- Email: [email protected]
Simultaneous determination of 200 pesticide residues in honey using gas chromatography–tandem mass spectrometry in conjunction with streamlined quantification approach
Accepted Manuscript Title: Simultaneous Determination of 200 Pesticide Residues in Honey using Gas Chromatography-Tandem Mass Spectrometry in Conjunction with Streamlined Quantification Approach Author: Amr H. Shendy Medhat A. Al-Ghobashy Moustapha N. Mohammed Sohair A. Gad Alla Hayam M. Lotfy PII: DOI: Reference:
S0021-9673(15)01712-4 http://dx.doi.org/doi:10.1016/j.chroma.2015.11.068 CHROMA 357082
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
2-10-2015 6-11-2015 22-11-2015
Please cite this article as: A.H. Shendy, M.A. Al-Ghobashy, M.N. Mohammed, S.A.G. Alla, H.M. Lotfy, Simultaneous Determination of 200 Pesticide Residues in Honey using Gas Chromatography-Tandem Mass Spectrometry in Conjunction with Streamlined Quantification Approach, Journal of Chromatography A (2015), http://dx.doi.org/10.1016/j.chroma.2015.11.068 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Simultaneous Determination of 200 Pesticide Residues in Honey using Gas
2
Chromatography-Tandem
3
Streamlined Quantification Approach
Mass
Spectrometry
in
Conjunction
with
ip t
4 5
Amr H. Shendya, Medhat A. Al-Ghobashyb,c,*, Moustapha N. Mohammeda, Sohair A. Gad Allaa
6
and Hayam M. Lotfyb,d
cr
7 8
Central Laboratory of Residue Analysis of Pesticides and Heavy Metals in Food, Agricultural Research
us
a
10
Center, Ministry of Agriculture and Land Reclamation, Giza, Egypt
12
c
13
d
Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt
an
11
b
Bioanalysis Research Group, Faculty of Pharmacy, Cairo University, Cairo, Egypt Pharmaceutical Chemistry Department, Faculty of Pharmaceutical Sciences and Pharmaceutical
14
Industries, Future University, Cairo, Egypt
15
M
9
16
*
17
Dr. Medhat A. Al-Ghobashy, Analytical Chemistry Department, Faculty of Pharmacy, Cairo
18
University, Cairo 11562, Egypt.
19
E-mail: [email protected]
21 22 23 24 25 26
d
te
Ac ce p
20
Correspondence:
27 28 29 30 31 1 Page 1 of 42
32 Abstract
34
A sensitive, accurate and reliable multi-class GC-MS/MS assay protocol for quantification and
35
confirmation of 200 common agricultural pesticides in honey was developed and validated
36
according to EU guidelines. A modified extraction procedure, based on QuEChERS method
37
(quick, easy, cheap, effective, rugged and safe) was employed. Mass spectrophotometric
38
conditions were individually optimized for each analyte to achieve maximum sensitivity and
39
selectivity in MRM mode. The use of at least two reactions for each compound allowed
40
simultaneous identification and quantification in a single run. The pesticides under investigation
41
were separated in less than 31 min using the Ultra-inert capillary column (DB-35MS). For all
42
analytes, neat standard calibration curves in conjunction with correction for matrix effect were
43
successfully employed. The detection limits of the assay ranged from 1.00 to 3.00 ng mL-1 for
44
the studied pesticides. The developed assay was linear over concentration range of 10.00 –
45
500.00 ng mL-1, with correlation coefficient of more than 0.996. At the LOQ, 81 % of the studied
46
pesticides were efficiently recovered in the range of 70.00-120.00 %, with CV % less than 15.00
47
% while 99.3 % compounds had mean percentage recovery of 60.00-140.00 %, with CV% less
48
than 21.00 % (N=18, over three different days). The proposed assay was successfully applied for
49
the analysis of the studied pesticide residues in one PT sample and 64 commercial honey
50
samples collected over one year from different districts around Egypt. Results revealed that only
51
one honey sample out of the 64 analyzed samples was contaminated with tau-Fluvalinate (10.00
52
µg Kg-1). This wide scope assay protocol is applicable for monitoring pesticide residues in honey
53
by national regulatory authorities and accredited labs; that should help ensure safety of such
54
widely used product.
56 57
cr
us
an
M
d
te
Ac ce p
55
ip t
33
58 59 60
Keywords
61
GC-MS/MS, honey; Pesticide residues; QuEChERS; Multi-class-multi-residue; Egyptian honey
62 2 Page 2 of 42
63 1.0 Introduction
65
Honey and bee products have the image of being natural, healthy and free of contaminants
66
although in many places they are produced in polluted environment [1-5]. Owing to the
67
extensive utilization and usual persistence in the environment, honey may get contaminated by
68
pesticides [1, 6-9]. It has been reported that pesticide residues can cause genetic mutations,
69
cellular degradation in addition to several public health problems [10-12]. This may occur
70
through direct contamination from beekeeping practices as well as indirect contamination from
71
environmental sources [13-15]. Acaricides, fungicides, insecticides and many other toxic
72
substances are used inside beehive colonies to control bee diseases especially varroaptosis and
73
ascospheriosis carrying the risk of direct contamination of honey and other hive products [1, 10,
74
16, 17]. On the other hand, the indirect contamination from environment occurs because of the
75
widespread use and extensive distribution of pesticides that helped introduce their residues into
76
honey by bees that have been fed on contaminated blossom [1, 4, 7, 11, 18, 19].
77
Reportedly, more than 150 different pesticides have been detected in colony samples [20]. In
78
general, the frequently detected pesticide residues are often from varroacides that have the ability
79
to migrate and accumulate in beeswax, pollen, and bee bread [21, 22]. Organophosphorus
80
(OPPs) and carbamates have almost replaced organochlorine pesticides (OCPs). However, owing
81
to the persistent nature of OCPs, they are still within the scope of recently developed analysis
82
procedures [19, 23]. According to EU regulations [24], honey is considered not suitable for
83
human consumption if residues are beyond the maximum residue levels (MRL) that are usually
84
in the range of 10.0 to 50.0 ng g-1 [11]. Based on the EU directive 96/23/EC (Annex Ι) [25] for
85
imported honey from the developing countries, many pesticide residue groups have been
86
identified as highly desirable to be monitored in honey samples. Thus, OCPs, OPPs, carbamates,
87
pyrethroids and polychlorinated biphenyls (PCBs) should be monitored in honey samples.
cr
us
an
M
d
te
Ac ce p
88
ip t
64
89
Determination of pesticides in honey at trace levels is a challenging task due to its complex
90
composition and particularly the presence of waxes and pigments. Conventional extraction
91
protocols using organic solvents followed by subsequent cleanup procedures prior to GC
92
determination have been a common practice [4, 7, 26-29]. The drawbacks of this traditional
93
approach are limited scope, large amounts of toxic solvents, prolonged analysis time and the 3 Page 3 of 42
need for large volume glassware. QuEChERS (quick, easy, cheap, effective, rugged and safe)
95
method on the other hand is based on liquid–liquid partitioning with acetonitrile followed by a
96
cleanup step via dispersive solid phase extraction (d-SPE) using primary secondary amine (PSA)
97
[30]. Coupling of QuEChERS protocol to GC-MS enabled multi-class, multi-residue analysis
98
over short analysis time. Determination of pesticide residues in honeybees using GC-MS/MS
99
have been previously reported [31]. To the best of our knowledge, very few studies were
100
reported for the simultaneous determination of pesticide residues in honey samples using
101
QuEChERS protocol coupled to tandem mass spectrometry. Paradise et al [32] reported the
102
simultaneous determination for 22 insecticides of three chemical families in honey using
103
QuEChERS/GC-MS/MS. The percentage recovery, correlation coefficient and LOD / LOQ were
104
63.00-139.00 % (CV% <25), 0.96-0.98 and 0.07-0.20 ng g-1 / 0.20-0.50 ng g-1; respectively. In
105
another study, Wiest et al [33] reported the determination of 80 environmental contaminants in
106
honey using a modified QuEChERS coupled to LC-MS/MS and GC-ToF. The percentage
107
recoveries and correlation coefficients using GC-ToF were 60.00-120.00 (CV% >25) and
108
>0.990; respectively while LOD and LOQ were 1.10- 47.50 ng g-1 and 10.80-128.00 ng g-1,
109
respectively. In both studies, a strong matrix effect was obtained and thus matrix matched
110
calibration was essential [32, 33].
