Investigating the efficiency of sorbents in the SBSE method for extraction of 17-OHPThe effect of sorbent type (PDMS and EG-Silicons) in the SBSE method to extract 17-OHP was investigated as described in Sect. “Comparison of two sorbents in the SBSE method for extraction of 17-OHP”. The desorption solutions were analyzed using HPLC instrument, and the obtained chromatograms and the related peak areas of 17-OHP using PDMS and EG-Silicone sorptive phase are depicted in Fig. 1. As is apparent in Fig. 1A, the extractions using PDMS and EG-Silicone stir bars displayed increased peak areas of 17-OHP compared to the result obtained by direct injection of the same concentration. Moreover, according to the peak area of 17-OHP (Fig. 1B), the PDMS stir bar performed much better than the EG-Silicone to extract the 17-OHP hormone. Based on the chemical structures of 17-OHP and the two sorbents used, it seems that the hydrophobic interactions between 17-OHP and the PDMS were more effective than the dipole–dipole interaction of hydroxyl groups in EG-Silicone with 17-OHP. Thus, PDMS was selected as the suitable sorptive phase for the rest of the experiments. Also, the performance of the stir bars in real samples of tap water, well water and urine were investigated, which is shown in Fig. 1C–E. As can be seen after spiking of 500 ng mL–1 17-OHP to each real sample, its peak was appeared in the chromatogram without interference.Figure 1(A) Comparison of the chromatograms of 17-OHP standard solution at the concentration level of 500 ng mL–1 without extraction (I), extraction by EG-Silicone stir bar (II), and extraction by PDMS stir bar (III). (B) Comparison of the extraction of 17-OHP (30 mL of 500 ng mL–1 solution) using PDMS and EG-Silicone stir bars under constant extraction conditions with three replications. Chromatograms obtained from the analysis of 17-OHP hormone in real samples (C) tap water, (D) well water, and (E) urine before (bottom) and after (top) addition of 500 ng mL–1 of 17-OHP to sample by PDMS-SBSE-HPLC-UV method:Optimization of the SBSE procedureOptimization of desorption conditionsAs explained in Sect. “Optimization of SBSE procedure”, one factor at a time for optimization of desorption solvent and full factorial for optimization of other factors were used to find the best desorption conditions. The type of desorption solvent was optimized using four different solvents (MeOH, ACN, H2O:MeOH 60:40 v/v, and MeOH:ACN 50:50 v/v), and the effect of a polar ionic liquid in the desorption phase was also investigated. To this end, 30 mL of the standard sample of 17-OHP with a concentration of 500 ng mL-1 was first prepared, and 5 g of NaCl was added to it. Then, the solution was extracted for 2 h using a PDMS stir bar at a speed of 750 rpm at room temperature. After the extraction step, the stir bars were placed in 250 μL of 5 different solutions in an ultrasonic bath for 15 min. As shown in Fig. 2, the best solvent for desorption was MeOH/ACN 50:50 v/v. Due to the combination of two solvents (ACN and MeOH), dipole–dipole and hydrogen bonding interactions with the analyte can be established. Thus, it has acted more strongly for the desorption of the analyte compared to the use of each of these solvents alone. On the other hand, the solubility of the analyte in methanol (organic phase) is higher than that in water; hence, the desorption efficiency in MeOH:H2O 40:60 v/v would be lower than other solvents. According to previous experiments43, the [Omim][BF4] ionic liquid was also used as a modifier in the optimized desorption solvent; however, as shown in Fig. 2, the addition of the ionic liquid did not have a positive effect on the extraction efficiency.Figure 2Comparison of different desorption solvents to optimize desorption solvent type and proportion (v/v) for extracting 30 mL of 500 ng mL–1 solution of 17-OHP under constant conditions.A 3-level full factorial design was used considering two blocks (two stir bars) to optimize the desorption time and temperature. The design matrix and obtained responses were represented in Table S2. The extraction conditions were considered constant during the optimization steps. In optimizing the desorption step, the values of the peak areas after the first desorption were considered as response one, and the peak areas obtained after the second desorption (without performing the extraction step again) for investigating the memory effect were considered as response 2. The changes of peak area according to the conditions of each experiment were analyzed. The main and binary interaction effects of factors for both responses were individually calculated, and their significance was investigated using ANOVA, which is reported