Determination of 229 pesticides residue in edible oil samples using conventional QuEChERS method and solid phase microextraction based on monolithic molecularly imprinted polymer fiber and analysis with GC-MS

Determination of 229 pesticides residue in edible oil samples using conventional QuEChERS method and solid phase microextraction based on monolithic molecularly imprinted polymer fiber and analysis with GC-MS | 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 Determination of 229 pesticides residue in edible oil samples using conventional QuEChERS method and solid phase microextraction based on monolithic molecularly imprinted polymer fiber and analysis with GC-MS Fatemeh kardani, Aniseh zarei Jelyani, Tahere Khezeli, Mohammad Hashemi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4545785/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 May, 2025 Read the published version in BMC Chemistry → Version 1 posted 13 You are reading this latest preprint version Abstract The goal of this research is development of solid phase microextraction based on monolithic molecularly imprinted polymer fiber (SPME-MMIPF) method to determine 229 pesticides in edible oil samples using gas chromatography-mass spectrometry (GC-MS) and comparison of it with the common QuEChERS method. In QuEChERS method, acetonitrile used as extraction solvent and magnesium sulfate used as water absorbing agent. For SPME-MMIPF method, an MMIPF was synthesized by polymerization of methacrylic acid in presence of ethylene glycoldimethacrylate and azo (bis)-isobutyronitrile. The optimal conditions for the SPME-MMIPF method are: extraction time 30 min, desorption time with toluene 20 min and string speed of the aqueous sample 600 rpm. Under optimal extraction condition, the figures of merit were obtained for two methods and compared. The linear range of 1-300 µg kg − 1 for SPME-MMIPF and 10–250 µg kg − 1 for QuEChERS was obtained. The detection limit of SPME-MMIPF (0.321–0.335 µg kg − 1 ) method was better than the QuEChERS (0.9–2.6 µg kg − 1 ) method. The results showed a quantification limit of 0.8–2.2 µg kg − 1 for SPME-MMIPF and 1.5–5.2 µg kg − 1 for QuEChERS. The recoveries were in the range of 92–102% and 68–127% for SPME-MMIPF and QuEChERS, respectively. SPME-MMIPF QuEChERS Pesticides Edible oil GC-MS Figures Figure 1 Figure 2 Figure 3 1. Introduction Nowadays, the utilization of advanced nanomaterials and nanocomposites for the detection and removal of wastes, contaminants, and unhealthy/poisonous compounds has been widely being investigated [ 1 – 7 ]. Pesticide residues are the most important categories of food contaminants that play a significant role in endangering human health problems [ 8 , 9 ]. Unreasonable and excessive consumption of pesticides may lead to an increase in the residues of these substances in food commodities. In addition to environmental effects, this issue is also considered a serious risk factor for consumers’ health Acute effects occur due to high exposure to pesticides [ 10 , 11 ].Recently, researchers have been concerned about the impact of chronic human exposure to pesticides and its connection with various diseases, including cancer, genetic disorders, infertility, hormonal disorders, IQ reduction, and the development of allergies, ultimately affecting human health. Maximum residue limits (MRLs) are set for pesticides to detect their legal levels in food [ 12 ]. Many countries and international authorities have set limits for them and it is necessary to regularly control them in food products. As an important source of human nutrition, vegetable oils are produced from various oil seeds such as olive, soybean, sunflower and cotton seed. Therefore, with the aim of controlling parasites and diseases, these products are exposed to a wide range of insecticides, fungicides and herbicides [ 13 , 14 ]. The residual portion of pesticides may remain in the final edible oil products, especially high-lipophilic pesticide. Multi residue method (MRM) with mass spectrometry is one of the best available methods for routine pesticide analysis. Because it analysis a large number of compounds at the same time, saves material, time, and even manpower [ 14 , 15 ]. The “QuEChERS” (quick, easy, inexpensive, effective, robust, safe) method was first introduced by Anastassiades et al. and attained global acceptance in analyzing pesticides thanks to its high advantages [ 16 , 17 ]. Despite the advantages, the QuEChERS method has some limitations such as using a lot of organic solvent and time, multi-stage, excessive use of chemicals, and lack of selectivity. The non-selectivity of the QuEChERS method is due to the application of the same experimental conditions in the analysis of different categories of compounds in simultaneous analysis, this limitation makes some target compounds to be extracted with high efficiency and some other unrelated compounds. Molecularly imprinted polymers (MIPs) are considered attractive polymers in recent years due to their easy preparation, mechanical, thermal, and chemical stabilities, and highly selective detection capabilities [ 18 – 20 ]. In MIPs preparation, three-dimensional structural voids can be created during polymerization and once template extraction [ 18 ]. Since the cavities are complementary in size, shape, and chemical functionality to the templates, MIPs have excellent recognition capabilities for template molecules. In this paper, the aim is to develop a selective, proprietary, chemically and physically strong SPME-MMIPF for the extraction of different classes of pesticides including organophosphorus, carbamate, synthetic pyrethroid, organo-nitrogen, and organochlorine in an easy and economical way from etched polymer. These fibers were obtained through non-covalent polymerization of the prepolymer solution inside the capillary columns. Then these fibers were used for selective extraction and sensitive determination of different categories of pesticides by the SPME-MMIPF/GC-MS method. Finally, the validation results using the SPME-MMIPF/GC-MS method were compared with the results of the QuEChERS method. 2. Experimental 2.1. Reagents and Chemicals Acetonitrile, toluene and formic acid (99.9%) were obtained from Sigma–Aldrich Company, and anhydrous magnesium sulfate (MgSO 4 ) were obtained from Merck (Darmstadt, Germany).C 18 sorbent and primary–secondary amines (PSA) were obtained from Supelco Company. A Milli Q purification system were used and ultrapure water (18.2X resistivity) was obtained. All the standard of pesticides was purchased from Merck (Darmstadt, Germany). The standard solutions of each pesticide were carefully prepared in acetonitrile and ethyl acetate in concentrations of 1000–2000 mg L − 1 . Finally, separate stock standard solutions for optimization and three mixed standard solutions for calibration were prepared from the stock standards. Methacrylic acid (MAA), ethylene glycoldimethacrylate (EGDMA) and azo (bis)-isobutyronitrile (AIBN) were purchased from were purchased from Sigma Aldrich (St. Louis, MO, USA). Triphenyl phosphate (TPP; used as internal standard), formic acid, anhydrous sodium acetate, trisodium citrate dehydrate, sodium chloride and anhydrous magnesium sulfate were purchased from Merck (Darmstadt, Germany). 2.2. Equipment The pesticides were analyzed by an Agilent gas chromatograph 7890B series that was coupled with an Agilent mass spectrometer model 5977A and auto sampler. The simultaneous analysis of 300 pesticides was performed by 1 µL injection of the sample in pulsed splitless mode at 280°C. An HP-5MS fused silica capillary column (30 m×0.25 mm, i.d. 0.25 µm) was used for separation with the oven temperature program started at 70°C, and heated up to 120°C at the rate of 25°C min − 1 ; next, it was heated up to 300°C with the heating rate of 5°C min − 1 and kept there for 20 min. The total run time was 56.8 min. The carrier gas was high-pure helium with 1.0 mL min − 1 flow rate. The temperature of the MS quadruple and ion source was maintained at 150°C and 230°C, respectively. The MS instrument was operated at an ion energy of 70 eV in electron impact (EI) in the range of m/z 50–550. Based on the usage of three qualifier ions, quantitative analysis was carried out in the selected ion monitoring (SIM) mode. The solvent delay time was adjusted at 3.0 min. the Masshunter and Chemstation software were used for data collection and processing procedures, respectively. In addition, instrument control, data gathering, and data processing were performed using Varian workstation software. Scanning electronic microscope (SEM) images were carried out by SEM instrument model Hitachi S5200 (Hitachi: Tokyo, Japan) in scanning mode at 30 kV. The confirmation of functional groups in MMIPF was performed using an FT-IR spectrometer (BOMEM MB-Series 1998 FT-IR spectrometer). 2.3. Optimization of GC-MS parameters For each analyte, the most intense ion with the highest m/z ratio, collision energies (CE), MS detection method, collision gas pressure, retention time, and precursor ion were optimized. For finding the best product ions, several scanning methods were created with different collision energies. A group of 300 pesticides was analyzed with collision energy ranging from 5 to 40 V and 5-V increasing intervals. After the findings were evaluated, the product with the highest possible intensity was chosen as the quantifier ion, and the second-highest intensity product was employed as the qualifier ion. Also, a technique for selective response monitoring (SRM) was developed by adjusting the collision gas pressure by injecting the same vial with various helium pressures (ranging from 1.0 to 2.0 mTorr with 0.1-mTorr increasing intervals). Furthermore, 1.5 mTorr was found to be the ideal pressure, producing an intense response for most pesticides. In the SRM mode, a new collision energy optimization was done, and no notable changes were found with the scan mode. Moreover, d-well time for fewer sensitive analytes was extended by shortening the d-well duration for analytes with a sufficient signal. 