Determination of Pesticide Residues in Five Different Corn-based Products Using a Single and Simple Solid–liquid Extraction without Clean-up Steps Followed by Comprehensive Two-dimensional Liquid Chromatography Coupled to Tandem Mass Spectrometry

Microchemical Journal 205 (2024) 111298 Available online 30 July 2024 0026-265X/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Determination of pesticide residues in five different corn-based products using a single and simple solid–liquid extraction without clean-up steps followed by comprehensive two-dimensional liquid chromatography coupled to tandem mass spectrometry Katia Arena a , 1 , Laura Martín-Pozo a , b , 1 , Roberto Lagan` a Vinci a , Francesco Cacciola c , * , Paola Dugo a , d , Luigi Mondello a , d a Messina Institute of Technology c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168 Messina, Italy b Department of Analytical Chemistry, Escuela Polit´ ecnica Superior, University of Seville, 41011 Seville, Spain c Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, 98125 Messina, Italy d Chromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168 Messina, Italy ARTICLE INFO Keywords: Pesticides LC×LC-MS/MS Solid–liquid extraction Corn products AGREE metrics RGB additive color model BAGI ABSTRACT Pesticide residues in food products are a critical issue concerning food safety. Recent studies have increasingly focused on developing selective, rapid, innovative, and environmentally friendly analytical methods for detecting pesticides in foodstuffs. This study presents a novel analytical method utilizing comprehensive twodimensional liquid chromatography-tandem mass spectrometry for the simultaneous determination of one hundred and thirteen multiclass pesticides in corn products. The method simplifies the process with a single-step solid–liquid extraction and benefits from two-dimensional separation to enhance resolution and minimize matrix effects. The methodology was validated according to European Commission DG-SANTE (SANTE/11312/2021) and European Committee for Standardization (EN 15662, 2018) guidelines, obtaining acceptable analytical parameters. Limits of detection ranged from 0.2 µg/L to 30.2 µg/L and limits of quantification from 0.3 to 91.6 μ g/L, all bellow regulatory limits. Accuracy, ranged from 60 to 120 % and precision, estimated by relative standard deviation, was consistently below 15.1 % in all cases. The applicability of the method was demonstrated by analyzing pesticides in five representative corn products (tortilla, corn flakes, corn cake, starch, and polenta). Sixteen pesticides were detected, all within the regulatory residue limits. Three metric tools were applied to assess the analytical process. Environmental sustainability was evaluated using the AGREE metric, while analytical performance and method applicability were assessed with the Red-Green-Blue and Blue Applicability Grade Index models, respectively, yielding promising scores (0.5, ≥0.55, and 0.65). These findings and the comparison to previous methods indicated that this approach represents a promising, alternative and userfriendly strategy for pesticide determination, offering effective productivity and reliable analytical performance. 1. Introduction In the last few years, food security has been threatened by a high increase in the number of pest epidemics, transboundary plant and animal diseases, with remarkable economic, social and environmental effects [1]. Globally, annual crop losses, due to plant pests, are estimated to be between 20 and 40 % of production, resulting in costs to the world economy of about $220 billion a year [2]. Prevention of plant diseases and pests has proven to be the most successful control strategy, and the use of plant protection products (PPP) e.g. pesticides might be of valid aid in increasing the productivity. Recently, the Food and Agricultural Organisation of the United Nations * Corresponding author. E-mail address: [email protected] (F. Cacciola). 1 Both authors contributed equally. Contents lists available at ScienceDirect Microchemical Journal journal homepage: www.elsevier.com/locate/microc https://doi.org/10.1016/j.microc.2024.111298 Received 10 April 2024; Received in revised form 8 July 2024; Accepted 8 July 2024 Microchemical Journal 205 (2024) 111298 2 (FAO) and the World Health Organisation (WHO) have drawn up a Guiding Principles around Sustainable Healthy Diets emphasizing the importance of consuming a wide variety of unprocessed or minimally processed foods, balanced among the various food groups, and limiting highly processed foods and beverages. In addition, they recommended greater consumption of whole grains, legumes, nuts and variety of fruits and vegetables to the detriment of foods with high energy density and rich in sugars, salt, proteins and fats [3]. In this regard, grains (corn, rice, wheat, rye, oat, barley, sorghum) are a staple diet food and one of the most foodstuffs consumed everywhere in the world, due to the high content of fibres, vegetable proteins and carbohydrates, as well being a precious source of polyunsaturated fats (linolenic acid), bioactive compounds (antioxidants), minerals (magnesium, iron) and vitamins (B and E groups) [4]. Cereals are grown in different countries, e.g. China, United States and India and their growth depends on climatic and environmental requirements but also on the eating habits of different populations [5]. The term cereals include several species all belonging to the Graminaceae family; corn (genus Zea) is the most produced crop globally with 1.1 billion