Food analysis is a tremendously broad field that is constantly evolving. New methods have emerged to increase productivity, such as modern miniaturized and robotic analytical techniques. In this paper, a microsolid-phase extraction system (μ-SPE) for clean-up was combined with a robotic autosampler to yield ready-to-analyze extracts.
Journal of Chromatography A 1652 (2021) 462384 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma Evaluation of the automated micro-solid phase extraction clean-up system for the analysis of pesticide residues in cereals by gas chromatography-Orbitrap mass spectrometry Elena Hakme∗, Mette Erecius Poulsen National Food Institute, Technical University of Denmark, Søborg, Denmark a r t i c l e i n f o Article history: Received 19 April 2021 Revised 23 June 2021 Accepted 28 June 2021 Available online July 2021 Keywords: μ-SPE clean-up Robotic system Cereals Pesticide residues Evaluation study a b s t r a c t Food analysis is a tremendously broad field that is constantly evolving New methods have emerged to increase productivity, such as modern miniaturized and robotic analytical techniques In this paper, a microsolid-phase extraction system (μ-SPE) for clean-up was combined with a robotic autosampler to yield ready-to-analyze extracts The system was evaluated for its applicability in routine laboratories The new, automated, high-throughput μ-SPE clean-up method was applied to acetonitrile extracts and was developed for the analysis of pesticide residues in cereals by gas chromatography-Orbitrap mass spectrometry (GC-Orbitrap-MS) The μ-SPE clean-up efficiency was demonstrated in the removal of matrix-interfering components and in the recovery of pesticides The sorbent bed mixture consisted of magnesium sulfate, primary-secondary amine, C18 , and CarbonX, and effectively retained matrix components without loss of target analytes Analysis of five types of cereals (barley, oat, rice, rye, and wheat) by GC-Orbitrap-MS showed that the method removed more than 70% of matrix components The clean-up method was validated for 170 pesticides in rye, 159 pesticides in wheat, 142 pesticides in barley, 130 pesticides in oat, and 127 pesticides in rice Spike recovery values were 70–120% for all pesticides and the repeatability, calculated as the relative standard deviation, was less than 20% The limits of quantitation achieved were 0.005 mg kg−1 for almost all analytes, ensuring compliance with the maximum residue limits © 2021 The Author(s) Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Introduction Pesticide residues, among the large variety of contaminants, are continuously monitored and controlled to ensure legislative compliance Pesticide residue analysis is crucial in estimating maximum residue limits, reviewing toxicological data, and ensuring food safety Similar to other food analysis applications, the sample preparation step is often the key parameter in method development, particularly in the isolation and detection of contaminants Besides the accuracy and validity of the method, the time required to complete the analytical process and the cost of the consumables (e.g., solvents and sorbents) used in the analysis are particularly considered It is estimated that 60–80% of the work activity and operational costs in analytical laboratories are spent preparing samples for analysis It is also estimated that this step is responsible of 50% of the error in the final reported data [20] There- ∗ Corresponding author E-mail address: elehak@food.dtu.dk (E Hakme) fore, faster, automated, cost-effective, and greener alternative sample preparation techniques with good accuracy are needed According to the literature, several sample preparation techniques, such as liquid-liquid extraction (LLE) [5], gel permeation chromatography (GPC) [14], solid phase microextraction (SPME) [24], and matrix solid phase dispersion [12], have been explored, and some have been successfully applied to the multiresidue analysis of pesticides in food Despite the effectiveness of these methods, the methods require large amounts of solvents, are time consuming and tedious, and require intense labor The sample preparation approach known as QuEChERS (quick, easy, cheap, effective, rugged, and safe), developed by Anastassiades et al in 2003 [2], met the changing needs of multiresidue analysis and has been successfully applied to the recovery of pesticide residues in food In 2007, the QuEChERS-d-SPE was published by the Association of Official Analytical Chemists (AOAC, [3]) and by the European Committee for Standardization [6] In its basic scheme, the method consists of an extraction with acetonitrile, partitioning with salts to promote water separation from the organic solvent, and clean-up of the final acetonitrile extract with dispersive solid phase extrac- https://doi.org/10.1016/j.chroma.2021.462384 0021-9673/© 2021 The Author(s) Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 tion (d-SPE) sorbents to remove organic acids, sugars, and polar pigments SPE sorbents, used in dispersive form or packed in a cartridge, are demonstrated suitable to a wide variety of food and agricultural products when appropriate adsorbing/sorbent materials are selected [4,10,13] The availability of pre-packaged dispersive kits has enabled fast sample preparation and has added advantages of the dispersive SPE (d-SPE) in terms of time and operational conditions However, some studies have shown that the clean-up efficiency with cartridge-SPE is better than with d-SPE, because there is better surface contact between the sorbent and the sample [1] Moreover, cartridge-SPE permits either solvent reduction or solvent exchange prior to clean-up, as well as the possibility of using different solvent mixtures that effectively elute the target analytes, which preserves the accuracy of the method [22,23] The main disadvantages of the cartridge-SPE are the extended operation time and procedure steps, the susceptibility to loss or degradation of the target analytes, and other potential sources of repeatability errors arising from the use of an SPE vacuum manifold In recent years, much effort