Magnetic Solid

For the analysis of pesticide residues in water samples, various extraction techniques are available. However, liquid–liquid extraction (LLE) and solid-phase extraction (SPE) are most commonly used. LLE and SPE extraction techniques each have their own disadvantages.

The aim of the study was to develop an environment-friendly multi-residue method for determination of multiclass pesticides in environmental water samples (ground water, agricultural field/irrigation run-off water, etc.).

The magnetic solid-phase extraction (MSPE) technique using surface-fabricated magnetic nano-particles was used for extraction of water samples, followed by quantification by gas chromatography tandem mass spectrometry. The developed multi-residue method was validated in terms of linearity, LOD, LOQ, recovery, and repeatability.

Recovery data were obtained at the spiking concentration level of 1, 5, and 10 µg/L, yielding recoveries in the range of 70–120%. Overall, non-polar pesticides from all the groups, i.e., synthetic pyrethroid, organophosphorus, organochlorine, herbicides, and fungicides, show acceptable recovery percentages. Good linearity (r2 value ≥ 0.99) was observed at the concentration range of 0.5–100 µg/L. RSD values were found ≤ 18.8.

The study shows that the method is specific, rapid, and low cost, as well as having a good linearity and recovery; thus, this method is applied in routine purposes for the analysis of pesticide residue in real water samples.

Due to better adsorption ability, permeability, and magnetic separability, the functionalized nano-particles were found effective in the enrichment of 22 multiclass pesticides including organo-phosphorus, organo-chlorine, synthetic pyrethroid, herbicides, and fungicides.

Pesticides are widely used for increasing agricultural and farming production through their ability to protect crops by controlling a wide range of threats from weeds, pests, and diseases. However, application of these toxic substances affects human health as well as the environment (1–4). With a specific end goal to control pesticide residue, governments and international organizations across the globe have set up maximum residue limits (MRLs) to ensure the safety of foodstuffs (5–7).

For solving environmental and biological issues, the analysis of pesticide residues in water samples is a must (8). There are various extraction techniques that have been used for the determination of multi residues of pesticides in various food commodities and environmental water samples such as solid-phase extraction (SPE) (9, 10), solid–liquid extraction (11, 12), liquid–liquid extraction (LLE) (13, 14), supercritical fluid extraction (SFE) (15, 16), stir bar sorptive extraction (SBSE) (17), solid-phase micro-extraction (SPME) (18, 19), liquid-phase micro-extraction (LPME) (20), etc. Among the various methods, LLE and SPE methods are most commonly used for monitoring pesticide residues in water. However, these methods have disadvantages, such as they are time consuming, consume large amounts of organic solvents, have problems like congestion of column or disc (21), and have a lengthy cleanup process which makes their application difficult in routine analysis of samples.

In recent years, magnetic solid-phase extraction (MSPE) has acted as a promising technique for sample preparation (22–27) because its MSPE method overcomes the above-mentioned problems and gives good recoveries. This method is based on the use of sorbent materials like molecular imprinted polymers, carbon hemimicelles, and magnetic nanoparticles (28). Due to high surface to volume ratio, the absence of internal diffusion resistance (29), and excellent absorption capacity of analyte, C18 functionalized magnetic nanoparticles (MNP) are used for pre-concentration or cleanup of non-polar and moderately polar pesticides. However, C18 gives poor recoveries for polar pesticide such as molinate and deethylatrazine (29, 30).

The most commonly used technique for the multi-residue method for trace level analysis of pesticide in water samples are gas chromatography (GC) and liquid chromatography (LC). In GC, various types of detectors are used for, e.g., electron-capture detection (ECD) (31), nitrogen-phosphorus detection (NPD) (32, 33), flame ionization detection (FID) (34), flame photometric detection (FPD) (35), and mass spectrometry (MS) (36, 37). The MS detector had great advantages such as higher selectivity, reproducibility, accuracy, etc. over other detectors. With the development of MS technology, high-detectable (IDL ≤ 0.2 pg/µL) tandem MS enhances these advantages (38).