2.4. Preparation of oils The samples inclusive sunflower and soybean oils were purchased from a super market (Ahvaz, Iran). The oil samples were prepared according to following procedure for QuEChERS and SPME-MMIPF methods. A 2.5 g of each sample (sunflower and soybean oil) was weighed in a 50 mL PTFE centrifuge tube. Then, 10 mL of acetonitrile and 100 µL of TPP as internal standard (10 mg L − 1 ) were added, and the sample was vortexed for 20 min, followed by centrifugation at -5 o C, 4500 rpm for 10 min. The supernatant was collected and stored for QuEChERS and SPME-MMIPF methods. 2.5. Fabrication of MMIPF In this study, the fiber for MIP was fabricated according to the method previously reported by Mirzajani and Kardani [ 18 – 20 ]. For this purpose, 2.2 mmol of template molecule (fenitrothion for organophosphorus, chloroneb for organochlorine, biphenyl for organonitrogen, cypermthrin for synthetic pyrethroid and pirimicarb for carbamate) were dissolved in 30 mL of acetonitrile. Then 25 mmol of MAA, 125 mmol of EGDMA, and 275 mg of AIBN were added. Afterwards 2.2 mL of the mixture was transferred to a vial and deoxygenated by N 2 gas for 8 min. To prepare integrated fibers in form of molds, capillary fused silica tubes with a length of 4 cm and an inner diameter of 0.3 mm were washed with methanol and dried by nitrogen flow. Then, the capillary was filled with the polymerization mixture, and then both ends of the capillary were closed with two small pieces of soft rubber. After that, the filled capillaries were placed in the oven and polymerization was done at 60°C for 14 h. Then the polymer monolith fibers immersed in 40% hydrofluoric acid solution for 2 h under gentle stirring. After dissolving the glass capillary, the MMIPF was immersed in a solution of methanol: acetic acid: water (4:1:1, v/v) for 2 h to remove the template, then were dried in the oven at 65°C for 2 h. Non-imprinted polymer fibers (NIP fibers) were prepared with a completely similar MIP method, except that the template molecule was not added. The resulting fiber offers excellent flexibility with high strength and chemical resistance. 2.6. Sample preparation steps using QuEChERS method For cleaning up oil sample, 6 mL of supernatant (section 2.4 .) was transferred into the 15 mL centrifuge tube containing 900 mg of magnesium sulfate, 300 mg of C 18 , and 300 mg of PSA. The sample was again vortexed and centrifuged with previous conditions. Afterward, 40 µL formic acid 5% was added to 4 mL of the final extract, evaporated under a gentle stream of nitrogen, reconstituted with 1 mL toluene, and filtered. The working method is shown in all the details in Fig. 1 . 2.7. Sample preparation steps using SPME-MMIPF For SPME procedure, the fibers were mounted on a laboratory-made SPME-MMIPF apparatus, then 5 mL of the supernatant (section 2.4 .) was added to a 10 mL glass vial containing a magnetic stir bar. The MMIPF was introduced into a stirring solution (600 rpm) for 30 minutes to extract the analyte. After 30 minutes, the fiber was drawn into the needle using the piston of the home-made SPME-MMIPF device and the device was removed from the vial. Then washed with mixed methanol: water (1:1 v/v) (by immersion, 60 seconds). Next, immersed in a glass vial containing 1 mL of toluene (as the eluent solvent) to extract the pesticides at 25°C for 20 minutes with a stirring speed of 500 rpm. After that, 1 µL of the rich phase solvent was withdrawn using a micro-syringe (10 µL, F-LC, SGE, Australia) and injected into the GC-MS injection port for further analysis. The general photograph of the SPME-MMIPF/GC-MS procedure is illustrated in Fig. 2 . In this figure, fenitrothion was as a model template that represents organophosphorus. The process was the same identical for other classes of pesticides with corresponding molecule template representatives. 2.8. Calibration curves To evaluate linearity of QuEChERS method, blank edible oil samples were pretreated according to optimized extraction and purification methods (section 2.4 and 2.6 ). For this purpose, edible oil samples (sunflower and soybean oil) from blank materials were mixed with multi-standard solutions containing 229 pesticides in concentration range of 10 to 250 µg kg − 1 . In this evaluation, 8 concentrations from the range of 10 to 250 µg kg − 1 were considered and the analytical method was repeated three times in each concentration to estimate the standard deviation and accuracy. For SPME-MMIPF method, 11 standard solutions in the concentration range of 1-300 µg kg − 1 of pesticides were prepared. 3. Results and discussion 3.1. Characterization of MMIPF Characterization experiments on MMIPF included SEM images (Fig. 1 S) and FT-IR spectra (Fig. 2 S), have been given in the supplementary information section. 3.2. Method validation To evaluate performance of both QuEChERS and SPME-MMIPF methods, the figures of merit including linearity, LOD, LOQ, inter-day and intra-laboratory precision, and accuracy of the methods were evaluated for the determination and extraction of pesticides in the edible oil matrix. The validation of the QuEChERS method was performed based on SANTE guidelines (European Commission, 2011) and SPME-MMIPF method was performed according to previous studies that have been done on other compounds [ 18 – 20 ]. 3.2.1. The linearity of QuEChERS and SPME-MMIPF The results for the linearity of QuEChERS and SPME-MMIPF methods for carbamates (as model) were presented in Table 1 and results for other pesticides reported in Table 1 S. The calibration curves for QuEChERS method were made using peak areas of analytes in different concentrations (10, 25, 50, 100, 150, 200, 230, and 250 µg kg − 1 ) versus the corresponding concentrations in the edible oil’s matrix. Chromatographic responses at 8 concentrations (10–250 µgkg − 1 ) had good linearity for all analytes. The coefficient of determination (R 2 ) > 0.99 was acceptable for each target analyte (Table 1 and Table 1 S). In order to estimate the linear range of SPME-MMIPF method, 11 standard solutions with specific concentrations of different groups of pesticides in the range of 1-300 µg kg − 1 were prepared and after extraction by SPME-MMIPF method and the obtained peak area was recorded by GC-MS for each concentration. The number of test repetitions in each concentration level were 5 times. Table 1 and Table 1 S show data obtained for calibration curves of different categories of pesticides. According to the obtained results, calibration curves in the concentration range of 1-300 µg kg − 1 were linear and had a high coefficient of determination (0.998). Table 1 Validation parameters of SPME-MMIPF and QuEChERS methods for the determination of other carbamates in edible oil samples. Carbamates QuEChERS method SPME method Linear range ( µ g kg − 1 ) R 2 LOD ( µ g kg − 1 ) LOQ ( µ g kg − 1 ) Linear range ( µ g kg − 1 ) R 2 LOD ( µ g kg − 1 ) LOQ ( µ g kg − 1 ) Butylate 10–250 0.9974 1.8 3.4 1-300 0.9963 0.329 1.1 Chinomethionat 10–250 0.9983 1.3 3.5 1-300 0.9987 0.328 1.2 EPTC 10–250 0.9988 2.3 3.4 1-300 0.9927 0.333 1.1 Dodemorph 10–250 0.9944 1.8 3.8 1-300 0.9903 0.321 0.9 Molinate 10–250 0.9985 1.3 3.6 1-300 0.9983 0.332 0.8 Novaluron 10–250 0.9972 1.2 4.5 1-300 0.9897 0.332 0.8 Pirimicarb 10–250 0.9996 2.1 3.2 1-300 0.9976 0.333 0.9 Promecarb 10–250 0.9967 2.5 3.4 1-300 0.9961 0.329 1.3 Propham 10–250 0.9968 2.1 3.6 1-300 0.9809 0.331 1.2 Propoxure 10–250 0.9987 1.6 3.5 1-300 0.9919 0.333 1.1 3.2. 2. Calculation of LOD and LOQ LODs and LOQs were calculated as 3 and 10×the standard deviation of 10 replicate measurements at lower concentrations per the slope of each calibration curve in edible oils (10 µg kg − 1 in QuEChERS and 1.0 µg kg − 1 in SPME-MMIPF), respectively (EURACHEM., 1998). The LODs obtained by QuEChERS method for all target analytes in the edible oil matrix ranged from 0.9 to 2.6 µg kg − 1 , while the LOQs ranged from 4.8 to 10.2 µg kg − 1 . The lowest LOD and LOQ were related to metalaxyl, fenitrothion, malathion, biphenyl, metribuzin, and mirex while the highest value was related to chlorpyrifos, propoxure, thiomton, and 2,4-DDE. The LOD and LOQ obtained in SPME-MMIPF method were in the range of 0.33 to 0.45 µg kg − 1 and 0.8 to 1.32 µg kg − 1 respectively. Table 1 and Table 1 S presented the results for carbamates (as model) and other pesticides, respectively. The LOD and LOQ obtained by the SPME-MMIPF method have lower than QuEChERS method, and also the difference detection range between all pesticides is very small and close to each other. 