tons, followed by wheat (genus Triticum) with 760.9 million tons and rice (genus Oryza) with 756.7 million tons [6]. Corn, like other agricultural products, is used as food (human and livestock) and for industry and biofuels fields [7,8]. Corn pesticides residues, deriving from treated plantations, can enter into food and feed products during production, processing, storage, transport and marketing and may cause adverse effect on human and animal health [9]. Consequently, monitoring pesticide residues in agricultural products is a very important tool to reduce the potential negative effects of these compounds on consumers and other organisms. To protect human health and guarantee food safety and quality, the European Commission (EC) established legally tolerated amounts of residues in food and feed, known as Maximum Residue Levels (MRLs), usually in the order of µg/kg or mg/ kg. Nowadays, due to the need to achieve low detection limits in line with MRLs, the huge variety of chemical properties of pesticides and the complexity of food samples require dedicated sample preparation and selective, reliable and sensitive analytical techniques. Among them, numerous sample pre-treatments for the determination of pesticide in various food matrices have been reported in the literature, such as solidphase extraction (SPE) [10], solid-phase micro-extraction (SPME) [11], matrix solid-phase dispersion (MSPD) [12,13], microwave-assisted extraction (MAE) [14], solid–liquid extraction (SLE) [10,15], liquid- –liquid extraction (LLE) [16], pressurized solvent extraction (PSE) [17], or the most common procedure, QuEChERS (quick, easy, cheap, effective, rugged and safe) method [18]. The latter involves two different steps: the first one is a salting-out solid–liquid extraction and the second one is a clean-up using dispersive-SPE (d-SPE). This method stands out for its large applicability on food commodities and the satisfactory extraction efficiency for a broad number of compounds. However, like other similar procedures reported in the literature, it involves several steps and a clean-up step, making the sample treatment process long and tedious. Therefore, despite being a relatively simple extraction system, it can be time-consuming, especially when dealing with a large number of samples for screening. Additionally, this procedure requires significant volumes of organic solvent. On the other hand, due to an ever-increasing demand for determining compounds at low concentration levels in complex matrices, new “green”solvents have been suggested as key subjects in Green Chemistry in order to reduce the intensity of anthropogenic activities related to analytical laboratories [19]. Among them, ionic liquids, superheated water, supercritical fluids, surfactants, bioderived solvents and deep eutectic solvents have been the most applied in the extraction process. Efforts are therefore aimed at reducing the amount of solvent used in sample treatment, and, if possible, substituting it with greener alternatives [19]. Regarding analytical techniques, the most used are GC–MS for the determination of volatile, semi-volatile or low-polarity pesticides [20,21] and HPLC-MS for the determination of polar or semi-polar compounds [12,22]. Furthermore, triple quadrupole mass spectrometry in Multi Reaction Monitoring (MRM) mode or in Selected Reaction Monitoring (SRM) mode is the most widely used. On the other hand, one-dimensional (1D) separation capability can be further improved by using a multi-dimensional (MD) technique such as comprehensive twodimensional gas chromatography (GC×GC) or liquid chromatography (LC×LC). Briefly, the analytes eluting from a first column ( 1 D) are collected and re-injected, using a modulator, into a secondary column ( 2 D) with a different selectivity. Regarding pesticides in food samples, the GC×GC technique has already been largely outlined. For example, it was used to detect different pesticides in green tea [23] and olive oils [24]. On the contrary, the LC×LC mode has been scarcely studied in the field of pesticides to date. To the best of the authors’knowledge, only two applications of LC×LC have been reported; the former supplied the determination of 24 pesticides (17 chiral and 7 achiral) and the setup consisted of the combination of a chiral column in the 1 D and an achiral column in the 2 D[25]; the latter involved the analysis of over a hundred of pesticides in starch and polenta samples by using a pentafluorophenyl column in the 1 D and a C18 column in the 2 D[26]. Other applications were developed through the heart-cutting mode (LC-LC), where only discrete fractions from the 1 D to the 2 D are transferred [27]. Sometimes, the chromatographic separation of pesticide residues in complex matrices is challenging and cannot be achieved using one-dimensional chromatography. To overcome these limitations, we decided to develop a method based on comprehensive twodimensional chromatography coupled with mass spectrometry (LC×LC-MS/MS), which allows for the reduction of potential matrix effects and considerably reduces (or even avoids) sample pretreatment [26,27]. Therefore, the main goals of the present study were: 1) to employ a previous validated analytical method based on LC×LC-MS/MS for the multiresidue determination of up to hundred pesticides in five different corn-based products viz. tortilla, corn flakes, corn cake, starch and polenta. 2) to develop a single and simple sample preparation step (SLE using circa 5 mL of acetonitrile), without further clean-up steps in order to exploit the potential of the LC×LC approach. 