Thierno Diallo

Yassine Makni

Adélaïde Lerebours

Hélène Thomas

Thierry Guérin

Julien Parinet
email: julien.parinet@anses.fr

Development and validation according to the SANTE guidelines of a QuECh-ERS-UHPLC-QTOF-MS method for the screening of 204 pesticides in bivalves

Keywords: Qualitative screening, LC-HRMS acquisition modes, Pesticides, Bivalves, QuEChERS, Taguchi Orthogonal Array

A qualitative screening high resolution mass spectrometry method was developed and validated according to the EU SANTE/12682/2019 guidelines for the analysis of 204 pesticides in seven commercial bivalve species spiked at three concentrations (0.01, 0.05 and 0.1 mg.kg -1 ). Samples were extracted using QuEChERS and analysed using ultra-highperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry.

The QuEChERS method was optimised by the Taguchi Orthogonal Array approach. The best conditions were obtained with pure ACN, MgSO 4 /NaCl as extraction salts, MgSO 4 /PSA/C18 as clean-up, and the non-dilution of extracts. The impact of different HRMS acquisition modes on detection and identification rates were also evaluated. The screening detection limits were determined to be 0.01 mg.kg -1 and 0.1 for 66% and 87% of pesticides, respectively. These screening procedure was finally applied to different bivalve samples using target and suspect analysis. This allowed the identification of diuron and its metabolite 1-(3,4-dichlorophenyl)-3methylurea in the investigated samples.

Introduction

During the last few decades, the use of pesticides to protect crops has facilitated mass food production. However, the use of pesticides can lead to chemical pollution in water located near crop fields due to transport into surface and groundwater [START_REF] Tudi | Agriculture Development, Pesticide Application and Its Impact on the Environment[END_REF]. Once in water, pesticides and their metabolites become bioavailable to aquatic organisms and can bioaccumulate in their tissues via the skin, gills and/or diet, depending on their solubility (van der Oost et al., 2003). Biomagnification of chemicals through the food chain can lead to toxic effects to predators and humans (van der Oost et al., 2003). Therefore, the use of pesticides near surface waters poses a high risk to the aquatic ecosystem and potentially to consumers.

Bivalves are indicator species of environmental quality. They filter large volumes of water and concentrate xenobiotics, allowing for the assessment of pollutant concentrations in their ecosystems [START_REF] Breitwieser | What is the relationship between the bioaccumulation of chemical contaminants in the variegated scallop Mimachlamys varia and its health status? A study carried out on the French Atlantic coast using the Path ComDim model[END_REF]. Moreover, bivalves are a reliable source of information on human exposure to contaminated seafood [START_REF] Rodríguez-González | Development of a Matrix Solid Phase Dispersion methodology for the determination of triazine herbicides in mussels[END_REF]. Bivalves are also crucial in aquatic environment since they provide habitats for other organisms (algae, macroinvertebrates) and contribute to nutrient cycling and storage [START_REF] Vaughn | Bivalve Impacts in Freshwater and Marine Ecosystems[END_REF]. In addition, bivalves accounted for 17.7 million tons of coastal and marine animal aquaculture in 2018, with an estimated market value of USD 34,6 billion (FAO, 2020). For all these reasons, high mortality of bivalves such as oysters [START_REF] Vohmann | Strong body mass decrease of the invasive clam Corbicula fluminea during summer[END_REF] and mussels [START_REF] Mcfarland | Potential impacts of blooms of the toxic dinoflagellate Karenia brevis on the growth, survival and juvenile recruitment of the non-native green mussel Perna viridis in southeastern United States[END_REF] worldwide is not only a major ecological issue but also an economic concern.

Analytically, whole-body bivalves are complex matrices. The presence of proteins and lipids in high concentrations (between 2 to 10% and between 10 to 18% of lipids and proteins, respectively) makes it difficult to analyse compounds such as pesticides with lower concentrations in the sample (US Department of Agriculture, 2016). This matrix effect needs to be reduced to avoid bias in the overall analysis. Sample preparation of bivalves normally requires time-consuming steps to remove these lipids and proteins in order to obtain acceptable recoveries for the target compounds. The most commonly used extraction techniques for bivalve samples are accelerated solvent extraction [START_REF] Galvao | Distinct bioaccumulation profile of pesticides and dioxin-like compounds by mollusk bivalves reared in polluted and unpolluted tropical bays: Consumption risk and seasonal effect[END_REF], pressurized liquid extraction [START_REF] Rodrigues | A single-step pesticide extraction and clean-up multiresidue analytical method by selective pressurized liquid extraction followed by online solid phase extraction and ultra-high-performance liquid chromatography-tandem mass spectrometry for complex matrices[END_REF], and microwave extraction [START_REF] Uluturhan | Seasonal variations of multi-biomarker responses to metals and pesticides pollution in M. galloprovincialis and T. decussatus from Homa Lagoon, Eastern Aegean Sea[END_REF].

Extraction methods based on these techniques are usually followed by solid phase analysis (SPE). The Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) extraction method is a preferable alternative to these different methods due to its fast protocol, reliable results, and affordable cost. This method has already been successfully applied for the analysis of pesticides in bivalves [START_REF] Cruzeiro | Development and application of a QuEChERS-based extraction method for the analysis of 55 pesticides in the bivalve Scrobicularia plana by GC-MS/MS[END_REF][START_REF] Álvarez-Muñoz | Fast analysis of relevant contaminants mixture in commercial shellfish[END_REF].

Despite the advantages of QuEChERS, to achieve the best extraction efficiency, many parameters need to be optimised, including sample amount, extraction solvent, solvent pH, partition salt, dispersive-SPE (dSPE) sorbent, concentration to increase sensibility of the compounds, and dilution of extracts to reduce matrix effects. Because of the high number of parameters that can influence the analysis, the possible interactions between them, and the time needed to process data, it is not feasible to analyse all of them independently. As a result, it is recommended to use so-called split factorial design methods to save time. One of these methods, called Taguchi Orthogonal Array (TOA), is characterized by its simplicity and efficiency in designing parameter combinations and in evaluating each parameter. Taguchi proposed a design matrix that allows the study of all parameters with selectively reduced experimental size [START_REF] Narenderan | Experimental design in pesticide extraction methods: A review[END_REF]. This matrix can be used to determine the influence of each designed parameter and to identify the best combination of parameters to obtain the best result [START_REF] Narenderan | Experimental design in pesticide extraction methods: A review[END_REF]. The TOA method was recently used to optimise the QuEChERS method for wide-scope multi-residue analysis of pesticides in beef [START_REF] Pang | Wide-scope multi-residue analysis of pesticides in beef by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry[END_REF].

