The present protocol describes the analysis of multiclass pesticide residues in avocado varieties using the Quick-Easy-Cheap-Effective-Rugged-Safe (QuEChERS) method with ammonium formate, followed by gas chromatography-tandem mass spectrometry.
Gas chromatography (GC) tandem mass spectrometry (MS/MS) stands as a preeminent analytical instrument extensively employed for the surveillance of pesticide residues in food. Nevertheless, these methods are vulnerable to matrix effects (MEs), which can potentially affect accurate quantification depending on the specific combination of analyte and matrix. Among the various strategies to mitigate MEs, matrix-matched calibration represents the prevailing approach in pesticide residue applications due to its cost-effectiveness and straightforward implementation. In this study, a total of 45 representative pesticides were analyzed in three different varieties of avocado (i.e., Criollo, Hass, and Lorena) using the Quick-Easy-Cheap-Effective-Rugged-Safe (QuEChERS) method with ammonium formate and GC-MS/MS.
For this purpose, 5 g of the avocado sample was extracted with 10 mL of acetonitrile, and then 2.5 g of ammonium formate was added to induce phase separation. Subsequently, the supernatant underwent a cleanup process via dispersive solid-phase extraction employing 150 mg of anhydrous MgSO4, 50 mg of primary-secondary amine, 50 mg of octadecylsilane, 10 mg of graphitized carbon black, and 60 mg of a zirconium oxide-based sorbent (Z-Sep+). The GC-MS/MS analysis was successfully performed in less than 25 min. Rigorous validation experiments were carried out to assess the performance of the method. The examination of a matrix-matched calibration curve for each variety of avocado revealed that the ME remained relatively consistent and less than 20% (considered as a soft ME) for most pesticide/variety combinations. Furthermore, the method´s limits of quantification were lower than 5 µg/kg for all three varieties. Finally, the recovery values for most pesticides fell within the acceptable range of 70-120%, with relative standard deviation values below 20%.
In chemical analysis, the matrix effect (ME) can be defined in various ways, but a widely accepted general definition is as follows: it refers to the change in the signal, particularly a change in the slope of the calibration curve when the sample matrix or portion of it is present during the analysis of a specific analyte. As a critical aspect, ME necessitates thorough investigation during the validation process of any analytical method, as it directly affects the accuracy of quantitative measurement for the target analytes1. Ideally, a sample pretreatment procedure should be selective enough to avoid extracting any components from the sample matrix. However, despite significant efforts, many of these matrix components still end up in the final determination systems in most cases. Consequently, such matrix components often compromise the recovery and precision values, introduce additional noise, and escalate the overall cost and labor involved in the method.
In gas chromatography (GC), ME arises due to the presence of active sites within the GC system, which interact with the target analytes through various mechanisms. On the one hand, the matrix constituents block or mask these active sites that would otherwise interact with the target analytes, resulting in frequent signal enhancement2. On the other hand, active sites that remain unobstructed may cause peak tailing or analyte decomposition due to strong interactions, leading to a negative ME. However, this can offer potential benefits in certain cases2. It is crucial to emphasize that achieving complete inertness in a GC system is exceedingly challenging, despite using highly inert components and proper maintenance. With continuous use, the accumulation of matrix components in the GC system becomes more pronounced, causing an increased ME. Nowadays, it is widely recognized that analytes containing oxygen, nitrogen, phosphorus, sulfur, and similar elements, exhibit a greater ME as they readily interact with these active sites. Conversely, highly stable compounds such as hydrocarbons or organohalogens do not undergo such interactions and do not show observable ME during analysis2,3.
Overall, ME cannot be fully eliminated, leading to the development of several strategies for compensation or correction when complete removal of matrix components is not feasible. Among these strategies, the utilization of deuterated internal standards (ISs), analyte protectants, matrix-matched calibration, the standard addition method, or the modification of injection techniques have been documented in scientific literature1,2,4,5. The SANTE/11312/2021 guidelines have also recommended these strategies6.
Regarding the application of matrix-matched calibration to compensate for MEs, sample sequences in practical situations encompass diverse types of foods or various samples from the same commodity. In this case, the assumption is made that employing any sample from the same commodity will effectively compensate for ME in all samples. However, there is a lack of sufficient studies in the existing literature that specifically investigate this issue7.
