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User Benefits Application News Fig. 2 Comparison of SPL and PTV Modes for Dichlofluanid Metabolite Fig. 1 GCMS-TQ 8040 RX The Multi-Mode Injection Unit (MMI) is a GC inlet that supports various injection modes, including programmed temperature vaporization (PTV) injection and large volume injection (LVI), in addition to conventional split/splitless injection (SPL). In the PTV mode used in this study, the inlet temperature at sample injection is set low, near or below the solvent boiling point, allowing the sample to be temporarily retained in the liner. The inlet is then rapidly heated according to a predefined temperature program, vaporizing the target analytes and transferring them onto the column. Because the sample is introduced at a low temperature in PTV mode, thermal decomposition of thermally labile pesticides can be effectively suppressed (Fig. 2). The Multi-Mode Injection Unit (MMI) Fig. 4 Example of MRM Transition Selection for Chlorpyrifos in Spinach Extract at an Equivalent Concentration of 10 ppb The simultaneous MRM acquisition method for pesticide residue analysis was developed using the Smart Pesticides Database. The database contains retention indices and MRM transitions for 530 pesticide compounds regulated in various countries (Fig. 3). By simply measuring n-alkanes, a multiresidue analytical method can be generated automatically, without using pesticide standard solutions, based on retention indices and the AART function. Because up to six transitions are registered for each pesticide, transitions that avoid matrix interference can be selectively used (Fig. 4). Pesticide residue analysis is critical for ensuring food safety. Because the number of target analytes can exceed several hundred, rapid and efficient multiresidue analytical methods are needed. In this study, a simultaneous analytical method for more than 300 pesticide compounds was investigated using the GCMS-TQ 8040 RX triple quadrupole gas chromatograph- tandem mass spectrometer (Fig. 1). Samples were introduced using a Multi-Mode Injection Unit (MMI) operated in programmed temperature vaporization (PTV) mode. Method development was facilitated by Smart Pesticides Database Ver. 2.2, which includes pre-registered optimized multiple reaction monitoring (MRM) conditions, enabling straightforward transition selection and reducing matrix-derived interferences. In addition, Peakintelligence for GCMS, an AI-based data analysis tool, was used to improve the accuracy and efficiency of peak detection and integration in complex chromatograms. The proposed method was evaluated quantitatively using spinach extracts. The results demonstrated its applicability to comprehensive pesticide residue analysis in food matrices. Introduction The GCMS-TQ 8040 RX and MMI-PTV enable analysis of more than 300 pesticide compounds in complex matrices. The Smart Pesticides Database automatically creates a multiresidue MRM acquisition method with optimized transitions. Peakintelligence for GCMS uses AI-based waveform processing for accurate analysis without parameter settings. Simultaneous Analysis of Pesticide Residues in Food Using Triple Quadrupole GC-MS/MS with PTV Mode of a Multimode Injection Unit (MMI) Kyoko Yamada, Kazuhiro