Tandem MS has two modes, one is SIM (single-ion monitoring) and the other is MRM (multiple-reaction monitoring). Due to enhanced sensitivity, selectivity and greater signal-to-noise (S/N) ratio, MRM mode reduces the noise by eliminating interferences of the sample. MRM mode has huge advantages over SIM mode. In both MRM and SIM mode, precursor ions were the same (38) but in the case of MRM, collision gas is used for fragmenting the precursor ions to produce product ions.

The aim of the present study was to develop a multi-residue method for the analysis of 22 pesticides of different chemical classes (organophosphorus, organochlorine, synthetic pyrethroid) in the environmental water samples (ground water, agricultural field/irrigation run-off water, etc.) with the help of magnetic core shell nanoparticles coated with C18 functionalized silica. The developed method is based on MSPE and quantification by gas chromatography tandem mass spectrometry (GC-MS/MS) in the MRM mode and was validated in the terms of specificity, linearity, LOD, LOQ, recovery/trueness/accuracy, and repeatability/precision.

The certified reference materials of different pesticides, namely trifluralin, gamma- HCH, delta-HCH, malathion, chlorpyrifos, parathion, pendimethalin, fipronil, o,p′-DDE, butachlor, p,p′-DDE, myclobutanil, o,p′-DDD, beta-endosulfan, ethion, p,p′-DDT, phosalone, permethrin, cyfluthrin, cypermethrin, fenvalerate, and deltamethrin pesticides of (purity in between 96–99%) were procured from M/s Sigma-Aldrich (India). HPLC-grade n-hexane and acetone was procured from M/s Merck (India).

For magnetic nanoparticles (MNP) synthesis, Iron (III) chloride hexahydrate, Iron (II) chloride tetrahydrate, trimethylamine, trimethoxyoctadecylsilane, and tetraethyl orthosilicate (TEOS) were purchased from M/s Sigma Aldrich (India). Absolute ethanol was procured from M/s Changshu Vangyuan Chemical (China) and polyvinylpyrrolidone K-30 (PVP K-30) was purchased from M/s Fluka (India).

Analysis of multiclass pesticides in environmental water samples were performed by using Shimadzu GC-MS/MS (model TQ8040) equipped with auto sampler AOC-20s, injector AOC-20i, and computer with GC-MS Lab solution (4.2 versions) software for data acquisition. Rtx-5Sil MS capillary column (30.0 m × 0.25 mm × 0.25 µm) was used for chromatographic separation of pesticides. Helium (99.99%) was used as carrier gas. Injection volume was kept at 1µL in splitless mode. Injector and interface temperatures were maintained at 250°C and 280°C respectively.

GC temperature was programmed as: initial temperature 50°C for 2 min and then increased at a rate of 15°C/minute up to 190°C with a hold time of 1 min and further increased up to 280°C at a rate of 5°C/minute with a hold time of 10 min. Total run time for the method was 40 min 33 s.

In MS/MS detector, 70 eV energy was used for electron ionization (EI) during MRM mode and 230°C ion source temperature. Solvent cut time was set to 2.5 min. Detector voltage was set relative to the tuning voltage. Argon was used as a collision ion dissociation (CID) gas with detector voltage 0.6 kV.

For MRM, one target ion and two reference ions were selected for each pesticide. Details of quantifier and qualifier ion transitions with retention time of each pesticide are shown in Table 1.

Retention time and instrumental determination parameters of various pesticides

CE = Collision energy.

Retention time and instrumental determination parameters of various pesticides

CE = Collision energy.

FeCl3·6H2O (2.7 g) and FeCl2·4H2O (1 g) were first dissolved in 100 mL of water. Then 10 mL of trimethylamine solution was slowly added under a nitrogen atmosphere, stirred mechanically for 2 h and reacted at 80°C. Subsequently, 28 mL of PVP-K 30 (7.0 g/L) was added into the solution and stirred for 12 h. The obtained nanoparticles were separated from the solution by a magnet and washed several times with pure water. Obtained nanoparticles then were dried on a rotatory evaporator.

