Analysis of Organophosphorus Pesticides in Milk Using SPME and GC-MS/MS. No. GCMS No. SSI-GCMS Shilpi Chopra, Ph.D.

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1 Gas Chromatograph Mass Spectrometer No. GCMS-1603 Analysis of Organophosphorus Pesticides in Milk Using SPME and GC-MS/MS Shilpi Chopra, Ph.D. Introduction Organophosphorus (OP) pesticides are a class of insecticides known for their acute toxicity towards a large variety of insects, and a relatively lower persistence in the environment compared to organochlorine pesticides. Therefore, the OP class of pesticides are more commonly used on food crops, despite the higher toxicity they present towards mammals. These compounds inhibit acetylcholinesterase enzymes in the central nervous system, eventually causing paralysis. Exposure in humans occurs through ingestion of contaminated food products, thus it is important to develop analytical techniques for complex food matrices to ensure safe consumption levels for OP pesticides are maintained. Gas chromatography mass spectrometry (GC/MS) instrumentation is a powerful tool for the separation and identification of volatile and semi-volatile compounds, including many OP pesticides. Gas chromatography coupled to a triple quadrupole mass spectrometer (GC-MS/MS) allows for even greater separation, as the mass spectrometer is operated in the Multiple Reaction Monitoring (MRM) mode with two stages of mass analysis. When using MRM mode, the sample is ionized at the ion source and a single, high-mass high-intensity ion is selected in the first quadrupole (Q1); this fragment is referred to as a precursor ion. The precursor ion is focused toward the second quadrupole, also called the Collision Cell, where it collides with an inert gas (argon) and is fragmented to produce product ions. A single product ion is selected using the third quadrupole (Q3) and detected by the electron multiplier. This mode of detection virtually eliminates background interference, making GC-MS/MS a powerful technique with improved sensitivity, selectivity, and specificity. interfering matrices. Detection and quantitation of pesticides in food products fit these criteria, and complimentary sample preparation techniques are needed to simplify the analyses to increase sample throughput through automation. Solid Phase Micro- Extraction (SPME) has produced promising results when used as a sample preparation technique for complex matrices. In the SPME process, a fused silica fiber treated with a compound-specific coating is either exposed to the equilibrated sample headspace, or directly immersed into the sample. The analytes are then absorbed or adsorbed onto the fiber, and subsequently thermally desorbed from the fiber in the GC inlet. SPME involves various parameters, such as incubation temperature and time, desorption time and temperature, and extraction time and temperature. If not properly optimized, these can affect detection levels, which is why a statistical tool is needed to evaluate these parameters. The SPME technique has several desirable advantages; most importantly it does not require the use of harmful organic solvents, and it is easily automated. This application note describes development of a fully automated SPME extraction method combined with GC-MS/MS analysis for detection and quantitation of OP pesticides in a complex milk matrix. The Shimadzu GCMS-TQ8040 triple quadrupole mass spectrometer used for this application note is shown in Figure 1. The SPME method development and validation were fully automated using the Shimadzu AOC-6000 Multifunctional Autosampler. GC-MS/MS analytical instruments are widely used in applications requiring detection of specific target compounds at low concentrations in complex, Figure 1: Shimadzu GCMS-TQ8040 Used for Detection and Quantitation of OP Pesticides

2 Experimental Sample Preparation Standard solutions of eight individual OP pesticides (demeton-s-methyl, sulfotep, disulfoton, methyl parathion, dichlorvos, fenitrothion, chlorpyrifos, and ethion) were acquired from Absolute Standard, Inc. (Hamden, USA). A stock working solution was prepared with each pesticide at 100 µg/ml (partsper-million, ppm). Milk was purchased from a local grocery store, and spiked with the working solution to 1.25 µg/ml for method development. Lower concentrations, in the ng/ml (parts-per-billion, ppb) range were used for calibration and method validation. Headspace - Solid Phase Micro Extraction (HS-SPME) The HS-SPME extraction was performed using a fiber coated with polydimethylsiloxane-divinylbenzene (PDMS-DVB, 65 μm) (Supelco, Bellefonte, USA). Individual aliquots of milk were placed in 20-mL SPME vials and spiked with the OP pesticide mix to yield a constant concentration of 1.25 µg/ml. To minimize the number of individual analyses required, a half factorial experimental design was used to optimize the experimental variables affecting the SPME extraction. The half-factorial experimental design provides the maximum amount of information with the least number of analyses. It can be used to determine the effect from each independent parameter, and to identify any interaction effects when more than one parameter influences the observed result. Five SPME parameters were optimized: sample volume, desorption time, incubation temperature, incubation time (sample equilibration time prior to introducing the SPME fiber), and extraction time (exposure of the SPME fiber to the sample). Two test values were established for each parameter, one at the high end of the range (H) and one at the low end (L), as shown in Table 1. This type of two-level experimental design is called half fraction factorial design, or factorial design, and is a common technique because it provides the greatest amount of information with the fewest number of instrument analyses. It also will provides information required to advance to a multi-level response surface experiment if necessary. Blanks were run between samples to verify that there was no carry over. Addition of a salt was not tested because adequate detection levels were achieved without the salt. Table 1: Half Factorial Experimental Design for SPME Method Optimization using PDMS-DVB Fiber Sample Volume (ml) Desorption Time (min) Incubation Temperature ( C) Incubation Time (min) Extraction Time (min) High Parameter (H) Low Parameter (L) Run # X1 Sample Volume X2 Desorption Time X3 Incubation Temperature X4 Incubation Time 1 H H H H H 2 H H H L L 3 H H L L H 4 H L L L L 5 L L L L H 6 H L H H L 7 H L L H H 8 H H L H L 9 L L L H L 10 L L H H H 11 L H H H L 12 H L H L H 13 L H L H H 14 L L H L L 15 L H L L L 16 L H H L H X5 Extraction Time