Following the sample preparation optimisation step comes the analytical step, to highlight the presence or absence of target compounds and to quantify them in the reference matrices. To determine pesticides in bivalves, gas chromatography (GC) and liquid chromatography (LC) coupled to triple quadrupole mass spectrometry operating in multiple reaction monitoring (MRM) mode are the most common techniques, due to their selectivity and sensitivity [START_REF] Cruzeiro | Development and application of a QuEChERS-based extraction method for the analysis of 55 pesticides in the bivalve Scrobicularia plana by GC-MS/MS[END_REF]. However, these low-resolution (LR) techniques are limited to the target compounds, so compounds not included in the method are not analysed. In addition, the number of compounds that can be screened simultaneously is limited [START_REF] López | Evaluation and validation of an accurate mass screening method for the analysis of pesticides in fruits and vegetables using liquid chromatography-quadrupole-time of flight-mass spectrometry with automated detection[END_REF]. More recently, there has been increasing interest in the benefits of high-resolution mass spectrometry (HRMS) [START_REF] Rajski | LC-ESI-QOrbitrap TM MS/MS within pesticide residue analysis in fruits and vegetables[END_REF]. This is partly due to its ability to perform simultaneous target, suspect, and non-target analyses to increase the number of analytes (the scope) that can be included in multi-residue methods [START_REF] Rajski | LC-ESI-QOrbitrap TM MS/MS within pesticide residue analysis in fruits and vegetables[END_REF]. HRMS increases selectivity needed to overcome interferences in more complex matrices [START_REF] Rajski | Improving the simultaneous target and non-target analysis LC-amenable pesticide residues using high speed Orbitrap mass spectrometry with combined multiple acquisition modes[END_REF]. It also allows for retrospective analysis by reprocessing the acquired data for potential new compounds discovered without reinjection of the sample [START_REF] Arroyo-Manzanares | High-resolution mass spectrometry for the determination of mycotoxins in biological samples[END_REF], which limits sample stability problems. Although HRMS, such as time-of-flight (TOF) instruments, has emerged as a conventional means for pesticide detection and identification [START_REF] López | Evaluation and validation of an accurate mass screening method for the analysis of pesticides in fruits and vegetables using liquid chromatography-quadrupole-time of flight-mass spectrometry with automated detection[END_REF], it is still a major challenge to analyse low-level and trace pesticides in matrices as complex as bivalves. Therefore, the quality of MS/MS spectra is critical to obtain high confidence in the identification of small molecules [START_REF] Schymanski | Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence[END_REF]. Currently, the main methods for acquiring a simultaneous MS-MS/MS mass spectrum are data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods. DDA and DIA are also referred to as IDA (information-dependent acquisition) and SWATH ® (sequential window acquisition of all theoretical fragment ion spectra) in Sciex instruments, respectively. The MS/MS data for the SWATH ® produce more fragments due to the ability of SWATH ® to fragment all ions, including isotopes. This results in spectra that are richer in information but also more complex.

In IDA mode, only the most intense ions are fragmented. The number of most intense ions (top-n) to be fragmented is set by the user. The MS/MS spectra in IDA mode are less rich than those obtained in SWATH, but clearer and easier to interpret [START_REF] Álvarez-Ruiz | Sequential window acquisition of all theoretical fragments versus information dependent acquisition for suspected-screening of pharmaceuticals in sediments and mussels by ultra-high pressure liquid chromatography-quadrupole time-of-flight-mass spectrometry[END_REF].

The aim of this study was to develop a simple, rapid and wide-scope QuEChERS sample extraction method, followed by a UHPLC-QTOF-MS comprehensive target screening analysis for 204 pesticides in bivalves. The list of target compounds included pesticides from different families with very distinct physicochemical properties, which therefore serve as a template to prepare the analysis in suspect screening. To our knowledge, this study is the first to perform HRMS analysis for such a comprehensive list of pesticides in bivalves. So far, only one study determined pesticides (12 in total) in bivalves by HRMS [START_REF] Álvarez-Muñoz | Fast analysis of relevant contaminants mixture in commercial shellfish[END_REF], while others have used LR mass spectrometers to analyse up to 60 pesticides in bivalves [START_REF] Cruzeiro | Development and application of a QuEChERS-based extraction method for the analysis of 55 pesticides in the bivalve Scrobicularia plana by GC-MS/MS[END_REF]. The TOA method was used to evaluate optimum parameters for the sample extraction step. To the best of our knowledge, this is the first time that the TOA method has been used to optimise a QuEChERS extraction for the analysis of pesticides in bivalves. Moreover, different IDA and SWATH methods were tested to optimise detection and identification rates. The selected method was also validated according to the European Union SANTE/12682/2019 guideline (European Commission, 2019). For each pesticide, the screening detection limits (SDLs) was established in bivalve samples. Although it is not required for the qualitative screening method validation according to the SANTE guidelines, the limit of identification (LOI) was also established. The number of false positives was assessed using blank bivalve samples out of the 204 pesticides. Once validated, the screening method was applied to the analysis of different bivalve samples, including mussels and oysters by target and suspect analysis.

Materials and methods

Chemical substances and analytical standards

Acetonitrile (ACN), obtained from Fisher Scientific (Illkirch, France), and ultrapure water (18.2 MΩ.cm), obtained by purifying distilled water with a Milli-Q system from Merck Millipore (Saint-Quentin-en-Yvelines, France), were used for the mobile phases and sample preparation. The standard consists of 204 pesticides (Table S1) divided into ten mixtures (100 mg.L -1 of each compound in ACN). The ten pesticide standard stock solutions were obtained from Restek Corporation (Les Ulis, France), transferred and stored in screw-capped amber glass tubes at -20 °C. A mixture working solution (2 mg.L -1 ) was prepared through suitable dilution of the ten intermediate solutions with ACN, and stored in screw-capped amber glass tubes at -20 °C.

Equipment and apparatus

A Genie 2 vortex mixer (Scientific Industries, Bohemia, NY, USA) was used for the extraction procedure. An IKA Ultra-turrax T25 digital homogenizer and a GM 200 Grindomix (Retsch Technology GmbH, Haan, Germany) were used for the extraction procedure. A Mettler Toledo (Columbus, USA) XS 204 analytical balance was used to weigh the samples.

An UHPLC Thermo Fisher Scientific U3000 system composed of a binary pump and autosampler (Waltham, MA, USA) coupled with a 5600 Q-TOF AB Sciex mass spectrometer (Darmstadt, Germany) with an electrospray ionization source (ESI) was used for analyses.

Orthogonal array design for optimisation of QuEChERS extraction and clean-up

Four factors (extraction solvent acidification, extraction salts, dSPE and dilution ratio) were considered for the optimisation of QuEChERS extraction and clean-up. Three levels were used for each factor (Table 1).

To optimise these four factors simultaneously, a Design of Experiment (DoE) was performed using L9 TOA. As shown in Table S2, nine experiments with three replicates for each were performed to evaluate responses such as pesticide detection rates (%) and signal-to-noise (S/N) of the 204 pesticides.

Bivalve samples

The bivalve samples (oysters, mussels, cockles, clams, sea almonds and two types of scallops) used for method optimisation and validation were purchased from local markets (Maisons-Alfort, France). To test the screening method applicability, a total of fourteen bivalves (seven mussels and seven oysters) were collected at three sites located in the Pertuis Charentais littoral area (southwest France) in May 2019, September 2019 and January 2020 and frozen at -20 °C.

Before shucking, the oysters were stored at 4 °C overnight and then brought to room temperature. The samples were homogenized with a GM 200 Grindomix (Retsch Technology GmbH, Haan, Germany) and kept at -20 °C until analysis.

Sample preparation

Extraction solvent acidification, extraction salts, dSPE and dilution ratio were simultaneously optimised using L9 TOA. For the enrichment experiment, 7.5 μL of the mix working solution were added to 50-mL polypropylene centrifuge tubes, which contained 1.50 ± 0.05 g of blank oyster sample, to provide a supplementation at 0.01 mg.kg -1 of pesticides. A contact time of 24 h at 4 °C was allowed for the pesticides to bind to the matrix. Then, 8.5 mL of Milli-Q water were added to each tube, and the sample was then ground using an IKA Ultra-Turrax mixer for 3 min at 5,000 rpm. Then, 10 mL of extraction solvent were added to each centrifuge tube to perform pesticide extraction, and tubes were vortexed again for 1 min. After that, the salts were added to the fortified samples, and the resulting mixture was centrifuged for 5 min at 4,000 rpm.

When EMR lipid was used as the dSPE reagent, 5 mL water was added to a 15-mL centrifuge tube containing the EMR-Lipid and vortexed for 3 s to activate the sorbent. Then, 5 mL of the supernatant (50-mL polypropylene centrifuge tube) were transferred to activated 15-mL centrifuge tube. The mixture was vortexed immediately for 1 min. The tube was centrifuged at 4,000 rpm for 5 min, and 5 mL of the supernatant was transferred to the polishing tube containing MgSO 4 to remove excess water. When using the other dSPE reagents, 6 mL of the supernatant were transferred to a 15-mL centrifuge tube containing the dSPE reagent. The tube was vortexed for 1 min, and then centrifuged for 5 min at 4,000 rpm. The supernatant was then combined with ACN according to the dilution ratio. The mixture was then vortexed, filtered, and deposited into a 2-mL auto-sampler vial for injection in the UHPLC-QTOF system.

To determine background contamination, matrix-free ACN was subjected to the same extraction and purification protocols as previously described.