Coating nanoparticles with silica were completed by a simple hydrothermal reaction. The above obtained Fe3O4 nanoparticles were homogeneously dispersed in a mixture of ethanol (160 mL) and water (40 mL) with 0.5 h of sonication. Ammonia solution (1 mL) and TEOS (300 µL) were added to the solution. The obtained mixture was stirred mechanically for 6 h at room temperature. The resultant Fe3O4@SiO2 core–shell nanoparticles were washed several times with water until the pH became neutral. Finally, the silica coated iron oxide nanoparticles were obtained after being dried in a vacuum. Thereafter, the obtained silica-coated nanoparticles (0.4 g) were added to ethanol (50 mL) through mechanical stirring. The slurry was heated to boiling point, and the ammonia solution (1.6 mL) and octadecyltrimethoxysilane (1.4 mL) were added in succession. The mixture was sealed into flask and stirred at 50°C for 2 h. The C18-modified MNPs were obtained, which were washed several times with ethanol and dried for further use.

Nanomaterials, produced in the lab, were characterized using Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM). The SEM and TEM analysis confirmed that the average particle size of functionalized magnetite nanoparticles was in the range of 52 ± 7 nm. Additional details on various characterization techniques used for the characterization of nanoparticles may be obtained from our previously published literature on the synthesis by Nair et al. 2015 (39).

Standard solutions of 22 pesticides of different classes (organophosphorus, organochlorine, and synthetic pyrethroids) were prepared in separate 10 mL volumetric flasks. Dissolve 2 mg of each pesticide in 1 mL of acetone and then make up the final volume up to 10 mL with hexane. A multipesticide standard mixture solution was prepared by using 10 mg/L concentration of each standard in hexane.

C18 functionalized silica magnetic nanoparticles (MNP) were synthesized by the co-precipitation method as described above and earlier publication by authors (39). The MSPE procedure was followed and the method was standardized (40). The schematic illustration of sample preparation by the MSPE procedure is presented in Figure 1.

Schematic illustration of the sample preparation by MSPE (MSPE procedure).

Calibration curves of multiclass pesticide mixture in MRM mode.

Calibration curves of multiclass pesticide mixture in MRM mode.

Calibration curves of multiclass pesticide mixture in MRM mode.

For validation, specificity, linearity, LOD, LOQ, recovery, and repeatability parameters were evaluated for the determination of multi-residue pesticides by GC-MS/MS.

For calibration curve, standards solutions of 0.5, 1, 5, 10, 20, 50, and 100 µg/L were prepared from a stock solution of a multipesticide mixture of 10 mg/L by serial dilution. The LOD and LOQ values were determined as per U.S. Environmental Protection Agency peak-to-peak noise method based on signal to noise ratios of 3:1 and 10:1, respectively. Recoveries and repeatability were evaluated at 3 different concentrations (1, 5, and 10 µg/L) with 6 replicates (n = 6) to check accuracy and precision.

Rtx-5Sil MS capillary column was chosen for the analysis in this study due to its inertness to active compounds and has wide usability, i.e., it can work well with chlorinated hydrocarbons, phthalates, organochlorine pesticide, organophosphorus pesticide, hydrocarbons, phenols, amines, drugs, and solvent impurities. A multiclass pesticide mixture was identified separately in an MS detector. The injector and interface temperatures were kept at 250°C and 280°C, respectively. The column oven temperature programming was started with an initial temperature of 50°C for 2 min which was raised to 190°C with an increasing rate of 15°C/min with a hold time of 1 min at 190°C and then further ascended to 280°C with an increasing rate of 5°C/min with a hold time of 10 min. A maximum temperature of the column oven temperature program was set at 280°C to minimize the carry over effect from the column. Electron ionization in MS causes fragmentation of molecules by high voltage electrons. Solvent cut time of 2.5 min was applied to protect ion source and MS by removing the solvents. To increase the response of detector, detector voltage is maintained with respect to tuning voltage.