3 GC-MS/MS The analyses were performed on a Shimadzu GCMS- TQ8040. Analytes were desorbed from the SPME fiber with inlet temperature of 250 C and using a splitless injection. Chromatographic separation was achieved using a capillary column (SH-Rxi-5HT Column; 30 m 0.25 mm ID 0.25 μm film) with helium carrier gas and a flow rate of 1.44 ml/minute. The oven temperature was programmed, with an initial temperature of 80 C for 3 minutes and a temperature ramp of 15 C/minute to 280 C, followed by a 2 minute hold. The GC-MS/MS was operated in the Multiple Reaction Monitoring (MRM) mode. Three transitions were monitored for each target compound, one for quantitation, and two additional transitions for confirmation. The Shimadzu Smart Pesticide Database was used as the foundation for creating the MRM analysis method. The Smart Pesticide Database includes up to six fully optimized MRM transitions and collision energies (CEs) for 479 pesticides and Retention Indices (RI) for accurately predicting compound retention times. The ion source was kept at 200 C, with an interface temperature of 260 C and solvent delay time of 3 minutes. The GC- MS/MS parameters are summarized in Table 2. Table 2: Optimized GC-MS/MS Operating Parameters GC-MS/MS Parameter Optimized Value Inlet Temperature 250 C Constant Linear Velocity Mode Column Flow 1.44 ml/minute Injection Mode Splitless Oven temperature Program 80 C (3 minutes) 15 C/minute to 280 C (2 minutes) Interface Temperature 260 C Ion Source Temperature 200 C MS/MS Mode MRM Results and Discussion GC-MS/MS The MRM method was established by using the three most prominent MRM transitions for the OP pesticides found in the Smart Database; compound retention times were predicted for the method using the principle of Retention Indices. Figure 2 shows an MRM chromatogram of the 8-pesticide mix at 10 µg/ml, with all peaks baseline resolved. Figure 2: MRM Chromatogram of the Eight OP Pesticides at 10 µg/ml

4 HS-SPME Method Development The results obtained from the evaluation of the significant parameters by half factorial design are summarized in the Pareto Chart of Standardized Effects, shown in Figure 3. The Pareto chart for each OP Pesticide shows the influence that the most significant of the five test parameters had on the instrument response for that specific compound, as well as any possible cross, or interactive effects among these parameters. Pareto charts illustrate the magnitude and the importance of any individual parameter, or combination of parameters, on the instrument response. Parameters with significant effects are labeled A, B, and C, along the Y-axis, with the length of the bar along the X-axis illustrating the relative magnitude of that effect. Where a combination of two or more parameters is determined to have a significant effect, it is included and labeled AB, AC, BC, etc. The horizontal bar for each parameter represents the normalized, or relative value of that effect. A vertical reference line on the chart indicates that any parameter which extends beyond this value is potentially important for the optimization of the instrumental method. Figure 3: Pareto Chart of Standardized Effects for 8 OP Pesticides Extracted by HS-SPME

5 As can be seen by the Pareto charts in Figure 3, only two of the five test variables were found to consistently have a significant effect on extraction of the pesticides during the SPME method development, as indicated by extension of the bar beyond the vertical red line. The Incubation Temperature (parameter A) crosses the vertical red reference line for seven of the eight pesticides, indicating that it was a decisive factor for HS-SPME extraction experiment. This is in agreement with HS- SPME principle, as the incubation temperature is a key factor for driving pesticides from sample into the headspace during the equilibration step. Extraction Time (parameter B) was significant for 6 of the 8 pesticides, indicating that it was also an important factor for SPME extraction of pesticides from the milk. The remaining 3 parameters, Incubation Time, Sample Volume, and Desorption Time, were found to have little or no effect on the final results using this approach. The optimized HS- SPME parameters are shown in Table 3. Table 3: Optimized HS-SPME Parameters SPME Parameter Incubation Temperature Incubation Time Extraction Time Desorption Time Sample Volume In addition to the Pareto charts, a three-dimensional surface plot of the factors can provide additional information. A surface plot displays a threedimensional view that provides a clear picture of the relationship between two variables. If the regression model contains only the main effect and no interaction effect, the fitted response will be flat plane. If the model contains interaction effects, it will be a curved surface. The response surface graph obtained from the results of the fractional factorial design for the OP pesticides revealed that there is a relationship between the Incubation Temperature (A) and Extraction Time (B). This effect is seen on the Pareto Charts by the bar labeled AB extending beyond the vertical line for six of the eight pesticides, and is further illustrated in the example of Dichlovos shown in Figure 4. The x- and y-axes are Incubation Temperature (A) and Extraction Time (B), respectively, and the z-axis is compound response measured as peak area. Optimized Value 100 o C 10 minutes 60 minutes 3 minutes 8 ml Figure 4: Response Surface of Diclovos