UHPLC-QTOF-MS conditions

Reversed-phase liquid chromatography separation was performed using A Phenomenex Aqua ® C18 column (150 x 2 mm, 3 µm particle size, 125 Å pore diameter; Phenomenex Inc, Torrance, CA, USA). A gradient using Milli-Q water (Solvent A) and acetonitrile (Solvent B) with both containing 0.01% of FA was used at a constant flow rate of 0.500 mL.min -1 . The gradient profile started with 10% B (0 min); increased linearly to 100% B for 9 minutes (0-9 min); fixed for 3 minutes (9-12 min) and returned to the initial conditions in 0.1 min (12.1 min), followed by a re-equilibration step for 3.9 minutes. The total run time was 16 min. The auto sampler was thermo-regulated at 15 °C to avoid sample degradation during analysis-runs. The injection volume was 10 µL and the column temperature was set at 50 °C. The detection was carried out in positive (ESI+) ionization mode. The instrument was tuned and calibrated before each batch, in high-resolution mode. The automated calibration device system (CDS) was scheduled to perform automatic calibration every five injections using APCI Positive Calibration Solution for the 5600 system (AB Sciex technology). ESI+ parameters were as follows: the ion spray voltage floating (ISVF) was set at 5.5 kV, the source temperature (TEM) was 500 °C, the curtain gas (CUR) was set at 30 psi, the nebulizing gas (GS1) at 40 psi, and the heater gas (GS2) at 45 psi.

Evaluation and comparison of IDA and SWATH ® workflows for pesticide detection and identification

In this study, four acquisition methods were implemented using the UHPLC-QTOF-MS system for pesticide detection and identification in spiked sample at 0.01 mg.L -1 of the target 204 pesticides. The source condition was the same for all methods as well as the mass range of 50-1,000 Da for TOF MS and TOF MS/MS.

Method A consisted in implementing the IDA method. The accumulation time for the TOF MS full scan was set to 490 ms, while in TOF MS/MS it was set to 100 ms. For the product ion scan, the parameters were set as follows: collision energy (CE) was fixed at 40 eV with ± 20 eV and declustering potential (DP) was 80 eV (+). Isotope exclusion was switched off. The 4 most intense ions (Top4) following precursor ion intensity greater than 50 cps with ion tolerance ≤ 10 ppm were monitored per cycle, and dynamic background subtraction was turned on. The total cycle run time was 940 ms.

Method B used IDA-PIL (Precursor Ions List). The parameters were the same as the IDA but with an inclusion list of the 204 target pesticides.

Method C and method D consisted in using the fixed (F-SWATH) and variable (V-SWATH) methods, respectively, with the specific settings listed in Table S3. Different numbers of SWATH ® windows (20, 30, 40, and 50) were evaluated with F-SWATH (F-SWATH20, F-SWATH30, F-SWATH40 and F-SWATH50) or V-SWATH (V-SWATH20, V-SWATH30, V-SWATH40 and V-SWATH50) window width for Q1 isolation (overlap 1 Da). F-SWATH selects and fragments all detected precursor ions with an m/z window size Q1 that is uniform throughout the LC-MS run. For V-SWATH, the m/z windows are variable depending on the density of precursor ions by adjusting the isolation window to the number of precursor ions in the window (Álvarez-Ruiz and Picó, 2019).

Different acquisition methods were evaluated and compared using the pesticide detection rates and identification rates. For the latter, two different MS/MS search algorithms (purity score and fit score) at two levels (>70% and > 50%) were evaluated according to the literature (Gómez-Romero et al., 2011).

Method validation

The QuEChERS-LC-HRMS qualitative screening method was validated according to the European Union SANTE/12682/2019 guidelines (European Commission, 2019). For this purpose, seven species of commercial bivalves were used: oysters, mussels, cockles, clams, sea almonds and two types of scallops (Pecten maximus and Mimachlamys varia). For each species, four groups were randomly formed. Individuals from each group were pooled, then 1.5 g of each was weighed and transferred into 50-ml polypropylene centrifuge tubes. Sub-samples were spiked before extraction with a mixture containing 204 pesticides at 0.01, 0.05, and 0.1 mg.kg -1 , (to a total of 28 samples for each concentration level). The SDL was established as the lowest concentration level at which a compound was detected (not necessarily meeting the MS-identification criteria) in at least 95% of the samples (i.e., 27 out of 28 samples analysed) (European Commission, 2019). SDL was estimated as the concentration level at which the thresholds of (i) T R (variation ± 0.1 min) and (ii) mass accuracy (± 5 ppm) of the precursor ion were satisfied (European Commission, 2019). The LOI (not required according to SANTE guidelines) was established as the lowest concentration tested for which a compound was satisfactorily identified in at least 95% of the samples. LOI used the same thresholds as for SDL, as well as an MS/MS library score. Non-spiked (blank) samples were also extracted and analysed to assess the number of false positives out of the 204 targeted pesticides. and fit score (Fit). Purity is guaranteed in the range of 0-100 and is a measure of the similarity between the library spectrum and the acquired spectrum [START_REF] Gómez-Romero | Automated identification of phenolics in plant-derived foods by using library search approach[END_REF]. Fit reflects the degree of agreement between the masses and intensities of the library spectrum and those found in the acquired spectrum, ignoring other masses contained in the acquired spectrum [START_REF] Gómez-Romero | Automated identification of phenolics in plant-derived foods by using library search approach[END_REF]. A high Fit and low Purity indicate that the acquired spectrum is likely impure but contains the library compound.

Software and statistical analysis

The SWATH TM Variable Window Calculator tool (version 1.1) was used to calculate V-SWATH using an IDA total ion chromatogram (TIC).

Statistical analyses were performed using RStudio (Boston, MA, USA). The normality of the data was verified by the Shapiro-Wilk test. According to the distribution of the data, analysis of variance (ANOVA) with Tukey's multiple comparison post-hoc test or Kruskal-Wallis's test with Dunn's post-test were used (confidence limit of 95%, p < 0.05).

Results and discussion

Optimisation of sample preparation using the TOA method

Several parameters can have an impact on the efficiency of a QuEChERS extraction. This is the case for the sample amount, the extraction solvent, the acidification of the solvent, the partition salt, the clean-up, the concentration and the dilution of the extracts. To simplify the optimisation of the QuEChERS method, the Taguchi approach was chosen to simultaneously optimise the acidification of the extraction solvent, extraction salts, dSPE and dilution ratio.

Responses evaluated were detection rates (%) and S/N of the 204 pesticides. The sample amount was not tested in this study as the aim of this work is to apply the optimised method to analyse a large number of bivalve samples (in particular oysters and mussels) pooled and individually (to test inter-individual variability). As the flesh of a mussel can weigh between 1 and 2 g, the choice of a sample weight of 1.5 g was considered appropriate.

The values of the mean detection rates oscillated between 56% and 86% (Table 2) and the S/N between 97 and 190 (Table 3) for all experiments. Overall, the best performances were found with experiment 9 and 1 for detection rates and S/N, respectively. The performance of each experiment depends on the pesticide considered as shown in the chromatograms of fenpropimorph, methoprotryne, hydramethylnon and azoxystrobin in Conversely, the steeper the slope, the more significant the factor. When the detection rate and S/N ratio are represented by column chart (as in the case of extraction solvent acidification, extraction salts and dSPE), the greater the difference between the best and worst performing level, the more significant the factor. Only dilution was a significant factor (p < 0.05) for the detection rates, while all factors were significant for the S/N (Table 2 and3).