te
111
d
M
an
us
cr
ip t
94
In the current study, QuEChERS method was revisited, modified accordingly and implemented
113
for the simultaneous determination of 200 pesticide residues, belonging to more than 50
114
functional and chemical classes (Table S1) and residues in honey samples using GC-MS/MS. A
115
streamlined quantification approach employing neat standard calibration curves in conjunction
116
with correction for matrix effect was used. The validation parameters have been evaluated for
117
each of the studied compounds according to EU guidelines [34-36]. This wide scope assay
118
protocol is applicable for monitoring pesticide residues in honey by national regulatory
119
authorities and accredited labs. The applicability of the developed protocol for the routine
120
monitoring of locally produced honey was investigated. This work is part of the national
121
initiative for developing a monitoring program for pesticide residues in Egyptian honey that
122
should help locally produced products to penetrate international markets.
123
2. Materials and methods
124
2.1 Chemicals, reagents and standard solutions
Ac ce p
112
4 Page 4 of 42
Pesticide reference standards were obtained from Dr. Ehrenstorfer GmbH (Germany), 99.00%
126
purity. An overview of the physicochemical properties of the studied compounds are
127
summarized in Table S1. Ethyl acetate, hexane, acetone, and acetonitrile of residue analysis
128
grade, were purchased from Sigma-Aldrich (USA). The QuEChERS kits (part no. 5682-5650)
129
with salt packets containing 4.00 g anhydrous magnesium sulfate, 1.00 g sodium chloride, 1.00 g
130
sodium citrate and 0.50 g sodium hydrogen citrate sesquihydrate, and 15 mL centrifuge tubes
131
with 150.00 mg anhydrous magnesium sulfate and 25.00 mg PSA for d-SPE (part no. 5982-
132
5021) were purchased from Agilent Technologies (USA). Stock solutions (1000 µg mL-1) of
133
each pesticide standard were prepared by dissolving 0.10 g of each pesticide in 100 mL toluene.
134
Working mixture standard solution of the studied pesticides (2.50 µg mL-1, each) was prepared
135
by diluting suitable aliquot of the stock solutions with toluene, and used to fortify honey
136
samples. A set of calibration standard solutions 10.00 - 500.00 ng mL-1 was prepared in
137
hexane/acetone (9:1 v/v). Stock standards and working solutions were stored at -20±2oC and 4-8
138
o
139
system (Millipore, Germany) with a resistivity of at least 18.2 MΩ.cm at 25oC and TOC below 5
140
ppb.
141
2.2 Instrumentation and analysis conditions
142
Analysis was carried out using an Agilent 7980A Gas Chromatography system equipped with
143
tandem mass spectrometer 7000B Quadrupole (Agilent Technologies, USA). Mass Hunter
144
software was employed for instrument control and data acquisition/processing (Agilent
145
Technologies, USA). NIST 08 mass spectral library, ver. 2.0f (Agilent P/N G1033A) was used
146
for confirmation of the studied compounds as well as identification of co-extractives.
147
Chromatographic separations were accomplished using the DB-35MS Ultra-inert capillary
148
column (20 m length x 0.18 mm id x 0.25 µm) that was obtained from Agilent Technologies
149
(USA). The GC oven temperature was programmed to initially be held at 70°C for 1.3 min then
150
increased to 135°C at 50°C min-1 (held for 0 min), and raised to 200°C at the rate of 6°C min-1
151
(held for 0 min), then increased from 200 to 310°C at 16°C min-1 (held for 8.2 min). The
152
injection volume was 1 µL and detection was achieved using EI source (-70 eV). Samples were
153
injected in a splitless mode and ultra-high purity helium (> 99.999%) was used as both the
154
carrier gas at flow rate of 0.7 mL min-1 and quench gas at 2.25 mL min-1, and nitrogen served as
155
the collision gas at 1.5 mL min-1. Injector temperature, transfer line temperature, ion source
an
us
cr
ip t
125
Ac ce p
te
d
M
C away from direct light, respectively. Ultra-pure water was obtained using a MilliQ UF-Plus
5 Page 5 of 42
temperature and quadrupole temperature were 250°C, 285°C, 280°C and 150°C, respectively.
157
The filament current (35 μA) was switched off during a solvent delay time of 6 min. Acquisition
158
was performed in MRM mode in which one MRM was used for quantification (quantifier peak)
159
and the others were used for confirmation (qualifier peaks). The MS/MS transitions and optimal
160
operational conditions used for analysis are summarized in Table 1. The correct identification of
161
the studied pesticides was based on the tR and the ion ratio of the qualifiers to quantifiers,
162
compared to that obtained via analysis of neat standard solutions. Regular maintenance was
163
carried out, where the liner (uni-taper) was replaced daily to avoid liner priming. In addition, 2 cm
164
from the front part of the column was trimmed to remove any accumulated non-volatile components
165
after about 500 injections.
166
2.3 Sample preparation
167
Initially, various conditions used for sample preparation as per QuEChERS protocol were
168
evaluated and optimized, if necessary. Locally produced organic honey samples were obtained
169
from GlobalG. AP-certified producers and used as blank samples. Aliquots of 5.00 ± 0.02 g were
170
accurately weighed into 50 mL polypropylene centrifuge tubes. Suitable volumes of the studied
171
pesticides working mixture standard solution were added to final concentration of 50.00 µg kg-1
172
of each compound. Spiked blank samples were vortex mixed for 30 s and stored away from light
173
at room temperature for 10 min. A modified QuEChERS protocol was optimized and applied,
174
briefly, deionized water (10.0 mL) was added to the sample then vortex mixed and incubated in a
175
water bath at 40oC until complete homogeneity was obtained. Acetonitrile (10 mL) was then
176
added and the content was shaken for 1 min using a mechanical shaker. The QuEChERS salt kit
177
was added and immediately shaken for further 1 min and subsequently centrifuged at 15,000 xg
178
at 4 – 8°C for 5 min. Thereafter, the whole acetonitrile fraction was transferred into a 15 mL d-
179
SPE polypropylene tube. The tube was shaken for 1 min and centrifuged for 2 min at 15,000 xg
180
using a cooling centrifuge. Finally, 2.00 mL of the supernatant was transferred into 50 mL round
181
bottom glass flask and evaporated under vacuum at 40 °C till complete dryness. The residue was
182
then reconstituted into 2.00 mL of hexane/acetone (9:1 v/v) containing 100.00 µg L-1 Aldrin
183
(Injection standard). Samples were then ultra-sonicated and filtered through a disposable 0.45
184
µm PTFE membrane filter into an amber glass vial and subjected to GC-MS/MS analysis.