3.2.3. Inter- and intra-day precision The concentrations of 10 and 50 µg kg − 1 and 1 and 50 µg kg − 1 were selected for estimation of inter and intra-day precision of QuEChERS and SPME-MMIPF methods, respectively. Intra-day and inter-day precision was assessed by five determinations at each spiking level on the same day. As the results show (Table 2 and Table 2 S), the recovery in the selected concentrations of the SPME-MMIPF method is in the range of 97–102% and the RSD for repeating the experiments in one day was between 1.2–2.7%, and for repeating the experiments in 5 days were obtained in the range of 3.3–5.8%. The recoveries obtained in QuEChERS method using selected concentrations are in the range of 68–132%. The RSDs obtained for the repeatability of tests in one day was in the range of 3.6–4.6% and for tests in 5 days it was obtained in the range of 4.8–6.3%. The results of QuEChERS method showed that the RSDs obtained for heptachlor, atrazine, procymidone, clomazone, metribuzin, propanil, and chlorfenapyr pesticides were high. The general results of the SPME-MMIPF method gave a very favorable recovery range for all the pesticides in comparison with QuEChERS method. Also, RSD value obtained by using this method is much less than QuEChERS method and also it is uniform for all pesticides. Table 2 Results from intra-day and inter-day precision (n = 5) of MIPF-SPME and QuEChERS for the determination of carbamates in edible oil samples. QuEChERS method SPME method Carbamates Intra-day Inter-day Intra- Inter-day Added 50 (µg kg − 1 ) Added 10 (µg kg − 1 ) Added 1 (µg kg − 1 ) Added 50 (µg kg − 1 ) Found Recovery RSD (%) Found Recovery RSD (%) Found Recovery RSD (%) Found Recovery RSD (%) Butylate 42.4 84.8 (3.9) 9.5 95.1 (6.4) 0.92 92.0 (3.9) 48.3 96.7 (3.6) Chinomethionat 47.6 95.2 (4.6) 9.1 91.1 (5.9) 0.93 93.0 (4.9) 49.2 98.5 (4.3) EPTC 46.4 92.8 (4.8) 8.8 88.6 (4.7) 0.94 94.0 (5.5) 45.5 91.0 (4.5) Dodemorph 37.6 75.2 (5.6) 9.4 94.9 (7.7) 0.92 92.0 (4.8) 47.7 95.5 (4.3) Molinate 43.2 86.4 (5.7) 9.0 90.5 (4.5) 0.91 91.0 (6.8) 46.6 93.2 (6.2) Novaluron 38.4 76.8 (5.6) 8.6 86.9 (6.5) 0.96 96.0 (4.7) 46.2 92.5 (4.1) Pirimicarb 44.1 88.2 (5.8) 8.6 86.3 (6.2) 0.98 98.0 (5.8) 46.5 93.1 (5.6) Promecarb 47.2 94.4 (4.2) 9.3 93.9 (6.3) 0.94 94.0 (4.1) 47.7 95.5 (3.8) Propham 52.6 105.2 (4.6) 10.3 103 (4.9) 0.9 90.0 (3.9) 49.5 99 (4.2) Propoxure 47.2 94.4 (4.4) 8.7 87 (4.4) 0.92 92.0 (4.6) 47.1 94.2 (4.6) 3.2.4 . Evaluation of the accuracy and performance of the method In this method, a certain concentration of standard compound solutions was added to each of the real samples prepared (the methods of preparing of real samples are explained in 2.4, 2.6 and 2.7 sections) and then recovery was obtained based on the following equation: $$\%R=\frac{{C}_{found}-{C}_{real}}{{C}_{added}}$$ To estimate after preparing the real samples with the proposed methods, the final solution was injected into the GC-MS and was obtained from the extrapolation of the calibration curves. The standard addition method was used to obtain the \({C}_{found}\) . In this way, the concentrations of the standard solution including 20 and 200 µg kg − 1 for both two methods (QuEChERS and SPME-MMIPF) were added to the edible oil samples. After the implementation experiments by both suggested methods. The peak area corresponding to each concentration was placed instead of y in the y = A(x) + B calibration curves equation. X represents the added concentration ( \({C}_{added}\) ) to real samples that equal to 20 and 200 µg kg − 1 for both methods (QuEChERS and SPME-MMIPF). The recovery of pesticides in different real samples for the two suggested methods is shown in Table 3S. The results show that the average recovery for all classes of pesticides using QuEChERS and SPME-MMIPF are more than 68%. The recovery in all two edible oil samples using QuEChERS and SPME-MMIPF method were obtained between 73 to 127% and 92 to102% for carbamates, 70 to 95% and 92 to 98% for the organochlorines, 68 to 127% and 95 to 102% for synthetic pyrethroid, 70 to 95% and 92 to 98% for organophosphorus, and 73 to 127% and 92 to102% for organonitrogens, respectively. In different edible oil samples, the RSDs using the SPME-MMIPF method were between 2.6 and 3.8%, and for the QuEChERS method, it was between 3.3 to 5.6% (Table 3S). Repeatability was calculated as the RSDs of repeated measurements (n = 3). All classes of pesticides were extracted using the SPME-MMIPF method showed favorable and satisfactory recovery range, but in the QuEChERS method, lipophilic pesticides such as hexachlorobenzene, datalymphos, aldrin, p, p'-DDE, o, p'. -DDT, o, p'-DDE, p, p'-DDT, leptophos, pentachloroaniline, transchlordane, pentachloronitrobenzene, trichloronate, dieldrin and prothiophos had very low recovery (between 35 and 67%). RSDs for this category of pesticides were also high which indicates the unfavorability of QuEChERS method for these pesticides. But the recovery for other OCs such as dicofol, chlorothalonil, o,p'-DDD, p,p'-DDD, and dieldrin was higher than 70%. Recovery of dimethoate and deoxidation in sunflower oil were much higher than soybean oil. The recovery of amitraz in all types of edible oil were unreliable due to its stability problem in the solvent. Accurate quantification was achieved in two types of oil at different concentrations, generally RSD less than 10% was obtained for more than 90% of the target compounds and RSD < 5% for more than 75% of the target compounds. The result that can be obtained from comparing the number of recoveries using the two methods is that SPME-MMIPF method is superior in extracting different categories of pesticides compared to QuEChERS method. The process of molecular mapping of the cavity is similar to the size and molecular structure of the template compound. Therefore, selective results are obtained from the effect of molecular mapping, due to spatial, molecular size detection, and specific interaction of MIP towards the template molecule and structurally similar compounds. Other compounds, which are not similar in terms of size, shape, and functional groups to the target compounds and the template molecule, must be adsorbed mainly by a non-specific adsorption mechanism. Selectivity is the most obvious feature of SPME-MMIPF technique compared to other techniques that extract compounds non-selectively. The QuEChERS method is a general and non-selective method that extracts all compounds simultaneously and under the same experimental conditions. In the meantime, the test conditions are favorable for some compounds and unfavorable for some compounds, which results in extracting some compounds well and extracting others unfavorably. 3.3. Pesticide residue in oil samples The SPME-MMIPF method was used to analyze 30 oil samples collected locally from Iranian markets (15 sunflower oil and 15 soybean oil samples). After testing all the collected oil samples, the identified pesticides by two proposed methods include beta HCH (5 samples of soybean oil, 5 sunflower samples), and beta endosulfan (1 samples of soybean oil, 2 sunflower samples) and 2,4-DEE (1 samples soybean oil, 2sunflower samples), Altrin (3 soybean oil samples, 2 sunflower samples), heptachlor-endo epoxide (1 soybean oil samples, 1 sunflower oil samples), heptachlor-oxo-, epoxide (1 soybean oil samples), chlorpyrifos (1 soybean oil samples, 2 sunflower samples) were found. However, the concentrations of all pesticides found, were lower than their MRL.According to the results of this investigation, in which the type and amount of pesticide residues were monitored in Iran’s oil samples, about 229 pesticide residues are less than their MRL levels and there is no risk or threatening harm to human health. Although the number of residual toxins is usually lower than the permissible limit, the contamination with fixed or banned toxins is still a serious environmental and public health problem that requires an efficient method of remediation and management. 4. Conclusions The method presented in the research includes the synthesis of selectable MMIPF and their application in SPME of 229 pesticides from different oils. Since the size and shape of the holes of MMIPFs match the size and shape of the template molecule, they have high selectivity toward target analytes. This method was compared with usual and popular QuEChERS method and very satisfactory results were obtained compared to QuEChERS. The QuEChERS method has disadvantages, such as multi-stage extraction, long extraction time, use of large sample volume and solvent, and wastage of compounds during the extraction process, all of which would lower the extraction efficiency of this method. Therefore, it was necessary to develop methods with high selectivity and speed to extract these compounds from real samples. The SPME-MMIPF method is a rapid method that avoids sample degradation during extraction and directly extracts the desired compounds without using intermediate steps from the sample matrix. The results showed that the method has a significant advantage over the QuEChERS method. This method has been able to extract different categories of pesticides with a high recovery with the least RSD. Declarations Funding : The Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, and the Food and Food and Drug Administration, Shiraz University of medical sciences of Shiraz, Iran, funded for this study. Ethics approval : This article does not contain any studies with human or animal subjects. Consent to participate : Not Applicable Consent for publication : Not Applicable Availability of data and material : Information and data will be provided to the journal if needed. Code availability : Not Applicable Authors' contributions : Methodology: Fatemeh Kardani Data curation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati, Seyyed Mohammad Ali Noori, Mohammad Hashemi, Formal analysis and investigation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati Writing original draft preparation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati, Marzieh rashedinia Writing, review and editing: Fatemeh kardani, Tahere Khezeli, Aniseh zarei jelyani, Mohammad Hashemi, Marzieh rashedinia, Saeedeh Shariati,Seyyed Mohammad Ali Noori References Tizhoosh NY, Khataee A, Hassandoost R, Soltani RDC, Doustkhah E: Ultrasound-engineered synthesis of WS2@ CeO2 heterostructure for sonocatalytic degradation of tylosin. 