3) to evaluate the sample procedure and analytical method with the AGREE metric, Red-Green- Blue model and BAGI. 2. Materials and methods 2.1. Chemicals, reagents and columns Acetonitrile (ACN), methanol (MeOH), acetone (Ace), ethyl acetate (EtOAc), formic acid (HCOOH) ammonium formate (NH 4 HCO 2 ) and water, all LC-MS grade ≥99.9 %, were acquired from Merck Life Science (Merck KGaA, Darmstadt, Germany). Pesticide reference standards (listed in Table S1), and triphenylphoshate (TPP) were acquired from Merck Life Science (Merck KGaA, Darmstadt, Germany) with a purity higher than 98 %. Stock solutions of pesticides (1000 mg/L) were dissolved in ACN, EtOAc, MeOH, Ace or water according to the single pesticide and stored in amber vials at −20 ◦C. TPP was used as internal standard (IS): a stock solution (1000 mg/L) was prepared and stored in a amber vial at −20 ◦C. The columns employed were: Ascentis Express F5 (150 ×1.0 mm I. D.; 2.7 μ md.p., 90 Å) and Ascentis Express C18 (50 ×4.6 mm I.D.; 2.7 μ md.p., 90 Å) for the 1 D and 2 D, respectively. Two C18 guard columns (5 ×4.6 mm I.D., 5 µmd.p.) were purchased from Merck Life Science (Merck KGaA, Darmstadt, Germany). 2.2. Samples, sample preparation and matrix-matched calibration curves In this study, five different corn-based products were analyzed. Tortilla, corn flakes, corn cake, starch and polenta were purchased in a local supermarket. K. Arena et al. Microchemical Journal 205 (2024) 111298 3 The samples were ground, 1.5 g of each sample was placed into an Eppendorf tube and extracted with 3 mL of ACN. Afterwards, each sample was shaken by hand for 5 min, vortexed for 15 min with an IKA MS 3 basic shaker (Werke GmbH &Co. KG-Staufen, Germany) and again shaken by hand for 5 min. In the next step, the sample was centrifuged at 3000 rpm for 5 min, using a Neya 16R centrifuge (Neya Centrifuge, Carpi, Italy). The upper ACN extract was evaporated (EZ-2 Genevac, SP Industries, Warminster, England), and redissolved in 1.5 mL of ACN. Before the LC×LC-MS/MS analysis the sample was filtered through a 0.45 μ m membrane. Calibration standard solutions were prepared in ACN, whilst matrixmatched calibration curves were built by using a spiked blank vegetable sample (with no detectable pesticides) at the following concentration points: 5, 10, 25, 50, 100, 500, and 1000 µg/L. The concentrations were selected, in correlation to the MRLs present in the EU Pesticides Database [28]. 2.3. LC×LC-MS/MS conditions The instrumental setup was the same previously developed and validated [26] LC×LC analyses were performed on a system consisting of Comprehensive Two-Dimensional Liquid Chromatograph Nexera-e (Shimadzu, Kyoto, Japan), equipped with a CBM-40 controller, two LC-40BX3 dual plunger parallel-flow pumps, a CTO-40C column oven, a SIL-40CX3 autosampler, two high speed/high-pressure two-position sixports switching valves with a micro-electric actuator (model FCV-32 AH, 1.034 bar; Shimadzu, Kyoto, Japan). The LC×LC instrument was coupled to an LCMS-8050 triple quadrupole mass spectrometer, through an ESI source (Shimadzu, Kyoto, Japan). The LC×LC-LCMS-8050 system and the switching valves were controlled by the Shimadzu Labsolution software version 6.116 (Shimadzu, Kyoto, Japan). 1 D separation: The separation was carried out at 30 ◦C with an Ascentis Express F5 with a flow rate of 25 µL/min and the injection volume was 1 µL. The binary mobile phases were water (A) and ACN containing 10 % of water v/v (B), both with 0.1 % HCOOH and 5 mM NH 4 HCO 2 v/v. Gradient elution was initiated with 2 % of B and it was maintained for 10 min, the B concentration was increased to 40 % at 20 min, then to 70 % at 40 min, at the end to 100 % at 51 min until 61 min. Modulation: Two high speed/high-pressure two-position, six-port switching valves with a micro-electric actuator equipped with two identical C18 guard columns. The modulation time was 1 min. 2 D separation: The separation was carried out at 30 ◦C with an Ascentis Express C18 with a flow rate of 2 mL/min. The binary mobile phases were water (C) and ACN containing 10 % of water v/v (D), both with 0.1 % HCOOH and 5 mM NH 4 HCO 2 v/v. A multi-segmented-in- fraction gradient was as follows: first step (from 0 to 22 min) the gradient elution was initiated with 30 % D, then the D concentration was increased to 60 % D at 0.89 min and at the end the column was equilibrated under the initial condition for 0.11 min. The second step (from 22 to 36 min) the gradient elution was initiated with 60 % D, then the D concentration was increased to 80 % D at 0.89 min and at the end the column was equilibrated under the initial condition for 0.11 min. In the third step (from 36 to 51 min) the gradient elution was initiated with 70 % D, then the D concentration was increased to 90 % D at 0.89 min, at the end the column was equilibrated under the initial condition for 0.11 min. MS condition: The flow eluting from the 2 D was split to obtain a flow of circa 800 µL/min to introduce in MS. Acquisition mode: scan, using a mass range from 100 to 600 m/zin positive and negative modes. Nebulizing gas flow (N 2 ) 3 L/min; drying gas flow (N 2 ) 10 L/min; event time 0.206 s for each event; interface temperature, 350 ◦C; desolvation line (DL) temperature, 600 ◦C; heat block temperature 300 ◦C. The multiple-reaction monitoring (MRM) was used for the MS/MS acquisition. Two MRM transitions were used for each target pesticide (Quantifier and qualifier). The transitions were selected based on the selectivity and intensity for the analytes. Table S1 reports the MRM transitions, with the precursor ion [M+H] + or [M−H] - , quantifier (Q) and qualifier (q) ions and collision energy (CE) for both transitions. 