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Matrix effects and interferences of different citrus fruits coextractives in pesticide residue analysis using ultra high-performance liquid chromatography–high resolution mass spectrometry Natalia Besil, Veronica Cesio, Horacio Heinzen, and Amadeo R. Fernández-Alba J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 25 May 2017 Downloaded from http://pubs.acs.org on May 25, 2017
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Journal of Agricultural and Food Chemistry
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Matrix effects and interferences of different citrus fruits co-extractives in
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pesticide residue analysis using ultra high-performance liquid
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chromatography–high resolution mass spectrometry.
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Natalia Besil1,2,3, Verónica Cesio2,3, Horacio Heinzen2,3 and Amadeo R. Fernandez-
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Alba1
7 8
1
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Pesticide Residues in Fruit and Vegetables. Pesticide Residue Research Group, Department of
Agrifood Campus of International Excellence (ceiA3). European Union Reference Laboratory for
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Chemistry and Physic, University of Almeria, La Cañada de San Urbano, 04120 Almeria, Spain
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2
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Química. CENUR Litoral Norte. Universidad de la República (UdelaR), Uruguay
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3
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Facultad de Química, Universidad de la República, General Flores 2124, 11800 Montevideo,
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Uruguay
Grupo de Análisis de Compuestos Traza. Departamento de Química del Litoral, Facultad de
Grupo de Análisis de Compuestos Traza. Cátedra de Farmacognosia y Productos Naturales,
16 17 18 19
Corresponding author: Agrifood Campus of International Excellence (ceiA3).
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European Union Reference Laboratory for Pesticide Residues in Fruit and
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Vegetables. Pesticide Residue Research Group, Department of Chemistry and
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Physic, University of Almeria, La Cañada de San Urbano, 04120 Almeria, Spain.
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E-mail address:
[email protected] , Tel: +34 950 015 034 Fax: +34 950 015 483
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ABSTRACT
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The matrix effects of ethyl acetate extracts from seven different citrus fruits on the
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determination of 80 pesticide residues using liquid chromatography coupled to high
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resolution time of flight mass spectrometry (UHPLC-(ESI)-HR-TOF) at 4 GHz
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resolution mode were studied. Only 20% of the evaluated pesticides showed
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noticeable matrix effects (ME) due to co-elution with natural products between tR=3-
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11 minutes. Principal components analysis (PCA) of the co-extractives detected
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grouped the mandarins and the orange varieties, but separated lemon, oranges, and
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mandarins from each other. Matrix effects were different among species but similar
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between varieties, forcing to determine pesticide residues through matrix matched
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calibration curves with the same fruit. Twenty-three natural products (synephrine,
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naringin, poncirin, glycosides of hesperitin, limonin, nomilin and a few fatty acids,
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among others) were identified in the analysed extracts. Twelve of the identified
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compounds co-eluted with 28 of the pesticides under study, causing different matrix
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effects.
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Keywords: Citrus sp; Pesticide residues; Matrix effects; Natural products, High
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resolution time of flight mass spectrometry
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Introduction
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Several approaches to determine pesticide residues in citrus fruit (1-5) and
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processed citrus products, such as fruit soft drinks (6, 7), juices (8, 9) and essential
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oils (10, 11), have been developed. The most notable, is the QuEChERS method
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(12) and its different versions (13, 14), as well as the ethyl acetate method (5). After
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chromatographic separation, the extraction methods are usually coupled to
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sophisticated and powerful mass spectrometry platforms. Despite the available
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analytical tools, the correct quantification of analytes during monitoring programs and
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routine analysis is affected by matrix effects (ME). The ME is the resultant of the
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presence of inferring matrix components at the time of measurement. The
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International Union of Pure and Applied Chemistry (IUPAC) (15) defined it precisely,
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as well as the SANTE guidelines, which also explicitly added that “ IThe response
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of some determination systems (e.g. GC-MS, LC-MS/MS) to certain analytes may be
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affected by the presence of co-extractives from the sample (matrix)0” (16). It is
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therefore clear that the commodity, the sample treatment procedure and others
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aspects such as the type of mass spectrometer or the acquisition mode influence
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directly on matrix effects (17). During the quantification step the ME can play an
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important role, such as diminishing or enhancing the signal for the analyte (18). To
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compensate it, matrix-matched calibration curves are commonly used and
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recommended in the DG-SANTE guidelines (16).
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Since 1992, the occurrence of ME in pesticide residue analysis using either
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conventional and/or mass spectrometer detectors was investigated by many
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researchers (19-26), as well as the influence of the clean-up step. (27-29).