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Chemical Engineering Journal 2016, 290:243-251. https://doi.org/10.1016/j.cej.2016.01.038 Moradi R, Khalili NP, Septiani NLW, Liu CH, Doustkhah E, Yamauchi Y, Rotkin SV: Nanoarchitectonics for Abused‐Drug Biosensors. Small 2022, 18(10):2104847. https://doi.org/10.1002/smll.202104847. Bhardwaj N, Bhardwaj SK, Bhatt D, Lim DK, Kim K-H, Deep A: Optical detection of waterborne pathogens using nanomaterials. TrAC Trends in Analytical Chemistry 2019, 113:280-300. https://doi.org/10.1016/j.trac.2019.02.019. Rad TS, Ansarian Z, Khataee A, Vahid B, Doustkhah E: N-doped graphitic carbon as a nanoporous MOF-derived nanoarchitecture for the efficient sonocatalytic degradation process. Separation and Purification Technology 2021, 256:117811. https://doi.org/10.1016/j.seppur.2020.117811. Vázquez PP, Hakme E, Uclés S, Cutillas V, Galera MM, Mughari A, Fernández-Alba A: Large multiresidue analysis of pesticides in edible vegetable oils by using efficient solid-phase extraction sorbents based on quick, easy, cheap, effective, rugged and safe methodology followed by gas chromatography–tandem mass spectrometry. Journal of Chromatography A 2016, 1463:20-31. https://doi.org/10.1016/j.chroma.2016.08.008. López-Blanco R, Moreno-González D, Nortes-Méndez R, García-Reyes JF, Molina-Díaz A, Gilbert-López B: Experimental and theoretical determination of pesticide processing factors to model their behavior during virgin olive oil production. Food Chemistry 2018, 239:9-16. https://doi.org/10.1016/j.foodchem.2017.06.086. Hernandez F, Cervera M, Portolés T, Beltrán J, Pitarch E: The role of GC-MS/MS with triple quadrupole in pesticide residue analysis in food and the environment. Analytical Methods 2013, 5(21):5875-5894. https://doi.org/10.1039/C3AY41104D. Shabeer TA, Girame R, Utture S, Oulkar D, Banerjee K, Ajay D, Arimboor R, Menon K: Optimization of multi-residue method for targeted screening and quantitation of 243 pesticide residues in cardamom (Elettaria cardamomum) by gas chromatography tandem mass spectrometry (GC-MS/MS) analysis. Chemosphere 2018, 193:447-453. https://doi.org/10.1016/j.chemosphere.2017.10.133. David F, Devos C, Dumont E, Yang Z, Sandra P, Huertas-Pérez JF: Determination of pesticides in fatty matrices using gel permeation clean-up followed by GC-MS/MS and LC-MS/MS analysis: a comparison of low-and high-pressure gel permeation columns. Talanta 2017, 165:201-210. https://doi.org/10.1016/j.talanta.2016.12.032. Hakme E, Lozano A, Ferrer C, Díaz-Galiano F, Fernández-Alba A: Analysis of pesticide residues in olive oil and other vegetable oils. TrAC Trends in Analytical Chemistry 2018, 100:167-179. https://doi.org/10.1016/j.trac.2017.12.016. Tankiewicz M: Determination of selected priority pesticides in high water fruits and vegetables by modified QuEChERS and GC-ECD with GC-MS/MS confirmation. Molecules 2019, 24(3):417. https://doi.org/10.3390/molecules24030417. Oellig C, Schmid S: Polyethyleneimine as weak anionic exchanger adsorbent for clean-up in pesticide residue analysis of fruits and vegetables. Journal of Chromatography A 2019, 1597:9-17. https://doi.org/10.1016/j.chroma.2019.03.020. Zayats MF, Leschev SM, Zayats MA: A novel method for the determination of some pesticides in vegetable oils based on dissociation extraction followed by gas chromatography-mass spectrometry. Food Additives & Contaminants: Part A 2016, 33(8):1337-1345. https://doi.org/10.1080/19440049.2016.1209575. He Z, Wang Y, Wang L, Peng Y, Wang W, Liu X: Determination of 255 pesticides in edible vegetable oils using QuEChERS method and gas chromatography tandem mass spectrometry. Analytical and Bioanalytical Chemistry 2017, 409:1017-1030. https://doi.org/10.1007/s00216-016-0016-9. Mirzajani R, Ramezani Z, Kardani F: Selective determination of thidiazuron herbicide in fruit and vegetable samples using molecularly imprinted polymer fiber solid phase microextraction with ion mobility spectrometry detection (MIPF-SPME-IMS). Microchemical journal 2017, 130:93-101. https://doi.org/10.1016/j.microc.2016.08.009. Mirzajani R, Kardani F, Ramezani Z: Preparation and characterization of magnetic metal–organic framework nanocomposite as solid-phase microextraction fibers coupled with high-performance liquid chromatography for determination of non-steroidal anti-inflammatory drugs in biological fluids and tablet formulation samples. Microchemical Journal 2019, 144:270-284. https://doi.org/10.1016/j.microc.2018.09.014. Kardani F, Mirzajani R, Tamsilian Y, Kiasat A, Farajpour FB: A novel immunoaffinity column based metal–organic framework deep eutectic solvents@ molecularly imprinted polymers as a sorbent for the solid phase extraction of aflatoxins AFB1, AFB2, AFG1 and AFG2 from cereals samples. Microchemical Journal 2023, 187:108366. https://doi.org/10.1016/j.microc.2022.108366. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.7.JUN.2024.docx Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in BMC Chemistry → Version 1 posted Editorial decision: Revision requested 06 Feb, 2025 Reviews received at journal 05 Feb, 2025 Reviewers agreed at journal 27 Jan, 2025 Reviews received at journal 31 Jul, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers invited by journal 19 Jun, 2024 Editor invited by journal 19 Jun, 2024 Editor assigned by journal 11 Jun, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 07 Jun, 2024 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. 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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-4545785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318375043,"identity":"7245e4d6-62cb-4ca4-a3af-7edad7a623a8","order_by":0,"name":"Fatemeh kardani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACCQiWYGBvb4AKMROrhefMAdK0MDDw3Egg0mGS7Wcf3vi5w4KBR/L5w888DHbyDOy8D/BqkeZJN7bsPQN0mHSOsTQPQ7JhAzO7AV4tcgxpbBK8bRIM9tI5bMw8DMwJDMxs+B0mx/+MTfIvUAuP5PFnQC31hLVIS6SxSYNs4ZFgMANqOUxYi+SMZ8zWsm0SPDw8OcaScwyOG7YR0iJxPo3x5tu2Ojke9uMPP7ypqJbn5z+GXwsM8EAoYFgRsGMUjIJRMApGATEAAP5GLYvGidj9AAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Chamran University of Ahvaz","correspondingAuthor":true,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"kardani","suffix":""},{"id":318375046,"identity":"c39e6b56-0d10-4d7e-b3f5-806fccc7d173","order_by":1,"name":"Aniseh zarei Jelyani","email":"","orcid":"","institution":"Shiraz University of medical sciences of Shiraz","correspondingAuthor":false,"prefix":"","firstName":"Aniseh","middleName":"zarei","lastName":"Jelyani","suffix":""},{"id":318375052,"identity":"15d9f23d-216a-4ec5-bfd8-d0a7fadf0573","order_by":2,"name":"Tahere Khezeli","email":"","orcid":"","institution":"Ilam University, Ilam, 69315-516, Iran","correspondingAuthor":false,"prefix":"","firstName":"Tahere","middleName":"","lastName":"Khezeli","suffix":""},{"id":318375056,"identity":"e741e3fb-d161-4113-a06e-cdc5906f4b1a","order_by":3,"name":"Mohammad Hashemi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Hashemi","suffix":""},{"id":318375059,"identity":"b626ae1a-167a-4dbc-98d6-8355ca843b15","order_by":4,"name":"Marzieh Rashedinia","email":"","orcid":"","institution":"Shiraz University of medical sciences of Shiraz","correspondingAuthor":false,"prefix":"","firstName":"Marzieh","middleName":"","lastName":"Rashedinia","suffix":""},{"id":318375061,"identity":"f8690bb9-545b-44e4-85dd-f8ed10be8b7d","order_by":5,"name":"Saeedeh Shariati","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences, Ahvaz","correspondingAuthor":false,"prefix":"","firstName":"Saeedeh","middleName":"","lastName":"Shariati","suffix":""},{"id":318375064,"identity":"ff64b88e-fa89-4b9c-96b8-c36aaa99660b","order_by":6,"name":"Masoud Mahdavinia","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences, Ahvaz","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Mahdavinia","suffix":""},{"id":318375065,"identity":"e76fcd93-85ee-4e34-a981-f11527707f7f","order_by":7,"name":"Seyyed Mohammad Ali Noori","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences, Ahvaz","correspondingAuthor":false,"prefix":"","firstName":"Seyyed","middleName":"Mohammad Ali","lastName":"Noori","suffix":""}],"badges":[],"createdAt":"2024-06-07 11:20:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4545785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4545785/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13065-025-01518-x","type":"published","date":"2025-05-24T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59131048,"identity":"b208d28a-e9c0-4aea-8fef-e412b15fc8b6","added_by":"auto","created_at":"2024-06-26 16:48:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":955625,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral procedure of the preparation process edible oil samples by QuEChERS method.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4545785/v1/5bd905906581c363c87a6c3d.png"},{"id":59131050,"identity":"b7b04eb1-71c6-4ffb-aaa6-d875d65b1382","added_by":"auto","created_at":"2024-06-26 16:48:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":602563,"visible":true,"origin":"","legend":"\u003cp\u003eThe general schematic of the SPME-MMIPF procedure\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4545785/v1/abf46bba61fa3886b61ac1ca.png"},{"id":59131049,"identity":"03383ff4-4551-415f-a28b-8da99e9ddb68","added_by":"auto","created_at":"2024-06-26 16:48:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466383,"visible":true,"origin":"","legend":"\u003cp\u003eChromatograms of (a) standard solution pesticides in toluene, (b) unspiked sunflower oil sample (c) unspiked soybean oil for dichlorvos pesticide.