has been devoted to eliminate these drawbacks This has led to the development of robotic automated techniques Currently, an automated micro-solid phase extraction (μ-SPE) clean-up method for acetonitrile extracts is available as an alternative to cartridge-SPE μ-SPE is a simple scaledown or miniaturization of the cartridge-SPE procedure The use of automated μ-SPE clean-up was originally reported by Morris et al [17] for the analysis of pesticide residues in avocado and citrus Automated mini-SPE clean-up was also evaluated, and was found to be efficient for the analysis of pesticide residues in spices, including chili powder, turmeric, black pepper, cumin, coriander, and cardamom [11] Lehotay et al demonstrated the clean-up efficiency of mini-SPE on avocado, salmon, pork loin, and kale [15] Ederina et al demonstrated the efficiency of robotic mini-SPE clean-up for the analysis of pesticides and their metabolites in catfish muscle [18] Pandey et al also demonstrated the high-quality results of this automated system for diverse types of analytes and food matricesf [19] The automation of the μ-SPE method for the cleanup and pre-concentration of polyfloroalkyl substances from surface water has also been demonstrated [16] Laboratory automation is expected to increase in food testing laboratories because of their time and space efficiency Thus, it is of great importance that laboratories adopt robotic automated systems that guarantee high sample throughput without much labor, and that, most importantly, are reliable The objective of this study was to evaluate the performance of the automated μ-SPE technique in the analysis of 172 pesticide residues in cereals, and to determine if the technique could be used in national and official routine analysis laboratories The μ-SPE clean-up method used in this study consisted of a removal/trapping strategy, where the matrix components were retained and the analytes of interest were eluted Since the procedure is intended to be scaled-up for application to all raw cereal products, five cereal matrices were selected for the validation study Most of the pesticides included in this study are included in the EU multi-annual control program [7] Two evaluation studies were designed to demonstrate the cleanliness of the extract and the clean-up efficiency In the first, blank extracts subjected to automated μ-SPE clean-up were compared to extracts subjected to d-SPE clean-up In the second study, acetonitrile extracts were spiked with pesticides prior to clean-up to demonstrate the recovery efficiency of the method Finally, the method was validated according to the guidance document on analytical quality control and method validation procedures for pesticide residues and analysis in food and feed [9] in terms of linearity, recovery, and repeatability The matrix effect of each cereal was evaluated for quantitation purposes Material and methods 2.1 Chemicals Pesticide standards (purity >96%) were purchased from SigmaAldrich and LGC Standards Pesticide standard stock solutions of mg mL−1 were prepared in toluene and stored at −18 °C in ampoules under an argon atmosphere A standard solution of 10 μg mL−1 was prepared from these stock solutions Working calibration standard solutions were prepared by diluting 1:1 (v/v) with acetonitrile to obtain five concentration levels: 0.2 μg mL−1 , 0.0667 μg mL−1 , 0.02 μg mL−1 , 0.0067 μg mL−1 , and 0.002 μg mL−1 Acetonitrile (HPLC Grade 5) was purchased from Rathburn Chemicals μ-SPE cartridges (Cart-uSPE-GC-QUE-0.3 mL) were purchased from CTC-Analytics Supel TM QuE QuEChERS tubes containing g magnesium sulfate (MgSO4 ), g sodium chloride (NaCl), 0.5 g sodium citrate sesquihydrate, and g sodium citrate dihydrate were purchased from Thermo Scientific The clean-up sorbent SupelTM QuE (EN) tubes were purchased from Supelco 2.2 Extraction method The samples were extracted using the citrate-buffered QuEChERS (EN 15662) (CEN 2008) method without clean-up In brief, g of each sample was prepared The procedural standard dichlorvosd6 was added to all samples before extraction Then, 10 mL cold water was added, followed by 10 mL acetonitrile To aid the extraction, a ceramic homogenizer was used The tubes were shaken for by hand Next, 4.0 g of MgSO4 , 1.0 g NaCl, 1.0 g sodium citrate dihydrate, and 0.5 g sodium citrate sesquihydrate were added After of shaking by hand and centrifugation for 10 at 4500 rpm, mL of the supernatant was transferred to a clean tube and stored at –80 °C for at least h The extracts were then thawed, and while they were still very cold, they were centrifuged at 4500 rpm for at °C Thereafter, mL of the cold supernatant was collected For the μ-SPE automated clean-up, the extracts were diluted (1:1 v/v) with acetonitrile and placed in mL glass vials on the sample tray of the robotic autosampler A minimum volume of 500 μL is recommended to avoid the aspiration of air bubbles into the 10 0 μL μ-SPE syringe, or else the syringe depth in the instrumental method should be modified accordingly Triphenyl phosphate (15 μl of a 0.1 μg mL−1 internal standard solution) which is used as an internal standard to check the performance of the injection system of the instrument, was added automatically on the robotic autosampler For the d-SPE clean-up, a dispersive sorbent mixture consisting of 150 mg PSA and 900 mg MgSO4 was added to the mL extract The tubes were shaken for 30 s, and then centrifuged at 4500 rpm for at room temperature After centrifuging, mL supernatant was collected and 5% formic acid was added The extracts were diluted (1:1 v/v) in mL glass vials with acetonitrile, and the internal standard (triphenyl phosphate) was added 2.3 Chromatographic separation and high-resolution mass spectrometry The analyses were performed on an GC-Exactive MS (Thermo Fisher Scientific), consisting of a Trace 1300 Series GC, a TriPlus RSH Autosampler GC-liquids, and an Exactive GC-Orbitrap-MS The samples were injected in a programmable temperature vaporizer (PTV) through a PTV baffle liner (2 × 2.75 × 120 mm) designed for Thermo GCs (Siltek) The injection volume was μL and the injection temperature was