Quick monitoring of routine samples of foods in the regulatory analysis of pesticide residues

Quick monitoring of routine samples of foods in the regulatory analysis of pesticide residues | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quick monitoring of routine samples of foods in the regulatory analysis of pesticide residues José Manuel Veiga-del-Baño, José Oliva, Miguel Ángel Cámara, Pedro Andreo-Martínez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5217790/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Archives of Environmental Contamination and Toxicology → Version 1 posted 5 You are reading this latest preprint version Abstract In this study, a rapid visualization method was developed to simultaneously evaluate the on-going performance of routine analysis and ensure that the concentration of multiple pesticides in food samples with high water content complies with Maximum Residue Limits (MRLs). In order to accomplish this, we used a bubble chart known as Fast Risk Estimation and Analysis (FREA). In this chart, each pesticide is represented by a bubble. By looking at its color, position on the graph, and the size of the bubble, you can quickly determine whether it meets the requirements for relative standard deviation (RSD), recovery, Index of Quality for Residues, and matrix effect. Onion and pepper were chosen as commodity group with high water content. A single matrix-matched calibration using pepper was performed to analyse all these products. The risk visualisation allows simultaneous checking of on-going validation and quality control parameters as recovery between 60–140% of the spike samples analysed at the same time, such as the historical RSD (value and alarm if is upper at 20%). At the same time, the bubble chart monitoring other parameters such as the exceeding of the MRL in the analyzed samples, the complex definition of the residue in different pesticides or inconsistencies such as limits of quantification higher than the MRL could be quickly identified. Control Chart On-going validation Pesticides Quality control Figures Figure 1 Figure 2 Figure 3 1. Introduction Pesticides are extensively utilized to safeguard livestock and crops in the agricultural sector. For example, in 2021, approximately 333,000 tonnes of pesticides were used, along with the introduction of over 400 new pesticides to the market (Eurostat, 2023 ). When these pesticides are used on crops, it is important to ensure that only a minimal amount of them ends up in the food supply. To address this, Maximum Residue Limits (MRLs) have been established by regulations worldwide to prevent health issues linked to the overuse of pesticides (Veiga-del-Baño, Cuenca-Martínez, Andreo-Martínez, Cámara, Oliva, & Motas, 2023 ). In the European Union (EU), for instance, the European Commission's (EC) consolidated version of Regulation 396/2005 sets MRLs for a range of foodstuffs (EC, 2005 ; Kuchheuser & Birringer, 2022 ). The development of appropriate methods for assessing food safety in accordance with the requirements of international quality standards is one of the main objectives in pesticide analysis (SANTE, 2021 ). To determine as many pesticides as possible in the most cost-effective way with the least effort, Multi-Residue Methods are needed. For this reason, pesticides are determined by gas chromatography (GC) and liquid chromatography (LC) coupled with mass spectrometry (MS), and the most widely used pesticide residue extraction method in routine laboratories today is the method with the acronym "Quick, Easy, Cheap, Effective, Rugged, and Safe" (QuEChERS). The QuEChERS method involves two steps, the first being an initial extraction with different salt formulations to drive the separation of the organic extraction solvent and water. The second, where an aliquot of the organic phase goes through a clean-up process in dispersive solid phase extraction (d-SPE) using different sorbents to remove the matrix component prior to injection by chromatographic analysis (Anastassiades, Lehotay, & štajnbaher, 2002 ). The extraction method in the QuEChERS has been subject to certain variations and modifications to cover a broad range of products, such as the addition of water (for spices, flour and other dry matrices) or the addition of a buffer method when analysing pH-sensitive pesticides as suggested by EN 15662 (ECS, 2008). The cleanup approaches are aimed to reducing the matrix effect (ME) in the variability of foods that can be analysed, such as foods with chlorophyll and other natural pigments (e.g. spices), fat or lipid content (e.g. nuts), essential oils and flavonoids (e.g. herbs), etc. (Rutkowska, Łozowicka, & Kaczyński, 2019 ). These ME lead to analytical problems that affect the accuracy of the results and can be seen in the recovery of pesticides by reducing or increasing the acceptable value for the purpose of validation from 70–120% (Damale et al., 2023 ; SANTE, 2021 ). One of the most widely approaches to reduce ME methods is the matrix-matched calibration (Cuadros-Rodrı́guez et al., 2003 ). Some of its advantages include the ability to utilize a wide range of matrices (Damale et al., 2023 ; Fu, Zhang, Qin, Dou, Luo, & Yang, 2022 ; Kardani et al., 2023 ; Rutkowska et al., 2019 ; UNE-CEN/TS-17061, 2019 ; Zhao et al., 2021 ). Annex A of the SANTE 11312/2021 (SANTE, 2021 ) document, which describes commodity groups such as high-water content (e.g. vegetables), high acid and high water content (e.g. citrus fruits), high starch and/or protein and low water and fat content (e.g. cereals) or unique commodities such as tea, spices or coffee, can be used to extrapolate some recommendations for the preparation of matrix calibrations. For a laboratory analysing a wide range of products and a large number of samples within the same product group, it is most effective to use a single matrix calibration for different matrices within the same product, which may result in reduced accuracy for some pesticides. In this sense, the design of an analytical Quality Assurance (AQA) system is very important, and the most efficient system for AQA is on-going validation or performance verification with the spike matrix to demonstrate applicability to other commodities in the same commodity group with the same matrix calibration. Any commercially available chromatography instrument has both acquisition and processing software to analyze AQA requirements, including retention times, sample recovery, blank, calibration, or MS/MS identification. However, these softwares do not allow an assessment of whether the recovery achieved is within the range of the average recovery and the relative estandar desviation (RSD) from on-going recovery results [within laboratory reproduciblity (RSD wR )]. Another major drawback when working with samples subject to MRLs, which may change over time, is knowing whether the result obtained for a particular pesticide exceeds the MRL, because although this is not defined as a specific AQA criterion, it is a criterion that affects the quality of the product and other criteria such as those defined in section D15 (SANTE, 2021 ) relating to confirmation of higher values exceeding the MRL. The great development of information technology in recent years has made it possible to provide commercial AQA software that allows, externally to the instrument software, many statistical and evaluation tools that allow easy and intuitive evaluation of the range of mean recovery and RSD wR through, for example, Shewhart's charts, which are commonly used for quick graphical visualisation of historical data (Agüera, López, Fernández-Alba, Contreras, Crespo, & Piedra, 2004 ; ISO-7870-2, 2023 ). However, this tool and software also have some disadvantages when used in a multiresidue method to analyze pesticides. For instance, there is a different interpretation of the classic Shewhart's chart when used in analytical chemistry, as shown in ISO/TS 13530 (ISO/TS-13530, 2009 ). Two plots are required, one for recovery and the other for precision for each pesticide, and it can be complex to study a multiresidue method with many pesticides (between GC and LC). In addition, other intrinsic characteristics of pesticide analysis, such as the MRLs values or the risk associated with the results of the pesticide versus MRLs, are not considered in this type of chart (Caldas, 2023 ; IEC, 31010; Li et al., 2023 ; Su et al., 2024 ). Therefore, the aim of this study is to create and to evaluate an alternative graphical tool to Shewart´s charts, called Fast Risk Estimation and Analysis (FREA), for multiresidue analysis of pesticides by chromatography and mass detector in a routine laboratory. The purpose of this graph is to simultaneously display information on the recovery, the RSD WR of the method, and ME for each pesticide and sample analyzed. This will provide insight into the on-going method performance verification and whether the product analyzed exceeds the MRL in the samples through the Index of Quality for Residues (IqR) parameter. This type of graph would enable a quick and visual assessment of the pesticides that have been analyzed with values above the limit of quantification (LOQ), as well as their associated risk. This would be reported in terms of AQA and also by their value in relation to the MRL. 2. Materials and Methods 2.1. Sample selection The commodity group used was High Water Content (G.HW) because of the wide variability of the different products it contains and the diversity of pesticides and MRLs. The products analyzed were onions and tomatoes, which are shown in Table 1 . A single matrix-matched calibration using pepper was performed to analyse all these products. Pepper was used because it was previously validated according to the SANTE 11312/2021 (SANTE, 2021 ). The pepper matrix was chosen based on internal validation studies previously carried out, and because it was considered very different from the other matrices to be analyzed (tomato and onion), so that the possible ME could be evaluated (SANTE, 2021 ). Table 1 also shows the samples used as blank and spike, and the samples used for analyzed as blind-incurred. The blank samples were supplied by an accredited laboratory for pesticide analysis in food (ISO/IEC-17025, 2017 ) located in the Region of Murcia (Spain), which are usually used by the laboratory as matrix calibration in its routine analyses. The blind-incurred samples were chosen based in the different classification in the EURL MRL database (EU, 2023b ) within the commodity group G.HW. These samples were obtained from local companies prior to sale in supermarkets. The subsampling and preparation of each G.HW sample were carried out according to R.D. 290/2003 (RD, 2003 ). The homogeneous products obtained were kept in the freezer at -20°C until extraction and analysis. Table 1 Samples analyzed by commodity group. Blank-spike samples G.HW Blind samples G.HW Commodity EURL database (Blind samples) Product EURL database (Blind samples) Onions Onions 0220000: Bulb vegetables 0220020: Onions Tomatoes Tomatoes 0230000: Fruiting vegetables 0231010: Tomatoes G.HW: High water content group; Commodity: Commodity group and code assigned in EURL database; Product: code assigned to the product inside the commodity EURL database. All the samples described in Table 1 were analyzed only once like any routine laboratory. 