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Actually, accurate high resolution mass spectrometry (HR-MS) coupled to
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chromatographic separation systems are being increasingly incorporated in the 3 ACS Paragon Plus Environment
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routine work of pesticide residues monitoring laboratories. Nevertheless, few studies
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have shown the performance of the different analytical methods using HR-MS for the
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determination of pesticides in citric matrices where the ME was briefly discussed (25,
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30). Recently, Gomez-Ramos et al (31) studied the ME caused by three different
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dispersive sample preparation methods in one orange variety among a wide variety
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of other fruits and vegetables. Although the phytochemical composition of citrus fruits
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has been largely described, the role of specific co-extractives on the ME in trace
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contaminant analysis has been only scarcely studied. Previous studies of ME in
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orange extracts using GC-MS reported by Sugitate et al (34) allowed the
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identification of 1-monopalmitin, 1-monolinolein, squalene, α-tocopherol and β-
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sitosterol.
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polyphenols as well as terpenoids were determined using liquid chromatography
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(HPLC) coupled to different detectors (32, 33), but the simultaneous identification
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and influence of specific co-extractives on MEs during citrus pesticide residues
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analysis using UHPLC-HR-MS-TOF has not been judiciously studied yet.
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Aiming to contribute to the understanding of the overall ME in the pesticide residues
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analysis in citrus fruits, four oranges, two mandarins and one lemon varieties were
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studied using an ethyl acetate extraction method (35) followed by UHPLC-HR/MS.
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The behaviour of different citrus species and varieties were compared. Furthermore,
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the influence of specific co-extracted matrix components was evaluated, taking into
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account the chemical diversity of the material under study.
Flavonoids,
coumarins,
permethoxylated
flavones
among
other
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Material and methods
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Standards and reagents
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High purity pesticides standards (Table 1) purchased from Dr. Ehrenstorfer GmbH
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(Augsburg, Germany), Sigma-Aldrich (Steinheim, Germany) and Riedel-de Häen
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(Selze, Germany) were stored at -30 °C. Individual pesticide stock solutions (1000-
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2000 µg/mL) were prepared by dissolving reference standards in the appropriate
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solvent. Each solution was stored in amber screw-capped glass vials in the dark at -
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20 °C. The working standard mix solution for spiking purposes was prepared at 10
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µg/mL concentration in acetonitrile (MeCN).
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Flavonoids standards were from Fluka AG and the other natural products reference
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standards were isolated in Pharmacognosy and Natural Product at the Faculty of
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Chemistry, UdelaR, and characterized using nuclear magnetic resonance (NMR) and
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electronic ionization mass spectrometry (EI-MS).
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HPLC-grade acetonitrile (MeCN) was obtained from Sigma-Aldrich (Steinheim,
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Germany) and analytical grade ethyl acetate (EtOAc) from Pharmco Products Inc.
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(Brookfield, CT. USA). Formic acid (98% purity for mass spectrometry) was from
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MACRON Chemicals (Netherlands). Sodium chloride from J.T. Mallinckrodt Baker
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Inc. (Phillipsburg, NJ, USA) and magnesium sulphate anhydrous from Scharlau
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(Spain) were employed throughout the whole study.
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Instrumentation
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Liquid chromatography high resolution mass spectrometry was used to separate and
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identify pesticides and natural products compounds. The UHPLC analysis was
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performed with an Agilent 1290 Infinity, equipped with a C8 Zorbax Eclipse Plus
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column (100×2.1mm, 1.8µm) from Agilent Technologies. A gradient program at 30
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°C, using of two solvents: A (0.1 % of HCOOH in ultrapure water) and B (0.1 % of
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HCOOH and 5 % of water in acetonitrile) was developed for the chromatographic 5 ACS Paragon Plus Environment
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separations. It started at 80: 20 (A: B) relationship for the first 2 min. Then, B was
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lineally increased to 100% from 2 to 15 min. Solvent B was maintained at 100% for 2
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min, decreased to 20% and kept from 17 to 19 min to re-equilibrate the column. The
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total run time was 19 min per sample. The flow rate was 0.3 mL/min with an injection
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volume of 4 µL.
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The liquid chromatograph was connected to an Agilent 6550 iFunnel QTOF-MS
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(Palo Alto, Agilent Technologies). The acceptable mass accuracy (within ± 2 ppm) of
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the QTOF-MS (used as TOF only) was calibrated before each analysis with a
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reference solution of HP-0921 (commercially available from Agilent) for scanning up
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to 1700 mass-to-charge ratio (m/z) and operated at 4 GHz(12000-16000 FWHM).