185
2.4 Calibration and validation
Ac ce p
te
d
M
an
us
cr
ip t
156
6 Page 6 of 42
A set of calibration standard solutions of 10.00 - 500.00 ng mL-1 was prepared in hexane/acetone
187
(9:1 v/v) containing 100.00 ng mL-1 of Aldrin, and analyzed as described. The neat standard
188
calibration curves were constructed by plotting the relative concentration (ng mL-1) versus
189
instrument relative response and correlation coefficients were estimated. In order to determine
190
the matrix effect; calibration curves were constructed using the conventional matrix matched
191
standards and results were compared to those of the neat calibration curves. In-house validation
192
was performed according to the criteria and recommendations of the European guidelines [34-
193
36]. The performance requirements for the assay were as follows: (1) Range: the MRL for each
194
analyte in the matrix should be encompassed inside the range, (2) Linearity: the correlation
195
coefficient should be ≥ 0.990, and the CV % of the response for each calibration point should be
196
˂ 20.0 %, (3) Precision: expressed as reproducibility (inter-day precision) should be < 20%, (4)
197
Trueness/Accuracy: expressed as mean recovery, shall be in the range of 70.00 – 120.00 %, (5)
198
Limit of quantification (LOQ): should be ≤ the lowest MRL and should comply with
199
requirements (3 - 4) and (6) Uncertainty values must comply with the EU requirements of a
200
maximum uncertainty of 50%. Uncertainty was calculated following the guidance of
201
EURACHEM [35, 37]. Blank honey samples (organic honey samples from GlobalG AP-certified
202
producers) were fortified at three levels: 10.00, 50.00 and 100.00 µg Kg-1. At each level the
203
analysis was performed on three different days using six replicates / day in order to verify the
204
validation parameters.
205
2.5 Application to commercial honey samples and PT sample
206
In order to ensure the reliability of the test results, the following samples were included with
207
every analysis batch: (1) blank extract, (2) reagent blank and (3) a spiked blank sample at 50.00
208
ng g-1. Calibration curves prepared and constructed in the same batch were employed in all
209
determinations. One point matrix matched standard (50.00 ng g-1) and an equivalent neat
210
standard were prepared and analyzed. Results were used to estimate the matrix effect and correct
211
for minor variability in test results. In routine analysis, recovery values between 60.00 and
212
140.00% were accepted in agreement to European guidelines [36]. The optimized assay protocol
213
was applied for the analysis of 64 commercial honey samples that were obtained from local
214
market in addition to a spiked strawberry sample, a proficiency testing sample (PT sample) for
215
pesticide residues (round 19178, 2014).
216
3. Results and discussion
Ac ce p
te
d
M
an
us
cr
ip t
186
7 Page 7 of 42
In this work, the analysis strategy was designed to detect and quantify as many pesticides as
218
possible in a single run. With more than 1000 pesticides being used world-wide, residue analysis
219
using LC-MS/MS and GC-MS/MS have become the gold standard. Several criteria were taken
220
into account while selecting which pesticides are to be included: (1) pesticides registered for crop
221
protection by local authorities, (2) surveying the literature for the commonly analyzed
222
compounds, especially those previously detected in Egyptian honey, (3) persistence of pesticides
223
in the environment and bioaccumulation, (4) international requirements for safety and suitability
224
for human use of honeybee products (CAC, EU, USA and EU-RASFF notifications) and (5)
225
amenability for GC-MS/MS analysis. It has been previously reported that a number of pesticide
226
classes are commonly used in Egypt such as OCPs, OPPs, carbamates, ureas, anilides and
227
pyrethroids [38-40]. In this study, we originally selected 200 pesticides of different classes:
228
OCPs, OPPs, synthetic pyrethroids, carbamates, fungicides, and herbicides. Physicochemical
229
properties of the selected pesticides along with their functional and chemical classification are
230
summarized in Table S1.
231
3.1 Instrumentation and analysis conditions
232
3.1.1 MS/MS optimization
233
Initially, optimum precursor ion for each of the studied compounds was identified to establish
234
the optimized MS/MS detection method. Thus, appropriate concentration of each analyte was
235
prepared and injected individually in full scan mode in the range of 50 - 550 m/z using EI
236
ionization mode at -70 eV. In general, the optimal precursor ions shall meet the criteria of the
237
most intense ion with the highest m/z, afterwards, several product ion scan methods were
238
created, each with different collision energy in order to find the optimum product ions. Within
239
each method, groups of approximately 10 pesticides were concurrently analyzed, and the tested
240
collision energies were 5 - 40 V with 5 V step increase. Fine optimization for some of the studied
241
compounds was carried out via slight increase or decrease of the collision energy in order to
242
obtain intense ions. As a consequence, the most intense ion was selected as the quantifier ion
243
while the less intense ones were used as qualifier ions. This yielded four identification points, 1
244
for the precursor ion and 1.5 for each product ion, in agreement with EU Guidelines [34, 36].
245
The MRM method was built and the D-well time was set at 10 ms for each transition. After
246
analyzing the obtained results, further optimization was required for the less sensitive
247
compounds. Consequently, the relevant segment was adjusted, whereas D-well time was
Ac ce p
te
d
M
an
us
cr
ip t
217
8 Page 8 of 42
increased for the less sensitive analytes such as deltamethrin by decreasing the D-well time for
249
analytes with intense signals. MS/MS transitions and optimal operational conditions for the
250
studied compounds are summarized in Table 1.
251
3.1.2 GC optimization
252
GC separation was optimized for all the studied pesticides that were from different chemical
253
classes. Two columns; DB-35MS (20 m length x 0.18 mm id x 0.25 µm) and HP-5MS (30 m
254
length x 0.25 mm id x 0.25 µm) were tested at constant flow rates of the carrier gas at 0.70, 1.20
255
and 1.80 mL min-1. Initially, separation for the studied compounds was achieved at a flow rate of
256
1.8 mL min-1 using HP-5MS column in conjunction with 30 segments and scan times. At such
257
conditions, the whole run time was relatively long (45 min) with asymmetric peaks. On the other
258
hand, when DB-35MS column was used at a flow rate of 1.8 mL min-1, the peaks eluted with
259
poor resolution. At such conditions, analysis was divided into ten segments, including too many
260
transitions and scan times of more than 1s. Efficient separation along with sufficient time
261
segments/scan times was obtained at flow rates of 0.70 and 1.2 mL min-1. Nevertheless, the flow
262
rate of 0.70 mL min-1 helped reduce the whole run time to less than 31 min with symmetrical
263
peaks when compared to HP-5MS column (tR of the last eluting peak, difenoconazole, was 23.6
264
min). As ultra-trace concentrations of the studied compounds were to be detected; splitless
265
injection mode for the final extracts was used for optimum sensitivity. Both of the injection
266
solvent type and volume; acetonitrile, hexane, acetone and hexane/acetone mixtures were tested
267
at different injection volumes (0.50, 1.00 and 2.00 µL), respectively. In contrast to the previously
268
published protocol [30], acetonitrile extract was not directly injected to the GC-MS/MS. This
269
could be attributed to the large expansion volume of acetonitrile upon injection under high inlet
270
temperatures that may result in irreproducible chromatograms, especially in routine analysis
271
[30]. Results showed that optimal sensitivity and peak symmetry for the studied compounds were
272
successfully accomplished upon conducting analyses using 1.00 µL of hexane/acetone (9:1 v/v)
273
as reconstitution solvent at the end of extraction step.
Ac ce p
te
d
M
an
us
cr
ip t
248
274 275
It should be noted that post-run, post-column backflush was used between each injection. The
276
use of backflush helped remove any high-boiling point matrix component that could be still
277
remaining in the column at the end of each run. In general, we recommend that after the injection
9 Page 9 of 42
of 25 samples, one point matrix matched standards of all analytes at concentration level of 50.00
279
µg Kg-1, each is injected to monitor possible deviation of analyte responses.
280
3.2 Sample preparation
281
Owing to the complexity of honey matrix that contains sugars, enzymes, proteins as well as other
282
minor components such as lipids and waxes, sample clean-up is generally employed [41].
283
QuEChERS method [30] has been in use for sample preparation prior to analysis of multi-class
284
residues of veterinary drugs in honey and milk samples [41-44] in addition to, pesticide residues
285
in various matrices including honey [5, 11, 38, 45-49]. Originally, QuEChERS method involved
286
an extraction step with acetonitrile followed by liquid – liquid partitioning with citrate buffer and
287
phase out separation using MgSO4 and NaCl salts. The obtained extracts were purified using d-
288
SPE with PSA and a large volume of the acetonitrile extract was directly injected to GC-MS
289
instrument [30]. Recent trends in sample preparation involves the development of multi-residue,
290
multi-class assays. Reduction in sample size, extraction time and extensive clean-up, thus solvent
291
consumption have always been the objectives [30, 50]. Accordingly, all relevant parameters for
292
efficient sample preparation were considered during the optimization of this extraction protocol,
293
as will be discussed in detail.