3. Results and discussion Nowadays, the need for analytical methods for the simultaneous determination of multiple chemical classes in a single analytical run is rising. It is well known that the MD-LC approach provides the separation power that could not be attained by conventional LC techniques, thanks to the two (or more) different separation mechanisms allowing varying selectivity for each compound. In this regard, this technique can be used for sample clean-up by achieving a reduction of the matrix effect and eliminating the interferences of the samples. The LC×LC-MS/MS system can provide a simple, robust and accurate method for the determination and quantification of pesticides in food products. In fact, in this work, all the advantages of the LC×LC were exploited to achieve a gain in simplicity in the sample preparation step, to reduce the matrix effects and to obtain more accurate quantification of the pesticides. In a previous work for the first time a multi-class analysis of over a hundred pesticides in two corn-based products, by using a “reduced” QuEChERS-based method as extraction and clean-up procedure prior to the analysis, was developed [26]. The novelty featuring the present research is correlated to the use of the LC×LC-MS/MS for the determination of one hundred and thirteen pesticides in different corn products, by a single sample preparation step (SLE using ACN), without additional cleaning step (Figure S1). 3.1. Method validation and other calculations The performance of the method validation was evaluated according to the linearity, limit of detection (LoD), limit of quantification (LoQ), precision (intra- and inter-day), accuracy, recovery and matrix effect following the European Commission DG-SANTE [29] and the European Committee for Standardization (CEN) [30] guidelines. Calibration curves of the standard compounds in pure solvent and in matrices were carried out considering a dynamic range from 5 to 1000 μ g/L and measured by the lack-of-fit test P-values (Plof %). A matrix-matched calibration is highly recommended and required due to the complexity of matrices, for this reason, was employed to evaluate the final method. P-values were >5 % in all cases, indicating good linearity within the concentration range for each calibration curve (confidence level 95 %). The LoD values in this study were between 0.1–30.2 μ g/L for chlorantraniliprole and cycloxydim, respectively. The LoQ values ranged from 0.3 to 91.6 μ g/L also for chlorantraniliprole and cycloxydim, respectively, with most of these lower than the MRL, as shown in Table 1. Thirteen values slightly exceeded the fixed limits (diethofencarb, ethoprophos, fluazifop, fludioxonil, foramsulfuron, isofetamid, mesosulfuron-methyl, metsulfuron-methyl, oxamyl, penoxsulam, propoxycarbazone, sulfoxaflor, terbufos-sulfoxide), but these can be acceptable according to the SANTE guideline, since the values were not exceeded the MRL when the expanded measurement uncertainty of 50 % is subtracted [29]. All results were considered compliant due to the uncertainty interval since they met x- U<MRL (where x is the measured value and U is x/2), that is, they did not exceed twice the MRL. Intra-day and inter-day precision were assessed by analyzing spiked samples at a concentration of 500 μ g/L and expressed as relative standard deviation (%RSD). The tests were conducted by performing six replicates on the same day, while triplicate analyses were conducted for three consecutive days for intra-day and inter-day, respectively. The LoDs, LoQs and precision values are shown in Table 1. Regarding the intra-day assay, the RSD values ranged from 0.6 % K. Arena et al. Microchemical Journal 205 (2024) 111298 4 Table 1 Evaluated pesticides in corn starch and polenta, along with EU MRL value ( μ g/L), linear dynamic range (LDR) ( μ g/L), LoDs and LoQs ( μ g/L), intra- and inter-day precision (500 μ g/L). Compound Corn starch Corn polenta EU MRL ( μ g/L) Precision (500 μ g/L) LDR LoD LoQ LDR LoD LoQ Intra-day (%) Inter-day (%) 3-Hydroxycarbofuran LoQ-1000 0.3 0.9 LoQ-500 0.7 2.2 10 5.8 8.1 Acephate LoQ-1000 0.2 0.6 LoQ-500 3.0 9.2 10 4.1 7.6 Ametoctradin LoQ-1000 3.0 9.2 LoQ-500 3.3 10.0 10 1.5 2.2 Ancymidol LoQ-1000 1.5 4.7 LoQ-500 1.7 5.2 10* 4.2 5.8 Asulam LoQ-1000 4.4 13.2 LoQ-1000 3.4 10.3 50 5.7 7.3 Atrazine LoQ-1000 6.8 20.6 LoQ-500 13.5 41.0 50 3.9 2.9 Azoxystrobin LoQ-1000 4.8 14.7 LoQ-500 5.6 16.9 20 1.0 6.6 Beflubutamid LoQ-1000 3.1 9.4 LoQ-500 0.8 2.4 10 1.4 1.1 Benalaxyl LoQ-1000 2.5 7.5 LoQ-500 1.8 5.5 10 1.5 1.2 Bensulfuron-methyl LoQ-1000 1.9 5.8 LoQ-500 2.1 6.2 10 1.9 1.4 Bifenazate LoQ-1000 0.9 2.7 LoQ-500 3.4 10.2 20 1.8 2.8 Bromoxynil LoQ-1000 5.8 17.5 LoQ-500 13.2 40.1 100 3.9 0.5 Bupirimate LoQ-1000 0.7 2.1 LoQ-500 3.3 9.9 10 1.4 7.5 Carbaryl LoQ-1000 12.1 36.7 LoQ-500 6.3 19.0 500 1.5 5.8 Carbetamide LoQ-1000 0.8 2.5 LoQ-500 2.1 6.4 10 2.4 5.5 Carbofuran LoQ-1000 2.7 8.2 LoQ-500 0.9 2.6 10 1.3 3.5 Carboxin LoQ-1000 7.5 22.7 LoQ-500 5.8 17.6 30 1.2 4.2 Chlorantraniliprole LoQ-1000 0.1 0.3 LoQ-500 6.1 18.5 20 8.6 4.3 Clethodim LoQ-1000 12.1 36.8 LoQ-500 14.9 45.2 100 4.1 2.5 Cycloxydim LoQ-1000 23.7 71.9 LoQ-1000 30.2 91.6 200 5.0 3.8 Cyflufenamid LoQ-1000 0.5 1.5 LoQ-500 1.8 5.4 10 4.4 2.5 Cyproconazole LoQ-1000 2.1 6.3 LoQ-500 4.7 14.2 100 8.2 3.7 Cyprodinil LoQ-1000 6.6 20.0 LoQ-500 5.7 17.2 20 2.0 5.4 Dazomet LoQ-1000 0.7 2.1 LoQ-500 1.8 5.4 10* 3.3 7.9 Diazinon LoQ-1000 2.2 6.7 LoQ-500 