2.2. Chemical and reagents All pesticides had certified reference standards of at least 95% purity and were purchased from LGC Standards (Teddington, UK). Gas chromatography and liquid cromatography solvents [(ethyl acetate, methanol, water (resistivity > 18 MΩ) and residual formic acid] were supplied by J.T. Baker (Center Valley, PA, USA). The ammonium formate, formic acid and acetonitrile were supplied by Merck (Darmstadt, Germany). Sodium chloride (1 g), disodium hydrogen citrate sesquihydrate (0.5 g), trisodium citrate dihydrate (1 g), sodium sulfate (4 g), primary secondary amine (PSA) for dispersive solid phase extraction (dSPE) were supplied in an extraction kit by Agilent Technologies (Santa Clara, USA). All reactives were supplied with analytical quality. 2.3. Preparation of solvent, matrix calibration and spiked samples The stock standard solution was prepared for individual pesticides at a concentration of about 1000 µg/ml. The solvent used for LC was acetone and for GC n-hexane/acetone (9:1, v/v). The standard working matrix calibration of multiple compounds was performed by serially dissolving the appropriate amounts of each stock solution. The matrix calibration used for G.HW was a blank pepper. The concentrations of the matrix calibration were in the range of 2 to 50 ng/ml. The fortified samples for the ME study were a white matrix of onion and another of tomato. 2.4. Pesticide analysis The QuEChERS extraction, based on EN 15662 (ISO, 2019 ), used 10 g into a 50 ml polypropylene tube without water addition. After, 10 ml of acetonitrile was added [E1 EN 15662 extraction (ISO, 2019 )] and the mixture was shaken for 1 min. After shaking, the salts extraction kit was added, and it was centrifuged for 5 min at 3000 rpm. A clean-up process using PSA [based in C2 EN 15662 clean-up (ISO, 2019 )] on the organic aliquot obtained and finally it was centrifuged again at 3000 rpm for 5 min to get the final aliquot for GC-MS/MS and LC-MS/MS analysis. A total of 23 pesticides were analyzed by GC-MS/MS using an Agilent (Santa Clara, USA) 7890A GC. A Gas Chromatography system coupled with a 7000A quadrupole tandem mass spectrometer. Chromatographic separation was performed on a RTx-5MS column (30 m, 0.25 mm, i.d., 0.5 µm) RESTEK. Helium was used as the carrier gas at a constant flow rate of 1.2 mL/min, while argon was used as the collision gas. The oven temperature was adjusted as follows: the initial temperature was set at 70°C for 2 min, then increased to 150°C at a rate of 25°C min − 1 , to 200°C at 3°C min − 1 with a hold time of 1 min, and finally to 280°C at 10°C min − 1 with a hold time of 10 min. The total run time was 45 min. The temperatures of the transfer line and ion source were set at 280 ºC and 250 ºC, respectively. The mass spectrometer was operated in multiple reaction monitoring (MRM) mode with three mass transitions. A total of 18 pesticides were analyzed by LC-MS/MS using an Agilent liquid chromatography system (Santa Clara, USA) coupled with a 6470 triple quadrupole tandem mass spectrometer. The chromatographic separation was performed on a Poroshell C18 column (150 mm, 2.1 mm i.d., 2.7 µm) (Agilent, USA) with a flow rate of 0.1 ml at 40°C. The elution solvent used was water 5 mM ammonium formate with 0.01% formic acid (A) and methanol 5 mM ammonium formate with 0.01% formic acid (B). The gradient elution was carried out as follows: 40% solvent B for 0–5 min, changing to 60% solvent B for 6–12 min and finishing with 100% solvent B for 17–20 min. Pesticides were analyzed using programmed MRM in positive and negative modes simultaneously. Ion source parameters include optimized drying gas temperature, drying gas flow, nebulizer pressure, sheath gas temperature and flow, capillary voltage, nozzle voltage and high and low radio frequency voltage. Limit of quantification (LOQ), validation information, MRM transitions and the retention times for tested pesticides can be found in Supplementary Table S1 . The number of pesticides studied using each technique, and the technique used, were chosen based on the history of positive results for the matrices analyzed in this study. 2.5. Performance of the method and quality assurance The method for pepper was previously validated according to the SANTE 11312/2021 (SANTE, 2021 ) requirements. The blind-incurred samples of G.HW were analyzed in the same sequence or batch according to the quality assurance section titled “on-going method performance verification during the routine analysis” from SANTE 11312/2021 (SANTE, 2021 ) guide. Therefore, together with the sequence of blind-incurred samples, the blank samples spiked at a LOQ of 0.005 mg/kg validated with all pesticides were analyzed. 2.6. Database creation and data analysis The Structured Query Language (SQL) database is one of the most widely used because it is a free tool and easy to implement in different programming languages (Strassemeyer, Daehmlow, Dominic, Lorenz, & Golla, 2017 ; Xia, Stinner, Brinkman, & Bennett, 2003 ). A database of MRLs in the EU was created by downloading information in Extensible Markup Language (XML) from the EU website (EU, 2023a ). In the same database, another table was created with information of the data with recoveries for each pesticide in the commodity group studied for automatic RSD wR calculations. Both tables are relational tables linked by the pesticide identifier (ID) present in the XML file from EU web page and they are periodically updated automatically, allowing both the MRL and the definition of the pesticide to be updated. The results of the samples analyzed (blind and spike) were exported through the Agilent MassHunter software (Anonymous, 2008 ) in an XML format to a processing computer code in Python (3.11.4) or PHP 7.0 language using open libraries for data analysis. The Phyton and PHP code generate a hypertext markup language (HTML) with a graphical through the open code Google chart (Google, 2023b ). The graphical option selected to generate a FREA chart was the bubble chart (Google, 2023a ). The Figures generated in HTML code that appear in this manuscript, as well as the source code used in PHP, together with the SQL databases, can be obtained on request from the authors and the author´s GitHub repository. 2.7. AQA data analysis The quality control based on the check of the routine recovery (Rec) of the spiked samples was calculated using Eq. 1. \(\:\%Rec=\frac{Measured\:concentration}{Spiked\:concentration}\times\:100\) Eq. 1 Where measured concentration is the concentration for each blind-incurred sample and each pesticide. Spiked concentration is the theorical concentration spiked, 0.005 mg/Kg in this case. The quality results, based on the reproducibility on-going method, are given by the Relative Standard Deviation (RSD wR ) of all the running verification samples (8 historical data) together the spiked samples analysed in the same time and batch, by the Eq. 2. \(\:\%RSD=\frac{Standard\:deviation}{Average\:}\times\:100\) Eq. 2 Where the standard deviation and mean are the results of calculating the standard deviation and mean for each historical routine recovery data for each pesticide together with the current recovery in the batch analysed. The limits for both calculations are given in section C43 of the SANTE Guide (SANTE, 2021 ). For routine analysis, a practical default of 60–140% can be used for individual recoveries, but with a maximum RSD of 20%. To assess the quality of the food in the samples analyzed, the index of quality for residues (IqR) could be used (Bibi, Rafique, Khalid, Samad, Ahad, & Mehboob, 2022 ; Mac Loughlin et al., 2018 ) as shown in the Eq. 3. \(\:IqR=\frac{PRC}{MRL\:}\) Eq. 3 Where PRC is the pesticide residue concentration (mg/kg) in the blind sample. The results thus obtained for IqR could be evaluated as good (0-0.6), adequate (0.6-1.0) and inadequate (> 1). The combination of the three equations gives information about the analytical compliance. These three variables were combined in a bubble chart. Figure 1 shows a bubble chart with data of different pesticides (each black bubble), recovery values (axis x), and RSD wR values (axis y). The diameter of the bubble indicates the value of IqR. The validation of a method for the analysis of multiple residues of pesticides must take into account that the limit of quantification is appropriate to the MRL of the pesticide and that there are pesticides with complex definitions that include different compounds to be validated, such as carbofuran (not analyzed in this study), where the MRL is set for various compounds and metabolites expressed as the sum of carbofuran (including any carbofuran formed from carbosulfan, benfuracarb or furathiocarb, and 3-OH carbofuran expressed as carbofuran). However, these variables may change rapidly depending on the changes introduced by Regulation 396/2005 (EC, 2005 ), such as the introduced value of IqR (IqRm) to add additional evaluations about the analytical compliance in the pesticide analysis. The Eq. 4 shows the modifications to identify two different cases: \(\:{IqR}_{m}=\frac{{PRC}_{m}}{MRL\:}\times\:100\:\text{I}\text{q}\text{R}\text{m}=\left\{\begin{array}{c}Case\:1:\:\:MRL<Spiked\:concentration\\\:Case\:2:MRL\:complex\:definitions\\\:\end{array}\right.\) Eq. 4 Case 1 Specific pesticides in diverse products present MRLs lower than the LOQ, e.g. carbofuran in wine grapes have a MRL of 0.002 mg/kg. Also, the MRLs values may be modified in future changes to the Regulation 396/2005 (EC, 2005 ). Updating XML files in the database allows detect MRLs < spiked concentration and show a 150% result of IqR m . Case 2 Some of pesticide are expressed and quantified through a single component (e.g. oxyfluorfen), but other pesticides have a complex residue definition because the MRL is based in the sum of more of one component that is analyzed separately (e.g endosulfan MRL is based in the analysis of alpha endosulfan, beta endosulfan and endosulfan sulphate). In these cases, only to evaluate the risk with the MRL value, it is necessary to consider that each component must comply with the MRL or use the residue definition in a specific manner taking into account the molecular weight conversion factor (EC, 2015 ). In the case of a complex definition, IqRm use the MRL of the sum for each component. All these cases were represented on different colored bubbles, as shown in the following scheme: Bubble chart = $$\:\left\{\begin{array}{c}Green:\:\:IqRm<60\%\:and\:RSD<20\:\%\:and\:recovery\:between\:60\%-140\%\:\\\:Yellow:\:IqRm\ge\:60\%\:and<100\%\:\:and\:\:RSD100\%\:or\:RSD>20\%\:or\:recovery140\%\\\:Grey:LMR20\%\:\:and\:at\:least\:two\:other\:non-compliance)\end{array}\right.