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Multiresidue Analysis of 100 Pesticides in
Food Samples by LC/Triple Quadrupole
Mass Spectrometry
Food Safety
In recent years, the established regulations regarding the maximum residue limits (MRLs) in commodities have become more and more strin- gent. The European Union (EU) has set new direc-tives for pesticides at low levels in vegetables in Yanyan Fang, Paul Zavitsanos, and Jerry A. Zweigenbaum order to meet health concerns. For fruits and veg- etables intended for production of baby food, an MRL of 10 µg/kg is applicable for all pesticides,and compounds without a stated regulation also Abstract
have the lowest MRLs at 10 µg/kg. The low MRLshave encouraged the development of more sensi- An analytical methodology for confirming the presence of
tive analytical methods to meet the requirements a group of 100 pesticides in vegetable and fruit samples
in complex samples. In this sense, liquid- was developed using the Agilent G6410AA Triple Quadru-
pole Mass Spectrometer (QQQ). One transition per parent
(LC-MS-MS) with triple quadrupole in multiple compound was monitored in a single chromatographic
reaction monitoring (MRM) mode has become, so run containing two time segments. The sensitivity
far, the most widely used technique for the moni- obtained meets the maximum residue levels (MRLs)
toring and quantitation of pesticides in food, as established by the European Union regulation for food
reported extensively in the literature. On the other monitoring programs. The analytical performance of the
hand, high-resolving power mass spectrometric method was evaluated for different types of fruit and veg-
techniques, such as time-of-flight mass spectrome- try (TOF-MS), have been applied recently for + orange, tomato, and green pepper + showing
little or no matrix effects. Linearity of response over two
screening purposes as well. Nevertheless, the sim- orders of magnitude was demonstrated (r > 0.99). This
plicity of methodologies using triple quadrupole as study is a valuable indicator of the potential of the QQQ
a detection technique, together with the low limits for routine quantitative multiresidue analysis of pesticides
of detection achieved and the MS/MS capability in vegetables and fruits.
make this technique a valuable tool for routine monitoring programs established in regulatory LC/MS/MS Instrumentation
official laboratories. The easiness of use is some- LC Conditions
times an essential for these types of regulatory agencies, which lack the high-skilled personnel required for more sophisticated techniques such as TOF-MS. Triple quadrupole technology is not new in the sense that it needs to be validated for moni- toring purposes and its basis is already well- Our study in this report is one of the first of its kind to examine the new Agilent Triple Quad for the analysis of pesticides in fruit and vegetables.
This topic was chosen because of the relevance of MS Conditions
these compounds and their significant use on foodcommodities. The sensitivity of the QQQ easily Positive ESI using the Agilent G6410AATriple Quadrupole Mass Spectrometer meets the levels required by the regulations onpesticides in food.
Sample preparation
Pesticide analytical standards were purchased from Dr. Ehrenstorfer (Ausburg, Germany). Individual pesticide stock solutions (around 1,000 µg/mL) were prepared in pure acetonitrile or methanol, depending on the solubility of each indi- vidual compound, and stored at –18 °C. Fromthese mother solutions, working standard solu- Results and Discussion
tions were prepared by dilution with acetonitrileand water.
Optimization of LC/MS/MS conditions
Vegetable samples were obtained from the local A preliminary study of the optimal MRM transi- markets. “Blank” vegetable and fruit extracts were tions for every compound was carried out by used to prepare the matrix-matched standards for injecting groups of analytes (around 10 analytes in validation purposes. In this way, two types of veg- one chromatographic run) at a concentration level etables and one fruit (green peppers, tomatoes, of 10 µg/mL. Various collision energies (5, 10, 15, and oranges) were extracted using the QuEChERS 20, and 25 V) were applied to the compounds method already described in a previous applica- under study. The optimum energies were those tion [1]. The vegetable extracts were spiked with that gave the best sensitivity for the main fragment the mix of standards at different concentrations ion and, as a general rule, left about 10% of parent (ranging from 2 to 100 µg/kg) and subsequently compound in the spectra, and they were selected as optimum ones. Only one fragment ion waschosen as the most abundant product ion for everytarget compound. Results are shown in Table 1.
