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User Benefits Application News Residual Pesticides Analysis in Plant-Based Meat by GC-MS/MS Simple sample preparation procedure for high sensitivity analysis Ready-to-use method with optimized MRM ion transitions from Shimadzu Smart Pesticides Database Elgin Ting, Cynthia Lahey Shimadzu (Asia Pacific) Pte Ltd. GCMS-TQ TM 8050 NX Introduction Plant-based meats are artificial meats that are created using plants as the main ingredient to look and taste like real meat. Plant-based meats are healthier and tend to have less environmental impact than real meats [1]. With more awareness of healthy lifestyles and climate change, the possibility of switching to a plant-based meat diet may increase in the near future. Plant-based meats are made of ingredients from plants, such as grains, legumes, vegetable proteins and vegetable oils. Pesticides are frequently used in farms to control weed growth and insect infestation in plants. Residual pesticides remain in the plants will cause health issues when consumed [2][3]. As such, residual pesticides are a food safety concern in plant- based meats. Hence, the detection and quantitation of pesticides in plant-based meats are crucial to ensure the food is safe for consumption. This study describes a triple quadrupole GC-MS/MS method coupled with Shimadzu Smart Pesticides Database TM Ver.2. 2 for screening targeted pesticides in four different types of plant- based meats. QuEChERS is used for sample extraction and clean-up. Experimental Analysis condition GCMS-TQ8050 NX with AOC-20i/s Plus (Shimadzu Corporation, Japan) was used in this work. GCMS-TQ8050 NX is equipped with a highly efficient detector and patented ion source technology for ultra trace analysis, which is suitable for this application. Analytical conditions, MRM transition ions and collision energies (CEs) used were obtained from the Shimadzu Smart Pesticides Database Ver.2.2. The Smart Pesticides Database is a database that contains a list of 530 residual pesticides (including internal standards), supplied with retention indices which allows simple method creation for MRM or SIM mode analysis of residual pesticides in food. Standard and sample preparation All the targeted compounds were mixed and diluted with acetonitrile to a concentration of 1 mg/L. The standard was then diluted with matrix solution (solution collected after sample preparation) to concentrations of 0.5, 1, 2, 5, 10, 20, and 50 pg/µL for matrix-match calibration curves. The sample preparation procedure is shown in Figure 1, according to EN 15662, with some modifications. An additional step of spiking the standard is required for recovery check after adding 10 mL of acetonitrile. Two microliters of standard and sample solutions were injected to the GC-MS/MS for subsequent analysis. Table 1: GC-MS/MS analytical conditions for analysis of residual pesticides in plant-based meat. Instruments and Column information GC-MS/MS GCMS-TQ8050 NX Auto Injector AOC-20i/s Plus Column SH-I-5Sil MS (P/N 221-75954-30) 30 m x 0.25 mm ID x 0.25 µm df GC-MS/MS parameter GC-MS/MS Method No. 3 from Smart Pesticides Database Ver.2.2 (P/N 225-45235-91) Weigh 10 g sample and add 5 mL of milli-Q water Add 10 mL of acetonitrile (For recovery check, the standard should be spiked after adding acetonitrile) Shake vigorously for 1 min Add Restek Cat.# 25852 (6 g MgSO 4 and 1.5 g NaOAc) and shake the tube for 1 min Centrifuge at 3000 rpm for 5 min Aliquot the top layer into centrifuge tube and freeze the fat overnight Take out the centrifuge tube from the freezer and immediately centrifuge at 3000 rpm for 2 min Transfer 1 mL of extract (do not take the fat) in Restek Cat.# 26216 (150 mg MgSO 4 , 25 mg PSA, and 25 mg C18-EC ) and shake the tube for 30 Sec Centrifuge at 3000 rpm for 5 min Filter the solution into GC 1.5 mL vial Inject 2 µL into GC-MS/MS for measurement Figure 1: Workflow for sample preparation of plant-based meat