set to 70 °C Helium was used as the carrier gas at a flow rate of 1.2 mL/min for analyte separation on a Thermo Scientific Trace GOLD TG-5SILMS column (30 m E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Fig Schematic of the TriPlus RSH robotic PAL autosampler length × 0.25 mm i.d × 0.25 μm film thickness) The GC oven program started with an initial temperature of 60 °C, which was held for 1.5 min, followed by a ramp of 25 °C/min to 90 °C This temperature was held for 1.5 min, followed by a ramp of 25 °C/min up to 180 °C, then up to 280 °C at °C/min Finally, to clean the column, the temperature was raised to 300 °C at a rate of 10 °C/min and held for 12 The analyses were performed in electron ionization (EI) positive mode Eluting peaks were transferred through an auxiliary transfer line into the EI source The EI source and the transfer line temperatures were set to 280 °C The instrument operated at a resolution of 60k and the automatic gain control (AGC) target was set to × 106 The MS data were acquired in a scan mode covering a mass range from 50 to 500 m/z The instrument was tuned using the Thermo Scientific Exactive GC Tune software (v 2.9 SP3 Build 290204) The vacuum inside the Orbitrap Analyzer was maintained below × 10−9 mbar The instrument method was developed on Thermo Scientific XCalibur software The full scan MS data were processed using a quantitation master method on the Thermo Scientific TraceFinder 4.1 software The studied compounds were transferred into the quantitation method from an in-house compound database The database included retention time, target ion, and at least confirming ions for each compound The Genesis algorithm was used for peak integration The method is shown in the Supplementary Material tosampler It uses two solvents: acetonitrile (fast wash station position 1) and a mixture of acetonitrile, methanol, and water (1:1:1 v/v/v) (fast wash station position 2) The μ-SPE tray holder, also attached to the PAL bus, has three slots The first slot is the sample tray where the crude extracts obtained from acetonitrile extraction were placed The second slot is the eluate tray where empty vials were placed to collect the cleaned-up μ-SPE extracts μ-SPE cartridges were placed in the third slot 2.5 μ-SPE clean-up workflow Table shows the automatic μ-SPE program steps and their duration In the automatic tool change station, the 10 0 μL μSPE syringe was automatically selected The syringe was robotically moved to the fast wash station module and rinsed with pure acetonitrile (2 rinsing cycles) A 300 μL aliquot of crude extract was loaded into the syringe after filling strokes The tool with the filled μ-SPE syringe was moved to the third slot to pick one μ-SPE cartridge and then back to the eluate tray to load the extract into the cartridge Approximately 240 μL cleaned extract was eluted at a flow rate of 30 μL s−1 and collected in the empty vial placed in the eluate tray Once the clean-up was completed, the syringe was moved back to the fast wash station to be rinsed again with Table Automatic μ-SPE program steps with a total duration of 13 2.4 TripPlus RSH autosampler The μ-SPE system is coupled to the GC-Orbitrap-MS The TriPlus RSH robotic PAL autosampler comprises three tools: the μ-SPE tool (LS3) that holds a 10 0 μL syringe, the analyte protectant or internal standard tool that holds a 25 μL syringe (LS2), and the injection tool (LS1) that holds a 10 μL syringe A schematic of the robotic PAL autosampler is presented in Fig The system also contains a standard wash module, a solvent station module, and a fast wash module The solvent station module was not used because the experiment was done without conditioning of the cartridges and without additional solvent elution, which also saved solvents and time The standard wash module tray holds mL glass vials, reserved for internal standards or analyte protectants, and three 25 mL glass vials, reserved for aliquots of blank extracts or acetonitrile for automated matrix-matched calibration curves and automated sample dilution, respectively The fast wash station for fast syringe washing is connected to the au- Time (mm:ss) Steps 0:30 Required tool selected Syringe wash: cycles at wash position Load sample onto μ-SPE Perform filling strokes Load sample onto μ-SPE cartridge: 300 μL Syringe wash: cycles at wash position Required tool selected Syringe wash: cycles at wash position Rinse Add 15 μL internal standard Perform filling strokes Add internal standard: 15 μL Required tool selected Syringe wash: cycle at wash position Rinse Move to sample at position Perform filling strokes Aspirate μL Inject sample 01:30 04:30 05:30 06:30 07:30 08:30 09:30 10:30 11:30 12:00 13:00 E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 acetonitrile (2 rinsing cycles) The tool holding the 25-μL syringe was then selected The syringe was moved to the fast wash module to be washed with acetonitrile (2 cycles) The syringe was then moved to the standard wash module After filling strokes, 15 μL internal standard was added to the cleaned-up extract Then, the required injection tool, holding a 10-μL syringe, was selected The syringe was washed with the acetonitrile, methanol, and water solvent mixture (one rinsing cycle) The syringe was moved to the eluate tray Three filling strokes were performed before μL was aspirated and injected For this purpose, a semi-procedural standard calibration was prepared by spiking a series of blank test portions of rye with different amounts of analyte just before the clean-up step It is referred to it as a “semi-procedural calibration” because the spiking was done just prior to clean-up, and not prior to the whole extraction method The extracts (0.5 g mL−1 matrix) obtained from the QuEChERS extraction were diluted 1:1 with a standard mixture of 172 pesticides prepared in acetonitrile at 0.2 μg mL−1 , 0.0667 μg mL−1 , 0.02 μg mL−1 , 0.0067 μg mL−1 , and 0.002 μg mL−1 The final concentrations prepared were 100 μg kg−1 , 33 μg kg−1 , 10 μg kg−1 , μg kg−1 , and μg kg−1 , respectively The vials were placed on the robotic autosampler for automated μ-SPE clean-up The amount of