$$ The scheme shows the different criteria used to plot the bubble chart. Note that for grey bubble chart could be for a non-compliance for at least two non-compliance about the IqRm, RSD wR or recovery. The ME could be estimated by comparing the slopes of matrix-matched calibration curves with the solvent calibration curves (Damale et al., 2023 ; de Sousa, Guido Costa, de Queiroz, Teófilo, Neves, & de Pinho, 2012). However, this presents a big problem for the analytical effort (cost and time) involved for each product in the same commodity group. For these reasons, an alternative calculation (Eq. 5) to evaluate ME was proposed. \(\:ME=\frac{{Absolute(Rec}_{1}-{Rec}_{2})}{\:{(Rec}_{1}+{Rec}_{2})/2}\times\:100\) Eq. 5 The ME proposed calculates the relative percentage difference (RPD) (Anonymous, 2017 ) between two different recoveries of the two different spike samples in the routine batch analyzed. In this study, Rec1 is the recovery of the tomato spike sample and Rec 2 is the recovery obtained from the onion spike. A maximum value for RPD of 20% was fixed, by similarity, to the value of RSD wR fixed in SANTE 11312/2021 (SANTE, 2021 ). 3. Results and discussion Table 2 shows the results obtained for the pesticides found in the blind samples analized of tomato and onions in a routine analysis by GC and LC. The concentrations are expressed with two significant figures according to SANTE 11312/2021 (SANTE, 2021 ) for all results over of the LOQ of 0.005 mg/kg. Table 2 also shows the MRL for both samples. Table 2 Pesticide results and MRL values. Pesticide Analysis mg/kg (Onion) mg/kg (Tomato) MRL (Onion) MRL (Tomato) Chlorpropham GC 0.053 < 0.005 0.01 -- Fenpropathrin GC 0.095 < 0.005 0.01 -- Fluopyram LC 0.079 0.090 0.07 0.5 Mandipropamid LC 0.0083 < 0.005 0.1 -- Spirotetramat (sum) LC 0.078 0.072 0.4 1 Spirotetramat enol LC 0.097 0.089 -- -- Pendimethalin GC 0.071 < 0.005 0.05 -- Acetamiprid LC < 0.005 0.028 -- 0.5 Azoxystrobine LC < 0.005 0.0082 -- 3 Boscalid LC < 0.005 0.043 -- 3 Chlorantraniliprole LC < 0.005 0.012 -- 0.6 Cypermethrin GC < 0.005 0.185 -- 0.5 Cyproconazole GC < 0.005 0.024 -- 0.05 Cyprodinil GC < 0.005 0.107 -- 1.5 Dimethomorph GC < 0.005 0.031 -- 1 Fenhexamid LC < 0.005 0.049 -- 2 Fludioxonil GC < 0.005 0.025 -- 3 Iprodione GC < 0.005 0.036 -- 0.01 Metaflumizone LC < 0.005 0.039 -- 0.7 Pyraclostrobin LC < 0.005 0.013 -- 0.3 Pyriproxyfen GC < 0.005 0.279 -- 1 Spinosad LC < 0.005 0.045 -- 0.7 Spirodiclofen LC < 0.005 0.026 -- 0.5 Spiromesifen LC < 0.005 0.055 -- 1 Thiacloprid LC < 0.005 0.049 -- 0.5 Spirotetramat (sum): Spirotetramat and spirotetramat-enol (sum of), expressed as spirotetramat: Spinosad: Spinosad (spinosad, sum of spinosyn A and spinosyn D); Cypermethrin: Cypermethrin [cypermethrin including other mixtures of constituent isomers (sum of isomers)]; Conc: Concentration in mg kg − 1 ; MRL: Maximum Residue Level from EURL database; --: MRL value not shown due to the current lack of MRL values in the EURL database, therefore the default value of 0.01 mg/kg can still be used. The number of pesticides with values above the LOQ or reported as positive were 7 in the onion sample and 21 in the tomato sample. The number of positive pesticides in tomato and onions were similar in number to other pesticide studies in these matrices (Jirata, Asere, Balcha, & Gure, 2024 ; Ouakhssase & Ait Addi, 2023 ). The number of pesticides with values in above the LOQ in onions is much lower than in tomatoes, could be due to the inhibitory effect of onion organosulphur compounds (propylpropane thiosulphinate and propylpropane thiosulphonate) on many pests (Falcón-Piñeiro et al., 2023 ; Skovgaard, Encinas, Jensen, Andersen, Condarco, & Jørs, 2017 ) and mainly because they are bulbs (grown under the soil). The three highest values for onion correspond to the pesticides fluopyram (0.079 mg/kg), fenpropathrin (0.095 mg/kg) and spirotetramat (0.097 mg/kg). In the case of spirotetramat, the value is obtained exclusively from the result of spirotetramat enol. As can be observed, all pesticides are below the MRL due to spirotetramat, even though it is a pesticide belonging to the derivatives of tetranic acid, it has a low persistence (Mandal, Joshi, Bansal, Sharma, & Kang, 2022 ). However, in the case of fluopyram it is above the MRL due to a greater persistence of the compound (Patel et al., 2016 ). In the case of tomato, the three pesticides found were cyprodinil (0.279 mg/kg), cypermethrin (0.185 mg/kg) and pyriproxyfen (0.107 mg/kg). These pesticides are commonly used in tomatoes, but they have low persistence due to factors such as photodegradation (Lin, Gerrard, & Shaw, 2005 ). In the case of cypermethrin, the results are the sum of different chromatographic peaks automatically via software in GC analysis (Khazri et al., 2016 ). Table 3 shows the data calculated of the spiked samples of onion and tomato (blank samples) such as % of recovery (%Rec), % RPD (as estimation of ME) described by Eq. 5, historical data for %RSD in the commodity group studied (G.HW), or the results for the IqRm for all the pesticides analyzed by GC and LC. Of note, pesticide concentrations in the blank matrix were not included in Table 3 because they were below the LOQ (< 0.005 mg/kg) in all cases. Table 3 Calculations obtained for the generation of FREA chart Pesticide %Rec (Onion) %Rec (Tomato) %RPD (ME) %RSD (G.HW) IqR m (Onion) IqR m (Tomato) Chlorpropham 64 86 29 23 530 0 Fenpropathrin 106 70 41 17 950 0 Fluopyram 89 104 16 22 113 0 Mandipropamid 109 75 37 19 8 0 Spirotetramat (sum) 115 91 23 20 20 0 Spirotetramat enol 115 91 23 20 24 0 Pendimethalin 116 103 12 18 142 0 Acetamiprid 106 87 20 17 0 6 Azoxystrobine 102 85 18 13 0 0 Boscalid 77 85 10 15 0 1 Chlorantraniliprole 111 80 32 22 0 2 Cypermethrin 87 108 22 17 0 37 Cyproconazole 113 90 23 13 0 48 Cyprodinil 105 89 16 9 0 7 Dimethomorph 92 69 29 16 0 3 Fenhexamid 81 107 28 18 0 2 Fludioxonil 96 109 13 21 0 1 Iprodione 99 115 15 10 0 360 Metaflumizone 99 83 18 15 0 6 Pyraclostrobin 78 91 15 16 0 4 Pyriproxyfen 94 84 11 14 0 28 Spinosad 118 115 3 15 0 6 Spirodiclofen 88 67 27 19 0 5 Spiromesifen 71 119 51 13 0 6 Thiacloprid 86 62 32 24 0 10 Spirotetramat (sum): Spirotetramat and spirotetramat-enol (sum of), expressed as spirotetramat: Spinosad: Spinosad (spinosad, sum of spinosyn A and spinosyn D); Cypermethrin: Cypermethrin (cypermethrin including other mixtures of constituent isomers (sum of isomers)). As can be seen in Table 3 , there are four pesticides in the onion with not adequate IqR m , but only the chlorpropham and fluopyram present a non-compliance in the RSD, according to the requirements fixed in the Eq. 5. However, in the tomato sample, only iprodione presented a not adequate IqRm but all other requirements are fulfilled. The ME effect, calculated with the RPD using the Eq. 5, shows that there are 12 (48%) pesticide with a non-compliance value upper of the LOQ. This could indicate that there is a ME to be considered when using pepper as matrix calibration in tomato or onion since the effects of signal suppression and co-extracted compounds can have a significant effect on the results (Gómez-Ramos, Rajski, Lozano, & Fernández-Alba, 2016 ; Wu & Ding, 2023 ). However, even taking into account the possible matrix effects, only the pesticides chlorpropham and fenpropathrin represent a high risk in onions due to the concentration of the pesticide with respect to the MRL that using Eq. 4 gives a very high value of the IqRm. As mentioned earlier, the FREA chart can be generated in two different ways depending on the objective pursued. Figure 2 illustrates how a graph can display information related to pesticides with values above the limit of quantification in a specific sample. These values must be evaluated from the perspective of both the AQA data analysis and the associated risk of that value against the MRL of the pesticide in that sample. In Fig. 2 , it is evident that the grey bubbles, representing chlorpropham, pose a high risk due to non-compliance in RSD, ME, and a larger radius indicating a high IqRm value. On the other hand, the position of fluopyram, the red bubble in the top right-hand corner of Fig. 2 , indicates a historical RSD > 20%, but it does not exhibit EM and has a > 100 value of the IqRm. However, its lower radius suggests that the quantified value of the pesticide compared to the MRL is lower in this pesticide than in chlorpropham. The FREA chart also allows numerical information to be displayed by hovering over each of the bubbles, as shown for the pesticide chlorpropham. An alternative to displaying positive pesticides as shown in Fig. 2 is to display pesticides with values below the LOQ as shown in Fig. 3 . In this way, the AQA criteria can be evaluated only and simultaneously. In Fig. 3 , those pesticides with adequate recovery values (60–140%) and RSD (< 20%) appear in green and in yellow when there is a non-compliance, as in the case of penconazole that does not comply with the RSD value by having a value of 21%. 4. Conclusions The results of this study showed that the bubble chart, called FREA chart, allows a rapid visualization in a larger number of pesticides of the classical on-going parameters RSD wR and recovery simultaneously. Using the FREA chart, it was easy to see that only 4 pesticides in the onion exceeded the limit of quantification analyzed by GC-MS/MS and LC-MS/MS. These pesticides are chlorpropham (IqRm of 530), fenpropathrin (IqRm of 950), fluopyram (IqRm of 113), and pendimetalin (IqRm of 142). This poses a risk that needs to be reviewed as the values in the sample exceeded the MRL. Hovering over the graph, it was observed that chlorpropham had an RSD value of 23% and an ME of 29%, which violates the AQA requirements. For fenpropathrin, only the ME (41%) was found to be non-compliant. In the case of fluopyram, non-compliance was shown for an RSD value over 20% (22%), while pendimetalin did not have any non-compliance issues. The FREA chart also showed its usefulness by indicating those pesticides that, although they were not positive in the samples, presented some type of non-compliance in the AQA as was the case with chlorpropham and penconazole with RSD values > 20%. Using the information from the MRL and the periodic updates of the existing databases, the IqRm index allows graphical visualisation both to assess the quality of the sample analyzed and to detect changes in the MRL or the definition of the pesticide that may affect the LOQ. This graph also allows to estimate the ME by RPD calculation between two recoveries of different samples analyzed in the batch. However, it has the limitation that it is only evaluated in a single concentration and sample, and a validation in a specific product or alternative solutions could be necessary (Kwon, Lehotay, & Geis-Asteggiante, 2012 ). This type of graph can be very helpful in making decisions about the analysis of pesticides, as well as determining the risk of a pesticide being present at a concentration above the LOQ, based on the concentration obtained and the MRL in place at the time for the specific type of sample being analyzed. Declarations Conflicts of Interest The authors declare no conflicts of interest. Funding This research received no external funding. References Agüera, A., López, S., Fernández-Alba, A. R., Contreras, M., Crespo, J., & Piedra, L. (2004). One-year routine application of a new method based on liquid chromatography–tandem mass spectrometry to the analysis of 16 multiclass pesticides in vegetable samples. Journal of Chromatography A, 1045 (1), 125-135. https://doi.org/https://doi.org/10.1016/j.chroma.2004.06.039. Anastassiades, M., Lehotay, S. J., & štajnbaher, D. (2002). 