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4545785/v1/f10e151c116063dc5297449e.png"},{"id":83459992,"identity":"ba041c5b-75a1-4709-9d72-824836b66e18","added_by":"auto","created_at":"2025-05-26 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3697677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4545785/v1/24640ed3-ad67-4bdf-bd27-7b2a567c1032.pdf"},{"id":59131051,"identity":"861744d8-bcc3-4111-9f5a-87faa9ee7006","added_by":"auto","created_at":"2024-06-26 16:48:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":444893,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.7.JUN.2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4545785/v1/fdc090be663093aeec5ad1ab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determination of 229 pesticides residue in edible oil samples using conventional QuEChERS method and solid phase microextraction based on monolithic molecularly imprinted polymer fiber and analysis with GC-MS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNowadays, the utilization of advanced nanomaterials and nanocomposites for the detection and removal of wastes, contaminants, and unhealthy/poisonous compounds has been widely being investigated [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pesticide residues are the most important categories of food contaminants that play a significant role in endangering human health problems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unreasonable and excessive consumption of pesticides may lead to an increase in the residues of these substances in food commodities. In addition to environmental effects, this issue is also considered a serious risk factor for consumers\u0026rsquo; health Acute effects occur due to high exposure to pesticides [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Recently, researchers have been concerned about the impact of chronic human exposure to pesticides and its connection with various diseases, including cancer, genetic disorders, infertility, hormonal disorders, IQ reduction, and the development of allergies, ultimately affecting human health. Maximum residue limits (MRLs) are set for pesticides to detect their legal levels in food [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Many countries and international authorities have set limits for them and it is necessary to regularly control them in food products. As an important source of human nutrition, vegetable oils are produced from various oil seeds such as olive, soybean, sunflower and cotton seed. Therefore, with the aim of controlling parasites and diseases, these products are exposed to a wide range of insecticides, fungicides and herbicides [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The residual portion of pesticides may remain in the final edible oil products, especially high-lipophilic pesticide. Multi residue method (MRM) with mass spectrometry is one of the best available methods for routine pesticide analysis. Because it analysis a large number of compounds at the same time, saves material, time, and even manpower [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The \u0026ldquo;QuEChERS\u0026rdquo; (quick, easy, inexpensive, effective, robust, safe) method was first introduced by Anastassiades et al. and attained global acceptance in analyzing pesticides thanks to its high advantages [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite the advantages, the QuEChERS method has some limitations such as using a lot of organic solvent and time, multi-stage, excessive use of chemicals, and lack of selectivity. The non-selectivity of the QuEChERS method is due to the application of the same experimental conditions in the analysis of different categories of compounds in simultaneous analysis, this limitation makes some target compounds to be extracted with high efficiency and some other unrelated compounds. Molecularly imprinted polymers (MIPs) are considered attractive polymers in recent years due to their easy preparation, mechanical, thermal, and chemical stabilities, and highly selective detection capabilities [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In MIPs preparation, three-dimensional structural voids can be created during polymerization and once template extraction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Since the cavities are complementary in size, shape, and chemical functionality to the templates, MIPs have excellent recognition capabilities for template molecules. In this paper, the aim is to develop a selective, proprietary, chemically and physically strong SPME-MMIPF for the extraction of different classes of pesticides including organophosphorus, carbamate, synthetic pyrethroid, organo-nitrogen, and organochlorine in an easy and economical way from etched polymer. These fibers were obtained through non-covalent polymerization of the prepolymer solution inside the capillary columns. Then these fibers were used for selective extraction and sensitive determination of different categories of pesticides by the SPME-MMIPF/GC-MS method. Finally, the validation results using the SPME-MMIPF/GC-MS method were compared with the results of the QuEChERS method.\u003c/p\u003e"},{"header":"2. Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Reagents and Chemicals\u003c/h2\u003e \u003cp\u003eAcetonitrile, toluene and formic acid (99.9%) were obtained from Sigma\u0026ndash;Aldrich Company, and anhydrous magnesium sulfate (MgSO\u003csub\u003e4\u003c/sub\u003e) were obtained from Merck (Darmstadt, Germany).C\u003csub\u003e18\u003c/sub\u003e sorbent and primary\u0026ndash;secondary amines (PSA) were obtained from Supelco Company. A Milli Q purification system were used and ultrapure water (18.2X resistivity) was obtained. All the standard of pesticides was purchased from Merck (Darmstadt, Germany). The standard solutions of each pesticide were carefully prepared in acetonitrile and ethyl acetate in concentrations of 1000\u0026ndash;2000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Finally, separate stock standard solutions for optimization and three mixed standard solutions for calibration were prepared from the stock standards. Methacrylic acid (MAA), ethylene glycoldimethacrylate (EGDMA) and azo (bis)-isobutyronitrile (AIBN) were purchased from were purchased from Sigma Aldrich (St. Louis, MO, USA). Triphenyl phosphate (TPP; used as internal standard), formic acid, anhydrous sodium acetate, trisodium citrate dehydrate, sodium chloride and anhydrous magnesium sulfate were purchased from Merck (Darmstadt, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Equipment\u003c/h2\u003e \u003cp\u003eThe pesticides were analyzed by an Agilent gas chromatograph 7890B series that was coupled with an Agilent mass spectrometer model 5977A and auto sampler. The simultaneous analysis of 300 pesticides was performed by 1 \u0026micro;L injection of the sample in pulsed splitless mode at 280\u0026deg;C. An HP-5MS fused silica capillary column (30 m\u0026times;0.25 mm, i.d. 0.25 \u0026micro;m) was used for separation with the oven temperature program started at 70\u0026deg;C, and heated up to 120\u0026deg;C at the rate of 25\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; next, it was heated up to 300\u0026deg;C with the heating rate of 5\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and kept there for 20 min. The total run time was 56.8 min. The carrier gas was high-pure helium with 1.0 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e flow rate. The temperature of the MS quadruple and ion source was maintained at 150\u0026deg;C and 230\u0026deg;C, respectively. The MS instrument was operated at an ion energy of 70 eV in electron impact (EI) in the range of m/z 50\u0026ndash;550. Based on the usage of three qualifier ions, quantitative analysis was carried out in the selected ion monitoring (SIM) mode. The solvent delay time was adjusted at 3.0 min. the Masshunter and Chemstation software were used for data collection and processing procedures, respectively. In addition, instrument control, data gathering, and data processing were performed using Varian workstation software.\u003c/p\u003e \u003cp\u003eScanning electronic microscope (SEM) images were carried out by SEM instrument model Hitachi S5200 (Hitachi: Tokyo, Japan) in scanning mode at 30 kV. The confirmation of functional groups in MMIPF was performed using an FT-IR spectrometer (BOMEM MB-Series 1998 FT-IR spectrometer).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Optimization of GC-MS parameters\u003c/h2\u003e \u003cp\u003eFor each analyte, the most intense ion with the highest m/z ratio, collision energies (CE), MS detection method, collision gas pressure, retention time, and precursor ion were optimized. For finding the best product ions, several scanning methods were created with different collision energies. A group of 300 pesticides was analyzed with collision energy ranging from 5 to 40 V and 5-V increasing intervals. After the findings were evaluated, the product with the highest possible intensity was chosen as the quantifier ion, and the second-highest intensity product was employed as the qualifier ion. Also, a technique for selective response monitoring (SRM) was developed by adjusting the collision gas pressure by injecting the same vial with various helium pressures (ranging from 1.0 to 2.0 mTorr with 0.1-mTorr increasing intervals). Furthermore, 1.5 mTorr was found to be the ideal pressure, producing an intense response for most pesticides. In the SRM mode, a new collision energy optimization was done, and no notable changes were found with the scan mode. Moreover, d-well time for fewer sensitive analytes was extended by shortening the d-well duration for analytes with a sufficient signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Preparation of oils\u003c/h2\u003e \u003cp\u003eThe samples inclusive sunflower and soybean oils were purchased from a super market (Ahvaz, Iran). The oil samples were prepared according to following procedure for QuEChERS and SPME-MMIPF methods. A 2.5 g of each sample (sunflower and soybean oil) was weighed in a 50 mL PTFE centrifuge tube. Then, 10 mL of acetonitrile and 100 \u0026micro;L of TPP as internal standard (10 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were added, and the sample was vortexed for 20 min, followed by centrifugation at -5 \u003csup\u003eo\u003c/sup\u003eC, 4500 rpm for 10 min. The supernatant was collected and stored for QuEChERS and SPME-MMIPF methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Fabrication of MMIPF\u003c/h2\u003e \u003cp\u003eIn this study, the fiber for MIP was fabricated according to the method previously reported by Mirzajani and Kardani [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For this purpose, 2.2 mmol of template molecule (fenitrothion for organophosphorus, chloroneb for organochlorine, biphenyl for organonitrogen, cypermthrin for synthetic pyrethroid and pirimicarb for carbamate) were dissolved in 30 mL of acetonitrile. Then 25 mmol of MAA, 125 mmol of EGDMA, and 275 mg of AIBN were added. Afterwards 2.2 mL of the mixture was transferred to a vial and