cleaned-up matrix injected in this experiment was 0.25 g mL−1 The semi-procedural standard calibration was compared to a matrix-matched calibration The set of matrix-matched calibration curves was prepared using the blank extracts (0.5 g mL−1 blank matrix extract obtained from the QuEChERS extraction) The extracts were placed on the robotic autosampler for μ-SPE cleanup After clean-up, the eluates were diluted 1:1 with a series of standards, giving a series of matrix-matched calibration samples at 100 μg kg−1 , 33 μg kg−1 , 10 μg kg−1 , μg kg−1 , and μg kg−1 The amount of matrix injected onto the GC system was 0.25 g mL−1 , which enabled the comparison of the two calibrations In this latter calibration, the pesticides were not loaded into the μ-SPE cartridge; therefore, the matrix-matched calibration was considered a reference calibration to evaluate pesticide recovery The slopes of the two calibration curves were compared The data obtained from this experiment were also processed to calculate the pesticide recovery after the robotic μ-SPE clean-up 2.6 Experiment 1: extract cleanliness assessment Whether cartridge-SPE, d-SPE, or μ-SPE, the critical piece in all SPE methods is the selection of the sorbent It is necessary to consider chemical and physical characteristics that allow maximal interaction between the sorbent and the analytes, which ensures selectivity of extraction, removal, or preconcentration of analytes present in analytical matrices In order to check the cleanliness of the extracts obtained with μ-SPE, blanks of barley, wheat, oat, rice, and rye were extracted using both the conventional QuEChERS extraction method with manual dispersive SPE clean-up and the robotic μ-SPE clean-up system Both extracts were injected onto the GC-Orbitrap-MS The same amount of matrix was used in both SPE methods, both in the clean-up step (0.5 g mL−1 ) and in the injection step (0.25 g mL−1 , following a 1:1 dilution with acetonitrile) In these comparison experiments, two factors were considered: differences in automation and sorbents In the manual dSPE clean-up, 25 mg of PSA/mL of extract and 150 mg of MgSO4 /mL of extract were used In the μ-SPE, 40 mg of PSA/mL of extract and 66 mg of MgSO4 /mL of extract were used, in addition to 40 mg/mL of C18 and 66 mg/mL of CarbonX The total ion chromatograms (TICs) of blanks obtained with the two different clean-up procedures were overlaid using XCalibur software For a closer examination of the clean-up effectiveness, the deconvolution plugin software (v 1.3), in conjunction with the TraceFinder software (v 4.1), was used The software automatically deconvoluted coeluted chromatographic peaks into multiple components by aligning mass spectral peaks, according to their slightly different retention times The software also automatically performed a peak search and a library search Combined with the unknown screening functionality of TraceFinder software, the deconvolution software was used to a cross-sample overlay of analytes 2.8 Experiment 3: method validation Method validation was performed according to SANTE/ 12682/2019 guidelines Five sets of semi-procedural calibration curves were prepared using extracts of blank samples of each of the five matrices (barley, oat, rice, rye, and wheat) as described in the previous section The extracts were cleaned-up using the automatic μ-SPE robotic system The linearity range was determined for the 172 compounds in the 0.0033 - 0.1 μg mL−1 range In gas or liquid chromatography systems, the matrix effect is caused by the unwanted interference of compounds during ionization in the MS source or during injection, and it can dramatically influence the analysis for both identification and quantification of an analyte Usually, the matrix effect is calculated as the percentage difference between the slopes of the matrixmatched calibration curves and the solvent calibration curve In this study, the matrix effect was investigated by comparing the slopes of the semi-procedural calibration curves, obtained with barley, rice, oat, and wheat, to the semi-procedural calibration curves prepared with rye, which was chosen as a representative matrix among the cereals included in the current study The rye matrix provides good protection of analytes and has a moderate matrix effect compared to those of other cereal matrices Five samples each of barley, oat, rice, rye and wheat matrices were spiked before extraction at each of three concentration levels (5 μg kg−1 , 10 μg kg−1 , and 50 μg kg−1 ) to study the extraction effectiveness Therefore, in total, the validation study was performed on 75 spiked samples The trueness and the precision of the method were evaluated by calculating the recovery and the repeatability, respectively Acceptable mean recovery is usually within the range of 70 – 120% Repeatability refers to the variation in repeated measurements made on the same subject under identical conditions Therefore, repeatability was evaluated by calculating the relative standard deviation (RSD) based on the recovery results of the five spiked samples of each matrix at each spiking concentration level The precision of the method was also investigated by 2.7 Experiment 2: calibration assessment In order to evaluate the possible loss of pesticides during cleanup by μ-SPE, and to assess if a procedural standard calibration was required, a preliminary evaluation study was carried out before proceeding with the validation study Matrix-matched calibrations are routinely used for quantitation of pesticide residues in food matrices, and they use standards prepared from blank extracts of the same matrix The blanks used to prepare matrix-matched calibrations were extracted in the same manner as the samples After extraction and clean-up, the blank extracts were diluted with a series of calibration standards The procedural standard calibration approach is typically used when dealing with difficult matrices to compensate for matrix effects and low extraction recovery associated with certain pesticide/commodity combinations It consists of spiking a series of blank test portions with different amounts of analytes