Kawamura Gas Chromatograph Mass Spectrometer Fig. 3 Registered Information in the Smart Pesticides Database SPL mode PTV mode 2.33e4 Q 200.10>45.00 (+) 16.6 16.8 17.0 17.2 17.4 0.00 % 100.00 1.03e5 Q 200.10>45.00 (+) 16.6 16.8 17.0 17.2 17.4 0.00 % 100.00 25.0 25.5 0.25 0.50 0.75 1.00 1.25 1.50 1.75 (x10,000) 313.90>285.90 313.90>257.90 25.0 25.5 0.5 1.0 1.5 2.0 2.5 3.0 3.5 (x10,000) 313.90>193.90 196.90>133.90 313.90>285.90 313.90>257.90 196.90>168.90 Automated Development of a Multiresidue Analysis Method Using the Smart Pesticides Database
Application News Sample Preparation and Pretreatment Table 1 Instrument Configuration and Analytical Conditions Fig. 5 Example of Sample Preparation Using QuEChERS Spinach was selected as the food matrix because it contains pigments and other complex coexisting components. The sample was homogenized by a pre-cooled dry ice grinding method, followed by extraction and cleanup using the QuEChERS method in accordance with AOAC 2007.01. The workflow is shown in Fig. 5. For extraction, a Q-sep QuEChERS extraction salt kit (Restek; AOAC 2007.01 compliant; P/N 25852) was used. For cleanup, Q-sep QuEChERS dSPE for extract purification (Restek; AOAC 2007.01 compliant; P/N 26219) was used. Calibration curves were prepared using matrix-matched standards. A mixed pesticide standard solution was added to the pretreated blank extract to obtain final concentrations of 0.1, 0.5, 1, 5, 10, 20, 50, 100, 200, and 400 ppb. Analytical Conditions The instrument configuration and analytical conditions are summarized in Table 1. Method 3 in the Smart Pesticides Database was used as the basis for the analytical method. To improve the peak shapes of low-boiling pesticides in acetonitrile, the initial GC oven temperature was changed to 70 ˚C. Data Analysis LabSolutions Insight, a quantitative data processing software package designed for high-throughput sample analysis, was used for data analysis, with Peakintelligence for GCMS applied as the peak integration algorithm (Fig. 6). Peakintelligence is an AI-based waveform processing algorithm that uses machine learning to emulate the peak processing performed by experienced analysts. It requires no operator-defined parameter settings and provides peak integration performance comparable to that of skilled users. For more information on improving the efficiency of pesticide data analysis using Peakintelligence for GCMS, please refer to Application News 01-00585-EN, “Time-Saving Data Processing for Pesticide Residues with Peakintelligence for GCMS.” Fig. 6 Peakintelligence for GCMS Configuration GC-MS : GCMS-TQ8040 RX Injection Unit : MMI-U Column : SH-I-5Sil MS (30 m × 0.25 mm I.D., 0.25 µm) (P/N: 221-75954-30) Guard Column : SH-I Guard / Retention Gap Column (5 m × 0.25 mm I.D.) (P/N: 227-36303-01) Liner : Xtra Inert Splitless Liner (P/N: 227-36701-01) GC MS MMI Mode : PTV injection Ion Source Temp. : 230 ˚C Injection Mode : Pulsed Splitless IF Temp. : 280 ˚C MMI Program : 60 ˚C (0.1 min) - (900 ˚C/min) - 300 ˚C Acquisition Mode : MRM Sampling Time : 1 min Loop Time : 0.4 sec Carrier Gas Control : Linear Velocity (44.1 cm/s) (He) Pulsed Injection : 250 kPa (1.5 min) Injection Volume : 2 µL Oven Program : 70 ˚C (3 min) - (10 ˚C/min) - 130 ˚C - (4 ˚C/min) - 200 ˚C - (8 ˚C/min) - 290 ˚C (12.25 min)