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Levels of organochlorine pesticides in onion and tomato samples from selected towns of Jimma Zone, Ethiopia. Heliyon, 10 (15), e35033. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e35033. Kardani, F., Jelyani, A. Z., Hashemi, M., Rashedinia, M., Shariati, S., Mirzaei, R., . . . Noori, S. M. A. (2023). Determination of 323 pesticide residues in Iran’s cereal by GC-MS and HPLC-UV combined with QuEChERS extraction and mixed-mode SPE clean-up method. Journal of Food Composition and Analysis, 124 , 105670. https://doi.org/https://doi.org/10.1016/j.jfca.2023.105670. Khazri, A., Sellami, B., Dellali, M., Corcellas, C., Eljarrat, E., Barceló, D., . . . Mahmoudi, E. (2016). Diastereomeric and enantiomeric selective accumulation of cypermethrin in the freshwater mussel Unio gibbus and its effects on biochemical parameters. Pesticide Biochemistry and Physiology, 129 , 83-88. https://doi.org/https://doi.org/10.1016/j.pestbp.2015.11.001. Kuchheuser, P., & Birringer, M. (2022). Pesticide residues in food in the European Union: Analysis of notifications in the European Rapid Alert System for Food and Feed from 2002 to 2020. Food Control, 133 , 108575. https://doi.org/https://doi.org/10.1016/j.foodcont.2021.108575. Kwon, H., Lehotay, S. J., & Geis-Asteggiante, L. (2012). Variability of matrix effects in liquid and gas chromatography–mass spectrometry analysis of pesticide residues after QuEChERS sample preparation of different food crops. Journal of Chromatography A, 1270 , 235-245. https://doi.org/https://doi.org/10.1016/j.chroma.2012.10.059. Li, Q., Zhang, J., Lin, T., Fan, C., Li, Y., Zhang, Z., & Li, J. (2023). Migration behavior and dietary exposure risk assessment of pesticides residues in honeysuckle (Lonicera japonica Thunb.) based on modified QuEChERS method coupled with tandem mass spectrometry. Food Research International, 166 , 112572. https://doi.org/https://doi.org/10.1016/j.foodres.2023.112572. Lin, H. M., Gerrard, J. A., & Shaw, I. C. (2005). Stability of the insecticide cypermethrin during tomato processing and implications for endocrine activity. Food Additives & Contaminants, 22 (1), 15-22. https://doi.org/https://doi.org/10.1080/02652030400027938. Mac Loughlin, T. M., Peluso, M. L., Etchegoyen, M. A., Alonso, L. L., de Castro, M. C., Percudani, M. C., & Marino, D. J. G. (2018). Pesticide residues in fruits and vegetables of the argentine domestic market: Occurrence and quality. Food Control, 93 , 129-138. https://doi.org/https://doi.org/10.1016/j.foodcont.2018.05.041. Mandal, K., Joshi, S., Bansal, T., Sharma, S., & Kang, B. K. (2022). Residual determination of spirotetrament and imidacloprid in onion and tomato crop by mass spectrometery. International Journal of Environmental Analytical Chemistry , 1-12. https://doi.org/https://doi.org/10.1080/03067319.2022.2103690. Ouakhssase, A., & Ait Addi, E. (2023). Monitoring 432 potential pesticides in tomatoes produced and commercialized in Souss Massa region-Morocco, using LC-MS/MS and GC-MS/MS. Environmental Pollution, 337 , 122611. https://doi.org/https://doi.org/10.1016/j.envpol.2023.122611. Patel, B. V., Chawla, S., Gor, H., Upadhyay, P., Parmar, K. D., Patel, A. R., & Shah, P. G. (2016). Residue decline and risk assessment of fluopyram + tebuconazole (400SC) in/on onion (Allium cepa). Environmental Science and Pollution Research, 23 (20), 20871-20881. https://doi.org/https://doi.org/10.1007/s11356-016-7331-8. RD. (2003). Real Decreto 290/2003, de 7 de marzo, por el que se establecen los métodos de muestreo para el control de residuos de plaguicidas en los productos de origen vegetal y animal. Available on: https://www.boe.es/buscar/doc.php?id=BOE-A-2003-4786. Rutkowska, E., Łozowicka, B., & Kaczyński, P. (2019). 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SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level. Crop Protection, 97 , 28-44. https://doi.org/https://doi.org/10.1016/j.cropro.2016.11.036. Su, Y., Lu, J., Liu, J., Li, F., Wang, N., Lei, H., & Shen, X. (2024). Optimization of a QuEChERS–LC–MS/MS method for 51 pesticide residues followed by determination of the residue levels and dietary intake risk assessment in foodstuffs. Food Chemistry, 434 , 137467. https://doi.org/https://doi.org/10.1016/j.foodchem.2023.137467. UNE-CEN/TS-17061. (2019). Foodstuffs - Guidelines for the calibration and quantitative determination of pesticide residues and organic contaminants using chromatographic methods (Endorsed by Asociación Española de Normalización in October of 2019.). Available on: https://www.normadoc.com/spanish/une-cen-ts-17061-2019.html. Veiga-del-Baño, J. M., Cuenca-Martínez, J. J., Andreo-Martínez, P., Cámara, M. Á., Oliva, J., & Motas, M. (2023). Uncertainty and associated risks in the analysis of pesticides in homogeneous paprika samples. Food Chemistry, 429 , 136963. https://doi.org/https://doi.org/10.1016/j.foodchem.2023.136963. Wu, X., & Ding, Z. (2023). Evaluation of matrix effects for pesticide residue analysis by QuEChERs coupled with UHPLC-MS/MS in complex herbal matrix. Food Chemistry, 405 , 134755. https://doi.org/https://doi.org/10.1016/j.foodchem.2022.134755. Xia, Y., Stinner, R. E., Brinkman, D., & Bennett, N. (2003). Agricultural chemicals use data access using coldfusion markup language and a relational database. Computers and Electronics in Agriculture, 38 (3), 217-225. https://doi.org/https://doi.org/10.1016/S0168-1699(03)00003-6. Zhao, J., Pu, J., Wu, X., Chen, B., He, Y., Zhang, Y., & Han, B. (2021). Evaluation of the matrix effect of pH value and sugar content on the analysis of pesticides in tropical fruits by UPLC-MS/MS. Microchemical Journal, 168 , 106375. https://doi.org/https://doi.org/10.1016/j.microc.2021.106375. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Archives of Environmental Contamination and Toxicology → Version 1 posted Editorial decision: Accept after minor revision 11 Mar, 2025 Reviewers agreed at journal 02 Dec, 2024 Reviewers invited by journal 02 Dec, 2024 Editor assigned by journal 07 Oct, 2024 First submitted to journal 07 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5217790","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363144469,"identity":"748ec78f-f8b0-45d4-b69b-e315badeef4c","order_by":0,"name":"José Manuel Veiga-del-Baño","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Manuel","lastName":"Veiga-del-Baño","suffix":""},{"id":363144470,"identity":"68628402-204f-47ad-aad3-300f6603921f","order_by":1,"name":"José Oliva","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Oliva","suffix":""},{"id":363144471,"identity":"58ed3283-a4ea-480e-8633-2635ddef0b37","order_by":2,"name":"Miguel Ángel Cámara","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"Ángel","lastName":"Cámara","suffix":""},{"id":363144472,"identity":"604912f4-8e95-41dc-9ff0-54b55044c0de","order_by":3,"name":"Pedro Andreo-Martínez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkUlEQVRIiWNgGAWjYJCCAwkVJOpgPPDgDIlamA8+bCNFvTn76YQDifMORzOwtz8gTotlT+6GA4nbDuc28JwxIE6LwQGYFokcIh1mcP4tUMscoBb550Q6zOAGyJYGkC0MRDrMcgbQloRj6bltPDlEajHnz9388UeNdW4/+3FiHQZjsBGnHlnLKBgFo2AUjAKcAAD/4TOH7Id9WAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6535-5492","institution":"Universidad de Murcia","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Andreo-Martínez","suffix":""},{"id":363144473,"identity":"6ec6da32-2ee3-4dc3-835d-96289109f694","order_by":4,"name":"Miguel Motas","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Motas","suffix":""}],"badges":[],"createdAt":"2024-10-07 11:35:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5217790/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5217790/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00244-025-01133-w","type":"published","date":"2025-06-14T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80362710,"identity":"2583f613-f6d2-4160-a514-e1fcf5c40ec1","added_by":"auto","created_at":"2025-04-11 04:27:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33361,"visible":true,"origin":"","legend":"\u003cp\u003eChart considering RSD\u003csub\u003ewR\u003c/sub\u003e a recovery in routine analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5217790/v1/fa5ea993c45f00eee9605232.png"},{"id":80362711,"identity":"5ce2ae75-b91b-4c5d-9e03-d423644f885a","added_by":"auto","created_at":"2025-04-11 04:27:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54513,"visible":true,"origin":"","legend":"\u003cp\u003eFREA chart for onion sample (only pesticides with values \u0026gt; LOQ from Table 2).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5217790/v1/bd52e7fa3cf092cb05b5d0e7.png"},{"id":80363606,"identity":"de631360-a663-4925-98a5-6430c1669e3e","added_by":"auto","created_at":"2025-04-11 04:43:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69319,"visible":true,"origin":"","legend":"\u003cp\u003eFREA chart for tomato sample (pesticides with values \u0026lt; LOQ from Table 2).