prior to extraction In order to assess the clean-up efficiency, the matrix effect, and the recovery of pesticides on the robotic μ-SPE system, rather than in the whole acetonitrile extraction method, blanks of rye were spiked before and after the cleanup step E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 calculating the reproducibility, which was derived from the range of recovery results obtained with different matrices of the cereals at each of the concentration levels 3.3 Experiment 2: calibration assessment The deviation between the slopes of the matrix-matched calibration and the semi-procedural-standard calibration was less than 25% for almost all of the compounds Additionally, pesticide recovery was calculated at each of the five spiked levels This recovery study is not a full validation study, but rather an estimation of the pesticide recovery (or loss) and an assessment of the robotic clean-up method Recovery results between 70 - 120% were considered successful Fig shows the percentage of compounds recovered after clean-up at the five different spiked concentration levels Almost all of the compounds were successfully recovered at a concentration level of 100 μg kg−1 For instance, at each of the concentration levels (100, 33, 10, 3, and μg kg−1 ), 98%, 94%, 112%, 99%, and 109% of toclophos-methyl were recovered, respectively Poorer results were obtained with ditalimphos, where only 58%, 59%, 57%, 55%, and 50% of the analyte were recovered from the samples with concentration levels of 100, 33, 10, 3, and μg kg−1 , respectively Some compounds, such as spiroxamine, fenhexamid, fenpropidin, deltamethrin, and iprodione were not recovered at high levels For the first two acidic compounds and the cationic potential compound (fenpropidin), recovery values obtained were less than 15%, probably due to interaction with PSA The two latter compounds exhibited a signal enhancement after passing through the μ-SPE, with recovery around 140% On average, 85% of the compounds were successfully recovered at the levels of 33, 10, and μg kg−1 The highest percentage of compounds (27%) was lost in the lowest spiking level (1 μg kg−1 ) during the clean-up, likely by adsorbing onto the μ-SPE bed sorbents Compound loss could also have occurred in the injector In the injector, matrix components protect the analytes from thermal decomposition and block them from adsorption onto the active sites of the GC system Thus, in a cleaner extract, compounds are no longer protected from degradation According to SANTE guidelines, recovery values outside the 70 - 120% range can be accepted if the results are consistent, and a correction factor can be applied However, due to the very low recovery of the compounds as mentioned above, a correction factor for recovery was not used Instead, a more accurate approach was adopted, which consisted of the use of a semi-procedural standard calibration for routine analysis Therefore, in the light of these results, a semi-procedural standard calibration was used for accurate quantitative method validation, and it is recommended for use in routine analysis to compensate for possible clean-up automation and extraction efficiency errors or the retention/loss of compounds, especially at the lowest concentration levels Results and discussion 3.1 Performance expectations of the automated sample preparation system The time needed for performing manual d-SPE clean-up on a batch of four samples was approximately 14 The addition of clean-up salts to the collected QuEChERS extracts took Mixing using an automatic agitator took Centrifugation was performed in min, according to the citrate-buffered QuEChERS (EN 15662) (CEN 2008) method Collecting the final extracts took up to min, and the addition of the internal standard was achieved in min, resulting in a total clean-up time of 14 Using the robotic μ-SPE, the automated clean-up and addition of the internal standard for a batch of samples took a total of 52 (13 min/sample) Surface-level thinking would lead to the conclusion that the automated μ-SPE system is not advantageous in terms of saving time However, a robotic system that could be operating 24/7 undoubtedly enables higher productivity because more samples can be processed outside of the normal work schedule or even overnight Hence, overall, the robotic system results in a significant reduction of labor Moreover, the automated μ-SPE system allows more consistent preparation by avoiding human laboratory errors in the final clean-up step Although the system was coupled to the GC-Orbitrap-MS, the autosampler was not equipped with a cooling system With a nonthermostatic autosampler, samples had to stand in the sample tray at room temperature for a long time, since the GC analysis time of each injection was 45 Therefore, in the case of non-availability of a thermostatic tray, a stand-alone robotic μ-SPE clean-up system is recommended Yet, a μ-SPE coupled to a chromatographic and spectrometric system would be advantageous, because the clean-up of an extract can be performed while another extract is being analysed 3.2 Experiment 1: extract cleanliness assessment The integrated area of the TIC obtained with a blank of wheat extract (0.5 g mL−1 ) cleaned-up with d-SPE was 1.2 × 109 The integrated area of the TIC of the same blank (0.5 g mL−1 ) after μ-SPE clean-up was 3.34 × 108 The μ-SPE method resulted in the removal of approximately 70% more matrix interferences than the dSPE method Fig shows the overlay of the TICs of the two blanks The TIC corresponding to d-SPE shows the most intense peaks at 9.34 (corresponding to linoleic acid (C18 H32 O2 )), at 16.7 (corresponding to linolenic acid (C18 H30 O2 )), and at 31.47 (corresponding to campesterol (C28 H48 O)) The comparison of these profiles showed that the extract obtained with d-SPE seems to have had a higher concentration of interfering compounds or matrix components In the μ-SPE sample, these unwanted matrix components remained bound to the μ-SPE sorbent and had a much lower concentration in the final extracts The two adsorbents (C18 and CarbonX) embedded in the μ-SPE cartridge also allowed the