deoxygenated by N\u003csub\u003e2\u003c/sub\u003e gas for 8 min. To prepare integrated fibers in form of molds, capillary fused silica tubes with a length of 4 cm and an inner diameter of 0.3 mm were washed with methanol and dried by nitrogen flow. Then, the capillary was filled with the polymerization mixture, and then both ends of the capillary were closed with two small pieces of soft rubber. After that, the filled capillaries were placed in the oven and polymerization was done at 60\u0026deg;C for 14 h. Then the polymer monolith fibers immersed in 40% hydrofluoric acid solution for 2 h under gentle stirring. After dissolving the glass capillary, the MMIPF was immersed in a solution of methanol: acetic acid: water (4:1:1, v/v) for 2 h to remove the template, then were dried in the oven at 65\u0026deg;C for 2 h. Non-imprinted polymer fibers (NIP fibers) were prepared with a completely similar MIP method, except that the template molecule was not added. The resulting fiber offers excellent flexibility with high strength and chemical resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Sample preparation steps using QuEChERS method\u003c/h2\u003e \u003cp\u003eFor cleaning up oil sample, 6 mL of supernatant (section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e.) was transferred into the 15 mL centrifuge tube containing 900 mg of magnesium sulfate, 300 mg of C\u003csub\u003e18\u003c/sub\u003e, and 300 mg of PSA. The sample was again vortexed and centrifuged with previous conditions. Afterward, 40 \u0026micro;L formic acid 5% was added to 4 mL of the final extract, evaporated under a gentle stream of nitrogen, reconstituted with 1 mL toluene, and filtered. The working method is shown in all the details in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Sample preparation steps using SPME-MMIPF\u003c/h2\u003e \u003cp\u003eFor SPME procedure, the fibers were mounted on a laboratory-made SPME-MMIPF apparatus, then 5 mL of the supernatant (section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e.) was added to a 10 mL glass vial containing a magnetic stir bar. The MMIPF was introduced into a stirring solution (600 rpm) for 30 minutes to extract the analyte. After 30 minutes, the fiber was drawn into the needle using the piston of the home-made SPME-MMIPF device and the device was removed from the vial. Then washed with mixed methanol: water (1:1 v/v) (by immersion, 60 seconds). Next, immersed in a glass vial containing 1 mL of toluene (as the eluent solvent) to extract the pesticides at 25\u0026deg;C for 20 minutes with a stirring speed of 500 rpm. After that, 1 \u0026micro;L of the rich phase solvent was withdrawn using a micro-syringe (10 \u0026micro;L, F-LC, SGE, Australia) and injected into the GC-MS injection port for further analysis. The general photograph of the SPME-MMIPF/GC-MS procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In this figure, fenitrothion was as a model template that represents organophosphorus. The process was the same identical for other classes of pesticides with corresponding molecule template representatives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Calibration curves\u003c/h2\u003e \u003cp\u003eTo evaluate linearity of QuEChERS method, blank edible oil samples were pretreated according to optimized extraction and purification methods (section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e and \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e). For this purpose, edible oil samples (sunflower and soybean oil) from blank materials were mixed with multi-standard solutions containing 229 pesticides in concentration range of 10 to 250 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In this evaluation, 8 concentrations from the range of 10 to 250 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were considered and the analytical method was repeated three times in each concentration to estimate the standard deviation and accuracy. For SPME-MMIPF method, 11 standard solutions in the concentration range of 1-300 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of pesticides were prepared.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characterization of MMIPF\u003c/h2\u003e \u003cp\u003eCharacterization experiments on MMIPF included SEM images (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS) and FT-IR spectra (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eS), have been given in the supplementary information section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Method validation\u003c/h2\u003e \u003cp\u003eTo evaluate performance of both QuEChERS and SPME-MMIPF methods, the figures of merit including linearity, LOD, LOQ, inter-day and intra-laboratory precision, and accuracy of the methods were evaluated for the determination and extraction of pesticides in the edible oil matrix. The validation of the QuEChERS method was performed based on SANTE guidelines (European Commission, 2011) and SPME-MMIPF method was performed according to previous studies that have been done on other compounds [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. The linearity of QuEChERS and SPME-MMIPF\u003c/h2\u003e \u003cp\u003eThe results for the linearity of QuEChERS and SPME-MMIPF methods for carbamates (as model) were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and results for other pesticides reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS. The calibration curves for QuEChERS method were made using peak areas of analytes in different concentrations (10, 25, 50, 100, 150, 200, 230, and 250 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) versus the corresponding concentrations in the edible oil\u0026rsquo;s matrix. Chromatographic responses at 8 concentrations (10\u0026ndash;250 \u0026micro;gkg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) had good linearity for all analytes. The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0.99 was acceptable for each target analyte (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS). In order to estimate the linear range of SPME-MMIPF method, 11 standard solutions with specific concentrations of different groups of pesticides in the range of 1-300 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were prepared and after extraction by SPME-MMIPF method and the obtained peak area was recorded by GC-MS for each concentration. The number of test repetitions in each concentration level were 5 times. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS show data obtained for calibration curves of different categories of pesticides. According to the obtained results, calibration curves in the concentration range of 1-300 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were linear and had a high coefficient of determination (0.998).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation parameters of SPME-MMIPF and QuEChERS methods for the determination of other carbamates in edible oil samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCarbamates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuEChERS method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eSPME method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear range\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLOQ\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinear range\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLOQ\u003c/p\u003e \u003cp\u003e(\u003cb\u003e\u0026micro;\u003c/b\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButylate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinomethionat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDodemorph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovaluron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePirimicarb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePromecarb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropoxure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. 2. Calculation of LOD and LOQ\u003c/h2\u003e \u003cp\u003eLODs and LOQs were calculated as 3 and 10\u0026times;the standard deviation of 10 replicate measurements at lower concentrations per the slope of each calibration curve in edible oils (10 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in QuEChERS and 1.0 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in SPME-MMIPF), respectively (EURACHEM., 1998). The LODs obtained by QuEChERS method for all target analytes in the edible oil matrix ranged from 0.9 to 2.6 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while the LOQs ranged from 4.8 to 10.2 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The lowest LOD and LOQ were related to metalaxyl, fenitrothion, malathion, biphenyl, metribuzin, and mirex while the highest value was related to chlorpyrifos, propoxure, thiomton, and 2,4-DDE. The LOD and LOQ obtained in SPME-MMIPF method were in the range of 0.33 to 0.45 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.8 to 1.32 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS presented the results for carbamates (as model) and other pesticides, respectively. The LOD and LOQ obtained by the SPME-MMIPF method have lower than QuEChERS method, and also the difference detection range between all pesticides is very small and close to each other.