Application News Quantitative Analysis Fig. 7 Distribution of Calibration Curve Linearity (R² ≥ 0.995), with numbers indicating the number of compounds Calibration curves were prepared over a range of 0.1 to 400 ppb (up to 200 ppb for some compounds) using matrix-matched standard solutions prepared in spinach extract. Of the 386 pesticides evaluated, 357 compounds (approximately 92%) showed good linearity, with coefficients of determination (R²) of 0.995 or higher (Fig. 7). Among these, 213 compounds (approximately 60%) were detectable at 0.1 ppb (Fig. 8). Repeatability was also evaluated (n = 6) using the calibration standard at 10 ppb. The results are summarized in Table 3. Despite the use of a challenging matrix extract, trueness values for many of the evaluated pesticides fell within the acceptance range specified in the SANTE guideline (70–120%). Repeatability (%RSD) also showed excellent performance, with most compounds well below the acceptance criterion of 20% (Fig. 9). These results indicate that reliable measurements can be achieved even in complex matrices through the combined effects of the high sensitivity of the GCMS-TQ 8040 RX for multiresidue analysis, stable sample introduction by PTV injection, appropriate transition selection using the Smart Pesticides Database, and accurate AI-based peak processing. Representative calibration curves and chromatograms near the LOQ are shown in Fig. 11. Data Analysis with Peakintelligence for GCMS Results obtained with Peakintelligence for GCMS were compared with those obtained by conventional peak processing (Chromatopac) (Fig. 10). Conventional processing occasionally resulted in incorrect peak integration, particularly at low concentrations in chromatograms with matrix-derived baseline elevation or adjacent peaks. In contrast, Peakintelligence provided appropriate peak processing for these chromatograms. Its use can reduce the time required for manual correction while enabling reliable quantitative results without operator-dependent variation. Conclusion In this Application News, pesticide residues in spinach extract were analyzed using the GCMS-TQ8040 RX in combination with a Multi-Mode Injection Unit (MMI) and advanced software tools. The GCMS-TQ8040 RX enables reliable detection of trace-level compounds in multiresidue analysis, while the PTV mode of the MMI and appropriate transition selection using the Smart Pesticides Database provide highly sensitive and selective measurements even in complex matrices. In addition, Peakintelligence for GCMS, an AI-based data analysis tool, improves integration accuracy for difficult peaks, streamlines data analysis, and reduces operator-dependent variability. Quantitative evaluation demonstrated excellent trueness and precision even for challenging matrix extracts. These results indicate that this integrated solution is well suited to routine pesticide residue analysis of complex multicomponent samples, helping laboratories improve both confidence in analytical results and operational efficiency. 