adsorption and removal of fatty acids and other matrix interference compounds Fig shows the cross-sample peak overlay of lignoceric acid methyl ester, a saturated fatty acid with the chemical formula C23 H47 COOH, in rye blanks after d-SPE and μ-SPE cleanup methods The most effective removal of this matrix interference compound was by μ-SPE Moreover, the number of peaks detected in a blank of rye after deconvolution analysis was 172 and 123 peaks for d-SPE and μ-SPE, respectively The same results were observed with the four other cereal matrices 3.4 Experiment 3: method validation The method showed a linear response over the studied concentration range of 1–100 μg kg−1 with the four matrices, and it had a coefficient of correlation greater than 0.99 The matrix effect percentage was calculated for each of the 172 pesticides Fig shows the percentage of compounds that exhibited a weak, moderate, and strong matrix effect The matrix effect is caused by coeluting compounds from the matrix, which generate a signal suppression or enhancement A strong matrix effect corresponds to a value above ±50% A weak matrix effect is less than ±25% A moderate matrix effect is between ±25% and ±50% An efficient extraction and clean-up method will generate clean extracts and retention of all matrix-interfering components, and thus will have a smaller matrix effect A weak matrix effect was observed for 75% of the compounds in wheat, in comparison to rye For oat and barley matrices, in comparison to rye, 65% of the compounds showed a weak matrix effect In rice, 45% of the compounds showed weak matrix effect A moderate matrix effect was observed for 14% of E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Fig Total ion chromatograms of a blank of wheat extracted with d-SPE (blue) and μ-SPE clean-up (red) Fig Cross-sample peak overlay of lignoceric acid methyl ester in 0.5 g mL−1 rye blank extracted with d-SPE (black; peak area: 5667.091), μ-SPE clean-up of 0.5 g mL−1 matrix (red; peak area: 75.399), and μ-SPE clean-up of 0.25 g mL−1 matrix (green; peak area: 420.236) Fig Percentage of compounds with recoveries within the range 70–120% after clean-up of acetonitrile extracts spiked at five concentration levels of spiking (1, 3, 10, 33, 100 μg kg−1 ) Fig Percentage of compounds showing a weak (±25%), moderate (ǀ25–50ǀ%), and strong (>± 50%) matrix effect in the barley, oat, rice and wheat matrices compared to the rye matrix the compounds in barley, 8% of the compounds in oat, 25% of the compounds in rice, and 17% of the compounds in wheat The obvious explanation for a weak-to-moderate matrix effect is that the matrix-interfering components were successfully retained by the μ-SPE sorbents Although matrix effect was not significant compared to rye, signal enhancement between and +20% was observed for almost all pesticides in wheat, but mainly in rice, barley, and oat For all compounds showing a weak-to-moderate matrix effect, the matrix-matched calibration prepared with the rye blank was used for the qualitative and quantitative analyses of those compounds in different kinds of cereal samples (barley, oat, rice, and wheat) Preparing semi-procedural calibration curves with each type of cereals is a tremendous effort in routine analysis laboratories, and a significant reduction of labor is achieved by preparing one semi-procedural calibration with rye A strong matrix effect was observed for 9% of the compounds in wheat, 14% of the compounds in barley, 27% of the compounds in oat, and 30% of the compounds in rice The more complex a ma6 E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Fig Extracted chromatograms of pirimiphos-methyl and chlormephos at a spiking level of μg kg−1 in rye, wheat, barley, oat, and rice trix, and the higher the amount of fatty components it contains, the stronger the matrix effect Cereal grain is a complex, heterogeneous mixture of a relatively wide range of chemical substances The gross composition differs among cereals The total amount of fatty acids in wheat, rye, barley, rice, and oat are 2, 2.3, 2.4, 2.9, and 6.5 g per 100 g matrix, respectively [21], which explains why the strongest matrix effect was observed in rice (30% of the compounds) and in oat (27% of the compounds) In cases of a strong matrix effect, the analyte should be quantified using standard ad- dition or an external matrix-matched calibration prepared with the same matrix as the sample Therefore, using a semi-procedural calibration of rye, the current study validated 170, 159, 142, 130, and 127 compounds in rye, wheat, barley, oat, and rice, respectively Table shows the limit of quantitation (LOQ), recovery, repeatability obtained for each compound at the spiking levels of 5, 10, and 50 μg kg-1, and the corresponding MRLs Successful results had a recovery between 70 and 120% and a relative standard deviation (RSD) of less than 20% These analytical figures validate the E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Table Compound recoveries (%), LOQs (mg kg−1 ), repeatability (%) for each spiking level in cereal matrices, and corresponding MRLs (EU pesticides database, 2021) [8] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 Compound MRL (mg.kg−1 ) Matrix LOQ (mg kg−1 ) Spiking levels (mg kg−1 ) Recovery% (RSD) 0.005 0.01 0.05 Acrinathrin 3-Hydroxycarbofuran Aldrin Atrazine Azinphos-ethyl Azinphos-methyl Azoxystrobin Bifenthrin Bitertanol Boscalid Bromophos-ethyl Bromopropylate Bromuconazole Bupirimate Buprofezin Cadusafos Carbofuran Carbosulfan Carboxin Chlorfenapyr Chlorfenson Chlorfenvinphos Chlormephos Chlorobenzilate Chlorpropham Chlorpyrifos Chlorpyrifos-methyl Clomazone Cyfluthrin Cyhalothrin-lambda Cypermethrin Cyproconazole Cyprodinil2 Demeton-S-methyl Diazinon Dichlorvos Dicloran Dicofol Dieldrin Difenoconazole Dimethoate Dimethomorph Diphenylamine Disulfoton Ditalimphos DMST Endosulfan-alpha Endosulfan-beta Endosulfan-sulfate Endrin EPN Epoxiconazole Ethiofencarb Ethion Ethoprophos Ethoxyquin Etofenprox Fenamiphos Fenamiphos-sulfone Fenarimol Fenazaquin Fenbuconazole Fenhexamid Fenitrothion Fenoxycarb Fenpropathrin Fenpropidin Fenpropimorph Fenson 0.01 0.013 