294
3.2.1 Sample size
295
Initially, reducing sample size without affecting the statistical reliability of the results was tested
296
in order to help improve the efficiency of the proposed assay and reduce interference from
297
matrix components. Different analytical portions of samples (1.00, 5.00, 10.00, 15.00 and 20.00
298
g) were taken from a bulk of 2.00 kg of honey samples spiked at 100.00 µg kg-1 with the target
299
compounds and analyzed in triplicate. Results showed good homogeneity with variability lower
300
than 20.00% for all compounds for sample sizes more than 1.00 g. As a result, we opted a sample
301
size of 5.00 g with the aim of reducing reagent consumption and time required for the
302
methodology. Although it has been previously reported that a subsample size of 10.00 – 15.00 g
303
is sufficiently representative [30, 50], our results showed the suitability of 5.00 g as the proposed
304
analytical portion for typical residue assays. This was in agreement to the recently published
305
report that recommended 2.00 – 5.00 g subsample for residue analysis [51].
306
3.2.2 Extraction and reconstitution solvents
307
Selecting the proper solvent for extraction depends on availability, cost, safety to the analyst and
308
the environment, in addition to, extraction efficiency with lowest possible matrix components. It
Ac ce p
te
d
M
an
us
cr
ip t
278
10 Page 10 of 42
has been previously reported that ethyl acetate would be the solvent of choice for extraction as it
310
provided good recoveries for a wide number of pesticides with different properties. In addition,
311
ethyl acetate is less polluting than chlorinated solvents, although it does also co-extract matrix
312
components [50]. Initially, the effect of extraction solvent type; ethyl acetate and acetonitrile in
313
conjunction with the effect of the PSA sorbent was investigated. In the absence of the PSA, the
314
overall mean percentage recoveries were 64.73% and 69.02% upon extraction with acetonitrile
315
and ethyl acetate, respectively. On the other hand, in the presence of the PSA, results indicated
316
that the overall mean percentage recovery for all of the studied compounds were relatively higher
317
than those obtained in absence of PSA (79.45% and 86.94% for acetonitrile and ethyl acetate,
318
respectively). Statistical analysis using one way ANOVA showed a significant difference
319
between the studied solvents either in the presence or absence of PSA (Table S2 and S3). Based
320
on the obtained results, it could be concluded that: (1) ethyl acetate was relatively more efficient
321
than acetonitrile in the absence or presence of PSA, (2) higher recoveries were obtained in the
322
presence of PSA regardless of solvent type, (3) extraction using ethyl acetate/PSA resulted in
323
relatively larger number of compounds recovered within the range of 70 – 120% when compared
324
to acetonitrile/PSA, however, almost all compounds were recovered using acetonitrile within the
325
range of 60 – 140% (Table S3 and Fig. S1, Raw data are provided in supplementary materials)
326
and (4) acetonitrile in conjunction with PSA was found capable of efficient extraction for the
327
compounds of concern with lowest possible co-extracts. This was concluded via comparing the
328
obtained chromatograms visually and by the aid of the NIST library. Thus, (acetonitrile/PSA)
329
was adopted in agreement with the original QuEChERS method as it provided efficient
330
extraction for almost all the studied pesticides with minimal amount of co-extractives. In the
331
current study, acetonitrile extract was not directly injected to the GC-MS/MS as discussed
332
earlier. Alternately, extracts were centrifuged at 15,000 xg, 4-8oC in order to enable further
333
sample clean-up via removal of solidified lipids and waxes. Evaporation under vacuum was
334
carried out at 40 oC till complete dryness using a rotary evaporator. At such conditions, studied
335
compounds were found to be not affected by either evaporation or degradation. In all future
336
experiments, 2.00 mL hexane/acetone (9:1 v/v) were used for reconstitution of evaporated
337
extracts. This volume was found appropriate in order to avoid long evaporation time.
338
3.2.3 Removal efficiency for matrix components
Ac ce p
te
d
M
an
us
cr
ip t
309
11 Page 11 of 42
Co-extracted non-volatile matrix components may cause serious problems in routine trace
340
analysis of pesticide residues [12, 52, 53]. Cleanliness of the final extracts should help improve
341
the selectivity of analysis. The use of acetonitrile in conjunction with PSA allowed for exclusion
342
of larger amount of co-extracted compounds such as enzymes, proteins, sugars, waxes and lipids
343
when compared to ethyl acetate. Co-extractives removal efficiency was estimated on the basis of
344
the weight of the remaining residue after evaporation of the extracts obtained from 5.00 g spiked
345
samples. Results showed that the residue obtained upon extraction of honey samples using ethyl
346
acetate/PSA and acetonitrile/PSA were ~55 mg g-1 and ~30 mg g-1, respectively. This conclusion
347
was further verified via inspection of the chromatograms of reconstituted residues using NIST
348
library as discussed above.
349
3.3 Confirmation and quantification
350
For all analytes under investigation, identification and quantification were based on four criteria:
351
(1) stability of chromatographic retention time, (2) matching between the retention time of
352
analytes in spiked samples and neat standards, (3) presence of the two or more relevant
353
transitions from the selected precursors with S/N>3 and (4) stability of the ion ratio (quantifier
354
and qualifier peaks), in accordance with the recommendations listed in CD 2002/657/EC and
355
SANCO/12571/2013
356
throughout the study in order to ensure that they were within the acceptable ranges [34, 36].
357
Matrix matched calibration is the most commonly adopted approach to compensate for signal
358
suppression/enhancement experienced during MS/MS analysis [41, 44, 54]. In the current study,
359
quantification was accomplished using a set of neat standard calibration curves via external
360
standardization approach. Results were corrected using one point matrix matched standard.
361
Assay validation and application to spiked honey samples, commercial samples and PT sample
362
were then carried out in order to verify the applicability of this approach.
363
3.3.1 Linearity and working range
364
A set of neat standard mixtures of all compounds was prepared in the presence of 100.00 ng mL-1
365
of the Aldrin. Analysis was carried out and the results were used to construct the neat standard
366
calibration curves. For all studied pesticides, the response was linear over the concentration
367
range (10.00 – 500.00 ng mL-1) with correlation coefficients more than 0.996 for all compounds.
368
The calibration data for the studied compounds are summarized in Table 2. A calibration level of
369
concentration 50.00 ng mL-1 of each compound was injected twenty times. The obtained results
d
M
an
us
cr
ip t
339
Ac ce p
te
[34, 36]. The ion ratio for each analyte was regularly monitored
12 Page 12 of 42
exhibited CV% values for the retention times of 0.001% - 0.764%, while for relative peak
371
responses the CV% ranged from 0.148% to 8.255%. Therefore, the results achieved could
372
indicate the stability of the chromatographic and mass conditions for the developed assay.
373
The matrix effect on instrument response was then investigated in order to reveal possible signal
374
suppression or enhancement [55]. Blank honey samples were extracted and fortified with
375
appropriate aliquots of studied compounds (10.00 – 500.00 ng mL-1). Matrix matched calibration
376
curves were constructed and regression equation parameters were compared to those obtained
377
using the neat standard calibration curves (Table 2). Results showed that both of the neat
378
standard and matrix matched calibration curves were linear with acceptable correlation
379
coefficients but with apparent slope differences.
us
cr
ip t
370
an
380
A set of six replicates of spiked honey samples (100.00 µg Kg-1) were analyzed and the
382
concentration was determined using both neat standard calibration and matrix-matched
383
calibration curves. Results showed acceptable mean percentage recoveries upon using matrix
384
matched calibration. On the other hand, results obtained using the neat standard calibration were
385
significantly lower than those obtained using the matrix matched calibration, as shown in Table
386
S4. Such results along with slope differences shown in Table 2, indicated matrix effect as will be
387
discussed in detail.