6.08e3 Q 110.10>64.00 (+) 15.50 15.75 16.00 16.25 16.50 0.0e0 1.0e3 2.0e3 3.0e3 4.0e3 5.0e3 6.0e3 6.08e3 Q 110.10>64.00 (+) 15.50 15.75 16.00 16.25 16.50 0.0e0 1.0e3 2.0e3 3.0e3 4.0e3 5.0e3 6.0e3 1.51e3 Q 168.10>70.00 (+) 27.0 27.2 27.4 27.6 0.0e0 2.5e2 5.0e2 7.5e2 1.0e3 1.3e3 1.5e3 1.51e3 Q 168.10>70.00 (+) 27.0 27.2 27.4 27.6 0.0e0 2.5e2 5.0e2 7.5e2 1.0e3 1.3e3 1.5e3 Fig. 10 Comparison of Peak Processing between Peakintelligence for GCMS and the Conventional Processing (Chromatopac) Peakintelligence for GCMS Conventional Peak Processing (Chromatopac) Propoxur (1 ppb) Triadimenol-2 (1 ppb) LOQ 0.1 ppb LOQ 0.5 ppb LOQ 1 ppb LOQ >5 ppb Fig. 8 Distribution of LOQs for 357 Compounds, with numbers indicating the number of compounds <5% 5-10％ 10-20％ >20% Fig. 9 Distribution of Quantitative Repeatability (%RSD) R² ≧ 0.995 R² ＜ 0.995
Application News Table 2 Summary of Quantitative Performance Evaluation Results (Excerpt) Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness (%) Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness (%) Chlofentezine deg. 9.9 99.3 2.5 -0.7 Terbacil 9.8 98.3 7.5 -1.7 Dichlorvos 8.4 84.1 4.9 -15.9 Isazofos 9.9 99.3 2.7 -0.7 Nereistoxin 10.3 103.0 3.0 3.0 Prohydrojasmon-1 9.6 95.6 3.6 -4.4 Allidochlor 9.5 94.7 6.9 -5.3 Tri-allate 9.8 98.0 6.5 -2.0 Dichlobenil 10.2 102.0 4.1 2.0 MCPA-thioethyl 9.7 97.0 4.1 -3.0 EPTC 9.9 98.9 4.6 -1.1 Etrimfos 9.7 97.1 5.1 -2.9 Biphenyl 10.1 100.9 5.9 0.9 Tefluthrin 10.1 101.4 4.7 1.4 Mevinphos-1 9.2 92.0 10.6 -8.0 Iprobenfos 9.7 96.6 4.7 -3.4 Mevinphos-2 8.8 87.8 8.4 -12.2 Oxabetrinil 9.4 94.4 18.2 -5.6 Butylate 10.3 103.1 3.5 3.1 Tebupirimfos 10.0 100.3 4.4 0.3 Chlormephos 10.0 99.9 4.0 -0.1 Benoxacor 10.3 102.8 3.8 2.8 Etridiazole 10.5 105.2 4.2 5.2 Prohydrojasmon-2 10.8 108.2 9.8 8.2 Thiocyclam 9.9 99.5 4.5 -0.5 Formothion 11.2 112.3 9.1 12.3 Methacrifos 9.5 94.8 3.4 -5.2 MCPB-ethyl 10.1 101.4 3.9 1.4 Crimidine 9.3 92.6 6.1 -7.4 Benfuresate 9.7 96.7 3.6 -3.3 Chloroneb 10.3 102.8 5.9 2.8 Phosphamidon-2 10.0 100.4 11.3 0.4 2-Phenylphenol 9.6 96.4 2.5 -3.6 Dimethenamid (Dimethenamid-P) 9.7 97.5 4.4 -2.5 Isoprocarb 8.8 87.9 6.0 -12.1 Dichlofenthion 9.8 98.3 3.3 -1.7 Molinate 10.2 101.6 4.0 1.6 Propanil 10.8 107.9 3.2 7.9 XMC 8.5 85.0 10.0 -15.0 Bromobutide 10.9 108.8 3.9 8.8 Omethoate 9.3 92.7 9.6 -7.3 Terbucarb 10.1 101.4 4.5 1.4 Tecnazene 10.9 109.1 4.9 9.1 Chlorpyrifos-methyl 9.6 95.7 7.0 -4.3 Xylylcarb 9.6 96.4 13.3 -3.6 Metribuzin 10.7 107.2 3.9 7.2 Propachlor 9.5 95.4 4.0 -4.6 Acetochlor 9.7 97.4 2.4 -2.6 Propoxur 9.7 96.8 6.8 -3.2 Oxpoconazole-formyl deg. 11.3 112.9 2.7 12.9 Chlorethoxyfos 11.0 110.2 3.7 10.2 Vinclozolin 9.5 94.6 7.3 -5.4 Demeton-S-methyl (Methyl demeton) 8.5 85.5 5.7 -14.5 Parathion-methyl 11.5 115.2 4.1 15.2 Diphenylamine 10.2 101.5 3.5 1.5 Tolclofos-methyl 9.6 96.0 5.4 -4.0 Ethoprophos 9.5 94.5 5.7 -5.5 Simeconazole 9.3 92.6 6.0 -7.4 Phenmedipham deg. 23.0 229.7 4.6 129.7 Alachlor 9.8 98.3 2.8 -1.7 Dichlofluanid metabolite 8.2 81.8 9.9 -18.2 Spiroxamine-1 10.0 100.3 4.3 0.3 Ethalfluralin 10.8 108.4 6.6 8.4 Simetryn 10.2 102.2 5.4 2.2 Chlorpropham 9.2 92.0 3.1 -8.0 Metalaxyl (Mefenoxam) 9.9 98.7 2.9 -1.3 2,6-Dichlorobenzamide 10.0 100.4 6.4 0.4 Fenchlorphos 9.2 92.5 5.2 -7.5 Dioxabenzofos (Salithion) 9.3 93.2 4.8 -6.8 Ametryn 9.8 98.3 2.4 -1.7 Dicrotophos 9.9 99.5 7.9 -0.5 Cinmethylin 9.5 95.1 5.5 -4.9 Flusilazole metabolite 18.4 183.6 2.9 83.6 Prometryn 10.5 105.0 4.2 5.0 Trifluralin 10.9 109.2 4.4 9.2 2-(1-Naphthyl)acetamide 8.5 84.9 3.3 -15.1 Sulfotep 10.3 102.6 3.7 2.6 Dithiopyr 