0.011 0.05 0.05 0.01 0.52 0.012 0.01 0.82 0.01 0.01 0.032 0.05 0.01 0.01 0.011 0.011 0.031 0.02 0.01 0.01 0.013 0.02 0.01 0.01 0.01 0.01 0.04 0.051 , 22 0.12 0.5 0.021 , 0.01 0.01 0.02 0.02 0.011 0.1 0.022 0.01 0.05 0.022 0.013 0.013 0.051 0.051 0.051 0.01 0.013 0.62 0.013 0.01 0.02 0.05 0.01 0.021 0.021 0.02 0.01 0.12 0.01 0.05 0.01 0.01 0.12 0.152 0.013 Barley, oat, rye, and wheat 0.005 104 (15) 86 (10) 79 (19) Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Rye and wheat Rye and wheat Barley, oat, rice, rye, and wheat Rye Barley, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rye, and wheat Barley, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, rice, rye, and wheat Barley, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rice, rye, and wheat Rye Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, rice and rye Barley, oat, rice, rye, and wheat Rye Barley, oat, rice, rye, and wheat Rye Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat 0.005 0.005 0.005 0.01 0.01 0.005 0.05 0.01 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.01 0.005 0.05 0.005 0.005 0.005 0.005 0.05 0.005 0.005 0.005 0.005 0.005 0.05 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.05 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.05 0.005 0.005 0.005 0.05 0.005 0.005 77 (7) 95 (9) 112 (11) 75 82 86 87 86 90 73 (8) 85 (6) 83 (10) 80 (13) 101 (9) 84 (10) 97 (6) 99 (16) 82 (7) 90 (9) 94 (8) 91 (7) 89 (6) 79 (8) 101 (12) 91 (5) 86 (9) 96 (6) 94 (8) 88 (9) 91 (11) 95 (7) 91 (7) 84 (5) 83 (8) 85 (13) 98 (15) 96 (14) 88 (12) 96 (8) 82 (9) 81 (10) 88 (5) 77 (20) 84 (8) 97 (8) 85 (6) 90 (17) 85 (12) 113 (11) 72 (12) 79 (6) 82 (17) 84 (18) 85 (6) 82 (6) 86 (8) 82 (6) 95 (10) 93 (9) 78 (14) 92 (9) 93 (6) 48 (17) 87 (10) 98 (15) 75 (7) 99 (11) 85 (14) 97 (8) 114 (18) 86 (8) 119 (11) 95 (12) 102 (14) 74 (17) 93 (5) 121 (8) 125 (9) 95 (9) 114 (7) 107 (8) 106 (8) 109 (14) 92 (5) 98 (6) 103 (8) 102 (6) 99 (16) 98 (10) 111 (12) 86 (8) 106 (9) 89 (10) 99 (9) 104 (9) 115 (6) (16) (8) (10) (14) (8) (7) 96 (20) 106 (10) 84 (5) 90 (6) 92 (5) 90 (7) 87 (7) 79 (12) 104 (16) 88 (7) 84 (6) 89 (6) 91 (5) 88 (9) 90 (36) 89 (7) 90 (13) 88 (8) 88 (8) 77 (7) 96 (11) 91 (12) 110 (9) 89 (9) 101 (13) 96 (8) 93 80 77 89 94 (11) 109 (16) 91 (9) 111 (12) 112 (12) 89 (7) 111 (11) 84 (5) 86 (6) 85 (11) 74 (10) 81 (7) 91 (11) 106 (12) 86 (9) 90 (11) 112 (11) 91 (10) 109 (14) 87 (11) 74 77 70 76 85 83 90 83 91 98 (11) (12) (11) (21) (5) (6) (5) (6) (8) (7) 113 (8) 106 (8) 81 (7) 95 (10) 109 (19) 115 (8) 109 (8) 110 (8) 112 (12) 91 99 67 82 97 98 95 95 90 (7) (8) (12) (8) (11) (4) (7) (10) (15) 112 (10) 100 (5) 115 (9) 85 (8) 98 (1) 94 (6) 92 (13) 98 (9) 78 (14) 87 (7) (7) (8) (12) (9) (continued on next page) E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Table (continued) Compound 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 Fenthion Fenthion-sulfone Fenthion-sulfoxide Fenvalerate Fipronil Fluazifop-butyl Fludioxonil Flufenoxuron Fluquinconazole Flusilazole Flutriafol Fluvalinate-tau Formothion Fosthiazate HCH-alpha HCH-beta Heptenophos Hexaconazole Imazalil Indoxacarb Iprodione Iprovalicarb Isofenphos-methyl Isoprothiolane Isoproturon Jodfenphos Kresoxim-methyl Lindane Linuron Malaoxon Malathion Mecarbam Mepanipyrim Metalaxyl Metconazole Methacrifos Methidathion Methiocarb Methiocarb-sulfone Methoxychlor Metribuzin Mevinphos Monocrotophos Monolinuron Myclobutanil Nuarimol Ofurace Omethoate Oxadixyl Paclobutrazol Paraoxon-methyl Parathion Parathion-methyl Penconazole Pencycuron Pendimethalin Permethrin Phenthoate Phosalone Phosmet Phosphamidon Pirimicarb Pirimicarb-desmethyl Pirimiphos-methyl Prochloraz Procymidone Profenofos Propargite Propiconazole Propoxur MRL (mg.kg−1 ) 0.01 0.011 0.011 0.22 0.051 0.01 0.01 0.01 0.01 0.01 0.152 0.052 0.01 0.02 0.01 0.01 0.013 0.01 0.01 0.01 0.01 0.01 0.013 0.012 0.01 0.013 0.082 0.01 0.01 81 81 0.01 0.01 0.011 , 0.062 0.01 0.022 0.11 0.11 0.01 0.1 0.01 0.02 0.01 0.01 0.013 0.013 0.012 0.01 0.01 0.021 0.052 0.021 0.01 0.05 0.05 0.05 0.013 0.01 0.052 0.01 0.05 0.013 0.52 0.21 , 0.01 0.01 0.01 0.092 0.05 LOQ (mg kg−1 ) Matrix Spiking levels (mg kg−1 ) Recovery% (RSD) 0.005 0.01 0.05 74 91 95 82 88 91 89 87 92 92 93 89 (6) (6) (10) (58) (9) (7) (6) (8) (13) (6) (8) (7) 90 86 88 85 81 91 91 86 72 88 98 88 89 94 96 76 (17) (27) (13) (10) (23) (6) (12) (30) (10) (7) (6) (12) (7) (6) (15) (10) (8) (7) (8) (7) (8) 82 (11) 95 (12) 99 (11) 78 (16) 94 (11) 97 (8) 98 (11) 82 (11) 93 (11) 86 (7) 101 (12) 87 (15) 72 (15) 74 (13) 80 (4) 80 (6) 77 (15) 96 (9) 94 (10) 83 (16) 105 (19) 97 (7) 90 (6) 98 (8) 96 (11) 91 (12) 91 (6) 83 (3) 97 (16) 94 (19) 73 (18) 83 (9) 95 (11) 91 (5) 102 (9) Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and oat Rye and wheat Barley, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye Barley, oat, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.05 0.01 0.005 0.005 0.005 0.05 0.005 0.01 0.05 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.01 0.05 0.005 0.005 0.005 0.005 0.005 84 (7) 112 (9) 120 (18) 117 (13) 105 (12) 107 (9) 106 (10) 111 (16) 103 (11) 115 (8) 117 (12) 82 (13) 65 (3) 107 (13) 102 (11) 103 (8) 117 (8) 95 87 86 89 92 Barley, oat, rye, and wheat Rye and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rice, rye, and wheat Rye and wheat Barley, oat, rye, and wheat Rye Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye Barley, oat, rice, rye, and wheat Barley, oat, rye, and wheat Barley, oat, rice, rye, and wheat Barley, oat, rice, rye, and wheat Rye 0.01 0.005 0.05 0.005 0.005 0.005 0.005 0.01 0.005 0.005 0.005 0.01 0.005 0.005 0.01 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.01 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 88 (29) 98 (10) 87 (10) 93 (25) 117 (18) 99 (8) 86 (19) 103 (16) 89 (6) 83 (7) 61 (43) 83 (1) 82 (12) 90 (7) 111 (11) 93 (9) 74 (12) 92 (6) 90 (8) 71 (9) 96 (11) 86 (9) 86 (7) 61 (45) 80 (9) 98 (21) 84 (7) 92 (8) 84 (18) 95 (1) 86 (8) 97 (14) 105 (5) 103 (5) 86 (6) 92 (8) 105 (17) 96 (6) 88 (19) 87 (8) 93 (11) 103 (13) 125 (10) 106 (8) 106 (12) 100 (8) 110 (7) 92 (10) 108 (10) 106 (9) 93 (10) 106 (10) 109 (16) 120 (11) 104 (10) 105 (10) 