completely in the supplementary material (Tables S3 and S4) for the two responses. According to the p-value < 0.05 and Pareto charts (Fig. 3A and B), desorption time (A), desorption temperature (B), and their interactions (AB) had significant effects on both responses. Moreover, the quadratic variables (AA and BB) did not significantly affect responses 1 and 2. It rejects the existence of curvature in the relationship between these factors and both responses. Moreover, the non-significance block effect indicated that the use of different stir bars did not significantly affect the extraction efficiency.Figure 3Plots related to optimizing the desorption step and memory effect using a three-level full factorial design. The left side illustrates the optimization graphs of response one, and the right side depicts the optimization graphs of response two; (A) and (B) Pareto, (C) and (D) response surface, and (E) and (F) relative residual plots according to experiment number.The value of R2 also indicated that the model for response 1 describes 95.77% of the results and was very close to the R2adj value (93.23). As is apparent from the response surface plots (Fig. 3C and D), and the amount of desorption increased by increasing the desorption time and temperature. At the same time, the value of memory effect decreased. In fact, by raising the temperature, diffusion into the sample increases, and the analyte would be transferred faster from the sorbent to the solution. The relative residual plots (Fig. 3E and F) for both responses also displayed a random distribution of data and the suitability of the models designed to optimize the desorption step. According to evaluated models, the optimal values for desorption time and temperature to maximize the peak area after the first desorption and to minimize the peak area after the second desorption were obtained to be 30 min and 50 °C, respectively. The experimental values obtained under this condition were very close to the predicted values from each response and exhibited a very small error percentage (1.25 and 7.02%), which indicates the validity of the fitted model used to optimize the desorption step for both responses (Table 1).Table 1 The optimum conditions for extraction of 17-OHP from aqueous media using the SBSE method.Optimization of extraction conditionTo optimize the extraction step, FCCD was used as described in Sect. “Comparison of two sorbents in the SBSE method for extraction of 17-OHP”, and the corresponding design matrix is represented in Table S5. Since different blocks did not significantly affect the desorption step’s optimization, the block effect was not considered in this design. The ANOVA results of the main and interaction factors are reported in Table S6. The results revealed that the main factors, including NaCl content (B), sample volume (C), extraction temperature (D), and extraction time (E), as well as the interaction factors, including AB, AC, BD, BE, CE, and DE, and quadratic parameters such as AA, BB, CC, DD, and EE, have a significant effect (p-value < 0.05) on the extraction efficiency at the confidence level of 95%.To investigate the effect of each of the factors, Pareto charts were also drawn (Fig. S1). Based on these results, NaCl content, temperature, and time positively affect the extraction efficiency, whereas the sample volume negatively affects it. The most significant effect was NaCl content because increasing the ionic species in the sample reduces the amount of water molecules to dissolve the analyte, and the conditions for extraction of the analytes to the sorbent would become more favorable44. Extraction temperature has two opposing effects on SBSE: the equilibrium is reached faster at higher temperatures; however, at the same time, the solubility of the analytes in water would increase (Ko/w decreases), which leads to a decrease in extraction efficiency. As shown in the results, increasing the extraction temperature in this study positively affected extraction efficiency. Based on the results obtained in this design, the effect of changing the sample volume on the extraction efficiency was negative, because by reducing the sample volume, the extraction kinetics were improved and the extraction reached the equilibrium faster45. It is also obvious from the Pareto chart that although the pH value did not significantly affect the extraction efficiency, its interactions with NaCl content and sample volume were significant.Some of the response surface plots resulting from the central composite design to optimize the extraction step are depicted in Fig. S2A–E. In each plot, two factors were considered supposed variable factors, and the remaining factors were considered fixed at