Ion transitions for MRM were optimized for each pesticide. A single compound can produce a lot of ion transitions (41). All the pesticides were run in full scan mode and then precursor ions were selected on the basis of high molecular weight and high intensity. The MRM method development includes creation of an MRM table. The product ions scan method was developed which was run under 15 different collision energies ranging from 3–45 V. After creation of the method, MRM optimization was done with the help of an MRM optimization tool provided by instrument software. Table 1 explains different m/z values of each pesticide for both quantifier and qualifier ions with their respective collision energy (CE) value and retention time.

The MSPE extraction is dependent upon several factors which play a key role in extraction and desorption efficiencies. These factors mainly include time of extraction, extraction solvents, and volume of extracting solvent. All these factors were optimized for maximum extraction and recovery. These factors were optimized with the spiked blank sample stock solution of 10 µg/L concentration. Then the spiked sample solution was extracted by MSPE in different conditions and analyzed by GC-MS/MS. The optimized conditions were determined according to the maximum peak area and recovery percentage of the analytes in aqueous medium.

The amount of C18 bound magnetic nanoparticles were optimized for maximum extraction efficiencies. The varying quantity of MNPs in the range of 10–120 mg was studied for maximum recoveries by using spiked sample concentration of 10 µg/L. The recovery of spiking sample concentration was maximum in 100 mg MNPs, and beyond 100 mg the rate of adsorption was in equilibrium and showed constant recovery in the range of 72.6–95.6%. The amount of 100 mg adsorbent MNPs was optimized for MSPE experiments (as shown in Figure 3).

Optimization of quantity (mg) of MNPs for efficient extraction.

To achieve a high enrichment factor from a water sample, an optimum sample volume is required. The effect of the sample volume on extraction performance of magnetic nanoparticles was investigated. For this purpose, a sample solution of different volumes of 10, 50, 100, 200, and 500 mL aqueous solutions was spiked with a pesticide mix at a concentration of 10 µg/L. All the spiked aqueous samples in varying volumes ranging from (10–500 mL) were extracted on 100 mg MNPs (optimized adsorbent). The highest quantitative recoveries (82–97%) among the studied sample volumes were obtained when the sample volume was 100 mL. As shown in Figure 4, a sample volume at 100 mL gives an optimum recovery which was well within the acceptable range. With a further increase of sample volume from 100 to 500 mL, the recovery percentage did not change significantly. However, in some cases it was observed that recovery percentages increased slightly up to 500 mL of the sample volume. As the large volume of sample did not show a significant effect on method performance, the recommended sample volume was 100 mL. Recommended volume gives optimum recovery percentage with ease of handling. Hence, a sample volume of 100 mL was selected for further experiments of MSPE.

Optimization of sample volume (mL) for maximum extraction.

Stirring speed plays a vital role in extraction of targeted analyte from sample to the adsorbent MNP. In the extraction process, the analytes from the aqueous sample solution diffuses to the stationary adsorbent. Continuous stirring is required for enhancing extraction efficiency of magnetic nanoparticles. Stirring exposes the extraction sites to the different analytes present in the aqueous sample (42) and results into maximum extraction performance of the adsorbent (43). To investigate the effect of stirring rate, extraction of analyte was performed at different stirring rates (50, 100, 150, 200, and 300 rpm) as shown in Figure 5. The peak area of anlytes were increased as stirring speed increased from 50–200 rpm, and after that no further enhancement in peak area was observed. Based on the above results, stirring speed of 200 rpm was optimized for further MSPE extraction work.

Optimization of stirring speed.