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Inter- and intra-day precision\u003c/h2\u003e \u003cp\u003eThe concentrations of 10 and 50 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1 and 50 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were selected for estimation of inter and intra-day precision of QuEChERS and SPME-MMIPF methods, respectively. Intra-day and inter-day precision was assessed by five determinations at each spiking level on the same day. As the results show (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eS), the recovery in the selected concentrations of the SPME-MMIPF method is in the range of 97\u0026ndash;102% and the RSD for repeating the experiments in one day was between 1.2\u0026ndash;2.7%, and for repeating the experiments in 5 days were obtained in the range of 3.3\u0026ndash;5.8%. The recoveries obtained in QuEChERS method using selected concentrations are in the range of 68\u0026ndash;132%. The RSDs obtained for the repeatability of tests in one day was in the range of 3.6\u0026ndash;4.6% and for tests in 5 days it was obtained in the range of 4.8\u0026ndash;6.3%. The results of QuEChERS method showed that the RSDs obtained for heptachlor, atrazine, procymidone, clomazone, metribuzin, propanil, and chlorfenapyr pesticides were high. The general results of the SPME-MMIPF method gave a very favorable recovery range for all the pesticides in comparison with QuEChERS method. Also, RSD value obtained by using this method is much less than QuEChERS method and also it is uniform for all pesticides.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from intra-day and inter-day precision (n\u0026thinsp;=\u0026thinsp;5) of MIPF-SPME and QuEChERS for the determination of carbamates in edible oil samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eQuEChERS method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003eSPME method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eCarbamates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIntra-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eIntra-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eInter-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAdded 50 (\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAdded 10 (\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAdded 1 (\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eAdded 50 (\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovery\u003c/p\u003e \u003cp\u003eRSD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRecovery\u003c/p\u003e \u003cp\u003eRSD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eFound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRecovery\u003c/p\u003e \u003cp\u003eRSD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRecovery\u003c/p\u003e \u003cp\u003eRSD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eButylate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.1 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.0 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e96.7 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChinomethionat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.2 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93.0 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e98.5 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEPTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.8 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.0 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e91.0 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDodemorph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.2 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.9 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.0 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95.5 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMolinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.4 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.5 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91.0 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e93.2 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNovaluron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.8 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.0 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92.5 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePirimicarb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.2 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.3 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98.0 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e93.1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePromecarb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.4 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.9 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.0 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95.5 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePropham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.2 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e90.0 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e99 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePropoxure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.0 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e94.2 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.2.4\u003c/em\u003e. \u003cem\u003eEvaluation of the accuracy and performance of the method\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn this method, a certain concentration of standard compound solutions was added to each of the real samples prepared (the methods of preparing of real samples are explained in 2.4, 2.6 and 2.7 sections) and then recovery was obtained based on the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\%R=\\frac{{C}_{found}-{C}_{real}}{{C}_{added}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo estimate\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e after preparing the real samples with the proposed methods, the final solution was injected into the GC-MS and \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003ewas obtained from the extrapolation of the calibration curves. The standard addition method was used to obtain the\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{found}\\)\u003c/span\u003e\u003c/span\u003e. In this way, the concentrations of the standard solution including 20 and 200 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for both two methods (QuEChERS and SPME-MMIPF) were added to the edible oil samples. After the implementation experiments by both suggested methods. The peak area corresponding to each concentration was placed instead of y in the y\u0026thinsp;=\u0026thinsp;A(x)\u0026thinsp;+\u0026thinsp;B calibration curves equation. X represents the added concentration (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{added}\\)\u003c/span\u003e\u003c/span\u003e) to real samples that equal to 20 and 200 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for both methods (QuEChERS and SPME-MMIPF). The recovery of pesticides in different real samples for the two suggested methods is shown in Table\u0026nbsp;3S. The results show that the average recovery for all classes of pesticides using QuEChERS and SPME-MMIPF are more than 68%. The recovery in all two edible oil samples using QuEChERS and SPME-MMIPF method were obtained between 73 to 127% and 92 to102% for carbamates, 70 to 95% and 92 to 98% for the organochlorines, 68 to 127% and 95 to 102% for synthetic pyrethroid, 70 to 95% and 92 to 98% for organophosphorus, and 73 to 127% and 92 to102% for organonitrogens, respectively.\u003c/p\u003e \u003cp\u003eIn different edible oil samples, the RSDs using the SPME-MMIPF method were between 2.6 and 3.8%, and for the QuEChERS method, it was between 3.3 to 5.6% (Table\u0026nbsp;3S). Repeatability was calculated as the RSDs of repeated measurements (n\u0026thinsp;=\u0026thinsp;3). All classes of pesticides were extracted using the SPME-MMIPF method showed favorable and satisfactory recovery range, but in the QuEChERS method, lipophilic pesticides such as hexachlorobenzene, datalymphos, aldrin, p, p'-DDE, o, p'. -DDT, o, p'-DDE, p, p'-DDT, leptophos, pentachloroaniline, transchlordane, pentachloronitrobenzene, trichloronate, dieldrin and prothiophos had very low recovery (between 35 and 67%). RSDs for this category of pesticides were also high which indicates the unfavorability of QuEChERS method for these pesticides. But the recovery for other OCs such as dicofol, chlorothalonil, o,p'-DDD, p,p'-DDD, and dieldrin was higher than 70%. Recovery of dimethoate and deoxidation in sunflower oil were much higher than soybean oil. The recovery of amitraz in all types of edible oil were unreliable due to its stability problem in the solvent. Accurate quantification was achieved in two types of oil at different concentrations, generally RSD less than 10% was obtained for more than 90% of the target compounds and RSD\u0026thinsp;\u0026lt;\u0026thinsp;5% for more than 75% of the target compounds. The result that can be obtained from comparing the number of recoveries using the two methods is that SPME-MMIPF method is superior in extracting different categories of pesticides compared to QuEChERS method. The process of molecular mapping of the cavity is similar to the size and molecular structure of the template compound. Therefore, selective results are obtained from the effect of molecular mapping, due to spatial, molecular size detection, and specific interaction of MIP towards the template molecule and structurally similar compounds. Other compounds, which are not similar in terms of size, shape, and functional groups to the target compounds and the template molecule, must be adsorbed mainly by a non-specific adsorption mechanism. Selectivity is the most obvious feature of SPME-MMIPF technique compared to other techniques that extract compounds non-selectively. The QuEChERS method is a general and non-selective method that extracts all compounds simultaneously and under the same experimental conditions. In the meantime, the test conditions are favorable for some compounds and unfavorable for some compounds, which results in extracting some compounds well and extracting others unfavorably.