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5217790/v1/a843b775f71c106f776ef6c8.png"},{"id":84726448,"identity":"508a11d6-474d-4f7b-a435-f33decf80a9d","added_by":"auto","created_at":"2025-06-16 16:03:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5217790/v1/3590637e-6539-407a-a18a-77333d8fe2f3.pdf"},{"id":80363012,"identity":"a6ffbcb5-158b-4999-bcdd-0624c7f5cdc1","added_by":"auto","created_at":"2025-04-11 04:35:27","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30583,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-5217790/v1/39b835f12599558562b3acab.docx"}],"financialInterests":"","formattedTitle":"Quick monitoring of routine samples of foods in the regulatory analysis of pesticide residues","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePesticides are extensively utilized to safeguard livestock and crops in the agricultural sector. For example, in 2021, approximately 333,000 tonnes of pesticides were used, along with the introduction of over 400 new pesticides to the market (Eurostat, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen these pesticides are used on crops, it is important to ensure that only a minimal amount of them ends up in the food supply. To address this, Maximum Residue Limits (MRLs) have been established by regulations worldwide to prevent health issues linked to the overuse of pesticides (Veiga-del-Ba\u0026ntilde;o, Cuenca-Mart\u0026iacute;nez, Andreo-Mart\u0026iacute;nez, C\u0026aacute;mara, Oliva, \u0026amp; Motas, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the European Union (EU), for instance, the European Commission's (EC) consolidated version of Regulation 396/2005 sets MRLs for a range of foodstuffs (EC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kuchheuser \u0026amp; Birringer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe development of appropriate methods for assessing food safety in accordance with the requirements of international quality standards is one of the main objectives in pesticide analysis (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To determine as many pesticides as possible in the most cost-effective way with the least effort, Multi-Residue Methods are needed. For this reason, pesticides are determined by gas chromatography (GC) and liquid chromatography (LC) coupled with mass spectrometry (MS), and the most widely used pesticide residue extraction method in routine laboratories today is the method with the acronym \"Quick, Easy, Cheap, Effective, Rugged, and Safe\" (QuEChERS). The QuEChERS method involves two steps, the first being an initial extraction with different salt formulations to drive the separation of the organic extraction solvent and water. The second, where an aliquot of the organic phase goes through a clean-up process in dispersive solid phase extraction (d-SPE) using different sorbents to remove the matrix component prior to injection by chromatographic analysis (Anastassiades, Lehotay, \u0026amp; štajnbaher, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe extraction method in the QuEChERS has been subject to certain variations and modifications to cover a broad range of products, such as the addition of water (for spices, flour and other dry matrices) or the addition of a buffer method when analysing pH-sensitive pesticides as suggested by EN 15662 (ECS, 2008).\u003c/p\u003e \u003cp\u003eThe cleanup approaches are aimed to reducing the matrix effect (ME) in the variability of foods that can be analysed, such as foods with chlorophyll and other natural pigments (e.g. spices), fat or lipid content (e.g. nuts), essential oils and flavonoids (e.g. herbs), etc. (Rutkowska, Łozowicka, \u0026amp; Kaczyński, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These ME lead to analytical problems that affect the accuracy of the results and can be seen in the recovery of pesticides by reducing or increasing the acceptable value for the purpose of validation from 70\u0026ndash;120% (Damale et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most widely approaches to reduce ME methods is the matrix-matched calibration (Cuadros-Rodrı́guez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Some of its advantages include the ability to utilize a wide range of matrices (Damale et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fu, Zhang, Qin, Dou, Luo, \u0026amp; Yang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kardani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rutkowska et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; UNE-CEN/TS-17061, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Annex A of the SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) document, which describes commodity groups such as high-water content (e.g. vegetables), high acid and high water content (e.g. citrus fruits), high starch and/or protein and low water and fat content (e.g. cereals) or unique commodities such as tea, spices or coffee, can be used to extrapolate some recommendations for the preparation of matrix calibrations.\u003c/p\u003e \u003cp\u003eFor a laboratory analysing a wide range of products and a large number of samples within the same product group, it is most effective to use a single matrix calibration for different matrices within the same product, which may result in reduced accuracy for some pesticides. In this sense, the design of an analytical Quality Assurance (AQA) system is very important, and the most efficient system for AQA is on-going validation or performance verification with the spike matrix to demonstrate applicability to other commodities in the same commodity group with the same matrix calibration.\u003c/p\u003e \u003cp\u003eAny commercially available chromatography instrument has both acquisition and processing software to analyze AQA requirements, including retention times, sample recovery, blank, calibration, or MS/MS identification. However, these softwares do not allow an assessment of whether the recovery achieved is within the range of the average recovery and the relative estandar desviation (RSD) from on-going recovery results [within laboratory reproduciblity (RSD\u003csub\u003ewR\u003c/sub\u003e)]. Another major drawback when working with samples subject to MRLs, which may change over time, is knowing whether the result obtained for a particular pesticide exceeds the MRL, because although this is not defined as a specific AQA criterion, it is a criterion that affects the quality of the product and other criteria such as those defined in section D15 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) relating to confirmation of higher values exceeding the MRL.\u003c/p\u003e \u003cp\u003eThe great development of information technology in recent years has made it possible to provide commercial AQA software that allows, externally to the instrument software, many statistical and evaluation tools that allow easy and intuitive evaluation of the range of mean recovery and RSD\u003csub\u003ewR\u003c/sub\u003e through, for example, Shewhart's charts, which are commonly used for quick graphical visualisation of historical data (Ag\u0026uuml;era, L\u0026oacute;pez, Fern\u0026aacute;ndez-Alba, Contreras, Crespo, \u0026amp; Piedra, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; ISO-7870-2, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this tool and software also have some disadvantages when used in a multiresidue method to analyze pesticides. For instance, there is a different interpretation of the classic Shewhart's chart when used in analytical chemistry, as shown in ISO/TS 13530 (ISO/TS-13530, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Two plots are required, one for recovery and the other for precision for each pesticide, and it can be complex to study a multiresidue method with many pesticides (between GC and LC). In addition, other intrinsic characteristics of pesticide analysis, such as the MRLs values or the risk associated with the results of the pesticide versus MRLs, are not considered in this type of chart (Caldas, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; IEC, 31010; Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study is to create and to evaluate an alternative graphical tool to Shewart\u0026acute;s charts, called Fast Risk Estimation and Analysis (FREA), for multiresidue analysis of pesticides by chromatography and mass detector in a routine laboratory.\u003c/p\u003e \u003cp\u003eThe purpose of this graph is to simultaneously display information on the recovery, the RSD\u003csub\u003eWR\u003c/sub\u003e of the method, and ME for each pesticide and sample analyzed. This will provide insight into the on-going method performance verification and whether the product analyzed exceeds the MRL in the samples through the Index of Quality for Residues (IqR) parameter. This type of graph would enable a quick and visual assessment of the pesticides that have been analyzed with values above the limit of quantification (LOQ), as well as their associated risk. This would be reported in terms of AQA and also by their value in relation to the MRL.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample selection\u003c/h2\u003e \u003cp\u003eThe commodity group used was High Water Content (G.HW) because of the wide variability of the different products it contains and the diversity of pesticides and MRLs. The products analyzed were onions and tomatoes, which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA single matrix-matched calibration using pepper was performed to analyse all these products. Pepper was used because it was previously validated according to the SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The pepper matrix was chosen based on internal validation studies previously carried out, and because it was considered very different from the other matrices to be analyzed (tomato and onion), so that the possible ME could be evaluated (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also shows the samples used as blank and spike, and the samples used for analyzed as blind-incurred. The blank samples were supplied by an accredited laboratory for pesticide analysis in food (ISO/IEC-17025, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) located in the Region of Murcia (Spain), which are usually used by the laboratory as matrix calibration in its routine analyses.\u003c/p\u003e \u003cp\u003eThe blind-incurred samples were chosen based in the different classification in the EURL MRL database (EU, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) within the commodity group G.HW. These samples were obtained from local companies prior to sale in supermarkets. The subsampling and preparation of each G.HW sample were carried out according to R.D. 290/2003 (RD, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The homogeneous products obtained were kept in the freezer at -20\u0026deg;C until extraction and analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSamples analyzed by commodity group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlank-spike samples G.HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlind samples\u003c/p\u003e \u003cp\u003eG.HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommodity EURL database (Blind samples)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProduct EURL database (Blind samples)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0220000: Bulb vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0220020: Onions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0230000: Fruiting vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0231010: Tomatoes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eG.HW: High water content group; Commodity: Commodity group and code assigned in EURL database; Product: code assigned to the product inside the commodity EURL database.\u003c/p\u003e \u003cp\u003eAll the samples described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were analyzed only once like any routine laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Chemical and reagents\u003c/h2\u003e \u003cp\u003eAll pesticides had certified reference standards of at least 95% purity and were purchased from LGC Standards (Teddington, UK).