Extraction solvent acidification

For the extraction solvent, only ACN was tested as extraction solvent because the use of ACN leads to much less interference from compounds such as lipophilic compounds, lipids and pigments than other extraction solvents such as acetone, ethyl acetate [START_REF] Anastassiades | Fast and easy multiresidue method employing acetonitrile extraction/partitioning and "dispersive solid-phase extraction" for the determination of pesticide residues in produce[END_REF]. In addition, ACN is the most widely used organic solvent for QuEChERS extraction due to its ability to extract a wide polarity range of pesticides from several animal and plant foodstuffs. Due to its miscibility with water, it is able to penetrate the aqueous fraction of the sample matrices, allowing for easy separation of the two phases upon salt addition. In order to increase the extraction efficiency of certain pesticides (e.g., acidic compounds), ACN can be acidified with acetic acid (AA) or formic acid (FA), for instance [START_REF] Pang | Wide-scope multi-residue analysis of pesticides in beef by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry[END_REF]. For this study, we therefore chose to evaluate the impact of ACN with 1% (v/v) FA or 1% (v/v) AA on the detection rates of target pesticides and their S/N. The best detection rates and S/N were obtained with pure ACN as extraction solvent (Fig. S3).

Extraction salts

In the QuEChERS method, MgSO 4 and NaCl are the original salts used for liquid-liquid phase separation by binding to water [START_REF] Anastassiades | Fast and easy multiresidue method employing acetonitrile extraction/partitioning and "dispersive solid-phase extraction" for the determination of pesticide residues in produce[END_REF]. MgSO 4 has good water absorption and allows the release of acetonitrile, while NaCl affects the polarity of the extraction solvents and, therefore, increases the selectivity of the extraction [START_REF] Kim | Review of the QuEChERS method for the analysis of organic pollutants: Persistent organic pollutants, polycyclic aromatic hydrocarbons, and pharmaceuticals[END_REF]. To avoid ionization or degradation of pesticides due to pH changes during extraction, two modifications of the original QuEChERS method were developed: (1) AOAC Method (acetate buffer) [START_REF] Lehotay | Determination of pesticide residues in foods by acetonitrile extraction and partitioning with magnesium sulfate: collaborative study[END_REF] and (2) EN Method 15662 (citrate buffer) [START_REF] Anastassiades | Recent Developments in QuEChERS Methodology for Pesticide Multiresidue Analysis[END_REF]. Both methods stabilize the pH at 5-5.5, which facilitates the extraction of pesticides sensitive to low pH or those with stability problems. However, the ability of the primary secondary amine PSA (used as a sorbent) may decrease to retain the co-extractives at this pH level, especially for samples with high fat content [START_REF] Kim | Review of the QuEChERS method for the analysis of organic pollutants: Persistent organic pollutants, polycyclic aromatic hydrocarbons, and pharmaceuticals[END_REF]. As shown in Fig. S3, a mixture of MgSO 4 and NaCl without buffer yielded better results in terms of detection rates and S/N than the methods using buffers.

dSPE

As illustrated in Fig. S3, the best detection rates and S/N were obtained with a mixture of MgSO 4 /PSA/C18 as dSPE reagent. As mentioned in the previous paragraph, MgSO 4 is commonly used as a drying salt for ACN extraction because it effectively removes residual water. PSA has been shown to remove many polar organic acids from the extract, as well as polar pigments, sugars and fatty acids, whereas C18 can retain fats, vitamins, and minerals [START_REF] Kim | Review of the QuEChERS method for the analysis of organic pollutants: Persistent organic pollutants, polycyclic aromatic hydrocarbons, and pharmaceuticals[END_REF]. Z-Sep/C18 is composed of zirconium oxide and C18. This mixture reduces fat and pigment levels in matrices and it is recommended for matrices with >18% of lipid (Álvarez-Ruiz et al., 2021), which is not the case for bivalve samples, whose lipid content is usually below 10%. EMR-Lipid, whose structure is a proprietary secret, removes lipids from sample matrices [START_REF] Álvarez-Ruiz | Development of multi-residue extraction procedures using QuEChERS and liquid chromatography tandem mass spectrometry for the determination of different types of organic pollutants in mussel[END_REF][START_REF] Pang | Wide-scope multi-residue analysis of pesticides in beef by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry[END_REF] but its ability to remove other interferents such as proteins has not yet been described in literature.

Dilution

As observed in Fig. S3, non-dilution was necessary to obtain the best detection rates and S/N.

Dilution combined with extraction and purification steps showed decreased matrix effects in apple, tea and broccoli during analysis [START_REF] Guo | Analysis of multiple pesticide residues in polyphenol-rich agricultural products by UPLC-MS/MS using a modified QuEChERS extraction and dilution method[END_REF]. However, sample dilution reduces the overall sensitivity of the method, thus a compromise is needed to reduce matrix effects while maintaining high method sensitivity. In addition to the dilution of extracts, the concentration of the sample can be important on the sensitivity of the target compounds and on matrix effects.

To have a simple, reliable and not time-consuming method to apply it to a large number of bivalve samples, the sample concentration was not considered in this work. However, this aspect should be tested in a future research to obtain a good compromise between increasing sensitivity and minimising matrix effects.

The conclusion based on the R values associated with the K1, K2, and K3 values and the main effects plot for both responses is that the optimal QuEChERS extraction conditions were obtained with pure ACN, Original QuEChERS method, MgSO 4 /PSA/C18 as dSPE, and the non-dilution of extracts.

Evaluation and comparison of IDA and SWATH ® workflows for pesticide detection and identification

As shown in Fig. 1a, detection rates ranged from an average of 75% to 96% for all methods, with the IDA and IDA-PIL methods being the most effective with detection rates of 96% on average, for both modes. The detection rates of F-SWATH mode ranged from an average of 75% for F-SWATH50 to 94% for F-SWATH20 and the detection rates of V-SWATH ranged from an average of 77% for V-SWATH50 to 94% for V-SWATH20. The detection similarity between IDA and IDA-PIL is expected because they have the same TOF-MS conditions. PIL only affects TOF-MS/MS (identification). The same observation was made with F-SWATH and V-SWATH which have the same detection rates when the number of windows is the same.

For the SWATH ® methods, the detection rates decreased with the increase of the number of windows. This is because, for the same accumulation time, if the number of windows increases, the number of cycles and points per peak decreases, which reduces the sensitivity of the precursor ions. In terms of detection, the performance of the IDA modes is equivalent to that of the best SWATH modes.

The TOF-MS used gave information on the accurate m/z of the targeted pesticides. However, pesticides were only considered identified in cases where the characteristic MS/MS of the compound was obtained. As can be seen in Fig. 1b-e, the IDA-PIL mode achieved a better identification rate than the IDA mode. This is due to the fact that in the IDA-PIL mode, the targeted precursor ions have the highest priority for MS/MS data acquisition, even though these precursor ions were not the most intense. For the IDA mode, only the top n most intense precursor ions are fragmented, which can be problematic in case of complex matrix or low concentration. The IDA modes were slightly impacted by the decrease of the MS/MS score threshold (from > 70% to > 50%) with an average increase of 5% of pesticides identified for both IDA and IDA-PIL. The same observation is made for the impact of the MS/MS search algorithm. From Purity to Fit, an average increase in pesticide identification rates of 2 and 6%

was observed for the IDA-PIL and IDA modes, respectively.

For SWATH modes, V-SWATH achieved overall higher identification rates than F-SWATH because it adjusts the isolation window to the number of precursor ions in the window. For F-SWATH, the m/z Q1 window size is uniform throughout the LC-MS run. Compared to IDA modes, SWATH modes are more affected by the decrease of the MS/MS score threshold (from >70% to >50%), especially for the Purity algorithm with an average increase of 15% of identified pesticides (Fig. 1b-c). For MS/MS search algorithms, the impact was even more pronounced. From Purity to Fit, an average increase of 20% in pesticide identification rates was observed. The Fit algorithm was therefore found to be the best for identifying compounds in SWATH modes.

Unlike what was observed for detection rates (Fig. 1a), which decrease with increasing the number of SWATH ® windows (decreasing precursor ion sensitivity), this is not always the case for identification rates (Fig. 1b-e). Increasing the number of SWATH ® windows with narrower widths, allows fewer precursor ions to be selected for MS/MS fragmentation, resulting in higher specificity and selectivity [START_REF] Demianova | Method validation procedures for pesticide residues analysis in food and feed[END_REF]. A compromise is therefore necessary to increase sensitivity while maintaining high precursor ion specificity and selectivity for confident pesticide identification.