Application News No. Name % Recovery using area count Sample A Sample B Sample C Sample D 1 Atrazine 98 98 97 109 2 Pyrimethanil 86 89 92 86 3 Terbacil 99 95 94 116 4 Tefluthrin 88 93 93 84 5 Vinclozolin 100 102 99 108 6 Transfluthrin 90 91 95 86 7 Anthraquinone 92 90 93 91 8 Chlorpyrifos 81 90 87 86 9 Triadimefon 105 100 106 107 10 Cyprodinil 84 91 91 69 11 Penconazole 99 97 99 97 12 Fipronil 125 114 128 109 13 Procymidone 99 110 105 96 14 Triflumizole 105 102 112 96 15 Paclobutrazol 114 105 114 111 16 Flutriafol 101 110 103 105 17 Fludioxonil 102 98 102 97 18 Profenofos 78 92 82 71 19 Bupirimate 97 115 107 91 20 Lenacil 97 96 92 101 21 Tebuconazole 101 99 104 97 22 Iprodione 69 78 68 82 23 Bifenthrin 72 80 77 71 24 Pyriproxyfen 82 83 85 73 25 Fenarimol 94 94 98 96 26 Permethrin-1 66 78 75 81 27 Permethrin-2 72 82 80 69 28 Cyfluthrin-1 87 102 103 84 29 Cyfluthrin-2 75 100 96 84 30 Cyfluthrin-3 79 98 94 81 31 Cyfluthrin-4 81 107 92 83 32 Cypermethrin-1 75 101 97 71 33 Cypermethrin-2 82 105 89 69 34 Cypermethrin-3 80 109 88 70 35 Flucythrinate-1 88 106 98 94 36 Cypermethrin-4 85 85 81 71 37 Etofenprox 78 79 77 71 38 Flucythrinate-2 87 108 98 89 39 Fluridone 104 107 104 115 40 Deltamethrin-1 (Tralomethrin deg.-1) 71 99 73 87 41 Deltamethrin-2 (Tralomethrin deg.-2) 76 79 70 77 42 Azoxystrobin 103 104 105 112 Name Area count of 10 pg/µL standard in acetonitrile Area count of 10 pg/µL (post-spike in Sample B) Matrix effect (%) Atrazine 5358 30395 467 Pyrimethanil 16199 115598 614 Terbacil 3193 81488 2452 Tefluthrin 83801 333775 298 Vinclozolin 12265 38893 217 Fludioxonil 14138 241907 1611 Bupirimate 2977 73582 2372 Lenacil 9612 298175 3002 Tebuconazole 3439 109475 3083 Atrazine Vinclozolin 10 pg/µL STD in Acetonitrile Post-spike 10 pg/µL std in Sample B Sample B blank 10 pg/µL STD in Acetonitrile Post-spike 10 pg/µL STD in Sample B Sample B blank Figure 2: Overlay MRM chromatogram of pesticides at 10 pg/µL in acetonitrile solvent (dark blue), post-spike 10 pg/µL in Sample B (green) and Sample B blank (light blue). Table 2: Percentage recovery using area count for all the four samples at 5 pg/µL. Table 3: Matrix effect of Sample B Sensitivity, Repeatability and Linearity Matrix-match calibration curves were plotted using Sample B matrix spiked with different concentrations of standard solution. As the pesticides displayed different sensitivity in the GC- MS/MS, the optimum calibration curve concentration range was selected for each compound. The limit of quantification (LOQ), which was set as the lowest level of a calibration curve, was determined based on the concentration at which the signal-to-noise ratio were greater than 10 and area repeatability (% RSD) were less than 15 %. About 60 % of the compounds had the lowest calibration level at 0.5 pg/µL, while 80% of compounds had at least 5-point calibration and good linearity (R 2 >0.999). The calibration information for each compound is tabulated in Table 4. Matrix effect The magnitude of the matrix effect was carefully considered before setting the calibration curves. Nine pesticides were selected as representatives to investigate the matrix effect. It was found that at the same concentration, compounds in post- spike matrix solution (Sample B) had higher area count than compounds prepared in acetonitrile solvent. Sample B blank is the matrix blank solution after sample pretreatment. Figure 2 shows the overlay of some pesticides prepared in acetonitrile solvent, post-spike in Sample B and Sample B blank. This shows that higher area counts in the post-spike standard in Sample B was due to the matrix effect. The matrix effect is tabulated in Table 3. Based on the matrix effect result, it was concluded that matrix-match calibration curves were more suitable for plant- based meat analysis. Results Recovery Four different plant-based meat samples (Sample A, B, C and D) were tested to determine whether QuEChERS sample preparation procedure was suitable. Recovery of area count of pre-spike over post-spike was calculated for each sample. Sample B could achieve area percentage recovery between 70 to 130 % for all the targeted compounds, while some pesticides in other samples were below 70% in recovery. This implies that modifications of sample preparation might be required for samples with different types of ingredients, for example, plant- based meats with much different fat content, sugar content, or additional coloring ingredients. In this experiment, the sample preparation in Figure 1, which used QuEChERS dSPE containing 150 MgSO 4 , 25 mg PSA, and 25 mg C18-EC (meant for foodstuffs with fats and waxes [4]), is appropriate for plant-based meats which are of similar ingredients with Sample B. Accordingly, Sample B was used for the next stage of the study. Note: Yellow indicates % recovery results which are outside 70- 130% window.