\u003c/p\u003e \u003cp\u003eGas chromatography and liquid cromatography solvents [(ethyl acetate, methanol, water (resistivity\u0026thinsp;\u0026gt;\u0026thinsp;18 MΩ) and residual formic acid] were supplied by J.T. Baker (Center Valley, PA, USA). The ammonium formate, formic acid and acetonitrile were supplied by Merck (Darmstadt, Germany). Sodium chloride (1 g), disodium hydrogen citrate sesquihydrate (0.5 g), trisodium citrate dihydrate (1 g), sodium sulfate (4 g), primary secondary amine (PSA) for dispersive solid phase extraction (dSPE) were supplied in an extraction kit by Agilent Technologies (Santa Clara, USA). All reactives were supplied with analytical quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Preparation of solvent, matrix calibration and spiked samples\u003c/h2\u003e \u003cp\u003eThe stock standard solution was prepared for individual pesticides at a concentration of about 1000 \u0026micro;g/ml. The solvent used for LC was acetone and for GC n-hexane/acetone (9:1, v/v).\u003c/p\u003e \u003cp\u003eThe standard working matrix calibration of multiple compounds was performed by serially dissolving the appropriate amounts of each stock solution. The matrix calibration used for G.HW was a blank pepper. The concentrations of the matrix calibration were in the range of 2 to 50 ng/ml. The fortified samples for the ME study were a white matrix of onion and another of tomato.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Pesticide analysis\u003c/h2\u003e \u003cp\u003eThe QuEChERS extraction, based on EN 15662 (ISO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), used 10 g into a 50 ml polypropylene tube without water addition. After, 10 ml of acetonitrile was added [E1 EN 15662 extraction (ISO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)] and the mixture was shaken for 1 min. After shaking, the salts extraction kit was added, and it was centrifuged for 5 min at 3000 rpm. A clean-up process using PSA [based in C2 EN 15662 clean-up (ISO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)] on the organic aliquot obtained and finally it was centrifuged again at 3000 rpm for 5 min to get the final aliquot for GC-MS/MS and LC-MS/MS analysis.\u003c/p\u003e \u003cp\u003eA total of 23 pesticides were analyzed by GC-MS/MS using an Agilent (Santa Clara, USA) 7890A GC. A Gas Chromatography system coupled with a 7000A quadrupole tandem mass spectrometer. Chromatographic separation was performed on a RTx-5MS column (30 m, 0.25 mm, i.d., 0.5 \u0026micro;m) RESTEK. Helium was used as the carrier gas at a constant flow rate of 1.2 mL/min, while argon was used as the collision gas. The oven temperature was adjusted as follows: the initial temperature was set at 70\u0026deg;C for 2 min, then increased to 150\u0026deg;C at a rate of 25\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, to 200\u0026deg;C at 3\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a hold time of 1 min, and finally to 280\u0026deg;C at 10\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a hold time of 10 min. The total run time was 45 min. The temperatures of the transfer line and ion source were set at 280 \u0026ordm;C and 250 \u0026ordm;C, respectively. The mass spectrometer was operated in multiple reaction monitoring (MRM) mode with three mass transitions.\u003c/p\u003e \u003cp\u003eA total of 18 pesticides were analyzed by LC-MS/MS using an Agilent liquid chromatography system (Santa Clara, USA) coupled with a 6470 triple quadrupole tandem mass spectrometer. The chromatographic separation was performed on a Poroshell C18 column (150 mm, 2.1 mm i.d., 2.7 \u0026micro;m) (Agilent, USA) with a flow rate of 0.1 ml at 40\u0026deg;C. The elution solvent used was water 5 mM ammonium formate with 0.01% formic acid (A) and methanol 5 mM ammonium formate with 0.01% formic acid (B). The gradient elution was carried out as follows: 40% solvent B for 0\u0026ndash;5 min, changing to 60% solvent B for 6\u0026ndash;12 min and finishing with 100% solvent B for 17\u0026ndash;20 min. Pesticides were analyzed using programmed MRM in positive and negative modes simultaneously. Ion source parameters include optimized drying gas temperature, drying gas flow, nebulizer pressure, sheath gas temperature and flow, capillary voltage, nozzle voltage and high and low radio frequency voltage.\u003c/p\u003e \u003cp\u003eLimit of quantification (LOQ), validation information, MRM transitions and the retention times for tested pesticides can be found in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe number of pesticides studied using each technique, and the technique used, were chosen based on the history of positive results for the matrices analyzed in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Performance of the method and quality assurance\u003c/h2\u003e \u003cp\u003eThe method for pepper was previously validated according to the SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) requirements. The blind-incurred samples of G.HW were analyzed in the same sequence or batch according to the quality assurance section titled \u0026ldquo;on-going method performance verification during the routine analysis\u0026rdquo; from SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) guide. Therefore, together with the sequence of blind-incurred samples, the blank samples spiked at a LOQ of 0.005 mg/kg validated with all pesticides were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Database creation and data analysis\u003c/h2\u003e \u003cp\u003eThe Structured Query Language (SQL) database is one of the most widely used because it is a free tool and easy to implement in different programming languages (Strassemeyer, Daehmlow, Dominic, Lorenz, \u0026amp; Golla, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xia, Stinner, Brinkman, \u0026amp; Bennett, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). A database of MRLs in the EU was created by downloading information in Extensible Markup Language (XML) from the EU website (EU, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the same database, another table was created with information of the data with recoveries for each pesticide in the commodity group studied for automatic RSD\u003csub\u003ewR\u003c/sub\u003e calculations. Both tables are relational tables linked by the pesticide identifier (ID) present in the XML file from EU web page and they are periodically updated automatically, allowing both the MRL and the definition of the pesticide to be updated.\u003c/p\u003e \u003cp\u003eThe results of the samples analyzed (blind and spike) were exported through the Agilent MassHunter software (Anonymous, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in an XML format to a processing computer code in Python (3.11.4) or PHP 7.0 language using open libraries for data analysis.\u003c/p\u003e \u003cp\u003eThe Phyton and PHP code generate a hypertext markup language (HTML) with a graphical through the open code Google chart (Google, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The graphical option selected to generate a FREA chart was the bubble chart (Google, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Figures generated in HTML code that appear in this manuscript, as well as the source code used in PHP, together with the SQL databases, can be obtained on request from the authors and the author\u0026acute;s GitHub repository.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. AQA data analysis\u003c/h2\u003e \u003cp\u003eThe quality control based on the check of the routine recovery (Rec) of the spiked samples was calculated using Eq.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\%Rec=\\frac{Measured\\:concentration}{Spiked\\:concentration}\\times\\:100\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere measured concentration is the concentration for each blind-incurred sample and each pesticide. Spiked concentration is the theorical concentration spiked, 0.005 mg/Kg in this case.\u003c/p\u003e \u003cp\u003eThe quality results, based on the reproducibility on-going method, are given by the Relative Standard Deviation (RSD\u003csub\u003ewR\u003c/sub\u003e) of all the running verification samples (8 historical data) together the spiked samples analysed in the same time and batch, by the Eq.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\%RSD=\\frac{Standard\\:deviation}{Average\\:}\\times\\:100\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eWhere the standard deviation and mean are the results of calculating the standard deviation and mean for each historical routine recovery data for each pesticide together with the current recovery in the batch analysed.\u003c/p\u003e \u003cp\u003eThe limits for both calculations are given in section C43 of the SANTE Guide (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For routine analysis, a practical default of 60\u0026ndash;140% can be used for individual recoveries, but with a maximum RSD of 20%.\u003c/p\u003e \u003cp\u003eTo assess the quality of the food in the samples analyzed, the index of quality for residues (IqR) could be used (Bibi, Rafique, Khalid, Samad, Ahad, \u0026amp; Mehboob, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mac Loughlin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as shown in the Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:IqR=\\frac{PRC}{MRL\\:}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003eWhere PRC is the pesticide residue concentration (mg/kg) in the blind sample. The results thus obtained for IqR could be evaluated as good (0-0.6), adequate (0.6-1.0) and inadequate (\u0026gt;\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eThe combination of the three equations gives information about the analytical compliance. These three variables were combined in a bubble chart. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a bubble chart with data of different pesticides (each black bubble), recovery values (axis x), and RSD\u003csub\u003ewR\u003c/sub\u003e values (axis y). The diameter of the bubble indicates the value of IqR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe validation of a method for the analysis of multiple residues of pesticides must take into account that the limit of quantification is appropriate to the MRL of the pesticide and that there are pesticides with complex definitions that include different compounds to be validated, such as carbofuran (not analyzed in this study), where the MRL is set for various compounds and metabolites expressed as the sum of carbofuran (including any carbofuran formed from carbosulfan, benfuracarb or furathiocarb, and 3-OH carbofuran expressed as carbofuran). However, these variables may change rapidly depending on the changes introduced by Regulation 396/2005 (EC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), such as the introduced value of IqR (IqRm) to add additional evaluations about the analytical compliance in the pesticide analysis. The Eq.