3.2 9.8 10 1.6 5.1 Diethofencarb LoQ-1000 3.6 10.9 LoQ-500 3.3 9.9 10 1.5 2.9 Dimethoate LoQ-1000 3.2 9.8 LoQ-500 3.3 10.0 10 6.4 7.4 Dimoxystrobin LoQ-1000 2.2 6.7 LoQ-500 3.1 9.4 10 5.6 1.6 Dinoseb LoQ-1000 0.2 0.7 LoQ-500 4.9 15.0 20 2.5 0.5 Ethametsulfuron-methyl LoQ-1000 0.3 0.9 LoQ-500 0.4 1.1 10 3.1 3.5 Ethoprophos LoQ-1000 3.5 10.6 LoQ-500 1.4 4.1 10 0.8 2.4 Etoxazole LoQ-1000 1.1 3.3 LoQ-500 1.2 3.7 10 2.5 4.1 Fenamidone LoQ-1000 1.0 3.0 LoQ-500 2.3 6.9 10 2.8 3.3 Fenamiphos LoQ-1000 1.4 4.2 LoQ-500 5.6 16.8 20 5.2 4.7 Fenazaquin LoQ-1000 1.2 3.7 LoQ-500 0.8 2.5 10 8.6 4.1 Fenoxycarb LoQ-1000 3.2 9.7 LoQ-500 0.4 1.1 10 2.7 5.7 Fenpyrazamine LoQ-1000 2.3 7.1 LoQ-500 1.3 4.0 10 1.4 7.8 Fenpyroximate LoQ-1000 0.8 2.4 LoQ-500 2.7 8.3 10 5.8 3.7 Fluazifop LoQ-1000 2.4 7.4 LoQ-500 4.2 12.7 10 4.3 0.7 Fludioxonil LoQ-1000 4.0 12.3 LoQ-500 2.6 7.7 10 1.6 3.2 Fluometuron LoQ-1000 1.2 3.6 LoQ-500 1.5 4.5 5 1.8 1.9 Fluopicolide LoQ-1000 3.1 9.4 LoQ-500 2.1 6.5 10 1.3 3.0 Flurtamone LoQ-1000 2.0 6.2 LoQ-500 1.1 3.2 10 1.7 1.5 Flusilazole LoQ-1000 0.5 1.4 LoQ-500 2.4 7.2 10 3.0 1.5 Flutianil LoQ-1000 1.6 4.7 LoQ-500 0.6 1.9 10 3.3 1.7 Flutriafol LoQ-1000 2.1 6.3 LoQ-500 3.0 9.1 10 3.2 1.2 Fluxapyroxad LoQ-1000 2.5 7.7 LoQ-500 1.3 3.9 10 1.1 8.2 Foramsulfuron LoQ-1000 4.1 12.4 LoQ-500 2.1 6.4 10 3.2 7.9 Fosthiazate LoQ-1000 1.1 3.4 LoQ-1000 2.6 7.9 20 3.3 2.5 Fuberidazole LoQ-1000 2.0 6.1 LoQ-500 1.5 4.5 10 2.3 6.2 Imazapic LoQ-500 0.8 2.4 LoQ-500 3.1 9.5 10 4.8 7.0 Imazapyr LoQ-1000 4.8 14.4 LoQ-500 5.3 16.1 50 2.5 9.6 Ioxynil LoQ-1000 0.4 1.1 LoQ-500 3.0 9.2 10 1.2 1.6 Iprovalicarb LoQ-1000 3.1 9.4 LoQ-500 2.0 6.2 10 5.6 3.6 Isofetamid LoQ-1000 1.3 4.0 LoQ-500 6.5 19.7 10 5.6 3.8 Isoprothiolane LoQ-1000 0.7 2.0 LoQ-500 3.0 9.0 10 3.3 13.8 Isoproturon LoQ-1000 1.0 3.0 LoQ-500 2.6 7.8 10 1.2 2.9 Isopyrazam LoQ-1000 3.2 9.8 LoQ-500 2.0 6.0 10 1.5 2.8 Isoxaben LoQ-1000 3.3 10.0 LoQ-500 1.4 4.3 100 1.7 5.0 Kresoxim-methyl LoQ-1000 1.4 4.1 LoQ-500 1.0 3.1 10 2.0 1.0 Mandestrobin LoQ-1000 2.2 6.6 LoQ-500 0.5 1.5 10 2.2 2.3 Mepronil LoQ-1000 2.8 8.5 LoQ-500 2.3 6.8 10 1.8 1.3 Mesosulfuron-methyl LoQ-1000 2.9 8.8 LoQ-500 3.8 11.4 10 3.9 3.4 Mesotrione LoQ-1000 2.2 6.7 LoQ-500 2.4 7.2 10 1.9 8.6 Metalaxyl LoQ-1000 6.6 19.9 LoQ-500 2.0 6.0 20 4.9 4.1 Metamitron LoQ-1000 3.3 10.0 LoQ-500 2.7 8.0 10 2.9 15.1 Methabenzthiazuron LoQ-1000 2.0 6.2 LoQ-500 1.3 4.0 10 0.6 2.9 Methamidophos LoQ-1000 2.8 8.4 LoQ-500 1.6 5.0 10 3.1 10.2 Methomyl LoQ-1000 4.8 14.6 LoQ-500 3.4 10.4 20 3.0 9.1 Methoprotryne LoQ-1000 3.1 9.5 LoQ-500 1.4 4.1 10* 7.6 2.8 Metribuzin LoQ-1000 1.5 4.4 LoQ-500 6.0 18.2 100 2.1 3.3 Metsulfuron-methyl LoQ-1000 5.3 16.2 LoQ-500 2.8 8.6 10 1.3 3.3 (continued on next page) K. Arena et al. Microchemical Journal 205 (2024) 111298 5 (methabenzthiazuron) to 8.6 % (chlorantraniliprole); considering the inter-day evaluation, the values were in the range from 0.4 % (triazophos) to 15.1 % (metamitron). Considering the accuracy (Table 2), it was evaluated at 50 μ g/L and 500 μ g/L levels. At the lower concentration, obtained values ranged from 80 % to 120 % and the average value was 100 %. While at the highest concentration, the values ranged from 60 % to 120 % and the average value was 97 %. The recovery and RSDs for the pesticides with the two spike levels (50 and 500 μ g/kg) are shown in Table 2. Recovery rates between 70 and 120 % were accepted. Concerning the lower concentration level, the recovery values ranged from 28 % (prochloraz) to 180 % (cycloxydim), with RSD values from 2 % to 23 %; at the highest concentration level, recovery values ranged from 16 % (asulam) to 175 % (tebufenozide), with RSDs in the range 1–20 %. Although not all the recovery values fall within the 70–120 % range, in accordance with SANTE/11312/2021 guidelines, the results obtained can be accepted given the satisfactory RSD values (almost always less than 20 %). Matrix effect values were calculated by comparing the relative peak area of the compounds in the matrix-matched standards and in the pure solvent (ACN). The obtained values were then expressed as a percentage (%ME) a value below 100 % describes ionization suppression, while a value over 100 % ionization enhancement. According to SANTE guidelines, ME values should be in the range of 70–120 %. This phenomenon in LC-MS usually depends on matrix constituents coeluting with each compound, making it difficult to quantify the analytes. To minimize matrix effects, a proper chromatographic separation of the analytes from matrix components can play an important role. For this purpose, LC×LC can be applied thanks to the two independent chromatographic separations, resulting in higher resolving power and enhanced chromatographic efficiency. As a result, better separation between analytes and matrix components can be expected, resulting in reduced matrix effects. All the calculated %ME values are reported in Fig. 1. The %ME values ranged from 10 % (Sethoxydim) to 161 % (Acephate), with a different value between the corn products observed. This high variability may be explained by the complexity of the matrix explored and even more by the limited sample preparation and the absence of the clean-up and thus of other matrix components (sugars, pigments) that may interfere with the compounds of interest. As can be seen in Fig. 1, tortilla, corn flakes and corn cake samples showed similar behavior, proving that more than half of the pesticides (78 % in tortilla, 88 % in corn flakes, 83 % corn cake), fell into the ME% range of 80–120 %, while the starch and polenta samples showed ME% values below 80 % (59 % in starch and 83 % in polenta). Only a 3 % of compounds showed signal enhancement. It can be correlated by the chemical composition of the foodstuff, i.e. starch and polenta are composed mostly of water (circa 14 g/100 g), carbohydrates (circa 80 g/ 100 g) and a good content of sugars, probably these external constituents with similar physiochemical properties to the target analyte, may Table 1 (continued) Compound Corn starch Corn polenta EU MRL ( μ g/L) Precision (500 μ g/L) LDR LoD LoQ LDR LoD LoQ Intra-day (%) Inter-day (%) Monocrotophos LoQ-1000 3.7 11.1 LoQ-500 3.8 11.4 20 5.3 9.8 Monolinuron LoQ-1000 3.1 9.5 LoQ-500 1.7 5.1 10 4.7 5.7 Napropamide LoQ-1000 0.5 1.6 LoQ-500 2.5 7.4 10 1.4 2.0 Nicosulfuron LoQ-1000 0.9 2.9 LoQ-500 0.7 2.0 10 4.9 10.9 Oxadixyl LoQ-1000 2.3 7.1 LoQ-500 1.3 4.1 10 2.6 2.7 Oxamyl LoQ-500 1.2 3.6 LoQ-500 4.0 12.0 10 4.4 6.7 Oxasulfuron LoQ-1000 2.7 8.2 LoQ-500 2.0 6.1 10 2.0 5.9 Oxathiapiprolin LoQ-1000 1.3 4.0 LoQ-500 2.3 7.0 10 2.6 3.2 Penoxsulam LoQ-1000 0.9 2.8 LoQ-500 3.7 11.1 10 3.9 5.2 Penthiopyrad LoQ-1000 1.7 5.2 LoQ-500 0.8 2.3 10 7.8 1.0 Picolinafen LoQ-1000 5.0 15.2 LoQ-500 3.2 9.8 50 4.3 1.4 Pinoxaden LoQ-1000 3.4 10.3 LoQ-500 3.1 9.5 20 2.1 4.4 Pirimicarb LoQ-1000 3.3 9.9 LoQ-500 2.7 8.2 50 2.8 1.7 Pirimiphos-methyl LoQ-1000 15.3 46.4 LoQ-500 1.3 3.9 500 2.5 10.4 Prochloraz LoQ-1000 2.9 8.8 LoQ-500 5.0 15.2 30 1.4 2.1 Profenofos LoQ-1000 3.2 9.8 LoQ-500 3.5 10.0 10 3.1 1.3 Propachlor LoQ-1000 5.1 15.5 LoQ-500 3.2 9.7 20 3.1 0.9 Propamocarb LoQ-500 1.3 4.0 LoQ-500 0.9 2.9 10 2.6 10.0 Propaquizafop LoQ-1000 3.3 10.0 LoQ-500 3.8 11.6 20 1.1 4.3 Propoxycarbazone LoQ-1000 0.3 1.0 LoQ-1000 9.7 29.4 20 2.9 3.5 Proquinazid LoQ-1000 2.9 8.7 LoQ-500 2.6 7.9 10 0.7 7.9 Pyrazophos LoQ-1000 3.0 9.1 LoQ-500 2.3 6.9 10 4.1 5.2 Pyriproxyfen LoQ-1000 1.5 4.6 LoQ-500 2.6 7.8 50 1.3 6.4 Pyroxsulam LoQ-1000 1.6 4.9 LoQ-500 3.1 9.3 10 8.6 6.8 Quinalphos LoQ-1000 2.9 8.7 LoQ-500 0.4 1.4 10 4.8 1.7 Sethoxydim LoQ-1000 12.1 36.6 LoQ-1000 10.1 30.5 100 2.0 0.5 Spirotetramat LoQ-1000 3.5 10.6 LoQ-500 4.0 12.1 20 2.7 3.4 Sulfoxaflor LoQ-1000 6.0 18.2 LoQ-500 2.2 6.7 10 2.3 2.0 Tebufenozide LoQ-1000 2.6 8.0 LoQ-500 1.5 4.4 10 1.0 5.3 Terbufos-sulfoxide LoQ-1000 5.3 16.2 LoQ-1000 5.4 16.4 10* 2.5 11.4 Terbuthylazine LoQ-1000 3.3 9.9 LoQ-500 3.2 9.7 20 1.9 2.4 Thiabendazole LoQ-1000 1.2 3.8 LoQ-500 1.7 5.1 10 2.0 2.5 Thionazin LoQ-1000 0.3 0.8 LoQ-500 3.1 9.5 10* 4.5 0.4 Tralkoxydim LoQ-1000 0.5 1.6 LoQ-500 3.1 9.5 10 2.1 2.4 Triasulfuron LoQ-1000 0.9 2.7 LoQ-500 3.1 9.3 10 1.9 4.8 Triazophos LoQ-1000 4.6 13.9 LoQ-500 2.1 6.5 20 2.0 0.4 Tricyclazole LoQ-1000 1.7 5.3 LoQ-500 2.1 6.3 10 2.7 5.7 Trifloxystrobin LoQ-1000 6.2 18.8 LoQ-500 1.7 5.2 20 0.6 7.1 Trinexapac-ethyl LoQ-1000 1.8 5.5 LoQ-500 2.9 8.9 10* 2.2 2.0 Valifenalate LoQ-1000 3.2 9.7 LoQ-500 1.4 4.2 10 3.6 2.2 Zoxamide LoQ-1000 4.3 12.9 LoQ-500 6.2 18.8 20 3.7 0.6 * For pesticides without an official MRL, a limit of 10 μ g/L was applied. K. Arena et al. Microchemical Journal 205 (2024) 111298 6 Table 2 Evaluated pesticides in Tortilla, Corn flakes, Corn cakes, Starch and Polenta, along with% recovery (Rec.) and accuracy (Acc.) values at two different concentration levels 50 and 500 ppb. Compounds Tortilla Corn flakes Corn cakes Starch Polenta 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) RSD (%) 3-Hydroxycarbofuran 77 ± 15 102 70 ± 14 113 72 ± 14 112 88 ±7 113 71 ± 18 85 71 ± 16 92 89± 11 107 96 ±9 110 87 ± 10 112 95 ± 12 100 Acephate 79 ± 13 116 88 ± 5 102 84 ± 15 96 81 ±8 120 75 ± 17 96 46 ± 11 81 108 ± 15 103 103 ± 14 86 88 ± 16 117 101 ± 9 100 Ametoctradin 58 ± 20 93 55 ± 8 95 71 ± 18 98 72 ±5 90 46 ± 11 95 42 ± 10 82 105 ± 10 109 87 ± 14 96 87 ± 11 111 98 ±6 95 Ancymidol 93 ±3 104 67 ± 13 103 78 ± 15 113 70 ± 18 108 70 ± 12 94 50 ± 10 82 108 ± 13 95 90 ±8 87 102 ± 17 101 95 ±7 92 Asulam 33 ± 11 82 27 ± 17 96 66 ±7 109 16 ±2 80 97 ± 12 114 58 ± 5 117 177 ± 8 80 96 ± 13 83 173 ± 6 84 96 ± 10 119 Atrazine 131 ± 12 118 70 ± 5 107 102 ± 18 84 106 ± 19 83 89 ±2 110 55 ± 2 98 112 ± 8 99 100 ± 9 102 72 ± 18 114 99 ± 13 112 Azoxystrobin 71 ± 14 86 72 ± 9 97 70 ±7 84 96 ±4 117 72 ± 18 98 71 ± 19 82 94 ±6 97 90 ± 10 89 93 ±6 106 86 ±9 103 Beflubutamid 48 ± 17 81 47 ± 13 82 21 ± 13 81 59 ± 15 92 35 ± 15 94 31 ± 16 75 95 ± 11 119 103 ± 9 93 77 ±5 107 100 ± 16 98 Benalaxyl 50 ± 13 84 52 ± 9 104 57 ±7 97 69 ±9 115 52 ± 11 84 64 ± 12 84 90 ±4 100 73 ±8 98 84 ± 18 113 84 ±9 107 Bensulfuron-methyl 94 ± 14 95 88 ± 7 89 86 ± 17 99 97 ±9 111 52 ±5 90 27 ± 19 88 87 ± 16 93 93 ± 11 87 102 ± 20 93 106 ± 10 94 Bifenazate 36 ±2 97 40 ± 9 60 50 ±9 102 34 ±5 110 49 ± 12 89 78 ± 16 78 93 ± 13 105 93 ± 10 95 93 ± 18 107 93 ± 16 102 Bromoxynil 84 ±2 94 59 ± 16 107 72 ± 20 111 74 ±9 112 81 ± 15 89 33 ± 1 84 91 ± 14 111 89 ± 13 82 70 ± 16 119 85 ± 16 100 Bupirimate 49 ± 18 93 39 ± 12 80 34 ±5 93 40 ±9 83 57 ± 17 119 66 ± 2 94 84 ± 12 102 77 ± 13 96 82 ± 14 112 88 ±8 96 Carbaryl 74 ± 17 92 72 ± 17 94 81 ± 17 92 72 ± 17 88 46 ± 13 98 55 ± 18 92 71 ± 20 91 75 ± 10 95 78 ± 20 109 84 ± 11 111 Carbetamide 59 ± 15 86 71 ± 4 118 87 ± 11 89 94 ± 18 112 70 ± 11 113 62 ± 14 85 94 ±8 114 89 ±9 100 95 ±8 89 113 ± 7 92 Carbofuran 74 ± 16 93 72 ± 7 101 71 ± 19 100 82 ± 16 97 52 ± 18 116 63 ± 19 84 93 ± 11 101 83 ± 10 97 102 ± 15 118 91 ±7 106 Carboxin 41 ±9 84 44 ± 10 82 61 ±8 106 64 ± 16 113 63 ±9 104 56 ± 3 95 92 ± 12 113 84 ± 12 97 83 ± 15 106 79 ± 13 94 Chlorantraniliprole 93 ±5 81 70 ± 16 85 73 ± 14 114 86 ±7 105 30 ± 12 86 43 ± 8 117 79 ± 10 110 101 ± 10 106 102 ± 15 87 81 ± 12 87 Clethodim 70 ±6 90 61 ± 6 116 90 ± 12 111 80 ±7 105 73 ±2 84 36 ± 19 