10.0 100.5 4.4 0.5 Benfluralin 10.8 108.2 4.7 8.2 Fenitrothion 10.8 107.9 5.1 7.9 Monocrotophos 8.4 84.0 14.7 -16.0 Pirimiphos-methyl 9.7 97.4 4.4 -2.6 Cadusafos 9.7 97.4 3.9 -2.6 Terbutryn 9.9 99.3 3.9 -0.7 Di-allate-1 10.0 100.3 5.7 0.3 Spiroxamine-2 9.5 94.7 5.8 -5.3 Phorate 10.1 100.6 5.8 0.6 Ethofumesate 9.3 92.9 6.4 -7.1 alpha-BHC 9.6 95.7 4.3 -4.3 Dichlofluanid 11.8 118.4 10.8 18.4 Di-allate-2 10.7 107.0 5.9 7.0 Quinoclamine 11.8 118.2 7.5 18.2 Thiometon 9.8 98.4 5.8 -1.6 (E)-Dimethylvinphos 9.7 96.6 8.6 -3.4 Dicloran 11.4 113.8 5.0 13.8 Bromacil 9.9 98.9 10.0 -1.1 Dimethoate 8.3 83.1 11.1 -16.9 Esprocarb 9.6 95.5 5.3 -4.5 Desmedipham deg. 11.2 112.1 5.1 12.1 Malathion 9.9 98.8 6.0 -1.2 Furilazole 10.3 102.7 3.7 2.7 Metolachlor (S- Metolachlor) 9.6 95.6 4.0 -4.4 Carbofuran 9.7 97.1 12.0 -2.9 Chlorpyrifos 9.7 96.9 5.2 -3.1 beta-BHC 9.6 96.3 5.3 -3.7 Thiobencarb 9.9 98.7 5.5 -1.3 Simazine 9.9 98.6 10.0 -1.4 (Z)-Dimethylvinphos 9.4 94.1 7.2 -5.9 Dimethipin 8.7 86.5 11.8 -13.5 Fenthion 9.4 93.7 4.0 -6.3 Quintozene 11.7 117.4 4.9 17.4 Chlorthal-dimethyl 9.2 91.6 6.4 -8.4 Swep 10.5 105.0 8.9 5.0 Cyanazine 10.6 105.7 7.7 5.7 Atrazine 9.0 90.3 6.9 -9.7 Diethofencarb 9.4 93.8 4.2 -6.2 Clomazone 9.0 90.3 2.9 -9.7 Parathion 10.6 105.7 5.2 5.7 Chlorbufam 11.3 112.9 4.5 12.9 Fenpropimorph 9.9 99.0 4.1 -1.0 gamma-BHC (Lindane) 9.8 98.5 3.9 -1.5 Phthalide 8.9 89.1 8.7 -10.9 Propazine 9.9 99.5 3.4 -0.5 Triadimefon 9.2 92.0 5.8 -8.0 Tolylfluanid metabolite 9.2 92.5 8.7 -7.5 Isocarbophos 10.2 102.4 8.4 2.4 Dioxathion deg. 12.0 120.1 5.5 20.1 Isofenphos oxon 10.0 99.7 7.8 -0.3 Cyanophos 9.1 91.5 5.3 -8.5 Tetraconazole 9.8 98.4 7.3 -1.6 Terbufos 9.9 99.0 3.6 -1.0 Bromophos 9.8 98.2 4.4 -1.8 Pyroquilon 9.2 91.5 6.1 -8.5 Nitrothal-isopropyl 12.6 125.8 4.0 25.8 Fonofos 9.7 97.2 3.1 -2.8 Fosthiazate-1 9.9 98.8 14.0 -1.2 Propyzamide 10.0 100.1 4.2 0.1 Diphenamid 8.6 85.6 4.7 -14.4 Chlorothalonil 10.7 106.6 15.9 6.6 Fosthiazate-2 9.7 96.6 12.3 -3.4 Phosphamidon-1 10.5 105.0 10.3 5.0 Thiamethoxam deg. 10.2 102.4 5.0 2.4 Pyrimethanil 9.2 92.4 5.5 -7.6 Cyprodinil 9.3 92.6 4.8 -7.4 Diazinon 9.5 95.3 4.7 -4.7 Pendimethalin 11.4 114.4 5.6 14.4 Disulfoton 9.9 99.1 4.8 -0.9 (E)-Chlorfenvinphos 9.3 92.8 6.3 -7.2 delta-BHC 9.1 90.7 5.4 -9.3 Penconazole 8.9 89.0 6.4 -11.0
Application News Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness (%) Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness (%) Tolylfluanid 10.9 109.4 10.4 9.4 Cyflufenamid 10.3 103.1 5.2 3.1 Dimethametryn 10.4 104.1 5.9 4.1 Nitrofen 10.9 109.0 6.0 9.0 (Z)-Pyrifenox 8.7 86.6 4.3 -13.4 Chlorthiophos-1 9.2 92.0 14.0 -8.0 Fipronil 9.9 98.9 3.3 -1.1 Fenoxanil 9.5 95.5 7.0 -4.5 Chlozolinate 11.5 115.5 3.3 15.5 1,1-Dichloro-2,2-bis(4- ethylphenyl)ethane 9.6 96.2 4.2 -3.8 Ethychlozate 9.2 92.3 5.4 -7.7 beta-Endosulfan 10.4 104.0 5.9 4.0 Thiabendazole 8.8 87.8 10.7 -12.2 Chlorthiophos-2 9.5 95.4 5.5 -4.6 Isofenphos 10.1 100.5 3.7 0.5 Chlorobenzilate 9.4 93.6 4.9 -6.4 (Z)-Chlorfenvinphos 9.3 92.9 6.7 -7.1 Chloropropylate 9.4 94.2 3.8 -5.8 Captan 32.9 329.4 3.6 229.4 Fensulfothion 10.2 101.7 5.8 1.7 Allethrin-1,2 8.6 86.0 8.9 -14.0 Diniconazole 9.3 92.9 4.4 -7.1 Mecarbam 10.6 105.7 15.5 5.7 (Z)-Pyriminobac-methyl 9.6 96.5 4.7 -3.5 Phenthoate 9.9 99.5 5.1 -0.5 Flufenpyr-ethyl 9.6 96.1 7.9 -3.9 