In terms of identification, the best performance was obtained with IDA-PIL, regardless of the algorithm or MS/MS threshold chosen, which demonstrates the importance of adding PIL to increase the probability of fragmenting the desired precursor ions. A previous study by Álvarez-Ruiz and Picó ( 2019) also compared the IDA mode (without PIL) with the F-SWATH and V-SWATH modes. In this mentioned study, the SWATH modes performed better than the IDA mode.

For the identification of pesticides in complex matrices such as bivalves, the quality of the MS/MS spectrum is also crucial. It was therefore taken into account in this work. As an example of the difference between the IDA modes and SWATH ® methods, Fig. 2 shows the results obtained with IDA-PIL and V-SWATH methods for zoxamide in spiked sample at 0.01 mg.L -1 . The MS spectra showed the two characteristic signals of zoxamide (one with 35 Cl and one with 37 Cl) (Fig. 2a). The MS/MS data of the SWATH ® acquisition showed zoxamide product ions with 35 Cl and 37 Cl (Fig. 2b). In the IDA acquisition, only the monoisotopic ion (zoxamide with 35 Cl) was selected and fragmented (Fig. 2c), so the MS/MS data lose isotopic abundance but with a cleaner spectrum than that obtained in SWATH ® . These results are consistent with the study conducted by Álvarez-Ruiz and Picó (2019) who found that the quality of MS/MS spectra is lower in SWATH than in IDA and more complex to interpret. Indeed, not all MS/MS data acquired with SWATH is useful as it may come from several precursor ions that are not all of interest. Thus, some fragments of other precursors may interfere with the signal of the analyte of interest, leading to additional verification by the user. Despite these described drawbacks, SWATH modes have shown good performance for the analysis of small molecules such as pharmaceuticals and personal care products in complex matrices, although these modes are more commonly used for protein analysis [START_REF] Álvarez-Ruiz | Sequential window acquisition of all theoretical fragments versus information dependent acquisition for suspected-screening of pharmaceuticals in sediments and mussels by ultra-high pressure liquid chromatography-quadrupole time-of-flight-mass spectrometry[END_REF][START_REF] Baduel | Development of sample extraction and clean-up strategies for target and non-target analysis of environmental contaminants in biological matrices[END_REF].

For the search algorithm, the library scores (Fig. 2b-c) showed that the Purity algorithm does not adapt the SWATH ® data, especially when compared to a home-made library in IDA. Of note, LibraryView TM does not allow for the implementation of a home-made spectral library in SWATH ® mode, which is an issue. It is therefore necessary to find ways in the future with improved software to take better advantage of SWATH modes. Indeed, SWATH modes have interesting unique features, namely the generation of chemical fingerprints (MS and MS/MS spectra) of the sample that can be exploited retrospectively as new knowledge is gained and the possibility of using samples in multiple studies.

According to the results obtained in terms of detection, identification rates and quality of MS/MS spectra, the IDA-PIL mode was selected for the validation method. The identification rates obtained with the evaluated algorithms being similar in the IDA-PIL mode, the Purity algorithm was chosen because it allows to evaluate the quality of the obtained spectra in terms of interfering peaks. As the score used does not have a great influence on the identification rates in the IDA-PIL mode, the score threshold > 70% was set to increase identification confidence.

Furthermore, for IDA-(PIL) modes, the Purity > 70% is most often used in the literature by authors working with the 5600 Q-TOF AB Sciex mass spectrometer [START_REF] Baduel | Development of sample extraction and clean-up strategies for target and non-target analysis of environmental contaminants in biological matrices[END_REF][START_REF] Romera | Determination of multiple mycotoxins in feedstuffs by combined use of UPLC-MS/MS and UPLC-QTOF-MS[END_REF].

Selectivity of the screening method

In order to determine the selectivity of the chosen screening method, the possible presence of false positives was also checked. In the SANTE guideline no criteria have been set with respect to the occurrence of false positives. However, it is important that the selectivity of the method is such that it reduces them to a sufficiently low number to avoid manual verification or confirmatory analysis that may reduce the effectiveness of the screening methods. The number of false positives was checked by analysing blank samples of each of the seven bivalve species prepared in triplicate. According to the results, a total of 5 false positives (2.5% of the 204 target pesticides) were obtained across all blank samples, when applying tolerances of ± 5 ppm and ± 0.1 min for mass accuracy and T R , respectively. False positive pesticides for fenpropimorph and propham (C 10 H 13 NO 2 ) were observed in two, three and five samples, respectively. When additional criteria were set, such as the MS/MS library score (Purity > 70%) the number of false positives dropped to zero. These results demonstrate the good selectivity of the method both in terms of detection and identification.

Method validation

The objective of this work was to develop and validate an analytical methodology according to the SANTE guidelines for the simultaneous detection and identification of 204 pesticides (used as model compounds) in bivalve matrices. Therefore, the validation of the qualitative screening methodology included the determination of SDL. Although it is not required for the SANTE guidelines, the LOI was also established. Seven species of bivalves were tested for method validation: oysters, mussels, cockles, clams, sea almonds and two species of scallops (Pecten maximus and Mimachlamys varia). Four groups of each species were spiked at three concentration levels (0.01, 0.05, and 0.1 mg.kg -1 ). For each individual pesticide, the number of positive detections and identifications at each concentration level was recorded and the SDL and LOI were established. Table S4 shows the number of detections and identifications for each compound in the 28 samples and the corresponding SDL and LOI. A summary of this table is also shown in Fig. 3. As it can be seen, about 93% of the pesticides could be detected at 0.1 mg kg -1 in bivalves, while the percentage of identification decreased down to 77% at this same concentration level. The SDL was determined to be equal to or less than 0.1 mg.kg -1 for 87% of the pesticides, whereas the percentage of pesticides with an LOI equal to or less than 0.1 mg.kg -1 decreased to 63%. The criteria of SDL and LOI were therefore not reached for 13 and 37% of pesticides, respectively. However, the proposed screening method was able to detect and identify them in most of the samples at the highest concentration level. In this regard, it is worth noting the complexity of bivalve matrices, which generally present a significant amount of co-extractives, even after sample clean-up, which may increase SDLs and LOI compared to other simpler matrices. For 50 pesticides (representing 25% of the selected pesticides), it was possible to establish the SDL at least at one of the three concentration levels, but not the LOI. For these compounds, validation was limited to detection (only SDL could be set-up). Regarding maximum residue limits (MRLs), no values have yet been established in bivalves for any of the pesticides studied, so the SDLs obtained, in most cases 0.01 mg.kg -1 , can be considered satisfactory and the proposed methodology suitable. In addition, no qualitative pesticide screening was performed on bivalves by QuEChERS-LC-HRMS to compare our results. Previous work has been done on other matrices such as fish fillet [START_REF] Munaretto | Liquid chromatography with high resolution mass spectrometry for identification of organic contaminants in fish fillet: screening and quantification assessment using two scan modes for data acquisition[END_REF], salmon feed [START_REF] Regueiro | Targeted approach for qualitative screening of pesticides in salmon feed by liquid chromatography coupled to traveling-wave ion mobility/quadrupole time-of-flight mass spectrometry[END_REF], and fruits and vegetables [START_REF] López | Evaluation and validation of an accurate mass screening method for the analysis of pesticides in fruits and vegetables using liquid chromatography-quadrupole-time of flight-mass spectrometry with automated detection[END_REF] for qualitative screening of 182, 156, and 199 pesticides, respectively. In these three studies, the percentage of pesticides with an SDL of 0.05 mg.kg -1 ranged from 79% to 84%. In our study, the SDL was determined to be equal to or less than 0.05 mg.kg -1 for 81% of the pesticides which is quite similar to the literature and done on more complex matrix like bivalves.