109 99 ± 20 93 95 ± 13 105 95 ± 11 110 83 ± 19 107 Cycloxydim 154 ± 8 86 41 ± 20 88 53 ±1 118 59 ±9 90 180 ± 20 116 78 ± 7 91 176 ± 15 95 66 ± 15 113 74 ± 18 96 91 ±6 118 Cyflufenamid 59 ± 18 80 59 ± 12 84 70 ± 10 82 78 ± 12 93 47 ±7 90 54 ± 11 83 83 ± 13 105 83 ± 19 93 105 ± 17 89 87 ± 12 94 Cyproconazole 166 ± 11 107 75 ± 12 85 96 ±8 83 126 ± 10 103 82 ±7 110 34 ± 5 103 87 ± 10 100 83 ± 17 96 44 ± 13 83 86 ± 10 91 Cyprodinil 87 ± 17 92 56 ± 9 82 77 ±7 85 78 ± 13 108 79 ±9 114 54 ± 18 89 99 ±8 104 97 ± 10 92 93 ± 16 83 105 ± 9 99 Dazomet 29 ± 19 83 18 ± 8 60 29 ±1 101 19 ±5 70 47 ± 16 99 23 ± 2 89 76 ± 20 119 92 ± 14 81 83 ± 12 103 88 ± 18 94 Diazinon 51 ± 20 82 55 ± 19 83 34 ± 12 103 89 ± 18 94 53 ± 12 113 34 ± 13 88 94 ± 10 93 105 ± 15 82 82 ± 16 108 92 ± 12 96 (continued on next page) K. Arena et al. Microchemical Journal 205 (2024) 111298 7 Table 2 (continued) Compounds Tortilla Corn flakes Corn cakes Starch Polenta 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) RSD (%) Diethofencarb 72 ± 19 118 80 ± 3 96 96 ± 10 99 65 ± 14 102 74 ±5 99 45 ± 6 81 85 ± 11 105 90 ± 13 95 81 ± 10 101 101 ± 15 89 Dimethoate 70 ± 10 119 60 ± 7 116 49 ±8 96 62 ± 16 111 70 ± 10 114 57 ± 20 83 96 ± 15 117 88 ±9 92 92 ± 11 115 98 ± 18 98 Dimoxystrobin 48 ± 12 88 52 ± 13 103 65 ± 10 105 74 ± 10 112 63 ± 14 83 38 ± 15 81 99 ±4 97 82 ± 10 97 83 ± 11 98 80 ±8 91 Dinoseb 84 ± 14 113 61 ± 11 78 99 ± 20 100 66 ± 10 89 79 ±8 99 43 ± 4 82 99 ±9 107 85 ±9 94 94 ± 18 92 92 ±7 93 Ethametsulfuronmethyl 109 ± 4 87 88 ± 17 106 76 ± 15 120 98 ± 15 117 78 ± 11 104 63 ± 16 92 111 ± 19 114 90 ± 14 93 84 ± 16 109 88 ± 16 99 Ethoprophos 53 ± 19 89 50 ± 15 103 62 ± 14 93 65 ±9 114 59 ±9 96 31 ± 16 85 71 ±4 103 86 ± 12 99 78 ± 20 98 78 ± 10 111 Etoxazole 46 ±8 114 56 ± 11 80 70 ± 20 93 66 ± 12 91 38 ± 13 98 31 ± 13 80 96 ± 15 100 79 ±9 98 90 ± 11 117 98 ± 15 100 Fenamidone 51 ± 15 81 72 ± 15 98 78 ± 16 101 96 ± 11 117 87 ± 16 116 48 ± 13 81 101 ± 16 88 95 ±8 97 71 ± 14 107 103 ± 16 106 Fenamiphos 54 ± 20 110 53 ± 6 112 71 ± 16 118 73 ± 16 102 72 ±6 90 42 ± 11 93 95 ±8 102 81 ±8 95 102 ± 8 104 75 ± 12 95 Fenazaquin 58 ±5 84 44 ± 6 67 74 ±6 93 65 ±8 87 37 ± 17 85 49 ± 16 80 114 ± 9 81 92 ± 11 95 97 ±8 111 89 ± 16 100 Fenoxycarb 64 ± 15 89 54 ± 8 73 88 ± 17 89 85 ± 19 84 49 ± 17 87 70 ± 12 88 65 ±9 93 64 ±8 89 83 ± 11 105 95 ± 10 92 Fenpyrazamine 71 ± 17 104 61 ± 13 97 62 ±5 105 82 ±7 120 52 ±5 112 60 ± 16 82 97 ± 10 111 87 ± 14 96 82 ±8 117 101 ± 10 99 Fenpyroximate 54 ± 16 109 54 ± 6 86 70 ± 19 93 85 ± 12 109 49 ± 17 93 53 ± 13 86 80 ±7 87 73 ± 16 99 94 ± 11 85 87 ± 11 100 Fluazifop 77 ± 19 105 70 ± 10 115 103 ± 11 118 77 ±4 115 45 ±4 89 70 ± 16 82 94 ± 10 109 86 ±9 95 80 ±7 106 101 ± 5 100 Fludioxonil 71 ± 13 100 72 ± 13 114 71 ± 16 87 85 ± 16 102 51 ± 15 89 48 ± 17 96 106 ± 12 83 85 ±8 106 95 ± 11 95 82 ± 10 102 Fluometuron 60 ± 11 91 64 ± 8 94 58 ± 11 97 63 ± 16 102 43 ± 11 104 46 ± 17 84 106 ± 12 109 78 ±8 96 94 ±6 110 92 ±6 96 Fluopicolide 79 ± 18 102 73 ± 11 99 60 ± 12 98 81 ±6 113 43 ± 10 113 71 ± 18 91 102 ± 8 106 94 ± 11 99 94 ±9 105 91 ±7 101 Flurtamone 77 ± 19 97 71 ± 11 106 78 ± 15 101 87 ±8 114 57 ± 13 96 61 ± 17 90 99 ± 12 109 91 ± 12 96 91 ± 13 109 91 ± 11 100 Flusilazole 80 ± 17 113 70 ± 2 88 70 ± 15 92 107 ± 15 97 76 ±6 94 70 ± 16 85 97 ± 19 97 92 ± 18 91 81 ± 19 108 95 ±8 97 Flutianil 72 ±7 89 73 ± 11 86 78 ±8 87 104 ± 16 103 72 ± 11 93 78 ± 14 94 118 ± 13 89 97 ± 14 94 94 ± 11 110 98 ±8 98 Flutriafol 87 ± 11 120 81 ± 12 103 108 ± 15 94 73 ±8 118 108 ± 2 104 64 ± 8 94 105 ± 10 105 99 ± 17 93 90 ± 11 97 90 ± 12 102 Fluxapyroxad 89 ± 13 89 99 ± 8 80 73 ± 19 89 73 ± 18 89 71 ±8 113 49 ± 18 80 85 ± 10 81 100 ± 9 106 76 ±7 105 85 ±7 90 Foramsulfuron 48 ±6 113 72 ± 12 93 50 ± 20 88 87 ± 13 96 51 ±3 85 40 ± 2 101 86 ±6 105 92 ± 17 103 103 ± 4 119 97 ±9 83 Fosthiazate 57 ±8 93 82 ± 16 109 81 ±8 110 64 ± 17 84 97 ± 15 89 29 ± 5 95 83 ± 12 119 91 ±3 86 79 ± 10 108 91 ± 16 96 Fuberidazole 66 ± 12 99 72 ± 5 101 95 ± 20 110 75 ± 15 111 43 ± 18 108 89 ± 10 94 98 ± 10 109 88 ± 11 96 95 ±7 98 95 ±8 99 Imazapic 78 ± 19 111 71 ± 9 115 87 ± 17 120 85 ± 16 116 53 ±8 111 73 ± 14 82 91 ± 11 113 92 ± 11 98 82 ± 15 104 101 ± 8 98 (continued on next page) K. Arena et al. Microchemical Journal 205 (2024) 111298 8 Table 2 (continued) Compounds Tortilla Corn flakes Corn cakes Starch Polenta 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) RSD (%) Imazapyr 62 ± 19 108 60 ± 9 109 57 ± 11 111 72 ±8 114 54 ± 15 107 35 ± 19 81 93 ± 12 116 88 ± 13 90 85 ±9 118 93 ± 14 102 Ioxynil 53 ± 18 85 53 ± 13 103 101 ± 14 102 67 ± 12 112 45 ± 13 95 44 ± 9 91 93 ± 19 115 97 ± 15 94 84 ± 11 101 94 ± 11 95 Iprovalicarb 57 ± 13 92 70 ± 9 108 76 ± 18 104 82 ±2 115 62 ± 16 119 71 ± 20 88 100 ± 9 111 89 ±8 91 95 ± 13 112 92 ±4 105 Isofetamid 47 ± 12 114 54 ± 13 102 35 ±9 97 66 ± 17 104 65 ± 18 115 33 ± 6 113 84 ± 16 101 98 ± 16 103 86 ± 19 108 106 ± 17 99 Isoprothiolane 41 ± 17 80 54 ± 18 95 19 ± 18 82 46 ±4 97 75 ±8 102 47 ± 7 102 100 ± 17 86 93 ± 15 81 86 ± 13 96 90 ±8 98 Isoproturon 76 ± 18 113 64 ± 12 103 64 ±7 101 77 ± 14 109 56 ± 11 105 61 ± 20 83 101 ± 8 107 92 ± 10 95 97 ±8 116 95 ±8 98 Isopyrazam 72 ± 11 85 72 ± 13 83 77 ± 20 102 81 ±6 111 56 ± 14 99 39 ± 19 84 93 ± 10 100 88 ±6 99 98 ±9 105 99 ± 10 97 Isoxaben 53 ± 14 107 56 ± 14 110 65 ± 19 96 78 ±7 112 62 ± 19 92 56 ± 15 94 87 ± 17 103 78 ± 11 88 91 ± 15 105 79 ± 10 97 Kresoxim-methyl 74 ±7 83 55 ± 17 94 53 ±7 97 80 ± 14 117 47 ±9 91 24 ± 17 92 98 ± 17 89 87 ± 15 96 77 ± 12 84 84 ±7 103 Mandestrobin 90 ± 19 96 63 ± 14 105 73 ± 10 80 72 ± 10 115 43 ±6 88 40 ± 14 87 113 ± 15 97 103 ± 9 94 86 ± 15 115 89 ± 10 