Diclocymet-1 9.4 94.0 3.0 -6.0 Oxadixyl 8.9 88.7 4.1 -11.3 Quinalphos 9.6 95.8 6.6 -4.2 Ethion 10.1 100.8 4.3 0.8 Dimepiperate 9.2 92.1 4.4 -7.9 Chlorthiophos-3 9.4 93.8 5.5 -6.2 Folpet 23.5 235.2 5.1 135.2 Mepronil 8.5 84.7 5.8 -15.3 Procymidone 9.0 89.5 5.0 -10.5 Fluacrypyrim 9.6 95.5 7.8 -4.5 Triadimenol-1 8.6 86.3 9.5 -13.7 Sulprofos 9.6 95.5 4.0 -4.5 Allethrin-3,4 (Bioallethrin) 9.9 99.3 4.7 -0.7 Triazophos 8.8 87.5 8.5 -12.5 Zoxamide deg. 9.7 96.5 4.4 -3.5 Azamethiphos 17.9 179.4 12.1 79.4 Chinomethionat 10.9 108.6 6.1 8.6 Chlornitrofen 11.7 117.4 3.5 17.4 Chlorbenside 10.6 105.7 3.3 5.7 Benalaxyl 9.1 90.9 7.9 -9.1 Methidathion 10.2 101.7 6.9 1.7 Isoxadifen-ethyl 10.5 104.8 4.1 4.8 Ferimzone 11.8 118.4 4.3 18.4 Carbophenothion 8.7 87.3 9.1 -12.7 Triadimenol-2 9.4 94.3 7.4 -5.7 Edifenphos 10.0 100.4 7.5 0.4 Bromophos-ethyl 9.5 95.5 3.4 -4.5 Carfentrazone-ethyl 9.5 95.3 5.7 -4.7 Diclocymet-2 9.7 96.6 3.4 -3.4 Cyanofenphos 8.9 89.5 7.6 -10.5 Methoprene 9.8 97.9 12.7 -2.1 Endosulfan sulfate 9.6 95.7 11.8 -4.3 (E)-Pyrifenox 8.8 87.6 7.0 -12.4 Norflurazon 8.8 88.3 5.9 -11.7 Propaphos 8.9 88.9 7.3 -11.1 Quinoxyfen 9.1 91.0 5.7 -9.0 Paclobutrazol 10.0 99.8 2.7 -0.2 Propiconazole-1 9.6 96.3 4.4 -3.7 Tetrachlorvinphos 9.7 96.8 9.5 -3.2 Lenacil 8.8 88.2 5.7 -11.8 Trichlamide 10.3 103.0 5.5 3.0 Trifloxystrobin 10.2 101.9 5.8 1.9 alpha-Endosulfan 9.7 97.1 4.7 -2.9 Propiconazole-2 9.7 96.5 4.6 -3.5 Disulfoton sulfone 10.1 100.7 11.0 0.7 (E)-Pyriminobac-methyl 9.4 94.1 4.2 -5.9 Fenothiocarb 9.9 99.3 4.8 -0.7 Pyraflufen-ethyl 9.4 94.0 4.9 -6.0 Butachlor 10.0 99.7 4.8 -0.3 Hexazinone 9.5 94.5 4.5 -5.5 Ditalimfos 9.8 98.5 6.3 -1.5 Thenylchlor 9.5 95.0 5.3 -5.0 Imazamethabenz-methyl-1 10.5 104.9 7.2 4.9 Tebuconazole 8.9 89.2 3.0 -10.8 Flutriafol 9.1 90.6 4.0 -9.4 Diclofop-methyl 9.1 91.2 6.6 -8.8 Butamifos 10.0 100.2 3.3 0.2 Propargite-1 14.9 149.0 7.3 49.0 Imazamethabenz-methyl-2 8.4 84.4 11.5 -15.6 Propargite-2 14.2 142.1 7.3 42.1 Napropamide 8.4 84.5 5.6 -15.5 Diflufenican 9.6 96.4 3.7 -3.6 Tricyclazole 6.5 64.9 13.8 -35.1 Resmethrin-1 9.7 97.0 10.1 -3.0 Chlorfenson 9.4 93.6 6.7 -6.4 Piperonyl butoxide 9.3 92.5 5.5 -7.5 Fenamiphos 9.9 98.9 4.2 -1.1 Nitralin 37.7 376.8 0.9 276.8 Hexaconazole 9.9 98.9 9.8 -1.1 Epoxiconazole 8.7 87.2 6.0 -12.8 Flutolanil 9.2 91.9 5.2 -8.1 Resmethrin-2 (Bioresmethrin) 9.4 93.5 5.7 -6.5 Prothiofos 9.5 95.3 5.3 -4.7 Zoxamide 9.8 97.7 11.8 -2.3 Fludioxonil 8.6 86.3 7.0 -13.7 Mefenpyr-diethyl 9.6 95.6 4.8 -4.4 (E)-Metominostrobin 9.4 93.9 3.3 -6.1 Pyributicarb 9.2 91.9 4.4 -8.1 Isoprothiolane 8.7 86.6 6.5 -13.4 Chlomethoxyfen (Chlormethoxynil) 11.3 113.3 5.6 13.3 Pretilachlor 9.7 96.9 5.8 -3.1 Pyridaphenthion 9.9 99.5 7.4 -0.5 Profenofos 9.1 90.9 9.6 -9.1 Iprodione 9.2 92.2 8.5 -7.8 Uniconazole (Uniconazole-P) 9.5 95.1 4.7 -4.9 Bromuconazole-1 9.2 91.8 5.2 -8.2 Tribufos 9.4 93.6 4.5 -6.4 Phosmet 9.4 94.3 6.2 -5.7 Myclobutanil 8.9 89.3 5.2 -10.7 Tetramethrin-1 9.2 91.9 6.7 -8.1 Oxadiazon 9.3 92.8 6.4 -7.2 EPN 11.5 115.0 4.5 15.0 Flamprop-methyl 9.3 92.6 5.2 -7.4 Bromopropylate 9.6 96.3 3.5 -3.7 Thifluzamide 9.2 92.1 5.0 -7.9 Piperophos 9.5 94.8 5.0 -5.2 Carboxin 10.1 100.8 3.4 0.8 Bifenthrin 10.0 99.5 3.7 -0.5 Imibenconazole-debenzyl 10.1 101.3 11.0 1.3 Picolinafen 9.2 92.3 6.2 -7.7 Diclobutrazol 9.3 93.4 6.3 -6.6 Tetramethrin-2 