388
3.3.2 Matrix effect and quantification
389
In literature, it has been reported that slope differences between matrix matched and neat
390
standard calibration curves of more than ±10% limit indicate matrix effect [54]. Later on, a more
391
detailed discussion of the correlation between slope differences and matrix effect was presented:
392
(1) ±20.00% indicates mild or tolerable matrix effect, (2) more than ±20.00 % up to ±50.00 %
393
represent medium matrix effect, while (3) differences of ±50.00% or more represent strong
394
matrix effect [45]. Recently, it has been reported that matrix effect would be considered minimal
395
and neat standard calibration curves could be applicable if slope differences were within ±30%
396
[56].
Ac ce p
te
d
M
381
397 398
In this study, slope difference percent was calculated for each compound (Table 2 and Fig. 1,
399
Raw data are provided in supplementary materials). Results demonstrated that only 93
400
compounds showed no matrix effect (within ±10% limit [54]). According to Camino-Sanchez et 13 Page 13 of 42
al. [45], 132 of the studied compounds presented mild suppression ranging from -19.91 % to -
402
0.87 % and 25 compounds showed mild enhancement at range of +0.34 % to +16.09 %. Medium
403
matrix suppression ranging from -20.30 % to -43.77 % was noticed for 35 compound, while 6
404
compounds presented medium matrix enhancement ranging from +20.69 % to +43.57 %. It
405
should be noted that only 3 compounds experienced severe matrix suppression ranging from -
406
55.79 % to -57.90 %. On the other hand, according to Nie et al. [56], 188 of the studied
407
compounds (~94.00%) showed slope difference percent within ±30%. These results might suggest
408
that using matrix matched calibrations was not critical in our case. However, ANOVA results for
409
comparison between the recoveries obtained from the neat standard and matrix matched
410
calibration curves revealed significant differences between the two approaches (Table S4).
411
Here, we suggest that for accurate quantification in multi-residue, multi-class assays of wide
412
scope, neat standard calibration curves could be used only after correction for matrix effect. In
413
order to estimate a correction factor for the matrix effect, a set of extracted blank honey samples
414
were fortified at four concentration levels (10.00, 50.00, 100.00 and 500.00 µg Kg-1, 6 replicates
415
each) of the studied compounds. Analysis was carried out as described and mean percentage
416
recoveries (N = 24) and CV% were calculated using the neat standard calibration curve for each
417
compound (Fig. S2, Raw data are provided in supplementary materials). Results indicated a
418
homogenous matrix effect with an overall suppression in the mean percentage recovery for all
419
compounds throughout the linear concentration range of each of the studied compounds.
cr
us
an
M
d
te
Ac ce p
420
ip t
401
421
Based on the obtained results, a correction factor was calculated for each compound, as
422
previously suggested for analysis of nitrofuran metabolites and nitroimidazoles in honey using
423
LC-MS/MS by our research group [41]. One point matrix matched standard was prepared at
424
50.00 µg Kg-1 and analysis results using neat standard calibration approach were used to estimate
425
the correction factor. The following mathematical equation was applied for the determination of
426
analyte concentration in samples after correction for the matrix effect:
427
14 Page 14 of 42
ip t cr
428 429 Cs, analyte concentration in sample (mg Kg-1)
431
Ci, found analyte concentration determined from calibration curve (mg Kg-1)
432
Vext, total volume of extraction solvent (mL)
433
Vevp, aliquot volume taken for evaporation (mL)
434
Vf, final volume after reconstitution of evaporated aliquot (mL)
435
W, sample weight (g)
436
Cmtx (labeled), labeled concentration of one point matrix matched standard (mg Kg-1)
437
Cmtx (found), found concentration of one point matrix matched standard (mg Kg-1)
438
Cmtx (labeled) / Cmtx (found), matrix effect correction factor
439
In order to extrapolate and verify the applicability of the proposed method of calculation for GC-
440
MS/MS analysis of pesticide residues, spiked honey samples (n=6) at concentration of 100.00 µg
441
Kg-1 of each compound were prepared and analyzed. The concentrations were determined using
442
the corresponding neat standard calibration curves and results were corrected as described.
443
Results were compared to those obtained using the matrix matched calibration curves.
444
Significant difference (one-way ANOVA) between the results obtained using the neat standard
445
calibration curve and the matrix matched calibration curve confirmed the need for correction for
446
matrix effect. On the other hand, no significant difference between the corrected results and
447
those obtained using the conventional matrix matched calibration. A summary of the obtained
448
results and the statistical analysis were summarized in Table S4. These results confirmed the
449
applicability of one point matrix matched calibration for streamlined quantification of pesticide
450
residues using GC-MS/MS.
451
3.4 Validation procedure
Ac ce p
te
d
M
an
us
430
15 Page 15 of 42
Assay validation was carried out in accordance with the procedures outlined in EU regulations
453
[34, 36]. The ruggedness of the assay was demonstrated on an ongoing basis through its use for
454
routine analysis of honey samples. It is worth mentioning that during validation for the optimized
455
assay protocol, great concern was given to isomeric compounds that may partially co-elute
456
together. Among isomeric compounds that gave the same ion transitions are DDT o,p- and DDD
457
p,p- and partially co-eluted. Although they could be distinguished individually by their tR
458
difference of 9.60s (DDT o,p- 18.35 min and DDD p,p- 18.51 min), it was opted to integrate and
459
quantify such compounds as one peak. For pyrethroids that formed multiple chromatographic
460
peaks (cyfluthrin, cypermethrin, permethrin and flucythrinate), all peaks were integrated together
461
and expressed as sum of isomers.
462
3.4.1 Specificity
463
A specificity study was conducted in order to verify the absence of potential interfering
464
compounds at the retention time of the studied analytes. The assay was applied for analysis of
465
twenty blank honey samples (organic honey samples from GlobalG AP-certified producers) of
466
different origins / matrix composition (viscosity, pigment content, pollen grain contents, etc).
467
Representative honey samples were fortified with all analytes at 50.00 µg Kg-1 and analysis was
468
carried out as described. No interfering peaks were detected in the region of interest for all
469
analytes as shown in the TIC of blank and fortified honey samples at 50.00 µg Kg-1 (Fig. 2).
cr
us an
M
d
te
Ac ce p
470
ip t
452
471
3.4.2 Accuracy and precision
472
The accuracy and precision of the assay were measured at 10.00, 50.00 and 100.00 µg Kg-1 for
473
each analyte (6 replicates/day, over three days). Results indicated acceptable performance of the
474
assay for all analytes over the studied validation levels. The percentage recoveries were in the
475
range of 51.13 – 126.55% with CV% of 0.43% - 20.53%, respectively. These results were in
476
agreement to the requirements of EU guidelines [34, 36] regarding the CV% for repeated
477
analysis of spiked or incurred material. The majority of the studied pesticides exhibited mean
478
percentage recoveries within the range of 70.00 – 120.00 % (Fig. 3, Raw data are provided in
479
supplementary materials). The long-term stability of analytes in the extracts and robustness of
480
the developed assay were evaluated by repeated injections (50 injections) of one point matrix
481
matched standards at concentration level of 50.00 µg Kg-1. Results demonstrated the stability of
482
responses after repeated injection. 16 Page 16 of 42
3.4.3 Limits of detection and quantification
484
In the current study, estimating the LOQ for each compound would be a commonly inaccurate
485
process using MS/MS due to non-existent electronic noise and/or highly variable chemical noise
486
that depends on the matrix [57, 58]. As a result, the fit-for-purpose approach was followed to
487
determine the lowest calibration level of the proposed assay via injecting such calibration level.