101 Mepronil 68 ± 14 91 63 ± 10 97 84 ± 19 95 84 ±6 116 45 ± 10 107 61 ± 18 80 102 ± 10 100 95 ± 11 100 94 ±8 109 93 ±7 100 Mesosulfuron-methyl 87 ±9 115 82 ± 15 99 53 ± 14 81 83 ± 14 117 51 ± 10 90 25 ± 12 73 93 ± 13 100 82 ± 14 90 108 ± 11 84 98 ± 15 85 Mesotrione 96 ± 19 88 79 ± 9 107 91 ±8 116 92 ± 16 101 52 ±2 82 52 ± 15 91 102 ± 18 84 103 ± 15 100 105 ± 17 96 99 ± 14 100 Metalaxyl 58 ± 17 85 73 ± 12 116 68 ±7 94 75 ± 19 109 52 ± 10 99 57 ± 14 81 98 ± 12 100 82 ± 14 100 95 ±9 112 88 ± 10 108 Metamitron 75 ± 17 104 56 ± 12 83 72 ± 10 95 50 ± 18 90 73 ±3 112 29 ± 10 103 99 ± 12 100 83 ± 13 104 91 ± 11 87 80 ± 11 100 Methabenzthiazuron 72 ± 18 91 71 ± 9 105 81 ±7 99 81 ± 16 120 54 ± 16 113 70 ± 14 85 102 ± 9 108 79 ± 10 100 101 ± 13 108 97 ±8 100 Methamidophos 81 ± 17 107 73 ± 10 89 58 ± 11 84 77 ± 14 88 50 ±2 87 53 ± 2 86 73 ±9 108 78 ±7 89 94 ± 12 115 94 ± 11 100 Methomyl 62 ± 18 84 75 ± 11 105 69 ± 14 89 84 ±8 111 59 ± 19 106 56 ± 13 82 101 ± 9 103 82 ± 12 96 100 ± 9 114 100 ± 11 101 Methoprotryne 68 ± 10 95 76 ± 8 96 65 ± 11 102 80 ± 11 108 43 ± 15 106 33 ± 10 83 98 ± 14 113 80 ± 14 91 96 ± 11 109 85 ±4 103 Metribuzin 72 ± 14 102 57 ± 12 95 66 ± 14 87 64 ± 15 117 57 ± 16 118 52 ± 15 87 92 ± 10 106 84 ±9 100 95 ± 18 108 89 ±7 104 Metsulfuron-methyl 96 ±6 109 108 ±4 96 56 ±3 81 65 ± 17 111 31 ± 16 93 19 ± 2 101 35 ±9 97 93 ±7 93 84 ± 11 101 98 ± 18 110 Monocrotophos 73 ± 19 97 75 ± 6 111 59 ± 12 93 66 ± 15 115 54 ± 11 98 40 ± 15 85 87 ±8 109 83 ± 11 93 93 ± 10 115 95 ±2 100 Monolinuron 75 ± 14 85 72 ± 9 93 67 ± 12 95 81 ± 17 100 44 ±9 102 63 ± 12 80 105 ± 6 105 86 ±7 94 98 ±5 119 103 ± 10 96 Napropamide 97 ± 17 110 58 ± 10 106 49 ± 15 106 58 ± 12 103 50 ±8 90 32 ± 19 101 131 ± 10 111 121 ± 10 98 99 ± 13 107 103 ± 9 97 Nicosulfuron 79 ± 16 89 73 ± 7 86 94 ± 14 106 96 ± 17 97 40 ± 13 86 64 ± 9 90 123 ± 12 109 93 ± 13 105 104 ± 10 113 104 ± 11 108 Oxadixyl 74 ±6 119 65 ± 5 117 61 ±3 86 73 ± 18 105 53 ± 15 89 49 ± 15 88 104 ± 11 114 83 ±9 100 97 ± 11 112 90 ±7 104 (continued on next page) K. Arena et al. Microchemical Journal 205 (2024) 111298 9 Table 2 (continued) Compounds Tortilla Corn flakes Corn cakes Starch Polenta 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb 50 ppb 500 ppb Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) Acc. (%) Rec. (%) RSD (%) Oxamyl 85 ± 16 120 74 ± 12 109 57 ± 12 87 82 ±8 113 42 ± 13 93 63 ± 19 84 102 ± 13 100 93 ±6 91 104 ± 6 119 96 ±7 95 Oxasulfuron 88 ± 12 96 81 ± 15 101 78 ± 16 90 94 ± 15 105 60 ± 12 99 30 ± 17 88 109 ± 13 110 107 ± 11 92 92 ± 11 94 88 ± 18 100 Oxathiapiprolin 93 ± 18 106 56 ± 14 104 104 ± 2 91 120 ± 19 108 72 ± 13 84 61 ± 19 112 107 ± 17 86 77 ± 13 114 73 ± 17 102 108 ± 18 93 Penoxsulam 104 ± 23 101 70 ± 19 96 94 ±6 94 81 ± 20 106 74 ±5 83 32 ± 10 83 119 ± 11 118 80 ± 15 92 84 ± 10 80 95 ± 14 109 Penthiopyrad 98 ±9 97 120 ±5 107 132 ± 13 115 135 ± 16 100 70 ± 10 111 54 ± 12 103 107 ± 6 102 105 ± 13 93 95 ± 15 101 86 ± 10 98 Picolinafen 83 ± 15 91 48 ± 14 99 66 ± 17 93 82 ± 10 84 46 ± 16 87 66 ± 12 119 60 ±7 114 61 ± 14 87 89 ± 10 101 94 ± 12 96 Pinoxaden 46 ± 17 87 48 ± 9 95 56 ± 14 117 76 ±8 114 57 ± 17 83 59 ± 14 87 103 ± 11 105 83 ±8 103 96 ± 17 105 93 ± 12 97 Pirimicarb 67 ± 16 92 81 ± 9 100 71 ± 19 95 76 ± 15 109 52 ± 13 109 56 ± 6 95 107 ± 12 103 88 ± 11 94 99 ±9 97 89 ±8 96 Pirimiphos-methyl 40 ± 20 87 35 ± 12 70 40 ± 12 85 38 ±7 97 41 ± 19 94 49 ± 10 100 106 ± 14 103 85 ± 11 93 87 ±7 107 92 ±9 105 Prochloraz 36 ±5 60 18 ± 11 71 28 ± 17 95 15 ±9 70 58 ± 18 97 23 ± 2 70 113 ± 5 102 90 ±8 95 103 ± 12 88 102 ± 8 101 Profenofos 112 ± 7 95 50 ± 15 85 67 ± 11 115 70 ±9 81 76 ± 18 110 55 ± 7 100 89 ±6 99 85 ± 11 96 102 ± 19 90 107 ± 19 96 Propachlor 72 ± 12 90 58 ± 15 92 77 ± 17 107 81 ± 11 96 84 ±6 115 87 ± 19 86 84 ± 17 100 88 ± 11 91 85 ± 17 107 95 ±8 101 Propamocarb 62 ± 16 107 70 ± 8 95 57 ± 11 104 76 ±8 107 56 ± 13 110 53 ± 14 83 81 ± 10 118 82 ± 12 86 66 ± 16 118 97 ± 18 100 Propaquizafop 95 ±7 92 78 ± 11 82 53 ± 19 90 89 ± 10 94 89 ± 15 94 32 ± 9 81 119 ± 16 91 113 ± 15 82 97 ± 10 88 99 ±7 99 Propoxycarbazone 62 ± 10 110 86 ± 17 105 89 ± 18 90 160 ± 13 85 42 ±9 111 35 ± 10 111 96 ±5 95 91 ± 20 84 52 ± 16 103 91 ± 17 104 Proquinazid 57 ± 13 99 47 ± 10 72 76 ±4 92 76 ±5 102 47 ± 18 81 35 ± 13 105 98 ± 15 109 91 ± 14 96 83 ± 13 114 98 ± 11 99 Pyrazophos 47 ± 11 117 38 ± 7 109 35 ± 11 116 50 ± 16 116 63 ± 15 113 49 ± 14 96 133 ± 18 113 95 ± 10 90 101 ± 10 116 73 ± 18 107 Pyriproxyfen 86 ± 13 92 56 ± 11 65 56 ± 11 88 69 ±9 87 38 ± 15 88 26 ± 10 98 129 ± 7 107 125 ± 11 90 75 ±9 116 98 ± 16 97 Pyroxsulam 84 ± 17 111 78 ± 7 119 90 ± 16 112 98 ± 19 107 60 ± 20 84 48 ± 19 81 124 ± 20 91 83 ± 11 94 77 ± 15 95 93 ± 13 100 Quinalphos 93 ± 11 115 58 ± 14 95 78 ±8 114 73 ± 11 104 70 ± 13 95 30 ± 4 87 96 ±9 102 102 ± 12 88 108 ± 17 93 94 ± 13 95 Sethoxydim 91 ± 10 86 27 ± 17 117 49 ±6 95 47 ± 16 115 37 ± 13 119 26 ± 11 102 106 ± 18 105 150 ± 11 84 66 ± 19 111 74 ±9 108 Spirotetramat 76 ±6 109 77 ± 7 112 76 ±5 118 105 ± 14 109 65 ± 12 91 63 ± 13 84 92 ±9 107 78 ± 14 97 98 ± 18 106 83 ± 12 104 Sulfoxaflor 75 ± 13 118 93 ± 8 106 97 ± 20 105 94 ± 10 109 51 ± 12 96 73 ± 15 106 99 ± 12 101 94 ±8 100 90 ±9 95 97 ±7 102 Tebufenozide 114 ± 10 117 144 ±7 104 132 ± 7 120 175 ± 14 114 96 ± 16 102 56 ± 20 100 106 ± 15 105 93 ± 19 104 83 ± 17 84 91 ± 15 102 Terbufos-sulfoxide 83 ±4 90 50 ± 12 106 107 ± 22 86 55 ± 13 86 76 ± 20 109 40 ± 13 101 118 ± 19 109 80 ± 20 103 44 ± 10 116 119 ± 19 102 Terbuthylazine 102 ± 14 119 59 ± 10 80 97 ± 18 107 86 ± 18 113 71 ± 18 93 51 ± 7 96 101 ± 18 115 86 ± 16 95 102 ± 4 82 85 ± 13 103 (continued on next page) K. Arena et al.