9.2 92.3 4.9 -7.7 Flusilazole 9.2 91.6 5.8 -8.4 Fenoxycarb 8.6 86.4 12.9 -13.6 Buprofezin 9.5 95.4 7.2 -4.6 Bifenazate 9.3 93.4 10.9 -6.6 Azaconazole 8.9 89.0 6.0 -11.0 Methoxychlor 10.3 102.8 6.5 2.8 Bupirimate 9.8 98.1 4.6 -1.9 Etoxazole 9.3 93.0 14.1 -7.0 Oxyfluorfen 12.0 120.4 3.0 20.4 Fenamidone 8.9 89.0 5.3 -11.0 (Z)-Metominostrobin 9.2 91.9 3.6 -8.1 Fenpropathrin 9.8 98.2 4.4 -1.8 Kresoxim-methyl 9.5 94.8 6.9 -5.2 Indanofan 10.4 104.1 5.6 4.1 Cyproconazole-1 9.8 97.7 10.1 -2.3 Bromuconazole-2 8.5 85.0 6.7 -15.0 Cyproconazole-2 8.8 88.1 6.8 -11.9 Anilofos 10.4 104.0 7.1 4.0 Chlorfenapyr 9.6 95.6 8.7 -4.4 Tebufenpyrad 9.1 91.0 3.8 -9.0 Isoxathion 10.4 104.3 10.0 4.3 Bifenox 20.4 204.3 3.5 104.3 Table 2 Summary of Quantitative Performance Evaluation Results (Continued)
Application News Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness (%) Compound Name Mean conc. (ppb) Recovery (%) Repeatability (%RSD) Trueness （%) Clomeprop 8.7 87.0 6.2 -13.0 Cyfluthrin-1 11.4 113.6 3.7 13.6 Furametpyr 8.5 85.5 4.2 -14.5 Cyfluthrin-2 12.4 124.0 5.8 24.0 Phenothrin-1 9.1 90.6 7.3 -9.4 Cyfluthrin-3 12.5 124.8 3.3 24.8 Tetradifon 9.2 92.0 3.9 -8.0 Cyfluthrin-4 10.4 103.8 10.0 3.8 Iprodione metabolite 11.0 109.8 15.0 9.8 Cypermethrin-1 14.6 145.7 54.7 45.7 Phosalone 9.2 92.1 8.6 -7.9 Halfenprox 10.1 101.2 2.5 1.2 Phenothrin-2 9.4 93.6 6.2 -6.4 Cypermethrin-2 14.1 140.7 41.0 40.7 Leptophos 8.9 89.4 6.6 -10.6 Quizalofop-ethyl (Quizalofop-P-ethyl) 8.9 88.6 7.0 -11.4 Pentoxazone 9.3 93.0 5.5 -7.0 Cypermethrin-3 13.2 131.9 60.3 31.9 Azinphos-methyl 10.6 106.1 9.4 6.1 Flucythrinate-1 9.5 94.6 5.1 -5.4 Pyriproxyfen 10.0 100.2 11.9 0.2 Cypermethrin-4 15.0 150.2 62.4 50.2 Mefenacet 9.3 92.7 6.9 -7.3 Etofenprox 9.5 94.8 4.2 -5.2 Cyhalothrin-1 11.6 116.0 5.6 16.0 Flucythrinate-2 9.9 99.2 4.9 -0.8 Cyhalofop-butyl 8.6 85.8 5.4 -14.2 Silafluofen 8.9 89.2 5.6 -10.8 Furametpyr metabolite 8.1 80.8 8.9 -19.2 Fluridone 9.0 89.6 14.0 -10.4 Cyhalothrin-2 11.6 115.8 3.9 15.8 Pyrimidifen 8.9 88.8 3.9 -11.2 Acrinathrin-1 12.2 121.8 10.3 21.8 Flumioxazin 9.7 97.2 9.3 -2.8 Fenarimol 8.8 88.1 2.8 -11.9 Fenvalerate-1 10.9 109.0 5.1 9.0 Pyrazophos 9.0 89.6 4.8 -10.4 Pyraclostrobin 8.6 85.9 13.4 -14.1 Acrinathrin-2 10.9 108.7 5.9 8.7 Fluvalinate-1 10.4 103.9 7.0 3.9 Azinphos-ethyl 10.1 101.4 5.9 1.4 Fenvalerate-2 (Esfenvalerate) 10.8 108.1 6.8 8.1 Dialifos 9.8 97.7 4.5 -2.3 Fluvalinate-2 11.2 111.7 9.4 11.7 Pyraclofos 9.6 96.5 10.9 -3.5 Difenoconazole-1 8.5 84.9 6.7 -15.1 Fenoxaprop-ethyl (Fenoxaprop-P-ethyl) 9.6 96.3 6.8 -3.7 Difenoconazole-2 8.2 82.3 4.5 -17.7 Spirodiclofen 10.4 104.0 30.5 4.0 Pyrazoxyfen 5.8 57.9 23.9 -42.1 Bitertanol-1 9.5 94.8 10.3 -5.2 Deltamethrin-1 (Tralomethrin deg.-1) 11.3 113.0 6.2 13.0 Permethrin-1 8.6 86.3 3.3 -13.7 Indoxacarb 8.2 82.2 12.7 -17.8 Bitertanol-2 9.0 89.6 16.1 -10.4 Deltamethrin-2 (Tralomethrin deg.-2) 11.0 109.9 5.7 9.9 Fluquinconazole 8.8 88.4 3.8 -11.6 Flumiclorac-pentyl 9.2 92.5 4.9 -7.5 Pyridaben 8.9 89.3 5.0 -10.7 Azoxystrobin 9.1 90.9 8.6 -9.1 Permethrin-2 10.0 99.7 3.6 -0.3 Dimethomorph-1 9.5 95.0 4.6 -5.0 Dioxathion 10.1 100.7 4.2 0.7 Famoxadone 8.8 88.4 8.0 -11.6 Oxpoconazole 8.5 84.9 8.6 -15.1 Tolfenpyrad 9.4 94.4 4.3 -5.6 Butafenacil 9.7 97.2 4.9 -2.8 Dimethomorph-2 8.9 89.0 5.8 -11.0 Etobenzanid 9.3 92.8 5.4 -7.2 Imibenconazole 9.2 92.3 9.3 -7.7 Cafenstrole 11.3 113.2 9.7 13.2 Cinidon-ethyl 9.4 93.5 3.6 -6.5 Fenbuconazole 8.4 83.8 6.5 -16.2 Fluthiacet-methyl 11.4 113.5 11.6 13.5 Table 2 Summary of Quantitative Performance Evaluation Results (Continued)