The extraction efficiency depends upon extraction time taken to desorb the pesticide from MNP, which is coated with C18, time taken to break the hydrophobic bond between C18, and pesticide is calculated as extraction duration. Extraction duration is the rate of transfer of targeted analytes from the sample to the extraction medium (44). Extraction duration evaluates the extraction efficiency: the lesser the extraction duration, the greater the extraction efficiency. Higher extraction efficiencies of different pesticides reduce the analytical time. The extraction time for the analysis of 22 pesticides were checked for 5, 10, 15, 20, and 25 min duration with a constant stirring speed of 200 rpm. The average maximum extraction efficiencies were obtained in 15 min and after that extraction efficiencies were equilibrates or slightly increased until 20 min and flattened or reduced upon further extraction duration, as shown in Figure 6. Extraction duration was optimized at 20 min for the multiclass pesticides. Conventional extraction techniques like SPE and SPME have a very long extraction time, i.e., 1 or 2 h. However, MNP takes only 20 min in extraction and gives good extraction efficiencies.

Optimization of extraction duration.

In any extraction method, extracting solvent plays a vital role in the extraction of targeted analyte from the sample. The best extracting solvent was selected on the basis of maximum capacity to desorb the analyte from the absorbent in a smaller volume and shorter period of time. The extracting solvent was chosen in line to qualify the mentioned criteria, i.e., desorbs the analyte quickly by using less volume of solvent. Comparison was done by varying the solvent type and keeping the other experiment variables at constant. The selected solvents used for desorption of analytes were acetone, methanol, acetonitrile, ethyl acetate, and hexane. Among these solvents, acetone gave maximum percentage recovery, i.e., 75.9–98%, as shown in Figure 7. Each solvent was used as desorption solvent in three replicates at room temperature.

Optimization of solvents for maximum extraction efficiency.

After selection of eluting solvent for maximum recovery of all the pesticides, solvent volume was optimized. The elution solvent volume influences the extraction efficiency. The recoveries of the pesticides increase with an increase of eluting solvent volume. For 100 mg MNP, extraction recoveries were increased up to 10 mL of acetone and after that recoveries were almost constant. This means that 10 mL of acetone is sufficient to get maximum recovery, as shown in Figure 8. Hence, 10 mL of acetone was adopted as the desorption solvent for MSPE experiments in this study.

Optimization of solvent volume (mL).

The pH value of a sample is the essential factor that plays an important role in extraction efficiency. The pesticide stability depends upon the pH values of the medium in which they are present. Most of the pesticides are unstable and undergo hydrolysis in alkaline conditions which results in inconsistent results. In addition, C18-bound MNPs are not stable in highly acidic as well as basic conditions and result in low adsorption behavior toward target analyte which consequently decreases the extraction efficiencies (45) (as shown in Figure 9). Therefore, pH values were maintained in the range of 6–8 during subsequent studies for consistent recoveries.

Optimization of sample pH..

Method validation was done in terms of linearity, accuracy, repeatability, selectivity, LOD, and LOQ in accordance with SANCO guidelines (46). Target compounds were identified according to retention time. Analytes were confirmed by matching MS spectra obtained with the reference spectra obtained under the same experimental conditions. This comparison was done by the software.