111 (10) 117 (12) 100 (9) 75 (17) 96 (9) 98 (13) 108 (11) 112 (11) 107 (15) 94 (9) 108 (8) 100 (10) 119 (15) 99 (9) 116 (15) 114 (19) 110 (9) 94 (4) 87 (13) 106 (11) 92 (12) 81 (14) 88 (5) 72 (19) 77 (18) 87 (21) 94 (6) 97 (8) 92 (9) 56 (23) 95 (6) 93 (8) 60 (34) 90 (9) 85 (10) 92 (6) 83 (10) 83 (8) 89 (6) 83 (8) 90 (6) 77 (14) 59 (20) 90 (5) 86 (6) 92 (8) 87 (6) 91 (6) 81 (10) 90 (11) 94 (8) 101 (15) (continued on next page) E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 Table (continued) 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 Compound MRL (mg.kg−1 ) Matrix LOQ (mg kg−1 ) Propyzamide Prosulfocarb Prothiofos Pyraclostrobin Pyrazophos Pyridaben Pyridaphenthion Pyrimethanil Pyriproxyfen Quinoxyfen Simazine Spirodiclofen Spiroxamine Tebuconazole Tebufenpyrad Tecnazene Tefluthrin Tetraconazole Tetradifon Thiamethoxam Thiometon Tolclofos-methyl Triadimefon Triadimenol Triallate Triazophos Tricyclazole Trifloxystrobin Trifluralin Triticonazole Vamidothion Vinclozolin Zoxamide 0.01 0.01 0.013 0.22 0.01 0.01 0.013 0.052 0.05 0.022 0.01 0.02 0.052 0.32 0.01 0.01 0.05 0.052 0.01 0.022 0.013 0.01 0.01 0.012 0.013 0.02 0.01 0.32 0.01 0.01 0.013 0.01 0.02 Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Rye and wheat Rye and wheat Rye and wheat Rye and wheat Barley, oat, rice, rye, and Barley, rye, and wheat Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, rye, and wheat Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Rye and wheat Rye and wheat Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, oat, rice, rye, and Rye and wheat Barley, oat, rice, rye, and Barley, oat, rice, rye, and Barley, rye, and wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat Spiking levels (mg kg−1 ) Recovery% (RSD) 0.005 0.01 0.05 0.005 0.005 0.005 0.01 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 96 (12) 106 (9) 100 (7) 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.05 0.005 0.005 0.005 0.05 0.005 0.05 113 (7) 112 (9) 83 (8) 96 (7) 101 (9) 94 (9) 93 (18) 80 (9) 94 (8) 103 (11) 112 (15) 92 (8) 109 (12) 85 (7) 89 (10) 86 (6) 82 (16) 87 (7) 84 (16) 89 (6) 74 (33) 87 (6) 83 (5) 82 (7) 91 (6) 81(10) 108 (10) 94 (6) 68 (43) 83 (7) 90 (6) 87 (8) 81 (8) 74 (18) 90 (8) 89 (7) 93 (11) 83 (12) 89 (7) 109 (9) 95 (7) 114 (10) 93 (7) 78 (8) 93 (9) 99 (8) 88 (6) 87 (5) 87 (5) 83 (9) 79 (49) 90 (8) 85 (9) 91 (7) 85 (8) 90 (8) 80 (9) 85 (6) 82 (7) 87(7) 100 (11) 97 (8) 78 (7) 83 (5) 94 (6) 90 (5) 78 (16) 75 (9) 88 (5) 90 (7) 100 (10) 89 (7) 92 (6) 65 (26) 94 (8) 80 (7) 97 (8) 77 (23) 88 (5) 91 (11) 115 (10) 106 (9) 113 (9) 88 (9) 103 (10) 99 (9) 99 (10) 112 (10) Metabolites included in the residue definition Assignment of MRLs for rye where MRLs for cereals’ category is not mentioned Application of a general default MRL of 0.01 mg.kg−1 where a pesticide is not specifically mentioned effectiveness of the developed method Reproducibility, estimated as the relative response deviation among cereal samples, was less than 20% for almost all of the studied compounds Fig shows the overlay of the ion chromatograms of pirimiphos-methyl and chlormephos in the five cereal matrices at a spiking level of μg kg-1 The μ-SPE method was not feasible for the determination of two compounds, 3-hydroxycarbofuran and methacrifos, which were not successfully validated, with 3-Hydroxycarbofuran being difficult to analyze by GC LOQs reached were below the MRLs except for 11 compounds Among these, some were also not validated in house with QuEChERS and d-SPE (hexaconazole and formothion) and others were validated using LC-MS/MS (fenhexamid, ethiofencarb, and vamidothion) Dichlorvos is very volatile which can explain its loss during the analysis and the relatively high LOQ obtained Additionally, and based on laboratory experience, the GCOrbitrap is not as sensitive as the triple quadrupole MS with the Advanced Electron Ion (AEI) source used in the laboratory routine analysis static autosampler, a larger size tray, and an automatic de-capping and capping system, would be optimal Authors’ contribution Elena Hakme (conception and design of the study, acquisition of data, analysis and/or interpretation of data, drafting the manuscript) Mette Erecius Poulsen (conception and design of the study, revising the manuscript critically for important intellectual content) Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgments The authors received funding from the European Union Reference Laboratory of pesticide residues in cereals and feeding stuff (EURL-CF) The authors thank Thomi Preiswerk from CTC Analytics for his technical support Conclusion The main benefit of μ-SPE is the increase in laboratory productivity and sample throughput, with an associated reduction of labor The best strategy for accurate pesticide determination and quantitation is the use of semi-procedural matrix calibration The automated μ-SPE system could be used as a standalone system, or it could be coupled to a high-sensitivity analytical instrument In the latter case, the addition of some features, such as a thermo- Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2021.462384 10 E Hakme and M.E Poulsen Journal of Chromatography A 1652 (2021) 462384 References [12] S.X Guan, Z.G Yu, H.N Yu, C.H Song, Z.Q Song, Z Qin, Multi-walled carbon nanotubes as matrix solid-phase dispersion extraction adsorbent for simultaneous analysis of residues of nine organophosphorus pesticides in fruit and vegetables by rapid resolution LC-MS-MS, Chromatographia 73 (1–2) (2011) 33–41, 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Collecting the final extracts took up to min, and the addition of the internal standard was achieved in min, resulting in a total clean-up time of 14 Using the robotic μ-SPE, the automated clean-up. .. are reliable The objective of this study was to evaluate the performance of the automated μ-SPE technique in the analysis of 172 pesticide residues in cereals, and to determine if the technique... miniaturization of the cartridge-SPE procedure The use of automated μ-SPE clean-up was originally reported by Morris et al [17] for the analysis of pesticide residues in avocado and citrus Automated