The multiresidue determination of pesticides in matrices containing an appreciable percentage of fat and pigments constitutes a challenging task. The considerable amount of coextracted material can significantly affect the extraction efficiency and interfere with the subsequent chromatographic determination, potentially damaging the column, source, and detector, and resulting in significant MEs8,9,10. Consequently, the analysis of pesticides at trace levels in such matrices necessitates a significant reduction of matrix components before analysis while ensuring high recovery values7. Obtaining high recovery values is crucial to ensure that pesticide analyses remain reliable, accurate, and compliant with regulatory standards. This is vital for ensuring food safety, environmental protection, and informed decision-making in agriculture and related fields.
Avocado is a fruit of high commercial value cultivated in tropical and Mediterranean climates worldwide and widely consumed both in its regions of origin and in the numerous export markets. From the analytical point of view, avocado is a complex matrix containing a significant number of fatty acids (i.e., oleic, palmitic, and linoleic), similar to nuts, a significant pigment content, as in green leaves, as well as sugars and organic acids, similar to those found in other fruits11. Due to its fatty nature, special attention must be given when employing any analytical method for analysis. While pesticide residue analysis has been conducted on avocados using GC-MS in some instances8,12,13,14,15,16,17,18,19,20, it has been relatively less frequent compared to other matrices. In most cases, a version of the Quick-Easy-Cheap-Effective-Rugged-Safe (QuEChERS) method has been applied8,12,13,14,15,16,17,18. None of these studies have investigated the consistency of MEs among different avocado varieties.
Therefore, the aim of this work was to study the consistency of MEs and recovery values for 45 representative pesticides across different varieties of avocado (i.e., Criollo, Hass, and Lorena) using the QuEChERS method with ammonium formate and GC-MS/MS. To the best of our knowledge, this is the first time that this type of study has been conducted on such fatty matrix samples.
1. Preparation of the stock and working solutions
NOTE: For safety reasons, it is advisable to wear nitrile gloves, a laboratory coat, and safety glasses throughout the protocol.
2. Sample collection
3. Sample preparation utilizing the QuEChERS method with ammonium formate
NOTE: Figure 1 illustrates a schematic representation of the QuEChERS method with ammonium formate.
4. Instrumental analysis using GC-MS/MS
5. Data acquisition
Comprehensive validation of the analytical method was conducted according to SANTE/11312/2021 guidelines6, encompassing assessments of linearity, ME, recovery, and repeatability.
For the linearity assessment, matrix-matched calibration curves were constructed using spiked blank samples at multiple concentration levels (ranging from 5 to 600 µg/kg). The determination coefficients (R2) for most of the selected pesticides were found to be higher than or equal to 0.99, indicating a highly linear relationship between concentration and response. The lowest calibration level (LCL) of 5 µg/kg was chosen, adhering to the established maximum residue limit (MRL) established of 10 µg/kg for food monitoring purposes22.
To evaluate the ME, the slopes of the multiclass pesticides´ calibration curves were compared between pure solvent and matrix-matched calibration conditions. As an illustrative example, Figure 2 shows the comparison of the curves in the solvent and each of the three matrices for carbofuran. The ME was calculated using equation (1)7, yielding percentages that signify signal enhancement (positive percentages) or signal suppression (negative percentages).
Matrix effect (%) = (1)
The presented ME classification system, based on percentage ranges, provides insights into the impact of the matrix on the pesticide signals, aiding in the interpretation of analytical findings. In all cases for carbofuran, a positive ME greater than 20% was obtained. However, the findings from the generation of matrix-matched calibration curves revealed a relatively consistent ME of less than 20% (classified as a soft ME) for most pesticide/variety combinations (see Table 2 and Figure 3).
To evaluate the accuracy and repeatability of the analysis, blank samples were spiked with pesticides at three different concentration levels (10, 100, and 400 µg/kg; n = 5 for each concentration). The results in Figure 4 demonstrate the count of pesticides whose average recovery percentages were within the acceptable range of 70-120% for each type of avocado. Furthermore, Table 3 presents detailed data for all the specific values obtained. A significant proportion of the tested pesticides exhibited recovery percentages falling within the specific range, with relative standard deviation (RSD) values below 20%.
Figure 1: Schematic representation of the QuEChERS method with ammonium formate employed for the extraction of pesticide residues from avocado samples. Abbreviations: QuEChERS = Quick-Easy-Cheap-Effective-Rugged-Safe; IS = internal standard; PSA = primary-secondary amine; GCB = graphitized carbon black; QC = quality control; GC-MS/MS = gas chromatography-tandem mass spectrometry. Please click here to view a larger version of this figure.