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The ionization mode was positive electrospray (+ESI), with the fragmentation voltage
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at 360 V. The nebulizer and drying gas was nitrogen at 350 °C.. In each analysis, the
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reference mass standard solution was constantly infused into the QTOF-MS for the
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monitoring and measurement of its mass accuracy with the reference masses of
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121.0509 and 922.0098 m/z.
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Sample preparation
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The oranges, mandarins and lemons samples used in the study, were obtained from
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several local growers in Uruguay. One of them came from a non-commercial,
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pesticide free orange tree grown in a familiar garden. Each sample employed
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weighted 2 kg of the different citrus fruit variety. The fruits were cut in four pieces,
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the two opposite ones were separated for analysis and the other two were discarded.
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The selected fruit pieces were chopped and homogenized using a kitchen blender.
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After composition and homogenization the final laboratory sample was stored at -20
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°C until analysis. The extraction method procedure chosen was a previously
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validated one by our group (35). Ten grams of the homogenized sample were 6 ACS Paragon Plus Environment
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weighed and placed into a 50 mL teflon centrifuge tube. Then, 10 mL of ethyl acetate
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were added and the tube was hand shaked vigorously for 1 min. Afterwards, 8 g of
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anhydrous magnesium sulphate and 1.5 g of sodium chloride were added. The
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mixture was shaked vigorously by hand for 1 min. The centrifuge tubes were placed
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in an ultrasonic bath for 15 min and centrifuged during 10 min at 5000 rpm. The ethyl
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acetate supernatant solution of 1 g/mL of sample was separated and concentrated
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under a N2 stream to dryness. The residue was re-dissolved in MeCN:H2O (1:9). The
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final extract was injected in the UHPLC system using the conditions described
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above.
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Matrix matched standards preparation
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To each citrus species and variety extract, the exact volume of pesticides working
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standard solution and MeCN:H2O (1:9) was added to re-dissolved it (final volume of
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100 µL). The fortified extracts were diluted 5 times with MeCN:H2O (1:9), yielding a
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final concentration in the fortified extract from 10 to 150 µg/L. The calibration curve
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used as reference was prepared in solvent at the same studied levels. The resulting
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concentrations in vial were from 2 to 30 µg/L respectively, ending up with the
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equivalent of 0.2 g of sample per mL of MeCN:H2O (1:9).
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Determination of matrix effect
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Matrix effects were evaluated injecting matrix-matched calibration curves and
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reference calibration curves in solvent. Matrix effects were quantified as the
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percentage of deviation of the response of the analyte in matrix vs. its signal intensity
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in the reference curve (IUPAC defines sensitivity as the slope of the calibration
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curve) calculated as:
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=
( ℎ ) − 1 × 100 ( )
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Positive values represent enhancements and negative values suppressions of the
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analyte signal induced by the matrix. In this work ME was classified, as currently, in
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3 levels; a) weak ME is considered when ME values is less or equal to 20 %. It can
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also be assumed as no matrix effects because the shift is between the repeatability
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values (36); b) medium ME is considered between 20 to 50%, and c) strong ME
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occurred when the values were above 50%.
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Results and discussion
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The ME caused by ethyl acetate extracts of three different citrus species, lemon,
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oranges, mandarins and varieties from the latter two, during pesticide residues
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analysis were characterized. ME calculation is usually performed using, at least, two
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different procedures. The first one compares the signal response between an analyte
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in a determined matrix and in solvent. This procedure is often done at different
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concentration levels as reported by Cervera (37).
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Other authors (5, 22) reported the determination of ME as the comparison between
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the slopes of matrix-matched standard curves against calibration standards curves in
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solvent. The requirements to accomplish this criteria are that: either the sum of least
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squares at each point must not exceed 20%, or the regression coefficient (r2) of both
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curves is higher than 0.99 The main drawback of this approach is that it can be
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inaccurate for pesticides with an r2 < 0.99, or whose sum of least squares at each
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point exceeds 20% (38). This was the criteria followed in the present study, and thus
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the slope adjustment to r2 ≥ 0.999 was verified throughout the experiment.
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Two types of matrix interferences are observed using ESI ionization followed by
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mass spectrometry detection. The most commonly effect of matrix co-extractives on
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residue determination occurs when they co-elute with the analytes from the
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chromatographic column in the ionization step. The pesticides of interest and the co-
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extractives placed in the surface of the solvent droplet compete in ion generation
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during the Coulomb explosion, causing in most cases a drop in the signal intensity
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(i.e., “suppression”). In addition to this effect, if the co-eluting compound generates
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ions with similar mass to those of the analyte (interferences), the ion ratio could
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change, leading to miss-interpretation of the results. The later is seen when
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determining pesticide residues through single quadrupole analyzers due to their poor
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mass resolution, and even in QqQ ones despite the selectivity improvement they
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have in these instruments. The problem could be partially avoided when high
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resolution spectrometers are employed, such as TOF or Orbitrap™.