488
For all studied compounds, it was found that the lowest concentrations that would be accurately
489
quantified ranged from 5.00 – 10.00 µg Kg-1. The LOQ was established at the concentration that
490
would provide a minimum S/N ratio of 10. Therefore, the LOQ for all analytes was compiled at
491
10.00 µg Kg-1. This value comply with the MRLs established in EU Regulation (EC) No.
492
396/2005 and its subsequent modifications [24]. For correct identification and accurate
493
quantification, the LOQ level was validated according to the quality criteria and requirement of
494
accuracy and precision initially established for the method. According to EURACHEM
495
guidelines [35]; it was normally sufficient to provide an approximate value for the LOD (the
496
level at which detection of the analyte becomes problematic). Thus, we can, however, estimate
497
that it is between 1.00 and 3.00 µg Kg-1 for the studied compounds at S/N of 3.
M
an
us
cr
ip t
483
d
498
501
Ac ce p
500
te
499
502
3.4.4 Measurement uncertainty (MU)
503
Uncertainty was calculated following the guidance of EURACHEM according to the document
504
“Eurachem/CITAC Guide CG4: Quantifying uncertainty in analytical measurement” [37]. The
505
calculated expanded uncertainty of the assay was 47.00%. This result was in agreement to the
506
European requirements for a maximum uncertainty of 50%.
507
3.5 Application of assay protocol
508
Throughout routine analysis, the following internal quality control (IQC) was carried out: (1)
509
blank extract; in order to identify false positives caused by any contamination during the
510
extraction procedure or by the presence of interfering compounds, (2) reagent blank; in order to
511
reveal any possibility of false positive due to contamination in the equipment or reagents and (3)
512
a spiked blank sample at 50.00 µg Kg-1 to assess the extraction efficiency.
513
5.1 Application to commercial honey samples 17 Page 17 of 42
In order to demonstrate the applicability of the optimized assay protocol as well as the correction
515
factor calculation, 64 commercial honey samples of different botanical origin were obtained
516
from local market of different governorates during the period of 2013 till the mid of 2014. The
517
validated assay protocol was then applied for the detection and determination of the studied
518
analytes. Results showed that all studied compounds were not detected in amounts above the
519
LOD or LOQ of the employed assay and were deemed suitable for human use except for one
520
sample that was found contaminated by tau-fluvalinate at concentration level of 10.00 µg Kg-1.
521
This result bears non associated heath risk to the consumer, since the MRL of tau-fluvalinate in
522
honey is 50.00 µg Kg-1 according to EU guidelines; 396/2005/EC [24]. It has been previously
523
reported that tau-fluvalinate is a pesticide with a high persistence in honey and its level does not
524
decrease with time, even during honey blending process or after storage at 35oC in the dark for 8
525
months [13]. Thus, repeated analysis for the naturally incurred honey sample was carried out
526
over six months. Results obtained for the repeated analysis of the tau-fluvalinate contaminated
527
sample further confirmed the validity of the assay for residues in honey samples.
528
3.5.2 Application to PT sample
529
Further verification of assay performance and calculation approach was carried out through
530
analysis of a PT sample (round 19178), as part of the Food Analysis Performance Assessment
531
Scheme (FAPAS). From a list of 220 pesticide residues, participating laboratories had to identify
532
and quantify those present in the PT of spiked strawberry sample. The assay was carried out as
533
described and results were calculated using the proposed calculation method. Results of the
534
optimized assay confirmed the presence of bromuconazole, parathion-ethyl, pirimiphos-methyl
535
and tebuconazole residues in the sample. Results reported in the FAPAS report (round 19178)
536
revealed that the z-score for the obtained results was found within the acceptable range |z| < 2
537
(Table S5). Satisfactory z-scores indicated the applicability of the proposed assay protocol for
538
the determination of the studied compounds and the possible applicability to other matrices.
539
4. Conclusion
540
An accurate and sensitive GC-MS/MS assay was developed and validated for the simultaneous
541
determination of 200 pesticide residues from different chemical classes in honey samples.
542
Analytes were extracted using modified QuEChERS protocol coupled to GC-MS/MS analysis.
543
Neat standard calibration curves were successfully employed in conjunction with correction for
544
matrix effect. Assay validation was carried out as per EU guidelines. The applicability of the
Ac ce p
te
d
M
an
us
cr
ip t
514
18 Page 18 of 42
545
method for the determination of the studied compounds was verified using spiked honey samples
546
and naturally incurred honey sample. The assay was deemed suitable for the regulatory
547
monitoring of pesticide residues in honey.
548
ip t
549 550
cr
551 552
us
553 554
an
555 556 557
M
558 559
563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579
te
562
Ac ce p
561
d
560
References
[1] S. Panseri, A. Catalano, A. Giorgi, F. Arioli, A. Procopio, D. Britti, L.M. Chiesa, Occurrence of pesticide residues in Italian honey from different areas in relation to its potential contamination sources, Food Control, 38 (2014) 150-156. [2] A. Giorgi, M. Madeo, J. Baumgartner, G.C. Lozzia, The relationships between phenolic content, pollen diversity, physicochemical information and radical scavenging activity in honey, Molecules, 16 (2011) 336-347. [3] A. Wilczynska, P. Przybylowski, Residues of organochlorine pesticides in Polish honeys, Apiacta, 42 (2007) 16-24. [4] G.P. de Pinho, A.A. Neves, M.E.L.R. de Queiroz, F.O. Silverio, Optimization of the liquidliquid extraction method and low temperature purification (LLE-LTP) for pesticide residue analysis in honey samples by gas chromatography, Food Control, 21 (2010) 1307-1311. [5] F.M. Malhat, M.N. Haggag, N.M. Loutfy, M.A.M. Osman, M.T. Ahmed, Residues of organochlorine and synthetic pyrethroid pesticides in honey, an indicator of ambient environment, a pilot study, Chemosphere, 120 (2015) 457-461.
19 Page 19 of 42
te
d
M
an
us
cr
ip t
[6] D. Debayle, G. Dessalces, M.F. Grenier-Loustalot, Multi-residue analysis of traces of pesticides and antibiotics in honey by HPLC-MS-MS, Anal. Bioanal. Chem., 391 (2008) 1011-1020. [7] B. Albero, C. Sanchez-Brunete, J.L. Tadeo, Analysis of pesticides in honey by solid-phase extraction and gas chromatography-mass spectrometry, J. Agric. Food Chem., 52 (2004) 5828-5835. [8] H. Yavuz, G.O. Guler, A. Aktumsek, Y.S. Cakmak, H. Ozparlak, Determination of some organochlorine pesticide residues in honeys from Konya, Turkey, Environ Monit Assess, 168 (2010) 277-283. [9] J. Regueiro, O. López-Fernández, R. Rial-Otero, B. Cancho-Grande, J. Simal-Gándara, A Review on the fermentation of foods and the residues of pesticides-biotransformation of pesticides and effects on fermentation and food quality, Crit. Rev. Food Sci. Nutr., 55 (2015) 839-863. [10] N. Al-Waili, K. Salom, A. Al-Ghamdi, M.J. Ansari, Antibiotic, pesticide, and microbial contaminants of honey: human health hazards, Sci. World J., 2012 (2012). [11] Z. Barganska, M. Slebioda, J. Namiesnik, Pesticide residues levels in honey from apiaries located of Northern Poland, Food Control, 31 (2013) 196-201. [12] R. González-Rodríguez, R. Rial-Otero, B. Cancho-Grande, C. Gonzalez-Barreiro, J. SimalGándara, A review on the fate of pesticides during the processes within the food-production chain, Crit. Rev. Food Sci. Nutr. , 51 (2011) 99-114. [13] R. Rial-Otero, E.M. Gaspar, I. Moura, J.L. Capelo, Chromatographic-based methods for pesticide determination in honey: An overview, Talanta, 71 (2007) 503-514. [14] J.F. Anderson, M.A. Wojtas, Honey bees (Hymenoptera: Apidae) contaminated with pesticides and polychlorinated biphenyls, J. Econ. Entomol., 79 (1986) 1200-1205. [15] M.W. Kujawski, E. Pinteaux, J. Namiesnik, Application of dispersive liquid-liquid microextraction for the determination of selected organochlorine pesticides in honey by gas chromatography-mass spectrometry, Eur. Food Res. Technol., 234 (2012) 223-230. [16] E. Korta, A. Bakkali, L.A. Berrueta, B. Gallo, F. Vicente, Study of an accelerated solvent extraction procedure for the determination of acaricide residues in honey by highperformance liquid chromatography-diode array detector, J. Food Protect. , 65 (2002) 161166. [17] G. Amendola, P. Pelosi, R. Dommarco, Solid-phase extraction for multi-residue analysis of pesticides in honey, J. Environ. Sci. Health: Part B, 46 (2010) 24-34. [18] S. Bogdanov, Contaminants of bee products1, Apidologie, 37 (2006) 1-18. [19] I. Mukherjee, Determination of pesticide residues in honey samples, Bull. Environ. Contam. Toxicol. , 83 (2009) 818-821. [20] C.A. Mullin, M. Frazier, J.L. Frazier, S. Ashcraft, R. Simonds, J.S. Pettis, High levels of miticides and agrochemicals in North American apiaries: implications for honey bee health, PLoS one, 5 (2010) e9754. [21] M. Frazier, C. Mullin, J. Frazier, S. Ashcraft, What have pesticides got to do with it?, Am. Bee J., 148 (2008) 521-524. [22] A.-C. Martel, S. Zeggane, C. Aurieres, P. Drajnudel, J.-P. Faucon, M. Aubert, Acaricide residues in honey and wax after treatment of honey bee colonies with Apivar or Asuntol 50, Apidologie, 38 (2007) 534-544.
