time was studied in the ranges of 2–4 min; the findings showed that high extraction efficiency was obtained at a short time. In DLLME-SFOD method, the equilibrium state is rapidly reached because of the high contact area between aqueous sample and extractant solvent and good dispersion of the analyte. Therefore, rapid and high mass transfer of the t,t-MA from aqueous media to the 1-undecanol is occurred.

As Fig. 3c indicates, the maximum extraction of t,t-MA was occurred at low concentration levels of sodium chloride and low volume of disperser solvent. In high volumes of disperser, increasing the salt can restrict the viscosity of the solution and mass transfer of the analyte. Moreover, increasing salt level and disperser solvent volume will increase the volume of floating organic phase which could decrease the enrichment factor and extraction efficiency31,40.

Figure 3d indicates the interaction between extraction time and disperser volume of the sample. At the constant volume of extractant solvent, by decreasing the disperser solvent volume and the extraction time the extraction of the target was increased (Fig. 3d). Enhancing the volume of disperser solvent affects the solubility of t,t-MA in the sample solution which decrease the extraction of t,t-MA. Moreover, following the generation of cloudy state, the extraction time is almost short due to the high contact area between aqueous phase and extractant solvent. This process facilitates the diffusion of target analyte into the extractant solvent with the lowest amount of disperser solvent. The use of small volume of organic solvent and also rapid extraction time are considered as the main advantages of the optimized method.

After confirming the validity of model, simplex optimization was used to find the optimum values of experimental conditions of the t,t-MA extraction procedure which were obtained as follow: pH of the sample: 3.0, the volume of extractant solvent: 49.0 µL, the volume of disperser solvent: 100 µL, salt amount: 2.0 w/v%, and extraction time: 2.0 min.

Matrix effects

Matrix effect was defined using the percentage of signal repression/enrichment obtained for urine samples by the proposed optimized method. In this regard, at a defined concentration level, the peak area acquired from the analysis of the fortified urine samples were compared with the relative spiked mobile phase. Based on the obtained results, the recoveries were 91–94% (n = 5). Trivial matrix effects was found when blank samples were fortified with 1.0 μg mL−1 t,t-MA.

Method validation

To assay the efficiency of the optimized method, the figures of merit such as the linear dynamic ranges (LDRs), squared correlation coefficients of the analytical curve (R2), limit of detection (LOD), limit of quantification (LOQ), precision, and relative recovery (RR) were all examined and the obtained findings are presented in Table 3. The linearity of the optimized method was investigated by spiking seven urine specimens with increasing concentrations of t,t-MA from the LOQ to 5 μg mL−1. As demonstrated in Table 3, a suitable linearity was attained in the ranges of 0.02–5 μg mL with a significant correction coefficent (R2 of 0.997). The LOD (S/N = 3) and LOQ (S/N = 10) were 0.006 μg mL−1 and 0.02 μg mL−1, respectively. The PF was estimated using the subsequent equation:

$${\text{PF}} = {\text{C}}_{{{\text{ex}},{\text{final}}}} /{\text{C}}_{{{\text{aq}},{\text{initial}}}}$$
(3)

where Cex, final and Caq, initial are the final and initial concentration levels of metabolite in extracting and aqueous phase, respectively. Under the optimize conditions, the PF of t,t-MA was 80. Precision study, characterized in term of RSD% for reproducibility (inter-day precision) and repeatability (intra-day precision), was determined by spiking urine specimens purchased by an unexposed volunteer in the analytical lab. The proposed technique was used for measurement of the concentration of t,t-MA in urine sample taken from unexposed person. According to the results, no levels of t,t-MA was detected in the obtained urine sample. In the next step, the aliquots of the collected urine sample were transferred to different test vials and fortified with various concentrations of t,t-MA. The prepared fortified samples were analyzed by the optimized method. According to Table 3, the RSD% were below 6.5 in all concentration levels, indicating a good precision achieved by this method. The proximity of the experimental findings from a developed procedure to the real amount of the target analyte is defined as the accuracy of an analytical procedure. The application of Certified Reference Materials (CRMs) is considered as an accurate way to examine the accuracy of a procedure. Since no CRM was available for the studied analyte, the trueness of the developed method was determined by the added-found procedure. To do this, the urine samples were spiked with t,t-MA analyte at three concentration levels (0.2, 1.0 and 2.0 μg mL−1) and were determined in three replicates according to the optimum conditions (pH of the sample: 3.0, the volume of extractant solvent: 49.0 µL, the volume of disperser solvent: 100 µL, salt amount: 2.0 w/v%, and extraction time: 2.0 min). As shown in Table 3, a good recovery obtained with the current method in the range of 86.9–91.3%.