Figure 2: Comparison of the calibration curves in the solvent and matrices for carbofuran. Solvent: y = 0.0028x – 0.0054 and R2 = 0.9974; Criollo: y = 0.0050x + 0.0050, R2 = 0.9994, and ME = 80%; Hass: y = 0.0037x – 0.0109, R2 = 0.9977, and ME = 30%; Lorena: y = 0.0041x + 0.0053, R2 = 0.9998, and ME = 42%. Abbreviations: ME = matrix effect; P-IS = procedural internal standard. Please click here to view a larger version of this figure.
Figure 3: Number of selected pesticides categorized by their respective ranges of ME for avocado varieties. The classification of ME is based on three categories: soft (values between −20% and 20%), medium (values ranging between −20% and −50% or between 20% and 50%), and strong (values exceeding 50% or falling below −50%). Abbreviation: ME = matrix effect. Please click here to view a larger version of this figure.
Figure 4: Number of pesticides that fall outside and within the acceptable recovery range spiked at 10, 100, and 400 µg/kg (n = 15) in the three avocado varieties. Please click here to view a larger version of this figure.
Table 1: Retention times, quantifier, and qualifier transitions utilized in GC-MS/MS analyses of the selected pesticides, along with the P-IS and I-IS. Abbreviations: P-IS = procedural internal standard; I-IS = injection internal standard; GC-MS/MS = gas chromatography-tandem mass spectrometry; HCB = hexachlorobenzene; α-HCH = alpha-hexachlorocyclohexane; β-HCH = beta-hexachlorocyclohexane; 4,4´-DDD = 4,4´-dichlorodiphenyldichloroethane; 4,4´-DDE = 4,4´-dichlorodiphenyldichloroethylene; 4,4´-DDT = 4,4´-dichlorodiphenyltrichloroethane; TPP = triphenyl phosphate; EPN = ethyl nitrophenyl phenylphosphonothioate. Please click here to download this Table.
Table 2: Matrix effect values (%) for the selected pesticides in different avocado varieties during the validation of the final analytical method. Abbreviations: HCB = hexachlorobenzene; α-HCH = alpha-hexachlorocyclohexane; β-HCH = beta-hexachlorocyclohexane; 4,4´-DDD = 4,4´-dichlorodiphenyldichloroethane; 4,4´-DDE = 4,4´-dichlorodiphenyldichloroethylene; 4,4´-DDT = 4,4´-dichlorodiphenyltrichloroethane; TPP = triphenyl phosphate; EPN = ethyl nitrophenyl phenylphosphonothioate. Please click here to download this Table.
Table 3: Recovery values and their corresponding RSDs in parentheses (n = 5 at each spiking level), both in %, for the selected pesticides in different avocado varieties during the validation of the final analytical method. Abbreviations: RSDs = relative standard deviations; HCB = hexachlorobenzene; α-HCH = alpha-hexachlorocyclohexane; β-HCH = beta-hexachlorocyclohexane; 4,4´-DDD = 4,4´-dichlorodiphenyldichloroethane; 4,4´-DDE = 4,4´-dichlorodiphenyldichloroethylene; 4,4´-DDT = 4,4´-dichlorodiphenyltrichloroethane; TPP = triphenyl phosphate; EPN = ethyl nitrophenyl phenylphosphonothioate. Please click here to download this Table.
Supplementary File 1: Mass spectrometric spectra of all pesticides. Abbreviations: HCB = hexachlorobenzene; α-HCH = alpha-hexachlorocyclohexane; β-HCH = beta-hexachlorocyclohexane; 4,4´-DDD = 4,4´-dichlorodiphenyldichloroethane; 4,4´-DDE = 4,4´-dichlorodiphenyldichloroethylene; 4,4´-DDT = 4,4´-dichlorodiphenyltrichloroethane; EPN = ethyl nitrophenyl phenylphosphonothioate. Please click here to download this File.