As described above, this study is the first to perform a qualitative multi-residue analysis of pesticides in bivalves. The other studies performed so far were quantitative analyses. This is the case for the studies carried out by [START_REF] Álvarez-Muñoz | Fast analysis of relevant contaminants mixture in commercial shellfish[END_REF] and [START_REF] Cruzeiro | Development and application of a QuEChERS-based extraction method for the analysis of 55 pesticides in the bivalve Scrobicularia plana by GC-MS/MS[END_REF] for the quantitative analysis of 13 and 55 pesticides, respectively. The authors of these two works obtained LOD and LOQ values below 0.01 mg.kg -1 for all the pesticides studied. Of all the pesticides investigated in the two studies mentioned above, nine (acetamiprid, azoxystrobin, difenoconazol, dimethoate, imidacloprid, metribuzin, simetryn, terbutryn and thiabendiazole) are included in the list of 204 pesticides analysed in the present study. For all of these nine pesticides, the SDL was established at least at one of the three concentration levels tested in the present study and only one of these pesticides (simetryn) had an LOI greater than 0.01 mg.kg -1 .

Application to real samples

Target screening analysis

To evaluate the applicability of the proposed method, 14 bivalve samples (seven mussels and seven oysters) from three sites (Ile de Ré, Sèvre Niortaise and Charente) located in the Pertuis Charentais littoral area were analysed. For target screening, the home-made database of the 204 target pesticides (with information on T R , MS, isotopic pattern, and MS/MS spectra) was used.

Pesticides were detected and identified using the criterion applied in this work. Table S5 summarizes pesticides detected and identified in mussel and oyster samples. Diuron was the most frequently detected and identified compounds (in 8 of the 14 bivalve samples). Although it has been banned in France since 2008, diuron is still detected at high concentrations in coastal and fresh waters [START_REF] Caquet | Risk assessment of herbicides and booster biocides along estuarine continuums in the Bay of Vilaine area (Brittany, France)[END_REF]. Other pesticide such as fungicide (fenpropimorph) was also detected and identified in one mussel sample in Sèvre Niortaise estuary in January 2020.

To best our knowledge, this study is the first to show the presence of fenpropimorph in bivalves.

Suspect screening analysis

In target screening, most of the peaks resulting from sample analysis are ignored during data processing. The feasibility of the developed method has also been demonstrated on "known unknown" compounds in bivalve real samples. Post-acquisition data processing was carried out using a suspect screening of accurate masses. The database of compounds (with information of exact mass and molecular formula) has been expanded to include approximately 545 organic contaminants, including many pesticides and their metabolites as well as some flame retardants and plasticizers. A total of five additional compounds were tentatively identified. The identification obtained from the suspect screening was considered tentative (level 3) until confirmed by a standard compound to reach identification confidence level 1 [START_REF] Schymanski | Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence[END_REF]. As in the case of diuron in the target analysis, its metabolite 1-(3,4dichlorophenyl)-3-methylurea was the most frequently tentatively identified. Other pesticide such as fungicide (diphenylamine) and personal insect repellent (N,N-diethyl-m-toluamide (DEET)) were also tentatively identified. Diphenylamine has been frequently detected in environmental waters because, in addition to its wide use as a fungicide, it is an important intermediate used in the chemical and pharmaceutical industries [START_REF] Chen | Sorption characteristics of nitrosodiphenylamine (NDPhA) and diphenylamine (DPhA) onto organo-bentonite from aqueous solution[END_REF]. DEET has previously been reported in mussels collected in San Francisco Bay, California with a concentration (mean 0.014 mg.kg -1 ) positively correlated with their lipid content [START_REF] Klosterhaus | Method validation and reconnaissance of pharmaceuticals, personal care products, and alkylphenols in surface waters, sediments, and mussels in an urban estuary[END_REF]. The organophosphate flame retardant and plasticizer (2-ethylhexyl diphenyl phosphate (EHDPP) and tris(2-chloroethyl) phosphate (TCEP)) were tentatively identified.

EHDPP and TCEP have previously been detected in bivalves collected on several points along the northern coast of Spain and along the Korean coast, with a concentration of around 0.002 mg.kg -1 for both [START_REF] Castro | Determination of 18 organophosphorus flame retardants/plasticizers in mussel samples by matrix solidphase dispersion combined to liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Choi | Organophosphate flame retardants and plasticizers in sediment and bivalves along the Korean coast: Occurrence, geographical distribution, and a potential for bioaccumulation[END_REF]. As shown in Table S6, all tentatively identified compounds were subsequently confirmed with their respective reference standard with MS, MS/MS and T R matching.

Conclusion

The development and validation of a QuEChERS-LC-HRMS method using IDA-PIL mode enabling screening of 204 pesticides in bivalves was carried out according to the European Union SANTE/12682/2019 guidelines. The list of target compounds included pesticides from different families with very distinct physicochemical properties. The use of L9 TOA for QuEChERS parameter optimisation greatly simplified the method development process and made it scientifically sound. We evaluated the impact of the different IDA and SWATH ® modes on a QTOF 5600 on detection and identification rates and demonstrated the effectiveness of the IDA-PIL approach, especially since LibraryView TM software does not allow for building of a SWATH library, only an IDA library. Most of the target list compounds were detected at all three concentration levels (0.01, 0.05 and 0.1 mg.kg -1 ) with false positives averaging 2.5% on blank matrices. Unequivocal identification was problematic at the lowest concentration (0.01 mg.kg -1 ) due to the lower abundance of product ions but was possible for most analytes at the highest concentration level (0.1 mg.kg -1 ). The SDL and LOI were equal to or less than 0.1 mg.kg -1 for 87% and 67% of pesticides, respectively.

The applicability of this approach for pesticide screening was finally demonstrated by analysing several samples of mussels and oysters collected in southwestern France. Diuron and fenpropimorph were the only identified analytes in the investigated samples using target analysis. The method also demonstrated its versatility to identify other organic contaminants (pesticides or other compounds) than those included in the method validation by suspect screening. This allowed the identification of diuron metabolite 1-(3,4-dichlorophenyl)-3methylurea and other contaminants such as flame retardants and plasticizers.

Although the proposed methodology is very suitable, it is still necessary to develop workflows on different open-source software to follow SANTE guidelines regarding identification (product ion mass accuracy ± 5 ppm). Importantly, MasterViewTM software can be used to automatically obtain the mass error only on the precursor ions and not on the product ions.

Another important point will be to find ways to realize an MS/MS library in SWATH methods in order to better exploit the advantages of these acquisition methods, especially on the richness of the generated MS/MS spectra and the retrospective analysis of the data. Fig. 1. Evaluation and comparison of IDA, IDA-PIL, F-SWATH and V-SWATH methods with 20, 30, 40 and 50 windows for pesticide detection (a) identification rates using Purity > 70% as search algorithm (b), identification rates using Purity > 50% as search algorithm (c), identification rates using Fit > 70% as search algorithm (d), and identification rates using Fit > 50% as search algorithm (e) at 0.01 µg.mL -1 in ACN of the target 204 pesticides.

Electronic Supplementary Material

Table S1: List of the target pesticides with their corresponding formula, adduct or in-source fragment, theoretical m/z and retention time.

Table S2. L9 TOA for factor optimisation.

Table S3. SWATH acquisition MS settings for fixed windows and variable windows size.

Table S4. Validation results for 204 pesticides across 28 samples mainly spiked at 0.01, 0.05 and 0.1 mg.kg -1 with corresponding SDL and LOI.

Table S5. Pesticide detected and identified by target screening analysis in mussel and oyster samples collected in the Pertuis Charentais littoral area.

Table S6. Pesticide detected and identified by suspect screening analysis in mussel and oyster samples collected in the Pertuis Charentais littoral area.

  Formic acid (FA) and acetic acid (AA) were supplied by Fisher Scientific (Illkirch, France), to prepare LC-MS mobile phases and the extraction solvent. QuEChERS extraction pouches based on the original method (4.0 g of magnesium sulphate (MgSO 4 ), 1.0 g of sodium chloride (NaCl)), AOAC Method 2007.1 (6.0 g anhydrous MgSO 4 , 1.5 g sodium acetate) and EN Method 15662 (4.0 g of MgSO 4 , 1.0 g of NaCl, 1.0 g of sodium citrate, and 0.5 g of sodium citrate sesquihydrate) were provided by Agilent Technologies (Santa Clara, CA, USA). A dispersive solid phase extraction (d-SPE) kit containing 900 mg of MgSO 4 , 150 mg of Primary and Secondary Amines (PSA) and 150 mg of octadecylsilane (C18), and Enhanced Matrix Removal-Lipid (EMR-lipid) d-SPE was obtained from Agilent Technologies (Santa Clara, CA, USA). Zirconium-based Z-Sep/C18 (120 mg Z-Sep, 300 mg Discovery DSC-18) was supplied by Supelco (Bellefonte, PA, USA).