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Pesticide residue in oil samples\u003c/h2\u003e \u003cp\u003eThe SPME-MMIPF method was used to analyze 30 oil samples collected locally from Iranian markets (15 sunflower oil and 15 soybean oil samples). After testing all the collected oil samples, the identified pesticides by two proposed methods include beta HCH (5 samples of soybean oil, 5 sunflower samples), and beta endosulfan (1 samples of soybean oil, 2 sunflower samples) and 2,4-DEE (1 samples soybean oil, 2sunflower samples), Altrin (3 soybean oil samples, 2 sunflower samples), heptachlor-endo epoxide (1 soybean oil samples, 1 sunflower oil samples), heptachlor-oxo-, epoxide (1 soybean oil samples), chlorpyrifos (1 soybean oil samples, 2 sunflower samples) were found. However, the concentrations of all pesticides found, were lower than their MRL.According to the results of this investigation, in which the type and amount of pesticide residues were monitored in Iran\u0026rsquo;s oil samples, about 229 pesticide residues are less than their MRL levels and there is no risk or threatening harm to human health. Although the number of residual toxins is usually lower than the permissible limit, the contamination with fixed or banned toxins is still a serious environmental and public health problem that requires an efficient method of remediation and management.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe method presented in the research includes the synthesis of selectable MMIPF and their application in SPME of 229 pesticides from different oils. Since the size and shape of the holes of MMIPFs match the size and shape of the template molecule, they have high selectivity toward target analytes. This method was compared with usual and popular QuEChERS method and very satisfactory results were obtained compared to QuEChERS. The QuEChERS method has disadvantages, such as multi-stage extraction, long extraction time, use of large sample volume and solvent, and wastage of compounds during the extraction process, all of which would lower the extraction efficiency of this method. Therefore, it was necessary to develop methods with high selectivity and speed to extract these compounds from real samples. The SPME-MMIPF method is a rapid method that avoids sample degradation during extraction and directly extracts the desired compounds without using intermediate steps from the sample matrix. The results showed that the method has a significant advantage over the QuEChERS method. This method has been able to extract different categories of pesticides with a high recovery with the least RSD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, and the Food and Food and Drug Administration, Shiraz University of medical sciences of Shiraz, Iran, funded for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: This article does not contain any studies with human or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e:\u0026nbsp;Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e: Information and data will be provided to the journal if needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e:\u0026nbsp;Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eMethodology: Fatemeh Kardani\u003c/p\u003e\n\u003cp\u003eData curation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati, Seyyed Mohammad Ali Noori, Mohammad Hashemi,\u003c/p\u003e\n\u003cp\u003eFormal analysis and investigation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati\u003c/p\u003e\n\u003cp\u003eWriting original draft preparation: Fatemeh Kardani, Aniseh Zarei jelyani, Saeedeh Shariati, Marzieh rashedinia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting, review and editing: Fatemeh kardani, Tahere Khezeli, Aniseh zarei jelyani, Mohammad Hashemi, Marzieh rashedinia,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSaeedeh Shariati,Seyyed Mohammad Ali Noori\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTizhoosh NY, Khataee A, Hassandoost R, Soltani RDC, Doustkhah E: Ultrasound-engineered synthesis of WS2@ CeO2 heterostructure for sonocatalytic degradation of tylosin. \u003cem\u003eUltrasonics Sonochemistry \u003c/em\u003e2020, 67:105114. https://doi.org/10.1016/j.ultsonch.2020.105114.\u003c/li\u003e\n\u003cli\u003eLaborda F, Bolea E, Cepri\u0026aacute; G, G\u0026oacute;mez MT, Jim\u0026eacute;nez MS, P\u0026eacute;rez-Arantegui J, Castillo JR: Detection, characterization and quantification of inorganic engineered nanomaterials: A review of techniques and methodological approaches for the analysis of complex samples. \u003cem\u003eAnalytica Chimica Acta \u003c/em\u003e2016, 904:10-32. https://doi.org/10.1016/j.aca.2015.11.008.\u003c/li\u003e\n\u003cli\u003eHassandoost R, Kotb A, Movafagh Z, Esmat M, Guegan R, Endo S, Jevasuwan W, Fukata N, Sugahara Y, Khataee A: Nanoarchitecturing bimetallic 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Moreno-Gonz\u0026aacute;lez D, Nortes-M\u0026eacute;ndez R, Garc\u0026iacute;a-Reyes JF, Molina-D\u0026iacute;az A, Gilbert-L\u0026oacute;pez B: Experimental and theoretical determination of pesticide processing factors to model their behavior during virgin olive oil production. \u003cem\u003eFood Chemistry \u003c/em\u003e2018, 239:9-16. https://doi.org/10.1016/j.foodchem.2017.06.086.\u003c/li\u003e\n\u003cli\u003eHernandez F, Cervera M, Portol\u0026eacute;s T, Beltr\u0026aacute;n J, Pitarch E: The role of GC-MS/MS with triple quadrupole in pesticide residue analysis in food and the environment. \u003cem\u003eAnalytical Methods \u003c/em\u003e2013, 5(21):5875-5894. https://doi.org/10.1039/C3AY41104D.\u003c/li\u003e\n\u003cli\u003eShabeer TA, Girame R, Utture S, Oulkar D, Banerjee K, Ajay D, Arimboor R, Menon K: Optimization of multi-residue method for targeted screening and quantitation of 243 pesticide residues in cardamom (Elettaria cardamomum) by gas chromatography tandem mass 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\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccjo","sideBox":"Learn more about [BMC Chemistry](https://bmcchem.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ccjo/default.aspx","title":"BMC Chemistry","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SPME-MMIPF, QuEChERS, Pesticides, Edible oil, GC-MS","lastPublishedDoi":"10.21203/rs.3.rs-4545785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4545785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe goal of this research is development of solid phase microextraction based on monolithic molecularly imprinted polymer fiber (SPME-MMIPF) method to determine 229 pesticides in edible oil samples using gas chromatography-mass spectrometry (GC-MS) and comparison of it with the common QuEChERS method. In QuEChERS method, acetonitrile used as extraction solvent and magnesium sulfate used as water absorbing agent. For SPME-MMIPF method, an MMIPF was synthesized by polymerization of methacrylic acid in presence of ethylene glycoldimethacrylate and azo (bis)-isobutyronitrile. The optimal conditions for the SPME-MMIPF method are: extraction time 30 min, desorption time with toluene 20 min and string speed of the aqueous sample 600 rpm. Under optimal extraction condition, the figures of merit were obtained for two methods and compared. The linear range of 1-300 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for SPME-MMIPF and 10\u0026ndash;250 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for QuEChERS was obtained. The detection limit of SPME-MMIPF (0.321\u0026ndash;0.335 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) method was better than the QuEChERS (0.9\u0026ndash;2.6 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) method. The results showed a quantification limit of 0.8\u0026ndash;2.2 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for SPME-MMIPF and 1.5\u0026ndash;5.2 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for QuEChERS. The recoveries were in the range of 92\u0026ndash;102% and 68\u0026ndash;127% for SPME-MMIPF and QuEChERS, respectively.\u003c/p\u003e","manuscriptTitle":"Determination of 229 pesticides residue in edible oil samples using conventional QuEChERS method and solid phase microextraction based on monolithic molecularly imprinted polymer fiber and analysis with GC-MS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 16:48:30","doi":"10.21203/rs.3.rs-4545785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-06T12:08:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-05T23:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-01-27T09:24:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T11:56:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-29T17:51:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49100193339004268079394154477278653561","date":"2024-07-16T08:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37405100277755909129416365177808153765","date":"2024-07-09T12:48:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90317934291474635371929154723002639999","date":"2024-07-08T10:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-19T14:26:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-19T12:02:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-11T13:15:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-11T01:44:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Chemistry","date":"2024-06-07T11:19:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccjo","sideBox":"Learn more about [BMC Chemistry](https://bmcchem.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ccjo/default.aspx","title":"BMC Chemistry","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ea5c546-ba78-4059-857f-6eb371f8f985","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:00:30+00:00","versionOfRecord":{"articleIdentity":"rs-4545785","link":"https://doi.org/10.1186/s13065-025-01518-x","journal":{"identity":"bmc-chemistry","isVorOnly":false,"title":"BMC Chemistry"},"publishedOn":"2025-05-24 15:57:20","publishedOnDateReadable":"May 24th, 2025"},"versionCreatedAt":"2024-06-26 16:48:30","video":"","vorDoi":"10.1186/s13065-025-01518-x","vorDoiUrl":"https://doi.org/10.1186/s13065-025-01518-x","workflowStages":[]},"version":"v1","identity":"rs-4545785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4545785","identity":"rs-4545785","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}