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Linearity
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An important drawback of TOF systems is their short dynamic range due to rapid
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saturation of the detector. The results obtained in this work confirmed previous
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reports (39, 40). In some cases pesticide levels as little as 0.01 mg/kg were enough
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to saturate the detector. The linear ranges are variable for each pesticide due to the
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combination of specific analytes and matrix components. This is the case of
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cyprodinil, which has a linear range up to 0.05 mg/kg in lemons and oranges, but
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reached 0.10 mg/kg in mandarins.
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Matrix effects
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All citrus varieties caused a differential response for a number of analytes when
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compared with the corresponding signal in solvent. ME values for each combination 9 ACS Paragon Plus Environment
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of pesticide and citrus variety are shown in Table 1. The three citrus species showed
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differential matrix effects for some pesticides under study. This fact stresses the
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need for protocol validation of each citrus species matrix to perform accurate
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residues determinations, despite the generally accepted statement that it is sufficient
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to validate a single matrix as representative for all the components in a matrix
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category.
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221 222 In all varieties and species an important percentage of analytes did not present any 223 matrix effect (see figure 1a). The ME in mandarins was negative for around 20% of 224 the compounds under study, while in orange and lemon only around 8 % of the 225 studied pesticides have the same behaviour. In addition, an enhancement of the 226 analyte response was observed in 12 % and 24 % of the pesticides in oranges and 227 lemons, 228 imidacloprid, acetamiprid, thiodicarb and pyridaben, showed positive matrix effect for 229 all the evaluated citrus varieties and their different species. The MEs are globally 230 different for HRTOF(MS) than for QqQ MS/MS. As stated above, in the present study 231 around 75 % of the compounds showed low matrix effects (100% in all the studied 288 species, the ME observed when using MM calibration curve of the specific variety 289 dropped down to values below 30%. On the other hand, spinosyn A and bupirimate, 290 both of which showed little ME in lemon, shifted to 60-70% in orange and mandarin. 291 This confirmed that more accurate results in pesticide residues analysis should be 292 obtained when MM calibration curves of the same fruit species are used. 293 Nevertheless, and as stated by the DG SANTE guidelines (16),for every positive 294 found, a second analysis at the concentration detected is highly recommended,. 295 296 Analysis of possible interferences 297 As stated above, pesticide co-eluting interferences having the same ion fragment as 298 the analytes under study are usual in single quadrupole and QqQ-MS/MS. Powerful 299 instruments such as HR-MS can partially overcome this problem by adjustment of 300 both: the retention time and exact mass windows. The possible interferences were 301 analysed for each pesticide in the different matrices. In each spiked citrus variety, a 302 narrow error windows of ±0.1 min in retention time and ±5 ppm in mass were set to 303 accurately extract the [M+H]+ fragment, and few interferences were observed. For 304 example, when the quantifier ion of imidacloprid (256.0596) is extracted in a narrow 305 window of 5 ppm, an isobaric interference appeared at the same retention time in all 306 the studied matrices (see Figure 2). Similar isobaric interferences could explain the 307 positive ME observed for many pesticides, albeit the phenomenon deserves a 308 deeper study. 309 310 Evaluation of co-extracted components 13 ACS Paragon Plus Environment Journal of Agricultural and Food Chemistry Page 14 of 40 311 Citrus extracts for pesticide residue analysis contain thousands of natural 312 compounds (45) that co-elute throughout the chromatographic run at different 313 concentration levels. The matrix co-extracted compounds can cause problems at the 314 ionization step and in the detection systems of the spectrometers. Those at high 315 concentrations saturate the TOF detector and could hamper the detection of the 316 sought residues for some minutes. The number, amount and chromatographic 317 distribution of matrix components depend on the particular matrix, even among those 318 included within the same commodity category according to EU guidelines (45). To 319 evaluate the effect properly, a sequential study was performed. The co-extracted 320 analytes were evaluated using the Molecular Feature Extractor (MFE) algorithm from 321 the Mass Hunter Workstation software. This tool analyses the chromatogram 322 searching for groups of all the ions that can be joined with a real