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Ltd, www.shimadzu.com.sg No. Name LOQ (pg/µL) Area %RSD (n=8) R 2 1 Atrazine 0.5 4.5 0.99993 2 Pyrimethanil 0.5 3.0 0.99994 3 Terbacil 0.5 5.6 0.99991 4 Tefluthrin 0.5 3.0 0.99997 5 Vinclozolin 1 4.3 0.99996 6 Transfluthrin 0.5 10.4 0.99989 7 Anthraquinone 0.5 2.7 0.99996 8 Chlorpyrifos 0.5 3.8 0.99987 9 Triadimefon 0.5 7.1 0.99996 10 Cyprodinil 0.5 7.0 0.99996 11 Penconazole 0.5 3.9 0.99994 12 Fipronil 0.5 6.6 0.99993 13 Procymidone 0.5 8.5 0.99996 14 Triflumizole 0.5 4.8 0.99991 15 Paclobutrazol 2 8.7 0.99993 16 Flutriafol 0.5 5.2 0.99994 17 Fludioxonil 0.5 5.6 0.99996 18 Profenofos 0.5 9.8 0.99993 19 Bupirimate 0.5 4.7 0.99993 20 Lenacil 1 2.6 0.99999 21 Tebuconazole 0.5 3.7 0.99997 22 Iprodione 5 7.1 1.00000 23 Bifenthrin 0.5 3.1 0.99999 24 Pyriproxyfen 0.5 5.6 0.99995 25 Fenarimol 0.5 6.9 0.99996 26 Permethrin-1 0.5 2.9 0.99997 27 Permethrin-2 1 4.7 0.99997 28 Cyfluthrin-1 2 8.2 0.99994 29 Cyfluthrin-2 5 1.8 0.99999 30 Cyfluthrin-3 5 4.6 0.99999 31 Cyfluthrin-4 2 5.7 0.99984 32 Cypermethrin-1 2 9.6 0.99980 33 Cypermethrin-2 5 4.7 1.00000 34 Cypermethrin-3 10 5.6 1.00000 35 Flucythrinate-1 0.5 6.5 0.99982 36 Cypermethrin-4 10 4.2 1.00000 37 Etofenprox 0.5 6.2 0.99999 38 Flucythrinate-2 0.5 6.0 0.99980 39 Fluridone 1 2.7 0.99989 40 Deltamethrin-1 (Tralomethrin deg.-1) 10 11.5 1.00000 41 Deltamethrin-2 (Tralomethrin deg.-2) 2 9.4 0.99988 42 Azoxystrobin 0.5 10.1 0.99993 First Edition: Jan. 2024 Revision A: Feb. 2026 04-AD-0272A-EN GCMS-TQ and Smart Pesticides Database are trademarks of Shimadzu Corporation or its affiliated companies in Japan and/or other countries. Conclusion Method development for pesticide residue analysis in plant- based meat was carried out using GCMS-TQ8050 NX. QuEChERS dSPE, containing 150 MgSO 4 , 25 mg PSA, 25 mg C18-EC, was used as a sample cleanup method which resulted in different recovery results for different plant-based meat samples. This suggests that modification of cleanup reagents could be required for different types of samples. The matrix effect was noticeable when comparing spiked pesticides in the matrix to those in acetonitrile; hence, matrix-match calibration curves were recommended for quantitation. More than half of the targeted compounds had LOQ of 0.5 pg/µL, while about 80% of the compounds had at least 5-point calibration curves with R 2 values