  Fig. S2. According to the R values for the mean detection rates and S/N, the most impactful factor is dilution (R=28.55 and 56.76), followed by extraction solvent acidification (R=3.98 and 9.69) or dSPE (R=2.39 and 43.19), and finally the extraction salts (R=1.40 and 8.92). The effects of the four studied factors on the pesticide detection rates and S/N are also shown in Fig. S3. Detection rates and S/N graphs can be read in two different ways depending on whether the levels are represented by column chart or connected by curves. When the levels are connected by curves (as in the case of dilution), the flatter the slope, the less significant the factor on the response studied.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Zoxamide MS spectrum with isotopic profile pattern (a), MS/MS spectrum acquired in SWATH ® (b) and IDA-PIL mode (c) in spiked sample at 0.01 µg.mL -1 . Fig. 3. Percentage of pesticides presenting SDL (a) and LOI (b) at 0.01, 0.05, 0.1 and > 0.1 mg.kg -1 under optimised conditions. Detection (c) and identification rates (d) across different bivalve samples spiked at 0.01, 0.05 and 0.1 mg.kg -1 .

Fig. S1 .

 S1 Fig. S1. Extracted Ion Chromatogram (top), MS spectra (bottom left) and MS/MS (bottom right) observed in the MasterView TM software interface.

Fig. S2 .

 S2 Fig. S2. Chromatograms of fenpropimorph (a), methoprotryne (b), hydramethylnon (c) and azoxystrobin (d) in oyster samples spiked at 0.01 mg.kg -1 .

Fig. S3 .

 S3 Fig. S3. Effects of extraction solvent acidification (i), extraction salts (ii), dSPE (iii) and dilution (iv) on pesticide detection rates (a) and pesticide S/N (b).

Fig

  Fig. 1.

  (C 20 H 33 NO) and formetanate-HCl (C 11 H 16 ClN 3 O 2 ) were each detected once across the different blank samples, whereas false positives for spiroxamine (C 18 H 35 NO 2 ), aminocarb (C 11 H 16 N 2 O 2 )

Table and Figure captions Table 1 .

 andcaptions1 Control factor and levels in the QuEChERS extraction and clean-up.

Table 2 .

 2 Pesticide detection rates of L9 TOA (3 4 ) for the optimisation of QuEChERS extraction and clean-up.

Table 3 .

 3 Pesticide S/N of L9 TOA (3 4 ) for the optimisation of QuEChERS extraction and cleanup.

Table 1 . Factor Level Solvent acidification Salt dSPE Dilution

 1

	1	ACN (1% AA)	AOAC Method*	MgSO 4 /PSA/ C18	Non-dilution
	2	ACN (1% FA)	EN Method**	Z-sep/C18	1:2
	3	ACN	Original Method***	EMR lipid	1:4

Acknowledgements

This work was funded by the French National Research Agency (ANR) project AlimOmic, ANR-19-CE21-0002. Authors are very grateful to AQUAECOS project (Amélioration de la QUAlité Environnementale en zones COnchylicoles des Pertuis Charentais), funded by the "Nouvelle Aquitaine Region", the "Comité Régional de la Conchyliculture de la Charente-Maritime" (CRC17), the "Comité Départemental de la Charente-Maritime" (CD17) and the "Office Français de la Biodiversité" (OFB). The authors would also like to thank the Pesticides and Marine Biotoxins Unit (UPBM) of the ANSES Food Safety Laboratory (LSAL) for its technical support.

Declaration of competing interest

The authors declare no conflicts of interest.


Suman Sau

Maha Kooli

Giorgio Di Natale

Alberto Bosio

Amlan Chakrabarti

SCHIFI: Scalable and flexible high performance FPGA-based fault injector

Keywords: Reliability, FPGA, Fault Injector, Hardware Faults, Memories

I. INTRODUCTION

System reliability has become an important design aspect for computer systems due to the aggressive technology miniaturization, which introduces a large set of different failure sources for hardware components [START_REF] Baumann | Soft errors in advanced computer systems[END_REF][2] [START_REF] Mukherjee | A systematic methodology to compute the architectural vulnerability factors for a high-performance microprocessor[END_REF]. The hardware system can be affected by faults caused by physical manufacturing defects, environmental perturbations (e.g., radiations, electromagnetic interference), or aging-related phenomena [START_REF] Kooli | Software testing and software fault injection[END_REF]. Faults propagate through the different hardware structures composing the full system (see Fig. 1). However, faults can be masked during this propagation either at the technological or at architectural level [START_REF] Vadlamani | Multicore Soft Error Rate Stabilization Using Adaptive Dual Modular Redundancy[END_REF] [START_REF] Mukherjee | A systematic methodology to compute the architectural vulnerability factors for a high-performance microprocessor[END_REF]. When a fault reaches the software layer of the system, it can corrupt data, instructions or the control flow. These errors may impact the correct software execution by producing erroneous results or prevent the execution of the application leading to abnormal termination or application hang.

The system reliability includes both the software and the hardware reliability. In order to target the overall system reliability, we have to consider: (i) faults affecting the software layer and (ii) the faults affecting the hardware layer. In the literature, several methods and tools have been proposed to accurately evaluate the impact of the faults affecting either the software layer (e.g., Mutation Testing) or the hardware layer (e.g., Fault Injection).

In this work, we mainly focus on the faults affecting the hardware layer that can actually really impacts on the software layers. The idea is to target a subset of all possible fault locations to speed up the fault injection campaign. For this purpose, we present in this paper a scalable and flexible high performance fault injector based on the use of FPGA board. The proposed tool main aims at giving to the user the possibility to easily build its own fault injector in order to target the desired set of fault locations. We present a set of preliminary results carried out on the open sourceAmber project [6], that is a complete embedded system implemented on the Xilinx Spartan-6 SP605[7] FPGA development board.

The remainder of the paper is organized as follows. Section II present the background and state-of-the-art about fault injections techniques. Section III discusses the architecture of the proposed fault injector. Section IV presents preliminary experimental results. Finally, Section V concludes the paper.

II. BACKGROUND

To evaluate the system reliability, fault injection [START_REF] Kooli | A survey on simulation-based fault injection tools for complex systems[END_REF] is a well-known and powerful technique to observe the impact of generated errors on the system behavior. It is based on the realization of controlled experiments to evaluate the system behavior in the presence of artificial faults. This work has been supported by the joint FP7 Collaboration Project CLERECO (Grant No. 611404).

hardware system. It uses either a manufactured processor prototype, a simulation of the processor architecture, or an implementation of the processor on an FPGA board. However, they are expensive in terms of execution time and injection facilities (i.e., the way how the faults are really injected in to the hardware). In addition, it is difficult to control the injection instant and location, which limits the fault evaluation.

A. Physical Fault-Injection Techniques

The physical fault injection techniques apply external perturbations on the circuit under test to evaluate the reliability [START_REF] Nicolaidis | Soft errors in modern electronic systems[END_REF]. Particle radiations, laser beams or pin forcing are used to create realistic faults:

• Radiation Methods: These methods test the device in its real environment by exposing it to the particle radiation [START_REF] Karlsson | Using heavy-ion radiation to validate faulthandling mechanisms[END_REF][11] [START_REF] Ecoffet | In-flightanomaliesonelectronicdevices[END_REF]. The advantage of this method is that the device is put in realistic conditions and the fault is correctly modeled. However, using this method it is difficult to control the fault location and instant.