chromatographic 323 peak and may represent a characteristic of a compound. In addition, the MFE 324 algorithm creates a compound list of all the peaks in the data file that represent real 325 molecules. In this work, only compounds with an absolute peak height equal to or 326 greater than 10,000 counts were considered. In oranges extracts between 10994 to 327 11522 components were found, of which 8059 are common for all the varieties 328 studied. In the case of mandarin extracts, between 12966 and 12555 components 329 were found, while in lemon 11142. Among the three evaluated species, only 5250 330 compounds, 50% of the total compounds, were common. Applying this type of data 331 analysis, 8017 co-extractives were detected in QuEChERS orange extracts after 332 analysis by UHPLC-TOF-MS (45). Therefore, ethyl acetate protocols extract 20 to 333 40% more matrix compounds than the CEN method (45). A preliminary comparison 334 of these results shows that QuEChERS extracts are cleaner than EtOAc ones for 335 citrus pesticide residue analysis. 14 ACS Paragon Plus Environment Page 15 of 40 Journal of Agricultural and Food Chemistry 336 The 2D diagram in Figure 3 shows the complexity of the EtOAc extracts and the 337 pesticide distribution and intensity throughout one UHPLC run. Many of the 338 pesticides eluted in the most co-extractive-crowded region of the chromatogram. 339 From their interaction stems the observed ME, but after 10 min of elution, the 340 presence of co-extractives was lower. 341 342 As a consequence, the matrix effect for the 37 pesticides that are detected between 343 10 and 14 min was lowered. This fact means that for more than 40% of the 344 pesticides under study, the matrix effects are negligible, pointing out the importance 345 of the chromatographic separation before the MS determination. If the samples are 346 injected directly to the TOF analyzer as it has been suggested (46), matrix effects 347 could not be avoided for these pesticides despite the high resolution power of new 348 instruments. They would reach the MS detector simultaneously with the matrix 349 components, not only interfering with the determination but also saturating the 350 detector. 351 Co-extractives identification 352 353 Taking advantage of HR-MS systems that allow screening and tentative identification 354 for both non-target and target compounds (47), the profile of co-extractives from 355 each matrix were analysed. Although no major differences in the composition of co- 356 extractives were observed between oranges and mandarins, a principal component 357 analysis (PCA) shows a clear differentiation between the three citrus species. 358 Separated clusters for each species/ were formed but the different varieties grouped 359 with together (see Figure 4). 15 ACS Paragon Plus Environment Journal of Agricultural and Food Chemistry Page 16 of 40 360 361 362 To identify the most important co-extractives, 50x, 25x and 5x dilutions were 363 performed. In these conditions, the detected compounds were mainly polyphenols 364 glycosides and aglycones of flavonoids, fatty acids, limonoids and an alkaloid. The 365 flavonoids hesperidin and some of its isomers (neohesperidin and meranzin 366 hydrate), naringenin, many polymethoxy flavones; and limonoids such as limonin 367 and nomilin were identified (see Table 2). The identification of the natural products 368 was performed based on their exact masses, their isotopic abundance patterns and 369 the comparison to true standards and fragmentation pathway. The use of isotopic 370 patterns for identification purposes is a valuable indicator of the high degree of 371 selectivity that can be obtained with HR-MS instruments (48). The accurate mass of 372 characteristic isotopic signals and the distance in the m/z axis between them are 373 combined by the software, providing an estimation of the similarity between the 374 experimental mass spectrum and the theoretical one obtained using the elemental 375 composition of the natural product. Figure 5 shows the fragmentation of hesperidin, a 376 flavanone glycoside. 377 378 379 380 To determine precisely the retention time and avoid detector saturation problems by 381 the different natural products, exact mass calculations were performed for the isomer 382 containing one or two 13 C atoms, as shown in Table 2. Many of the identified natural 16 ACS Paragon Plus Environment Page 17 of 40 Journal of Agricultural and Food Chemistry 383 products were present in all the citrus matrices, but in different amounts. The most 384 different matrix was lemon, where citric acid could be identified but the alkaloid 385 synephrine and some flavonoids were not detected. 386 387 388 Influence of specific co-extractives in the matrix effects 389 The presence of known natural products from citrus fruits, such as flavonoids, acids 390 and limonoids can explain the global matrix effect that some analytes suffered during 391 the analysis. The relative amounts of each coextractive in the studied citrus species 392 and varieties played an important role For example, synephrine and citric acid co- 393 eluted with the triazine cyromazine. In sweet oranges, the citric acid content was the 394 lowest for the three species while in lemons was largely the highest. Synephrine was 395 absent in lemons and at lower concentrations in oranges but it was significant in 396 mandarins. The observed ME followed this trend. Mandarin II, whose sum in both 397 metabolites content was the highest, showed the greatest ME on cyromazine, 398 followed by lemon. In the four orange varieties ME was negligible (