greater than 0.999. Table 4: Calibration information for all the targeted compounds prepared in Sample B matrix. Figure 3: Calibration curves of pesticides prepared in Sample B matrix. Conc. (ppb) 0 5 10 15 20 25 30 35 40 45 50 Area 0.0e0 2.0e4 4.0e4 6.0e4 8.0e4 1.0e5 1.2e5 1.4e5 1.6e5 1.8e5 2.0e5 2.2e5 2.4e5 2.6e5 2.8e5 3.0e5 3.2e5 3.4e5 3.6e5 3.8e5 Atrazine y = -14.41772x² + 7996.483x + 1818.201 R² = 0.9999322 R = 0.9999661 Curve Fit: Quadratic Weighting: Default (None) Zero: Default (Not Forced) Conc. (ppb) 0 5 10 15 20 25 30 35 40 45 50 Area 0.0e0 2.0e5 4.0e5 6.0e5 8.0e5 1.0e6 1.2e6 1.4e6 1.6e6 1.8e6 2.0e6 2.2e6 2.4e6 2.6e6 2.8e6 3.0e6 3.2e6 3.4e6 3.6e6 Tefluthrin y = -64.64755x² + 72957.50x + 16010.79 R² = 0.9999692 R = 0.9999846 Curve Fit: Quadratic Weighting: Default (None) Zero: Default (Not Forced) Conc. (ppb) 0 5 10 15 20 25 30 35 40 45 50 Area 0.0e0 5.0e4 1.0e5 1.5e5 2.0e5 2.5e5 3.0e5 3.5e5 4.0e5 4.5e5 5.0e5 5.5e5 6.0e5 6.5e5 7.0e5 7.5e5 8.0e5 8.5e5 9.0e5 9.5e5 Procymidone y = -20.65336x² + 18447.42x + 5600.282 R² = 0.9999604 R = 0.9999802 Curve Fit: Quadratic Weighting: Default (None) Zero: Default (Not Forced) Conc. (ppb) 0 5 10 15 20 25 30 35 40 45 50 Area 0.0e0 1.0e4 2.0e4 3.0e4 4.0e4 5.0e4 6.0e4 7.0e4 8.0e4 9.0e4 1.0e5 1.1e5 1.2e5 1.3e5 1.4e5 1.5e5 1.6e5 1.7e5 1.8e5 1.9e5 2.0e5 2.1e5 2.2e5 2.3e5 Profenofos y = -9.792444x² + 4823.565x + 1680.474 R² = 0.9999298 R = 0.9999649 Curve Fit: Quadratic Weighting: Default (None) Zero: Default (Not Forced) Atrazine R 2 = 0.99993 Terfluthrin R 2 = 0.99996 Procymidone R 2 = 0.99996 Profenofos R 2 = 0.99993 Note: Green indicates pesticide compounds with LOQ of 0.5 pg/µL References 1. Jayson L Lusk et al 2022 Environ. Res. Lett. 17 024035 2. EPA United States Environmental Protection Agency, Why We Use Pesticide, accessed 28 July 2022 https://www.epa.gov/safepestcontrol/why-we-use- pesticides#:~:text=Pesticides%20are%20used%20to%20control,be%20 effective%20against%20specific%20pests. 3. ScienceDirect, Crop yields Increase, accessed 20 September 2022 https://www.sciencedirect.com/topics/agricultural-and-biological- sciences/crop-yields-increase 4. RESTEK, Q-sep QuEChERS dSPE Tubes for Extract Cleanup, accessed 20 September 2022 https://www.restek.com/row/products/sample- preparation--air-sampling/sample-preparation- products/quechers/7186/
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