Analytical Conditions and Limits of Detection (LOD) for Each of the Compounds Tested
Retention Protonated
Collision LOD
Compound name
time (min)
molecule [M+H]+
Segment 1
Segment 2
Analytical Conditions and Limits of Detection (LOD) for Each of the Compounds Tested (Continued)
Retention Protonated
Collision LOD
Compound name
time (min)
molecule [M+H]+
The MRM transitions were included in the method example, the calibration curve generated for with a dwell time of 15 msec, and two different atrazine is shown in Figure 2. As it can be time segments were recorded in the chromato- observed in this figure, the linearity of the analyti- graphic run (each one of them containing about cal response across the studied range is excellent, half of the pesticides studied). Figure 1 shows the with a correlation coefficient of 0.998. Similar chromatogram corresponding to 100 pg on column results were obtained for the rest of the for all the compounds studied. Extracted ion chro- matograms are overlaid for each one of the targetanalytes according to their respective protonated The limits of detection (LOD) were estimated from molecule and product ion MRM transition.
the injection of standard solutions at concentra-tion levels corresponding to a signal-to-noise ratio Linearity and Limits of Detection
of about 3. The results obtained are included inTable 1 as well. The best limits of detection were Linearity was evaluated by analyzing the stan- obtained for the triazines (from 100 fg to 2 pg on dards solutions at five different concentration column) and the highest limits of detection were levels in the range 2 to 100 pg on column. As an for fluoroxypyr and spinosad D (above 100 pg).
+ MRM (282.0 ≥ 212.0) mix100_100 pg_5May.d 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Figure 1.
Product ion chromatograms of a mix of 100 pesticides (concentration: 100 pg on column).
Figure 2.
Calibration curve for atrazine using a linear fit with no weighting and no
origin treatment.
Application to Vegetable Matrices
requirements regarding the MRLs imposed by theexisting European regulations.
To confirm the suitability of the method for analy-sis of real samples, matrix-matched standardswere analyzed in three different matrices + green Reference
pepper, tomato, and orange + at two different con- 1. Imma Ferrer and E. Michael Thurman, “Deter- centration levels (10 and 100 µg/kg). Figure 3 mination of Fungicides in Fruits and Vegetables shows the analysis of a green pepper spiked with by Time-of-Flight and Ion Trap LC/MS” (2005) the pesticide mix at 10 µg/kg (10 pg on column).
Agilent Technologies, publication 5989-2209EN As it can be observed in two of the MS/MS extracted product ion chromatograms, fordimethoate and azoxystrobin, compounds can beeasily identified in these complex matrices due to For More Information
the selectivity of the MRM transitions, thus fulfill-ing the regulation limits imposed by the EU direc- For more information on our products and services, tives. In general, the LOD obtained meet the visit our Web site at
+ TIC MRM (** ≥ **) mix100_10pg_green pepper.d + MRM (230.0 ¡ 199.0) mix100_10pg_green pepper.d + MRM (404.0 ¡ 372.0) mix100_10pg_green pepper.d 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Figure 3.
MRM chromatogram of a spiked green pepper sample at 10 µg/kg. Product ion chromatograms for
(a) dimethoate and (b) azoxystrobin.

Agilent shall not be liable for errors contained herein or for incidental or consequentialdamages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to changewithout notice.
Printed in the USAAugust 9, 20065989-5469EN


Data Representation DATA REPRESENTATION Data Types Complements Fixed Point Representations Floating Point Representations Other Binary Codes Error Detection Codes Computer Organization Computer Architectures Lab Data Representation Data Types DATA REPRESENTATION Information that a Computer is dealing with * Data - Numeric Data Numbers( Integer, real)

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