the middle level. The values of R2 and R2adj of the predicted model were 99.84% and 99.58%, respectively, which were very close to each other and indicated that this model described more than 99% of the results well and is a suitable model for optimizing the extraction conditions. Moreover, the relative residual plot of the proposed model is illustrated in Fig. S2F. Due to the random distribution of the residues, it was confirmed that the designed model is suitable for optimizing the factors.The optimal value for each factor was obtained from the predicted model (reported in the Supplementary file), and the results are listed in Table 1. The best extraction time was predicted to be approximately two hours (close to the equilibrium conditions), and the relative error percentage of the obtained results was appropriate (6.40%). Since 17-OHP is on the border between polar and non-polar properties, a longer extraction time would be required to reach equilibrium than non-polar compounds. Under this situation, kinetically-controlled extraction (i.e. extraction at constant times less than the time needed to reach the equilibrium) can be applied46. To this end, the extraction time was kept constant at 90 min, and the optimum levels for other factors were found according to the proposed model (Table 1).Method validationConsidering that the aim of the present study was the analysis of 17-OHP in aqueous media and urine, the selectivity of the method was investigated using real samples. The obtained chromatograms related to the analysis of six different real samples of water and urine before and after the spike of 500 ng mL–1 17-OHP to the blank samples of water and urine are depicted in Fig. S3. Since there were no interfering peaks in water and urine samples at the retention time of 17-OHP (7 min), the method exhibited good selectivity.Under the optimum conditions, the calibration curves were separately obtained for blank water and urine samples at the concentration levels mentioned in Sect. “Optimization of SBSE procedure”. Using the least-squares method, equations of the lines and the correlation coefficients were obtained. Validation parameters to determine 17-OHP in water and urine are reported in Table 2. The values of R2 for calibration curves prepared in water and urine were calculated to be 0.9998 and 0.9967, respectively. The ANOVA table (Table S7) indicated that the F value was greater than the F critical at a 95% confidence level and the regressions were significant for both calibration curves. In addition, the lack of fit (LOF) F values for both calibration curves (4.200 and 3.870 for water and urine, respectively) were smaller than F critical at a 99% confidence level (4.695 and 4.202), there was no significant LOF and the models were suitable for the calibration curves (Table S7). The linearity range of the method was observed from 1.21 to 1000.00 and 2.43 to 2000.00 ng mL–1 for water and urine samples, respectively. LOD and LOQ values were calculated using Eqs. (1) and (2), which were obtained to be 0.40 and 1.21 ng mL–1 for water samples and 0.80 and 2.43 ng mL–1 for urine samples, respectively.Table 2 Analytical performance for the determination of 17-OHP in water and urine samples.The proposed method’s intra- and inter-day accuracy and precision in water and urine media were obtained based on the mentioned procedure in Sect. “Optimization of SBSE procedure”. The results (reported in Table 2) demonstrated excellent recovery values in the ranges of 87–103% and 87.5–101% for water and urine samples, respectively. According to the obtained results, the RSD% values for the two media were within the ranges of 0.4–3.6% and 0.1–5.2%, respectively; indicating good precision of the proposed method for the analysis of 17-OHP in water and urine. CF, EF, and EE% were also calculated according to the Eqs. (3)–(5) at 100 ng mL-1 concentration level of 17-OHP, which were obtained to be 40, 13, and 32.5, respectively.Real sampleTo demonstrate the efficiency of the PDMS stir bar for extraction of 17-OHP from real samples, tap water, well water, and urine samples of a healthy person were tested. A standard solution of 17-OHP with a concentration of 500 ng mL–1 was spiked to each of the water and urine samples, and the extractions were performed under optimal conditions for real and spiked samples. The obtained chromatograms (before and after the spike of standard solution to each real sample) are depicted in Fig. 1C–E. The recovery and RSD% were also calculated for real samples, reported in Table S8. As can be seen, for two samples of tap water and well water as well as a urine sample, the recovery values were in the range of 98–105%, and the RSD% was found to be ≤ 