The study of linearity of multi-residue mixture was conducted in the range of 0.5–100 µg/L in MRM. Calibration graphs were drawn by concentration versus relative peak area (Figure 2a–c). Linearity values were calculated as determination of coefficient (r2) which was in the range of 0.97–0.99. LOD and LOQ were calculated by analyzing blank spiked samples of farm ground water, used for irrigation purpose. LOD is termed as the lowest concentration at which chromatographic peak can be determined at S/N of 3 and 10 (42). LOQ (S/N = 10) was calculated, respectively, according to LOD (S/N = 3). Regression coefficient (r2value), LOD, and LOQ are listed in Table 2. Selectivity was determined by running control blank samples. No peak interference at same retention time as target analytes showed that there were no other interferences. Accuracy was evaluated by calculating recovery percentage of pesticides from water samples spiked by a multipesticide mixture through MNP. Three levels of concentration 1, 5, and 10 µg/L for recovery were calculated (Table 3). Recovery percentage for herbicides and fungicides ranged from 70.56–98.76%, organochlorine pesticide ranged from 69.81–117.59%, organophosphorous class of pesticides ranged from 70.37–118.44, and synthetic pyrethroids ranging from 69.63–119.63%. All the classes of pesticide molecules showed acceptable recovery in all three concentration levels and thus were found suitable for sample analysis with MNP through the MSPE procedure in routine application. Repeatability experiments were performed by six replicates for each concentration level. Relative standard deviation was equal to or less than 20%. High recovery percentage for pyrethroids using C8 and C18 membranes was reported (43). Therefore, according to the above-mentioned results our method meets the requirements of the quantitative method.

Regression coefficient (r2value), Method LOD, and LOQ of pesticides

0.490

0.490

Regression coefficient (r2value), Method LOD, and LOQ of pesticides

0.490

0.490

Average recovery percentage of pesticides at different spiking concentration levels

RSD = Relative standard deviation.

Average recovery percentage of pesticides at different spiking concentration levels

RSD = Relative standard deviation.

In order to authenticate the use of the developed and validated MSPE method, forty one (41) agriculture field water/irrigation run-off water samples from farmlands of state Haryana were analyzed for the presence of the pesticide residues. The MNP-based extraction procedure was followed, and it was observed that 49% of the water samples were found to be contaminated with pesticide residues viz. chlorpyrifos, pendimethalin, butachlor, malathion, permethrin, fipronil, cypermethrin, and deltamethrin. Among the contaminated samples, majority of pesticides were found to be below 0.5 µg/L concentration level. Only in some cases chlorpyrifos and fipronil were found to be present in the range of 0.5–1.00 µg/L concentration level. It is evident from the monitoring study results that the present MSPE method can well be utilized for regular monitoring of environmental water samples.

Multiclass pesticide analysis was conducted by GC-MS/MS and the method was validated for the analysis of 22 pesticides in ground water samples. Real samples of agriculture field water/irrigation run-off water from farmlands of state Haryana were analyzed for the presence of the pesticide residues. Use of magnetic nanoparticles for the extraction of pesticides from the sample add the merits of better adsorption and desorption, magnetic separation through the MSPE procedure. GC-MS/MS condition was optimized which resulted in high sensitivity and selectivity. Method was validated in terms of linearity, accuracy, repeatability, selectivity, LOD, and LOQ according to SANCO guidelines. Recovery percentage ranged from 70–120%. Good linearity was observed at the concentration range of 0.5–100 µg/L (r2 value ≥ 0.99) with RSD values ≤ 18.8. It was observed from the study that apart from having good linearity and acceptable range of recoveries, the MSPE method is specific due to specific adsorption behavior of surface coatings, rapid as the extraction step takes maximum 20 min and low cost, i.e., approximately USD 1.5 compared to USD 7.0 for SPE and USD 19.0 for LLE. Moreover, the method requires a low volume of sample which in turn makes the method highly suitable for use in monitoring studies. Furthermore, the method is environment friendly due to its minimal use of organic solvents. Hence, it can be concluded that the present MSPE method will be very much suitable for long-term monitoring studies and analysis of environmental water samples like ground water, agricultural run-off water, surface water and river water, etc. Additional studies on the method may prove its applicability for analyzing drinking water samples also.

The authors are thankful to the IPFT for providing necessary infrastructural facilities and the Department of Chemicals and Petrochemicals, Ministry of Chemicals and Fertilizers, Government of India for providing the financial assistance.

No potential conflict of interest was there.