Application News www.shimadzu.com/an/ Shimadzu Corporation © Shimadzu Corporation, 2026 For Research Use Only. Not for use in diagnostic procedures. This publication may contain references to products that are not available in your country. Please contact us to check the availability of these products in your country. The content of this publication shall not be reproduced, altered or sold for any commercial purpose without the written approval of Shimadzu. See https://www.shimadzu.com/about/trademarks/index.html for details. Third party trademarks and trade names may be used in this publication to refer to either the entities or their products/services, whether or not they are used with trademark symbol “TM” or “”. Shimadzu disclaims any proprietary interest in trademarks and trade names other than its own. The information contained herein is provided to you "as is" without warranty of any kind including without limitation warranties as to its accuracy or completeness. Shimadzu does not assume any responsibility or liability for any damage, whether direct or indirect, relating to the use of this publication. This publication is based upon the information available to Shimadzu on or before the date of publication, and subject to change without notice. 1.34e2 Q 313.90>285.90 (+) 24.3 24.4 24.5 24.6 24.7 24.8 0.00 % 100.00 1.43e2 Q 283.00>96.00 (+) 26.7 26.8 26.9 27.0 27.1 27.2 0.00 % 100.00 1.08e2 Q 364.10>147.10 (+) 35.1 35.2 35.3 35.4 35.5 35.6 35.7 0.00 % 100.00 5.24e2 Q 173.10>99.00 (+) 24.1 24.2 24.3 24.4 24.5 24.6 24.7 0.00 % 100.00 2.44e2 Q 277.00>260.00 (+) 23.6 23.7 23.8 23.9 24.0 24.1 0.00 % 100.00 1.44e2 Q 304.10>179.10 (+) 20.0 20.2 20.4 20.6 20.8 0.00 % 100.00 First Edition: Jul. 2026 01-01233-EN GCMS-TQ, Smart Pesticides Database and LabSolutions Insight are trademarks of Shimadzu Corporation or its affiliated companies in Japan and/or other countries. Pyridaben Procymidone Chlorpyrifos 0.1 ppb 0.5 ppb 0.1 ppb Fig. 11 Calibration Curves and Chromatograms at the Limit of Quantification (LOQ) 0.1 ppb 0.1 ppb 0.1 ppb Conc. (ppb) 0 100 200 300 400 Area 0.0e0 5.0e5 1.0e6 1.5e6 2.0e6 2.5e6 3.0e6 3.5e6 Malathion y = 8528.792x - 6127.447 R 2 = 0.9998143 R = 0.9999071 Malathion Conc. (ppb) 0 100 200 300 400 Area 0.0e0 2.0e5 4.0e5 6.0e5 8.0e5 1.0e6 1.2e6 Fenitrothion y = 3341.022x - 10136.34 R 2 = 0.9996909 R = 0.9998454 Fenitrothion Conc. (ppb) 0 100 200 300 400 Area 0.0e0 2.0e5 4.0e5 6.0e5 8.0e5 1.0e6 1.2e6 Diazinon y = 3198.240x - 641.4892 R 2 = 0.9999349 R = 0.9999674 Diazinon Conc. (ppb) 0 100 200 300 400 Area 0.0e0 1.0e5 2.0e5 3.0e5 4.0e5 5.0e5 6.0e5 7.0e5 8.0e5 9.0e5 Chlorpyrifos y = 2328.031x + 890.1781 R 2 = 0.9996288 R = 0.9998144 Conc. (ppb) 0 100 200 300 400 Area 0.0e0 2.0e5 4.0e5 6.0e5 8.0e5 1.0e6 1.2e6 1.4e6 1.6e6 1.8e6 2.0e6 Procymidone y = 5213.941x + 5285.381 R 2 = 0.9995439 R = 0.9997719 Conc. (ppb) 0 100 200 300 400 0.0e0 1.0e5 2.0e5 3.0e5 4.0e5 5.0e5 6.0e5 Pyridaben y = 1548.924x - 1570.198 R 2 = 0.9998198 R = 0.9999099 Area
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