Ac ce p
580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
20 Page 20 of 42
te
d
M
an
us
cr
ip t
[23] C. Blasco, M. Fernandez, A. Pena, C. Lino, M.I. Silveira, G. Font, Y. Pico, Assessment of pesticide residues in honey samples from Portugal and Spain, J. Agric. Food Chem., 51 (2003) 8132-8138. [24] European Commission Regulation 2005 (396/2005/EC) on maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending Council Directive 91/414/EEC, http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2005:070:0001:0016:en:PDF [25] European Commission. 1996. Commission Directive 96/23/EC. Off. J. Eur. Comm., http://ec.europa.eu/food/food/chemicalsafety/residues/council_directive_96_23ec.pdf [26] J. Al-Rifai, N. Akeel, Determination of pesticide residues in imported and locally produced honey in Jordan, J. Apicult. Res., 36 (1997) 155-161. [27] M.R. Driss, M. Zafzouf, S. Sabbah, M.L. Bouguerra, Simplified procedure for organochlorine pesticides residue analysis in honey, Int. J. Environ. Anal. Chem. , 57 (1994) 63-71. [28] U. Menkissoglu-Spiroudi, G.C. Diamantidis, V.E. Georgiou, A.T. Thrasyvoulou, Determination of malathion, coumaphos, and fluvalinate residues in honey by gas chromatography with nitrogen-phosphorus or electron capture detectors, J. AOAC Int., 83 (2000) 178-182. [29] Z. Barganska, M. slebioda, J. Namiesnik, Determination of pesticide residues in honeybees using modified QUEChERS sample work-Up and liquid chromatography-tandem mass spectrometry, Molecules, 19 (2014) 2911-2924. [30] M. Anastassiades, S.J. Lehotay, D. Stajnbaher, F.J. Schenck, Fast and easy multiresidue method employing acetonitrile extraction/partitioning and dispersive solid-phase extraction for the determination of pesticide residues in produce, J. AOAC Int., 86 (2003) 412-431. [31] S. Walorczyk, B. Gnusowski, Development and validation of a multi-residue method for the determination of pesticides in honeybees using acetonitrile-based extraction and gas chromatography–tandem quadrupole mass spectrometry, J. Chromatogr. A, 1216 (2009) 6522-6531. [32] D. Paradis, G. Bérail, J.-M. Bonmatin, L.P. Belzunces, Sensitive analytical methods for 22 relevant insecticides of 3 chemical families in honey by GC-MS/MS and LC-MS/MS, Anal. Bioanal. Chem., 406 (2014) 621-633. [33] L. Wiest, A. Buleté, B. Giroud, C. Fratta, S. Amic, O. Lambert, H. Pouliquen, C. Arnaudguilhem, Multi-residue analysis of 80 environmental contaminants in honeys, honeybees and pollens by one extraction procedure followed by liquid and gas chromatography coupled with mass spectrometric detection, J. Chromatogr. A, 1218 (2011) 5743-5756. [34] European Commission. 2002. Commission Decision 2002/657/EC of August 12, 2002: Implementing Council Directive 96/23/EC (2002) Concerning the performance of analytical methods and the interpretation of results, Off. J. Eur. Comm. L221: 8–36, http://ec.europa.eu/food/food/chemicalsafety/residues/lab_analysis_en.htm [35] Eurachem guide 2014: The fitness for purpose of analytical methods-a laboratory guide to method validation and related topics, https://www.eurachem.org/images/stories/Guides/pdf/MV_guide_2nd_ed_EN.pdf [36] SANCO/12571/2013, Guidance document on analytical quality control and validation 385 procedures for pesticide residues analysis in food and feed, http://ec.europa.eu/food/plant/pesticides/guidance_documents/docs/qualcontrol_en.pdf
Ac ce p
624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669
21 Page 21 of 42
te
d
M
an
us
cr
ip t
[37] Eurachem/CITAC guide: Quantifying uncertainty in analytical measurement, 2012, (https://www.eurachem.org/images/stories/Guides/pdf/QUAM2012_P1.pdf) [38] Y. Al Naggar, G. Codling, A. Vogt, E. Naiem, M. Mona, A. Seif, J.P. Giesy, Organophosphorus insecticides in honey, pollen and bees (Apis mellifera L.) and their potential hazard to bee colonies in Egypt, Ecotoxicol. Environ. Saf., 114 (2015) 1-8. [39] M.I. Badawy, Use and impact of pesticides in Egypt, Int. J. Environ. Health Res., 8 (1998) 223-239. [40] S.A. Mansour, Pesticide exposure—Egyptian scene, Toxicology, 198 (2004) 91-115. [41] A.H. Shendy, M.A. Al-Ghobashy, S.A.G. Alla, H.M. Lotfy, Development and validation of a modified QuEChERS protocol coupled to LC-MS/MS for simultaneous determination of multi-class antibiotic residues in honey, Food Chem., 190 (2016) 982-989. [42] J. Wang, D. Leung, The challenges of developing a generic extraction procedure to analyze multi-class veterinary drug residues in milk and honey using ultra-high pressure liquid chromatography quadrupole time-of-flight mass spectrometry, Drug Test. Anal.,, 4 (2012) 103-111. [43] M.M. Aguilera-Luiz, J.L.M. Vidal, R. Romero-Gonzalez, A.G. Frenich, Multi-residue determination of veterinary drugs in milk by ultra-high-pressure liquid chromatographytandem mass spectrometry, J. Chromatogr., A, 1205 (2008) 10-16. [44] Z. Barganska, J. Namiesnik, M. Slebioda, Determination of antibiotic residues in honey, Trends Anal. Chem., 30 (2011) 1035-1041. [45] F.J. Camino-Sanchez, A. Zafra-Gomez, J. Ruiz-Garcia, R. Bermudez-Peinado, O. Ballesteros, A. Navalon, J.L. Vilchez, UNE-EN ISO/IEC 17025: 2005 accredited method for the determination of 121 pesticide residues in fruits and vegetables by gas chromatography-tandem mass spectrometry, J. Food Comp. Anal., 24 (2011) 427-440. [46] M. LeDoux, Analytical methods applied to the determination of pesticide residues in foods of animal origin. A review of the past two decades, J. Chromatogr., A, 1218 (2011) 10211036. [47] L. Zhang, S. Liu, X. Cui, C. Pan, A. Zhang, F. Chen, A review of sample preparation methods for the pesticide residue analysis in foods, Cent. Eur. J. Chem., 10 (2012) 900-925. [48] D. Lu, X. Qiu, C. Feng, Y. Lin, L. Xiong, Y. Wen, D. Wang, G. Wang, Simultaneous determination of 45 pesticides in fruit and vegetable using an improved QuEChERS method and on-line gel permeation chromatography-gas chromatography/mass spectrometer, J. Chromatogr., B, 895 (2012) 17-24. [49] X. Hou, M. Han, X. Dai, X. Yang, S. Yi, A multi-residue method for the determination of 124 pesticides in rice by modified QuEChERS extraction and gas chromatography-tandem mass spectrometry, Food Chem., 138 (2013) 1198-1205. [50] J.L. Vidal, F.J. Liebanas, M.J. Rodiguez, A.G. Frenich, J.L. Moreno, Validation of a gas chromatography/triple quadrupole mass spectrometry based method for the quantification of pesticides in food commodities, Rapid Commun. Mass Spectrom., 20 (2006) 365-375. [51] S.J. Lehotay, J.M. Cook, Sampling and sample processing in pesticide residue analysis, J. Agric. Food Chem., 63 (2015) 4395–4404. [52] T. Cajka, C. Sandy, V. Bachanova, L. Drabova, K. Kalachova, J. Pulkrabova, J. Hajslova, Streamlining sample preparation and gas chromatography-tandem mass spectrometry analysis of multiple pesticide residues in tea, Anal. Chim. Acta, 743 (2012) 51-60. [53] J. Hajslova, J. Zrostlikova, Matrix effects in (ultra) trace analysis of pesticide residues in food and biotic matrices, J. Chromatogr., A, 1000 (2003) 181-197.