The primary limitation associated with matrix-matched calibration arises from the use of blank samples as calibration standards. This leads to an augmented number of samples to be processed for analysis and an increased injection of matrix components in each analytical sequence, potentially leading to higher instrument maintenance demands. Nonetheless, this strategy is more suitable than standard addition, which would generate a much larger number of samples to be injected due to the need to perform a calibration curve for each sample. Consequently, in both cases, the use of sample preparation techniques that minimize such co-extraction while remaining cost-effective, fast, and reliable is required. In this context, the QuEChERS method has demonstrated its usefulness in analyzing pesticide residues in avocado samples8,12,13,14,15,16,17,18. However, none of those approaches have explored the application of the QuEChERS method employing ammonium formate. This choice aims to mitigate the drawbacks of using magnesium and sodium salts in MS analysis23,24,25,26,27. Both magnesium and sodium salts have low vapor pressures that have the propensity to form solid deposits on surfaces within the MS source, which may potentially affect instrument performance. While this phenomenon occurs in liquid chromatography (LC) systems, it also poses challenges in the context of GC, where these can accumulate in the inlet liner, necessitating more frequent replacements of the liner27. To overcome these limitations and enhance compatibility with MS detection, the substitution of these salts with highly volatile alternatives has been implemented. Ammonium salts are preferred as they can be easily evaporated and/or decomposed, thereby overcoming the disadvantages. The current investigation represents the first instance of utilizing the QuEChERS method employing ammonium formate for the analysis of pesticide residues in avocados. In particular, the extraction process comprised subjecting the avocado sample to an extraction step using acetonitrile, with the addition of 0.5 g of ammonium formate per gram of sample to facilitate salting out (Figure 1).
As the second step of the QuEChERS method, the dSPE step is crucial because it serves to remove undesired matrix components that could potentially lead to analytical interferences26. However, achieving an effective d-SPE step often requires a combination of various sorbents to address the diverse co-extractives originating from the sample matrix. When dealing with avocados, this step may include anhydrous MgSO4 to remove excess water and improve pesticide partitioning, PSA to eliminate fatty acids, organic acids, and sugars, C18 to enhance the removal of nonpolar components, GCB for chlorophyll removal, and zirconia materials such as Z-Sep+ to eliminate high amounts of fat15,26,28. Consequently, the avocado extracts were transferred to centrifuge tubes containing specific amounts of each sorbent: 150 mg of anhydrous MgSO4, 50 mg of PSA, 50 mg of C18, 10 mg of GCB, and 60 mg of Z-Sep+ (Figure 1).
To initiate the validation process involving the extraction and cleaning steps, the calibration curves were rigorously examined. This involved assessing matrix-matched calibration curves for each analyte/avocado variety combination, in addition to acetonitrile-only calibrations (Figure 2). In both scenarios, a previously proposed analyte protectants mixture29, consisting of 3-ethoxy-1,2-propanediol, L-gulonic acid γ-lactone, D-sorbitol, and shikimic acid, was employed. The evaluation encompassed linearity across a 5 to 600 µg/kg concentration range. The LCL of 5 µg/kg falls below the stringent MRL of 10 µg/kg as set by international regulations governing the analysis of pesticide residues in food commodities22. Furthermore, the LCL of 5 µg/kg yielded a signal-to-noise ratio higher than 10 for all the selected multiclass pesticides. Visual inspection of calibration plots was also performed to verify the precision of slope values employed for calculating ME. Results indicated that most of the selected pesticides exhibited R2 values higher than or equal to 0.99 across all four calibration curves for each of them. The overall assessment of calibration outcomes demonstrated the accuracy and suitability of these equations for precise ME calculations in each avocado variety.
The ME was determined to be soft (ME ≤ 20%) for most of the pesticides in each of the three avocado varieties under investigation (Table 2 and Figure 3). In this context, three key points are worth highlighting. Firstly, the final sample extracts were relatively clean due to the effectiveness of the implemented sample preparation protocol, thereby resulting in minimal interferences. Secondly, in GC systems, MEs are subject to the influences stemming from interactions transpiring within the matrix and the interactions occurring at active sites within the system29. The mixture of analyte protectants used comprehensively covered nearly the entire spectrum of pesticides. However, pesticides eluting early (propoxur, dichlorvos, carbofuran, and diphenylamine), as well as those eluting later (pyriproxyfen, fenvalerate, esfenvalerate, and deltamethrin), exhibited the highest and less consistent ME values. Thirdly, considering these differences, it was decided to utilize the matrix-matched calibration of each variety separately for conducting the recovery study. It is important to note that one variety can reasonably represent the other varieties for the remaining pesticides.
