A subscription to JoVE is required to view this content. Sign in or start your free trial.
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 equ...
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 f...
The authors have no conflicts of interest to disclose.
We would like to thank EAN University and the University of La Laguna.
Name | Company | Catalog Number | Comments |
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 |
Request permission to reuse the text or figures of this JoVE article
Request PermissionExplore More Articles
This article has been published
Video Coming Soon
Copyright © 2025 MyJoVE Corporation. All rights reserved