6 Method Validation Calibration standards were prepared and analyzed using the optimized HS-SPME and GC-MS/MS parameters, with concentrations ranging from 20 to 1000 µg/l (ppb). Figure 5 shows the individual calibrations curves for all 8 pesticides, with the corresponding correlation coefficients illustrating linearity. Figure 5: Calibration Plots for all 8 Pesticides in Milk using HS-SPME and GC-MS/MS

7 Of the eight OP pesticides analyzed, seven were linear across the calibration range with correlation coefficients above 0.99, while one compound, demeton-s-methyl, displayed linearity only from µg/l using this method. There are two possible reasons for the non-linear response of demeton-smethyl. First, while optimizing the SPME method, it was observed that the peak area for demeton-smethyl increased considerably at low extraction temperatures, and presented comparatively lower peak areas at the higher temperatures. Using a liquid syringe injection technique for comparison, there were no unexpected peaks in the chromatogram. However, when using the HS-SPME extraction technique, an additional peak was seen at minutes, which is most likely a degradation product of the target analyte caused by the higher temperatures. (Figure 6.) The second, less likely possibility could be attributed to the chemistry of the fiber. As PDMS-DVB operates on an adsorption mechanism, competition for adsorption sites may explain the loss of response for Demeton-S-methyl Figure 6: Comparison of MRM Chromatograms of Demeton-S-methyl Analyzed using Liquid Syringe Injection (6a) and HS-SPME (6b) Table 4 lists the method validation statistics as defined by IUPAC guidelines, which include linearity, repeatability as % RSD, and accuracy as % Recovery for extraction of pesticides in milk by the HS-SPME method developed here. All pesticides had repeatability of less than 12.8 %, and recovery between 82 and 105 %, as calculated using a spiking level of 20 µg/l.

8 Table 4: Method Validation Statistics Pesticide Conc. in µg/l (R 2 ) % RSD (20 µg/l) % Recovery (20 µg/l) Dicholorovos (0.998) Demeton-S-Methyl (0.993) Sulfotep (0.998) Disulfoton (0.995) Methyl Parathion (0.997) Fenitrothin (0.996) Chlorpyrifos (0.998) Ethion (0.998) Conclusions A method based on HS-SPME, presented here for the analysis of organophosphorus pesticides in milk, was developed using a fully automated AOC-6000 Autosampler. SPME- GC-MS/MS in multiple reaction monitoring (MRM) mode was used for the trace analysis of pesticides in milk. The method was calibrated from 20 µg/l to 1000 µg/l, and all validation statistics were within an acceptable range. This is attributed to effective extraction using automated SPME which ensures precision by eliminating analysis variables in sample preparation and MS-MS which allows monitoring of transitions specific to each pesticide, delivering high selectivity and resulting low noise. References 1. The Smart Forensic Database, Shimadzu Corporation (Japan), is part of the Smart Database Series. The database contains 480 pesticides and approximately 2,680 transitions, and eliminates the need to configure complicated analysis conditions. Retention Indices (RI) are registered for all components, enabling easy updating of retention times via the AART function. Analysis by the internal standard method is also supported, with MRM transitions and RIs registered for compounds commonly used as internal standards for pesticides. 2. Antony,Jiju: Design of Experiments for Engineers and Scientists: Butterworth- Heinemann, Sep 5, 2003, Pages Volante, Marco; Pontello, Mirella: Application of Solid Phase Micro-Extraction (SPME) to the analysis of pesticide residues in vegetables: 56(7); , First Edition: January 2016 SHIMADZU Corporation SHIMADZU SCIENTIFIC INSTRUMENTS 7102 Riverwood Drive, Columbia, MD 21046, USA Phone: / , Fax: URL: For Research Use Only. Not for use in diagnostic procedures. The contents of this publication are provided to you as is without warranty of any kind, and are subject to change without notice. Shimadzu does not assume any responsibility or liability for any damage, whether direct or indirect, relating to the use of this publication. Shimadzu Corporation, 2016

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