1, which confirmed the effectiveness of the proposed method for extraction and analysis of this hormone from these media.Evaluation of the greenness of the PDMS-SBSE-HPLC–UV methodAs mentioned in Sect. “Evaluation of the greenness of the PDMS-SBSE-HPLC–UV method”, in this study, three tools, AES, GAPI and AGREE, were used to check the greenness of the proposed method. The principles of calculating the amount of greenness with each of the methods were briefly explained in Sect. “Evaluation of the greenness of the PDMS-SBSE-HPLC–UV method”. The results obtained in Table 3 showed that since the amount of penalty points obtained using the AES method was equal to 86, the method proposed in this article was placed in the “excellent” category in terms of the amount of greenness. In the investigation using GAPI method, as shown in Table 3, since most of the parts of the pentagons in the obtained pictogram were green and yellow, it can be concluded that the proposed method in this study was green. AGREE was used as the last method to check the degree of greenness. The calculations of this method were done using AGREE software and the result is reported in Table 3. Since the value obtained using this method was closer to one and was higher than 0.5, the greenness of the method was also confirmed by the AGREE method.Table 3 Evaluation of the greenness of the PDMS-SBSE-HPLC-UV method using eco-scale, GAPI and AGREE tools.Comparison of the introduced method with previous methodsThe method proposed in this study was compared to other methods used to analyze 17-OHP (Table 4)47,48,49,50,51,52. Since the HPLC–UV technique was used in the present investigation, higher detection limit values in comparison with those obtained by LC–MS/MS might be expected; however, the obtained detection limit for the proposed method exhibited appropriate value for diagnosis of CAH disease via the analysis of 17-OHP in urine4,14,52,53. Moreover, the RSD value of the method was less than 5.2%, and the stir bars demonstrated repeatable results for multiple extractions up to 18 independent extractions when the RSD value was less than 6.4% (Fig. S4). The method proposed in this study utilizes the HPLC technique and a commercial sorbent for extraction. This method is more cost-effective compared to other methods such as LC–MS/MS and UPLC-MS/MS which has been used to determine this hormone in other reported articles (Table 4). Recently, Manousi et al. have clearly shown that methods using HPLC–UV techniques are considered simple and readily available in most laboratories. However, methods using LC–MS/MS or UPLC-MS/MS are categorized as not being normally available in most laboratories54. On the other hand, previous studies have not evaluated the degree of greenness of their methods. According to the parameters that are important in calculating the greenness of a method with the help of tools such as AGREE, Eco-Scale, and GAPI, the previous methods consume a large amount of solvent due to the use of extraction techniques such as SPE and LLE.Table 4 Comparison of reported methods for determination of 17-OHP.ConclusionIn this research, a commercial PDMS stir bar joint to HPLC–UV technique was used to determine 17-OHP in water and urine samples. The effective factors in the desorption stage were optimized using a full factorial design and one-factor-at-a-time method. In contrast, the influential parameters during the extraction step were optimized using a central composite design. The method validation results for water and urine samples showed high good recovery and acceptable detection limit (0.40 and 0.80 ng mL–1 for aqueous and urine samples, respectively). The coefficient of determination of the introduced method was good (0.9998 and 0.9967 for aqueous and urine samples, respectively). Since radioimmunoassay methods suffer from poor selectivity, according to the obtained results, this method can be considered as good alternative to immunoassay methods for analysis of the selected hormones. On the other hand, due to the use of commercial stir bar and HPLC instrument, the developed method can be a routine and less expensive method compared with LC–MS or LC-MSMS techniques for analyzing this hormone. In addition, the greenness of the proposed method in this study has been proven by using different methods of calculating greenness. Moreover, it can be used to analyze this hormone in other biological fluids, such as blood serum. It is suggested to investigate the use of the proposed method in other environments such as blood serum and plasma in future studies. Also, the use of this method in the medical diagnosis laboratory and checking its effectiveness is one of the things that can be considered.