The recovery and reproducibility assessment were performed at three different concentration levels (10, 100, and 400 µg/kg) in quintuplicate (n = 15). To achieve this, avocado samples were spiked at the beginning of the application of the QuEChERS method. Recoveries were calculated by comparing the ratios of pesticide peak area to the peak of the P-IS (atrazine-d5) obtained from matrix-matched calibration. Each replicate was injected in triplicate within the same sequence to ensure consistency. The use of an isotopically labeled IS enables compensation for potential pesticide losses during the protocol, while also accounting for methodological errors and instrumental variability. The results showed that most pesticides met the acceptable criteria, with recoveries ranging from 70 to 120% and RSD below 20% at each spiking level6 (Figure 4), indicating the method's effectiveness and repeatability. However, certain pesticides exhibited recoveries beyond this acceptable range (Table 3). This is the hexachlorobenzene (HCB) case, showing recoveries in the range of 28-55% for all concentration levels and matrices. This can be attributed to HCB's planar molecular structure, which leads to a strong affinity with GCB, causing its retention and reducing extraction efficiency30. Despite the lower recoveries for HCB and a few other cases, the method still demonstrated consistent and reliable recovery for these pesticides, with RSD values remaining below the recommended limit.
In conclusion, the analysis of pesticide residues in food samples encounters ME, which can impact the accuracy of GC-MS/MS. Matrix-matched calibration proves to be a straightforward and effective strategy for mitigating these effects, even in matrices such as avocados, which are rich in fatty acids and other co-extractive materials such as pigments. Through the application of the QuEChERS method employing ammonium formate together with matrix-matched calibration and analyte protectants, highly accurate quantification is achieved. Consequently, this approach ensures reliable and enforceable pesticide residue analysis in avocado samples, making it suitable for regulatory applications.
The authors have nothing to disclose.
We would like to thank EAN University and the University of La Laguna.
3-Ethoxy-1,2-propanediol | Sigma Aldrich | 260428-1G | |
Acetonitrile | Merk | 1006652500 | |
Ammonium formate | Sigma Aldrich | 156264-1KG | |
AOAC 20i/s autosampler | Shimadzu | 221-723115-58 | |
Automatic shaker MX-T6-PRO | SCILOGEX | 8.23222E+11 | |
Balance | OHAUS | PA224 | |
Centrifuge tubes, 15 mL | Nest | 601002 | |
Centrifuge tubes, 2 mL | Eppendorf | 4610-1815 | |
Centrifuge tubes, 50 mL | Nest | 602002 | |
Centrifuge Z206A | MERMLE | 6019500118 | |
Choper 2L | Oster | 2114111 | |
Column SH-Rxi-5sil MS, 30 m x 0.25 mm, 0.25 µm | Shimadzu | 221-75954-30 | MS GC column |
Dispensette 5-50 mL | BRAND | 4600361 | |
DSC-18 | Sigma Aldrich | 52600-U | |
D-Sorbitol | Sigma Aldrich | 240850-5G | |
Ethyl acetate | Merk | 1313181212 | |
GCMS-TQ8040 | Shimadzu | 211552 | |
Graphitized carbon black | Sigma Aldrich | 57210-U | |
Injection syringe | Shimadzu | LC2213461800 | |
L-Gulonic acid γ-lactone | Sigma Aldrich | 310301-5G | |
Linner splitless | Shimadzu | 221-4887-02 | |
Magnesium sulfate anhydrus | Sigma Aldrich | M7506-2KG | |
Methanol | Panreac | 131091.12.12 | |