\u0026nbsp;4 shows the modifications to identify two different cases:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{IqR}_{m}=\\frac{{PRC}_{m}}{MRL\\:}\\times\\:100\\:\\text{I}\\text{q}\\text{R}\\text{m}=\\left\\{\\begin{array}{c}Case\\:1:\\:\\:MRL\u0026lt;Spiked\\:concentration\\\\\\:Case\\:2:MRL\\:complex\\:definitions\\\\\\:\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;4\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCase 1\u003c/strong\u003e \u003cp\u003eSpecific pesticides in diverse products present MRLs lower than the LOQ, e.g. carbofuran in wine grapes have a MRL of 0.002 mg/kg. Also, the MRLs values may be modified in future changes to the Regulation 396/2005 (EC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Updating XML files in the database allows detect MRLs\u0026thinsp;\u0026lt;\u0026thinsp;spiked concentration and show a 150% result of IqR\u003csub\u003em\u003c/sub\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCase 2\u003c/strong\u003e \u003cp\u003eSome of pesticide are expressed and quantified through a single component (e.g. oxyfluorfen), but other pesticides have a complex residue definition because the MRL is based in the sum of more of one component that is analyzed separately (e.g endosulfan MRL is based in the analysis of alpha endosulfan, beta endosulfan and endosulfan sulphate). In these cases, only to evaluate the risk with the MRL value, it is necessary to consider that each component must comply with the MRL or use the residue definition in a specific manner taking into account the molecular weight conversion factor (EC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the case of a complex definition, IqRm use the MRL of the sum for each component.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAll these cases were represented on different colored bubbles, as shown in the following scheme:\u003c/p\u003e \u003cp\u003eBubble chart =\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left\\{\\begin{array}{c}Green:\\:\\:IqRm\u0026lt;60\\%\\:and\\:RSD\u0026lt;20\\:\\%\\:and\\:recovery\\:between\\:60\\%-140\\%\\:\\\\\\:Yellow:\\:IqRm\\ge\\:60\\%\\:and\u0026lt;100\\%\\:\\:and\\:\\:RSD\u0026lt;20\\:\\%\\:and\\:recovery\\:between\\:60\\%-140\\%\\\\\\:Red:IqRm\u0026gt;100\\%\\:or\\:RSD\u0026gt;20\\%\\:or\\:recovery\u0026lt;60\\%\\:or\u0026gt;140\\%\\\\\\:Grey:LMR\u0026lt;spike\\:concentration\\:or\\:(ME\u0026gt;20\\%\\:\\:and\\:at\\:least\\:two\\:other\\:non-compliance)\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe scheme shows the different criteria used to plot the bubble chart. Note that for grey bubble chart could be for a non-compliance for at least two non-compliance about the IqRm, RSD\u003csub\u003ewR\u003c/sub\u003e or recovery.\u003c/p\u003e \u003cp\u003eThe ME could be estimated by comparing the slopes of matrix-matched calibration curves with the solvent calibration curves (Damale et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; de Sousa, Guido Costa, de Queiroz, Te\u0026oacute;filo, Neves, \u0026amp; de Pinho, 2012). However, this presents a big problem for the analytical effort (cost and time) involved for each product in the same commodity group. For these reasons, an alternative calculation (Eq.\u0026nbsp;5) to evaluate ME was proposed.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:ME=\\frac{{Absolute(Rec}_{1}-{Rec}_{2})}{\\:{(Rec}_{1}+{Rec}_{2})/2}\\times\\:100\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;5\u003c/p\u003e \u003cp\u003eThe ME proposed calculates the relative percentage difference (RPD) (Anonymous, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) between two different recoveries of the two different spike samples in the routine batch analyzed. In this study, Rec1 is the recovery of the tomato spike sample and Rec 2 is the recovery obtained from the onion spike. A maximum value for RPD of 20% was fixed, by similarity, to the value of RSD\u003csub\u003ewR\u003c/sub\u003e fixed in SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results obtained for the pesticides found in the blind samples analized of tomato and onions in a routine analysis by GC and LC. The concentrations are expressed with two significant figures according to SANTE 11312/2021 (SANTE, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for all results over of the LOQ of 0.005 mg/kg. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also shows the MRL for both samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePesticide results and MRL values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003cp\u003e(Onion)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emg/kg\u003c/p\u003e \u003cp\u003e(Tomato)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMRL\u003c/p\u003e \u003cp\u003e(Onion)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRL\u003c/p\u003e \u003cp\u003e(Tomato)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpropham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenpropathrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluopyram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandipropamid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirotetramat (sum)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirotetramat enol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePendimethalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetamiprid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoxystrobine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoscalid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorantraniliprole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCypermethrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproconazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyprodinil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethomorph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenhexamid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFludioxonil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIprodione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetaflumizone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyraclostrobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyriproxyfen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinosad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirodiclofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpiromesifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiacloprid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpirotetramat (sum): Spirotetramat and spirotetramat-enol (sum of), expressed as spirotetramat: Spinosad: Spinosad (spinosad, sum of spinosyn A and spinosyn D); Cypermethrin: Cypermethrin [cypermethrin including other mixtures of constituent isomers (sum of isomers)]; Conc: Concentration in mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; MRL: Maximum Residue Level from EURL database; --: MRL value not shown due to the current lack of MRL values in the EURL database, therefore the default value of 0.01 mg/kg can still be used.\u003c/p\u003e \u003cp\u003eThe number of pesticides with values above the LOQ or reported as positive were 7 in the onion sample and 21 in the tomato sample. The number of positive pesticides in tomato and onions were similar in number to other pesticide studies in these matrices (Jirata, Asere, Balcha, \u0026amp; Gure, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ouakhssase \u0026amp; Ait Addi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The number of pesticides with values in above the LOQ in onions is much lower than in tomatoes, could be due to the inhibitory effect of onion organosulphur compounds (propylpropane thiosulphinate and propylpropane thiosulphonate) on many pests (Falc\u0026oacute;n-Pi\u0026ntilde;eiro et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Skovgaard, Encinas, Jensen, Andersen, Condarco, \u0026amp; J\u0026oslash;rs, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and mainly because they are bulbs (grown under the soil).\u003c/p\u003e \u003cp\u003eThe three highest values for onion correspond to the pesticides fluopyram (0.079 mg/kg), fenpropathrin (0.095 mg/kg) and spirotetramat (0.097 mg/kg). In the case of spirotetramat, the value is obtained exclusively from the result of spirotetramat enol. As can be observed, all pesticides are below the MRL due to spirotetramat, even though it is a pesticide belonging to the derivatives of tetranic acid, it has a low persistence (Mandal, Joshi, Bansal, Sharma, \u0026amp; Kang, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, in the case of fluopyram it is above the MRL due to a greater persistence of the compound (Patel et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the case of tomato, the three pesticides found were cyprodinil (0.279 mg/kg), cypermethrin (0.185 mg/kg) and pyriproxyfen (0.107 mg/kg). These pesticides are commonly used in tomatoes, but they have low persistence due to factors such as photodegradation (Lin, Gerrard, \u0026amp; Shaw, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the case of cypermethrin, the results are the sum of different chromatographic peaks automatically via software in GC analysis (Khazri et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the data calculated of the spiked samples of onion and tomato (blank samples) such as % of recovery (%Rec), % RPD (as estimation of ME) described by Eq.\u0026nbsp;5, historical data for %RSD in the commodity group studied (G.HW), or the results for the IqRm for all the pesticides analyzed by GC and LC. Of note, pesticide concentrations in the blank matrix were not included in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e because they were below the LOQ (\u0026lt;\u0026thinsp;0.005 mg/kg) in all cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculations obtained for the generation of FREA chart\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Rec\u003c/p\u003e \u003cp\u003e(Onion)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%Rec\u003c/p\u003e \u003cp\u003e(Tomato)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%RPD\u003c/p\u003e \u003cp\u003e(ME)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%RSD\u003c/p\u003e \u003cp\u003e(G.HW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIqR\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(Onion)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIqR\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(Tomato)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpropham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenpropathrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluopyram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandipropamid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirotetramat (sum)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirotetramat enol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePendimethalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetamiprid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoxystrobine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoscalid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorantraniliprole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCypermethrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyproconazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyprodinil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethomorph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenhexamid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFludioxonil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIprodione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetaflumizone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyraclostrobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyriproxyfen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinosad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirodiclofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpiromesifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiacloprid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpirotetramat (sum): Spirotetramat and spirotetramat-enol (sum of), expressed as spirotetramat: Spinosad: Spinosad (spinosad, sum of spinosyn A and spinosyn D); Cypermethrin: Cypermethrin (cypermethrin including other mixtures of constituent isomers (sum of isomers)).