Mansour S.A. (

Malhat F.M. , Loutfy N.M., Greish S.S., Ahmed M.T. (

De Solla S.R. , Fernie K.J. (

Vorkamp K. , Christensen J.H., Riget F. (

EU Pesticides database04/09/

Part 180-Tolerances and exemptions for pesticide chemical residues in food. https://www.govinfo.gov/content/pkg/CFR-2014-title40-vol24/xml/CFR-2014-title40-vol24-part180.xml (accessed on September 15, 2020)

Pesticide index. http://www.fao.org/fao-who-codexalimentarius/codex-texts/dbs/pestres/pesticides/en/ (accessed on September 15, 2020)

Barcelo D. (

Stajnbaher D. , Zupancic K.L. (

Sabin G.P. , Prestes O.D., Adaime M.B., Zanella R. (

González-Curbelo M.Á. , Herrera-Herrera A.V., Hernández-Borges J., Rodríguez-Delgado M.Á. (

Uclés S. , Belmonte N., Mezcua M., Martínez A.B., Martinez-Bueno M.J., Gamón M., Fernández-Alba A.R. (

Rajput S. , Kumari A., Arora S., Kaur R. (

Wu J. , Lu J., Wilson C., Lin Y., Lu H. (

Norman K.N.T. , Panton S.H.W. (

Lehotay S.J. , Valverde-García A. (

Purdešová A. (

Li X. , Gan P., Peng R., Huang C., Yu H. (

Volante M. , Pontello M., Valoti L., Cattaneo M., Bianchi M., Colzani L. (

Menezes H.C. , Paulo B.P., Paiva M.J.N., Cardeal Z.L. (

Ozcan S. , Tor A., Aydin M.E. (

Liu J.C. , Tsai P.J., Lee Y.C., Chen Y.C. (

Zhao X. , Cai Y., Wang T., Shi Y., Jiang G. (

Wu J.H. , Li X.S., Zhao Y., Gao Q., Guo L., Feng Y.Q. (

Ding J. , Gao Q., Luo D., Shi Z.G., Feng Y.Q. (

Gao Q. , Luo D., Ding J., Feng Y.Q. (

Ballesteros-Gómez A. , Rubio S. (

Chang Y.C. , Chen D.H. (

Bruzzoniti M.C. , Sarzanini C., Costantino G., Fungi M. (

Bortoluzzi E.C. , Rheinheimer D.S., Gonçalves C.S., Pellegrini J.B.R., Maroneze A.M., Kurz M.H.S., Bacar N.M., Zanella R. (

Ismail M. , Sayed M., Khan H.M., Cooper W.J. (

Park J.H. , Mamun M.I.R., Abd El-Aty A.M., Na T.W., Choi J.-H., Ghafar M.W., Choi W.J., Kim K.S., Kim S.D., Shim J.-H. (

Dores E.F.G.C. , Navickiene S., Cunha M.L.F., Carbo L., Ribeiro M.L., De-Lamonica-Freire E.M. (

Bavcon M. , Trebse P., Zupančič-Kralj L. (

Ahmadi F. , Assadi Y., Hosseini S.M.R.M., Rezaee M. (

Djozan D. , Ebrahimi B. (

Bonansea R.I. , Amé M.V., Wunderlin D.A. (

Jiang Y. , Zhong M., Gou Y., Peng W., Zhou J., Wu P., Ma Y. (

Nair K.K. , Kaur R., Iqbal N., Hasan A., Alam S., Raza S.K. (

Srivastava N. , Kumari S., Nair K., Alam S., Raza S.K. (

Jiang Y.B. , Zhong M., Ma Y.Y. (

Shen G. , Lee H.K. (

Lee S. , Gan J., Kabashima J. (

Asati A. , Satyanarayana G.N.V., Patel D.K. (

Zhao X. , Shi Y., Wang T., Cai Y., Jiang G. (

SANCO/11813/2017, Guidance document on analytical quality control and method validation procedures for pesticide residues and analysis in food and feed, https://ec.europa.eu/food/sites/food/files/plant/docs/pesticides_mrl_guidelines_wrkdoc_2017-11813.pdf (accessed on April 18, 2020)