Milli-Q ultrapure (type 1) water | Millipore | F4H4783518 | |
Pipette tips 10 – 100 µL | Biologix | 200010 | |
Pipette tips 100 – 1000 µL | Brand | 541287 | |
Pipette tips 20 – 200 µL | Brand | 732028 | |
Pipettes Pasteur | NORMAX | 5426023 | |
Pippette Transferpette S variabel 10 – 100 µL | BRAND | 704774 | |
Pippette Transferpette S variabel 100 – 1000 µL | BRAND | 704780 | |
Pippette Transferpette S variabel 20 – 200 µL | SCILOGEX | 7.12111E+11 | |
Primary-secondary amine | Sigma Aldrich | 52738-U | |
Shikimic acid | Sigma Aldrich | S5375-1G | |
Syringe Filter PTFE/L 25 mm, 0.45 µm | NORMAX | FE2545I | |
Triphenyl phosphate (QC) | Sigma Aldrich | 241288-50G | |
Vials with fused-in insert | Sigma Aldrich | 29398-U | |
Z-SEP+ | Sigma Aldrich | 55299-U | zirconium oxide-based sorbent |
Pesticides | CAS registry number | ||
4,4´-DDD | Sigma Aldrich | 35486-250MG | 72-54-8 |
4,4´-DDE | Sigma Aldrich | 35487-100MG | 72-55-9 |
4,4´-DDT | Sigma Aldrich | 31041-100MG | 50-29-3 |
Alachlor | Sigma Aldrich | 45316-250MG | 15972-60-8 |
Aldrin | Sigma Aldrich | 36666-25MG | 309-00-2 |
Atrazine | Sigma Aldrich | 45330-250MG-R | 1912-24-9 |
Atrazine-d5 (IS) | Sigma Aldrich | 34053-10MG-R | 163165-75-1 |
Buprofezin | Sigma Aldrich | 37886-100MG | 69327-76-0 |
Carbofuran | Sigma Aldrich | 32056-250-MG | 1563-66-2 |
Chlorpropham | Sigma Aldrich | 45393-250MG | 101-21-3 |
Chlorpyrifos | Sigma Aldrich | 45395-100MG | 2921-88-2 |
Chlorpyrifos-methyl | Sigma Aldrich | 45396-250MG | 5598-13-0 |
Deltamethrin | Sigma Aldrich | 45423-250MG | 52918-63-5 |
Dichloran | Sigma Aldrich | 45435-250MG | 99-30-9 |
Dichlorvos | Sigma Aldrich | 45441-250MG | 62-73-7 |
Dieldrin | Sigma Aldrich | 33491-100MG-R | 60-57-1 |
Diphenylamine | Sigma Aldrich | 45456-250MG | 122-39–4 |
Endosulfan A | Sigma Aldrich | 32015-250MG | 115-29-7 |
Endrin | Sigma Aldrich | 32014-250MG | 72-20-8 |
EPN | Sigma Aldrich | 36503-100MG | 2104-64-5 |
Esfenvalerate | Sigma Aldrich | 46277-100MG | 66230-04-4 |
Ethion | Sigma Aldrich | 45477-250MG | 563-12-2 |
Fenamiphos | Sigma Aldrich | 45483-250MG | 22224-92-6 |
Fenitrothion | Sigma Aldrich | 45487-250MG | 122-14-5 |
Fenthion | Sigma Aldrich | 36552-250MG | 55-38-9 |
Fenvalerate | Sigma Aldrich | 45495-250MG | 51630-58-1 |
HCB | Sigma Aldrich | 45522-250MG | 118-74-1 |
Iprodione | Sigma Aldrich | 36132-100MG | 36734-19-7 |
Lindane | Sigma Aldrich | 45548-250MG | 58-89-9 |
Malathion | Sigma Aldrich | 36143-100MG | 121-75-5 |
Metalaxyl | Sigma Aldrich | 32012-100MG | 57837-19-1 |
Methidathion | Sigma Aldrich | 36158-100MG | 950-37-8 |
Myclobutanil | Sigma Aldrich | 34360-100MG | 88671-89-0 |
Oxyfluorfen | Sigma Aldrich | 35031-100MG | 42874-03-3 |
Parathion-methyl | Sigma Aldrich | 36187-100MG | 298-00-0 |
Penconazol | Sigma Aldrich | 36189-100MG | 66246-88-6 |
Pirimiphos-methyl | Sigma Aldrich | 32058-250MG | 29232-93-7 |
Propiconazole | Sigma Aldrich | 45642-250MG | 60207-90-1 |
Propoxur | Sigma Aldrich | 45644-250MG | 114-26-1 |
Propyzamide | Sigma Aldrich | 45645-250MG | 23850-58-5 |
Pyriproxifen | Sigma Aldrich | 34174-100MG | 95737-68-1 |
Tolclofos-methyl | Sigma Aldrich | 31209-250MG | 5701804-9 |
Triadimefon | Sigma Aldrich | 45693-250MG | 43121-43-3 |
Triflumizole | Sigma Aldrich | 32611-100MG | 68694-11-1 |
α-HCH | Sigma Aldrich | 33377-50MG | 319-86-8 |
β-HCH | Sigma Aldrich | 33376-100MG | 319-85-7 |