Table 3 Analytical characteristics of the method.
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Figure 4 presents the chromatograms of a real urine sample collected from unexposed healthy man before and after spiking with 1.0 μg/mL of t,t-MA. As shown in Fig. 4, no chromatogram was observed at the retention time of target metabolite, indicating acceptable specificity and not require to the additional clean up step to remove interferences before HPLC analysis. As seen in Fig. 4, the analyzed real sample was free from analyte and t,t-MA could be efficiently extracted from urine samples using DLLME-SFOD technique.

Figure 4
figure 4

Representative HPLC chromatograms of a real sample from a non-exposed healthy man before (blank) and after spiking with t,t-MA at concentration level of 1 μg/mL. (The extraction conditions were as follow: pH of the sample: 3.0, the volume of extractant solvent: 49.0 µL, the volume of disperser solvent: 100 µL, salt amount: 2.0 w/v%, and extraction time: 2.0 min).

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Applicability of the optimized method for urine analysis of workers who occupationally exposed to benzene

The optimized procedure was utilized for preconcentration and measurement of t,t-MA in urine samples taken from workers who occupational exposed to benzene in a petrochemical industry. Five end-shift urine specimens were collected from non-smoker employees who were occupationally exposed to benzene. All of urine samples were treated according to the optimized technique of DLLME-SFO, and analysis were performed using HPLC–UV. The results showed that t,t-MA were identified in all of taken samples. The results were expressed as urinary measured t,t-MA to creatinine (mg/g creatinine). The modification of urinary dilution was performed using urinary creatinine because the urinary level of t,t-MA is affected by urine volume. Table 4 presents the findings of the real urine analysis. On the other hand to make another estimation from the accuracy, the results were compared by the solid phase extraction technique (SPE) using SAX cartridge and HPLC–UV detection2, which are shown in Table 4 as well. As shown, the present technique is comparable with SPE method as a routine and well-evaluated technique in occupational laboratories.

Table 4 Analysis of urine samples taken from workers of a petrochemical industry using two different extraction technique: DLLME-SFOD and SPE.
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Comparison of the optimized method with other pretreatment methods

The analytical performance of the proposed method was compared with other previously reported techniques for the extraction and measurement of t,t-MA. As illustrated in Table 5, different solid and liquid phase extraction/ micro extraction procedures have been introduced for monitoring of t,t-MA from urine sample. As seen, the introduced method has an acceptable LOD which is comparable with other analytical techniques used for the measurement of t,t-MA. Moreover, the very low volume of the used organic extractant solvent with less toxicity in the present method are the major advantages of our suggested method. The proposed technique uses a few microliters of an organic extractant solvent without any other toxic agents such as chlorinated organic solvents. In addition, the extraction process last less than 15 min, which is more rapid than other techniques such as conventional LLE or SPE (> 30 min). Although other extraction techniques especially solid phase-based methods have a lower LOD, these techniques require proprietary instruments, expensive SPE cartridges, and complicated steps of sample preparation for the extraction of analyte. These comparisons reveal that the introduced method (DLLME-SFOD) is easy, fast, cost effective, and environmentally friendly. Moreover, 1-undecanol is an inexpensive and easily available solvent, which is appropriate for fast extraction of large-scale samples.

Table 5 Comparison of the SFO-DLLME method with other preconcentration techniques for monitoring of urinary t,t-MA.
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Ethics approval

The manuscript has been approved by the ethics committee of Shiraz University of Medical Sciences (Ethical ID: IR.SUMS.REC.1398.1051). No clinical trial was performed in the study but for urine sampling as the experiments involving human participants informed consent have been obtained.

Conclusion

In the proposed method, a novel, simple, and sensitive DLLME-SFOD method was optimized and validated for the preconcentration and trace measurement of t,t-MA by HPLC–UV in urine sample. In this study, an organic extractant solvent with an appropriate melting point was used; after solidification, it was easily collected. In comparison with other previous techniques, this method enjoys the advantages such as simplicity, low cost, low usage of organic solvent, rapidity, and environmental friendly. In this work, for the first time, DLLME-SFOD was used for the trace determination of t,t-MA in urine samples, which revealed wide linearity, satisfactory relative recovery and good precision. Especially, the LOD of DLLME-SFOD method was 0.006 μg/mL, which is lower or comparable with other previous techniques. Furthermore, the most important advantages of this method is that the extraction of t,t-MA occurs in a short time, which can be used as an efficient method in analytical labs. Because the optimized method does not require any complicated instrument and expensive material, it can be used as a simple protocol for monitoring of employees who occupationally exposed to benzene.