• Laser Methods: The laser beams generate a photon material interaction instead of a particle material interaction. These methods can better control the fault location. Researchers prove that both the radiation and laser methods offer accurate results [START_REF] Velazco | Laser injection of spot defects on integrated circuits[END_REF][14] [START_REF] Miller | Effects of beam spot size on the correlation between laser and heavy ion seu testing[END_REF].

• Pin Forcing: The pin values at the input/output of the device are directly modified [16][17] to create the same effect of the radiation and the laser methods. This solution is cheaper, but it is only applied on simple circuits [START_REF] Nicolaidis | Soft errors in modern electronic systems[END_REF].

B. Simulation-based Fault-Injection Techniques

The simulation-based fault injection techniques do not operate on the physical device, but they employ a model of the device described using a simulation language, such as VHDL [START_REF] Pontes | An accurate single event effect digital design flow for reliable system level design[END_REF][19] [START_REF] Sieh | Verify: Evaluation of reli-ability using vhdl-models with embedded fault descriptions[END_REF]. They can inject faults in the VHDL model either at run-time or at compile-time. Compared to the physical fault injection techniques, the simulation-based techniques are cheaper in term of set-up and can better control where the fault is injected. In addition, they present no risk to damage the hardware system under evaluation. However, they create a computational overhead depending on the complexity of the device under evaluation [START_REF] Ebrahimi | A fast, flexible, and easy-to-develop FPGA-based fault injection technique[END_REF].

C. FPGA-based Fault-Injection Techniques

The FPGA-based fault injection techniques implement the device on an FPGA board and perform the fault injection campaigns on the different device components. Compared to the simulation-based techniques, they can precisely control where the fault is injected. Furthermore, they offer a faster fault evaluation [START_REF] Ebrahimi | A fast, flexible, and easy-to-develop FPGA-based fault injection technique[END_REF]. The fault injection process applies one of the following mechanisms:

• Reconfiguration Mechanism: The bits of the FPGA board are reconfigured to inject the fault in the specified location [22][23]. The fault injection takes place either at run-time or at compile-time. However, the reconfiguration process creates a time overhead.

• Instrumentation Mechanism: Additional circuit elements in the different components of the system are built to inject the fault in the target location, which are called Saboteurs [24][25]. The activation of these elements generates the fault. The instrumentation mechanism is therefore faster than the reconfiguration mechanism.

III. PROPOSED FAULT INJECTOR ARCHITECTURE

In modern processors-based computation systems, the concept of memory hierarchy is now always implemented. Actually, the main idea behind the memory hierarchy is to reduce as much as possible the need for transferring data from and to the main memory (RAM) and the processor. For this reason, cache memories have been introduced to store data/instructions in order to accelerate their future requests by the processor. To be cost-effective and to enable efficient use of data/instructions, the caches are relatively small compared to the RAM. This cache-based architecture introduces a sort of hardware redundancy for increasing performances. This means that the same variable can have, at the same time, several copies in different locations. It can reside the RAM (the data segment or the stack), the data cache or/and the CPU (the registers), as shown in Fig. 2. The same description can be applied to the register bank embedded in to the processor itself. In this work we mainly target the components of the memory hierarchy as possible locations for faults that could reach the software layer (e.g.,by corrupting a variable or an instruction). The target fault model is the well-known Single Event Upset (SEU) that corresponds to the inversion of the logic value stored in a single memory element (i.e., the single bit). It models soft errors caused by electromagnetic interference, external radiations (such as thermal neutrons, cosmic rays creating energetic neutrons and protons and alpha particles from package decay) [START_REF] Nicolaidis | Soft errors in modern electronic systems[END_REF]. In Fig. 3, the hardware block diagram of the implemented Fault Injector (FI) is shown. We simple design a saboteur mask for select the desired memory address and flip a random bit on the data corresponding to that address. Bit flip is done by simply using a XOR gate as shown in the Fig. 3. The memory block depicted in the Fig. 3 can be the RAM, the cache or the register bank. The user can actually specify what is the target.

The Injection mechanism is managed by a Finite State Machine depicted in Fig. 4. "S0" is the FI initial state. When the user set the start command, it goes to "S1" state. In this state depending on the saboteur mask result, the next state will be decided. If the Address of the target memorymatches with the random Address for fault injection with other satisfied conditions, then next state will be"S4" otherwise next state will be "S3".Actually, the state "SA" corresponds to the activation of the injection. Here, the output of the saboteur is set to logic '1', so that the target bit is flipped by the XOR gate as shown in the Fig. 3. On the other hand, in the state "S3" the injection is not activated. Here, the output of the saboteur is set to logic '0' so that the XOR does not invert the target bit. Next state of both "S3" and "S4" will be the "S0". From "S0", if the user set the end command, the next state will be "S3". The Fig. 5 sketches the full flowchart of a complete fault injection campaign. First of all the proposed fault injector is written in VHDL, synthesized by using the Xilinx ISE14. 4[7]. The resulting bitstream is then download on the target board Xilinx Spartan 605 [7].

The A, we designed a fault injector with amber core [6] where it is synthesized to generate the bit file. The download.bit file is downloaded to FPGA board via the JTAG port from Workstation A. From another Workstation B, the same board is connected using two different ways: Ethernet and Serial connection.

The target application corresponding to a given software C code is compiled to obtain the executable (.elf) file. The executable is downloaded on the Amber main memory implemented on the FGPA through the Ethernet connection. The Trivial File Transfer Protocol (TFTP) is exploited for the download. After that, the injection campaign automatically starts. First of all a golden run is done to store the expected application outputs. The, at each injection, a random address (i.e., the address can be a RAM, cache or register location) and mask are generated to inject the fault. After the execution of the faulty application, the system compares the obtained outputs with the goldenoutputs.The comparison result is classified into three categories:

• Masked: The software produces correct results. The faults is masked.

• Silent Data Corruption: The application outputs are different from the fault free outputs.

• Detected: The fault is detected by the application.

• Crash/Unresponsive: The application stops working or it never stops.

The Serial connection is used to monitor the Fault Injector behavior by using a HyperTerminal. The injections run up to anuser defined value N.

Finally, the Fig. 6 shows the real Lab setup implementing the flow above described. The Table 1 gives the results obtained by the injector on the considered applications. For each application, we implement three different cache sizes. The goal was to see the impact of the cache size on the global system reliability (i.e., the impact on the running application). For each sub column, we report the percentage of Silent Data Corruption. This is the only outcome since we target only the RAM containing the data segment of the application; therefore the instructions are not involved. As we can see, increasing the cache size positively impacts the reliability of the whole system since the percentage of SDC decreases. It is also important to mention that the difference of SDC between the standard and the duplicated version is due to the capability to correct many of the injected faults. Finally, the whole injection campaigns were of 10k injections for each configuration. The required injection time is on the order of minutes.

V. CONCLUSIONS

In this work, we presented a scalable and flexible high performance fault injector based on the use of FPGA board. The proposed tool main aims at giving to the user the possibility to easily build its own fault injector in order to target the desired set of fault locations. We carried out a set of preliminary results carried out on the open source Amber project [6], that is a complete embedded system implemented on the Xilinx Spartan-6 SP605 [7] FPGA development board.

Fig. 1 .

 1 Fig. 1. System Layers and Fault Propagation

  Hardware-based fault injection technique allows injecting physical faults (e.g., bit flip fault, stuck at fault) in the target

Fig. 2 .

 2 Fig. 2. Data Location in System

Fig. 3 .

 3 Fig. 3. Fault Injector Hardware Model

Fig. 4 .

 4 Fig. 4. State Diagram Graph of the FSM controlling the Fault Injection Block

Fig. 5 .

 5 Fig. 5. Work flow diagram of the FPGA-based Fault Injector

Fig. 6 .

 6 Fig. 6. Real experimental setup with FPGAIV. PRELIMINARY RESULTSIn this section we present a set of preliminary results carried out by the proposed fault injector. The target application is a simple matrix multiplication program with

TABLE I

 I

		.	RESULTS
	Application	Silent Data Corruption% 32 Kbytes 64 Kbytes 128 Kbytes
	Standard	68.83	66.47	64.69
	Duplicated	29.39	27.6	23.86