\u003c/p\u003e \u003cp\u003eAs can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there are four pesticides in the onion with not adequate IqR\u003csub\u003em\u003c/sub\u003e, but only the chlorpropham and fluopyram present a non-compliance in the RSD, according to the requirements fixed in the Eq.\u0026nbsp;5. However, in the tomato sample, only iprodione presented a not adequate IqRm but all other requirements are fulfilled.\u003c/p\u003e \u003cp\u003eThe ME effect, calculated with the RPD using the Eq.\u0026nbsp;5, shows that there are 12 (48%) pesticide with a non-compliance value upper of the LOQ. This could indicate that there is a ME to be considered when using pepper as matrix calibration in tomato or onion since the effects of signal suppression and co-extracted compounds can have a significant effect on the results (G\u0026oacute;mez-Ramos, Rajski, Lozano, \u0026amp; Fern\u0026aacute;ndez-Alba, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu \u0026amp; Ding, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, even taking into account the possible matrix effects, only the pesticides chlorpropham and fenpropathrin represent a high risk in onions due to the concentration of the pesticide with respect to the MRL that using Eq.\u0026nbsp;4 gives a very high value of the IqRm.\u003c/p\u003e \u003cp\u003eAs mentioned earlier, the FREA chart can be generated in two different ways depending on the objective pursued. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates how a graph can display information related to pesticides with values above the limit of quantification in a specific sample. These values must be evaluated from the perspective of both the AQA data analysis and the associated risk of that value against the MRL of the pesticide in that sample.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is evident that the grey bubbles, representing chlorpropham, pose a high risk due to non-compliance in RSD, ME, and a larger radius indicating a high IqRm value. On the other hand, the position of fluopyram, the red bubble in the top right-hand corner of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, indicates a historical RSD\u0026thinsp;\u0026gt;\u0026thinsp;20%, but it does not exhibit EM and has a\u0026thinsp;\u0026gt;\u0026thinsp;100 value of the IqRm. However, its lower radius suggests that the quantified value of the pesticide compared to the MRL is lower in this pesticide than in chlorpropham.\u003c/p\u003e \u003cp\u003eThe FREA chart also allows numerical information to be displayed by hovering over each of the bubbles, as shown for the pesticide chlorpropham.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn alternative to displaying positive pesticides as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is to display pesticides with values below the LOQ as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In this way, the AQA criteria can be evaluated only and simultaneously. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, those pesticides with adequate recovery values (60\u0026ndash;140%) and RSD (\u0026lt;\u0026thinsp;20%) appear in green and in yellow when there is a non-compliance, as in the case of penconazole that does not comply with the RSD value by having a value of 21%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe results of this study showed that the bubble chart, called FREA chart, allows a rapid visualization in a larger number of pesticides of the classical on-going parameters RSD\u003csub\u003ewR\u003c/sub\u003e and recovery simultaneously.\u003c/p\u003e \u003cp\u003eUsing the FREA chart, it was easy to see that only 4 pesticides in the onion exceeded the limit of quantification analyzed by GC-MS/MS and LC-MS/MS. These pesticides are chlorpropham (IqRm of 530), fenpropathrin (IqRm of 950), fluopyram (IqRm of 113), and pendimetalin (IqRm of 142). This poses a risk that needs to be reviewed as the values in the sample exceeded the MRL. Hovering over the graph, it was observed that chlorpropham had an RSD value of 23% and an ME of 29%, which violates the AQA requirements. For fenpropathrin, only the ME (41%) was found to be non-compliant. In the case of fluopyram, non-compliance was shown for an RSD value over 20% (22%), while pendimetalin did not have any non-compliance issues.\u003c/p\u003e \u003cp\u003eThe FREA chart also showed its usefulness by indicating those pesticides that, although they were not positive in the samples, presented some type of non-compliance in the AQA as was the case with chlorpropham and penconazole with RSD values\u0026thinsp;\u0026gt;\u0026thinsp;20%.\u003c/p\u003e \u003cp\u003eUsing the information from the MRL and the periodic updates of the existing databases, the IqRm index allows graphical visualisation both to assess the quality of the sample analyzed and to detect changes in the MRL or the definition of the pesticide that may affect the LOQ. This graph also allows to estimate the ME by RPD calculation between two recoveries of different samples analyzed in the batch. However, it has the limitation that it is only evaluated in a single concentration and sample, and a validation in a specific product or alternative solutions could be necessary (Kwon, Lehotay, \u0026amp; Geis-Asteggiante, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis type of graph can be very helpful in making decisions about the analysis of pesticides, as well as determining the risk of a pesticide being present at a concentration above the LOQ, based on the concentration obtained and the MRL in place at the time for the specific type of sample being analyzed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAg\u0026uuml;era, A., L\u0026oacute;pez, S., Fern\u0026aacute;ndez-Alba, A. 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Evaluation of the matrix effect of pH value and sugar content on the analysis of pesticides in tropical fruits by UPLC-MS/MS. \u003cem\u003eMicrochemical Journal, 168\u003c/em\u003e, 106375. https://doi.org/https://doi.org/10.1016/j.microc.2021.106375.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-environmental-contamination-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aect","sideBox":"Learn more about [Archives of Environmental Contamination and Toxicology](https://www.springer.com/journal/244)","snPcode":"244","submissionUrl":"https://submission.nature.com/new-submission/244/3","title":"Archives of Environmental Contamination and Toxicology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Control Chart, On-going validation, Pesticides, Quality control","lastPublishedDoi":"10.21203/rs.3.rs-5217790/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5217790/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, a rapid visualization method was developed to simultaneously evaluate the on-going performance of routine analysis and ensure that the concentration of multiple pesticides in food samples with high water content complies with Maximum Residue Limits (MRLs). In order to accomplish this, we used a bubble chart known as Fast Risk Estimation and Analysis (FREA). In this chart, each pesticide is represented by a bubble. By looking at its color, position on the graph, and the size of the bubble, you can quickly determine whether it meets the requirements for relative standard deviation (RSD), recovery, Index of Quality for Residues, and matrix effect. Onion and pepper were chosen as commodity group with high water content. A single matrix-matched calibration using pepper was performed to analyse all these products. The risk visualisation allows simultaneous checking of on-going validation and quality control parameters as recovery between 60\u0026ndash;140% of the spike samples analysed at the same time, such as the historical RSD (value and alarm if is upper at 20%). At the same time, the bubble chart monitoring other parameters such as the exceeding of the MRL in the analyzed samples, the complex definition of the residue in different pesticides or inconsistencies such as limits of quantification higher than the MRL could be quickly identified.\u003c/p\u003e","manuscriptTitle":"Quick monitoring of routine samples of foods in the regulatory analysis of pesticide residues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-11 04:27:22","doi":"10.21203/rs.3.rs-5217790/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept after minor revision","date":"2025-03-11T09:26:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-12-02T20:37:29+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-02T20:33:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-07T12:40:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Environmental Contamination and Toxicology","date":"2024-10-07T07:34:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-environmental-contamination-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aect","sideBox":"Learn more about [Archives of Environmental Contamination and Toxicology](https://www.springer.com/journal/244)","snPcode":"244","submissionUrl":"https://submission.nature.com/new-submission/244/3","title":"Archives of Environmental Contamination and Toxicology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eebeb99c-3a58-42ff-b422-5bb45e418e07","owner":[],"postedDate":"April 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T15:58:56+00:00","versionOfRecord":{"articleIdentity":"rs-5217790","link":"https://doi.org/10.1007/s00244-025-01133-w","journal":{"identity":"archives-of-environmental-contamination-and-toxicology","isVorOnly":false,"title":"Archives of Environmental Contamination and Toxicology"},"publishedOn":"2025-06-14 15:56:53","publishedOnDateReadable":"June 14th, 2025"},"versionCreatedAt":"2025-04-11 04:27:22","video":"","vorDoi":"10.1007/s00244-025-01133-w","vorDoiUrl":"https://doi.org/10.1007/s00244-025-01133-w","workflowStages":[]},"version":"v1","identity":"rs-5217790","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5217790","identity":"rs-5217790","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}