Ac ce p
670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715
22 Page 22 of 42
us
cr
ip t
[54] B.K. Matuszewski, M.L. Constanzer, C.M. Chavez-Eng, Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS, Anal. Chem., 75 (2003) 3019-3030. [55] F. Gosetti, E. Mazzucco, D. Zampieri, M.C. Gennaro, Signal suppression/enhancement in high-performance liquid chromatography tandem mass spectrometry, J. Chromatogr. A, 1217 (2010) 3929-3937. [56] J. Nie, S. Miao, S.J. Lehotay, W.-T. Li, H. Zhou, X.-H. Mao, J.-W. Lu, L. Lan, S. Ji, Multiresidue analysis of pesticides in traditional Chinese medicines using gas chromatographynegative chemical ionization tandem mass spectrometry, Food Addit. Contam.: Part A, 32 (2015) 1287-1300. [57] U. Koesukwiwat, S.J. Lehotay, N. Leepipatpiboon, Fast, low-pressure gas chromatography triple quadrupole tandem mass spectrometry for analysis of 150 pesticide residues in fruits and vegetables, J. Chromatogr., A, 1218 (2011) 7039-7050. [58] D.N. Heller, S.J. Lehotay, P.A. Martos, W. Hammack, A.R. Fernndez-Alba, Issues in mass spectrometry between bench chemists and regulatory laboratory managers: Summary of the roundtable on mass spectrometry held at the 123rd AOAC International Annual Meeting, J. AOAC Int., 93 (2010) 1625-1632.
an
716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733
M
734 735 736
d
737
te
738
740 741 742 743
Ac ce p
739
Figure captions
744
Figure 1: A bar chart representing the slope difference percent for the studied pesticides in honey
745
samples between matrix matched and neat standard calibration curves representing matrix-
746
induced suppression/enhancement in GC-MS/MS response. Compound numbers are as indicated
747
in Table 2. Raw data and an equivalent plot but versus compound name are provided in
748
supplementary materials.
749 750
Figure 2: Typical total ion chromatogram for the studied pesticides in fortified honey sample at
751
50.00 µg Kg-1 in comparison to the corresponding blank sample. 23 Page 23 of 42
752
Figure 3: Results of the validation study carried out over three days, at three different
753
concentration levels (10.00, 50.00 and 100.00 µg Kg-1, 6 replicate each), showing the mean
754
percentage recovery (N = 18) of the studied pesticide residues in honey samples. Compound
755
numbers are as indicated in Table 2. Raw data is provided in supplementary materials.
ip t
756 757
cr
758 759
Acrinathrin
RT (min) 19.48
Alachlor
15.18
d 15.62
te
Aldrin (ISTD)
15.66
Ac ce p
Ametryn
Precursor ion (m/z) 289a 208 181.1 188.1a 160.05 293 263a 262.9 227.15 227.15a 227.15 211a 211 200.1 200.1 200.1a 160.05 160.05a 160.05a 160.05 204a 148 166a 151 181.1a 181 170a 170 342
M
Compound(s)
an
us
Table 1: MS/MS transitions and optimal operational conditions used for pesticide residue analysis
Atraton
13.16
Atrazine
13.59
Azinphos-ethyl
21.03
Azinphos-methyl
20.78
Benalaxyl
18.85
Bendiocarb
5.65
Bifenthrin
19.00
Bitertanol
20.74
Boscalid
22.38
Product ion (m/z) 93a 181 152.1 160.1a 130 186 193a 190.9 170.1 152.1a 170.1 196a 169 122.1 103.9 94.1a 102 77.1a 77.1a 132.1 176a 105 151a 81.2 166.1a 165 141a 115 140
D Well (ms) 20 20 20 10 10 20 20 20 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
CE (V) 5 5 25 10 30 30 25 40 30 20 30 15 15 10 20 20 15 15 20 5 5 17 10 10 15 25 20 35 10
24 Page 24 of 42
16.51
Bromopropylate
19.42
Bromuconazole I
19.86
Bromuconazole II
20.26
Bupirimate
17.87
Buprofezin
17.70
Butachlor
16.94
Butralin
15.94
Cadusafos
11.60
d
Ac ce p
Chlordane trans- (gamma)
te
Chlordane cis- (alpha)
17.29
17.13
Chlorfenapyr
17.92
Chlorfenvinphos
16.57
Chlorobenzilate
18.10
Chlorpropham 11.34 Chlorpyrifos
16.03
Chlorpyrifos-methyl
15.32
Chlorthal-dimethyl
16.00
CE (V) 10 15 5 20 35 20 20 15 15 15 15 15 5 5 15 15 10 5 5 25 10 10 10 8 5 20 30 25 30 25 10 10 10 20 40 20 12 15 30 5 5 20 14 15 40 20 20 25
ip t
Bromophos-methyl
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 30 30 30 10 10 10 10 10 10
cr
16.97
Product ion (m/z) 112a 302.9a 331 315.9a 285.9 185 183 155a 173 145a 173a 145 208.2 193a 108 57a 104 188.3 160.3a 105 220.2a 190.2 174.1 131 130.9 97a 265.9 263.9a 266.1a 264.1 59 31a 29 159a 81 159 139a 111 75 171 127 65a 258a 168.9 107 270.9 93a 222.9
us
Bromophos-ethyl
Precursor ion (m/z) 140a 358.9a 358.7 330.9a 330.9 341 341 183a 294.8 172.9a 294.8a 172.9 316.1 273a 273 172a 105 236.8 236.8a 175.9 265.9a 265.9 265.9 159 158.7 158.7a 372.9 372.9a 372.7a 372.7 408 59a 59 267a 267 267 251a 139 139 213 171 127a 313.8a 196.9 196.9 286 286a 300.9
an
RT (min)
M
Compound(s)
25 Page 25 of 42
16.62
Clodinafop-propargyl ester
18.92
Cyanophos
14.44
Cyfluthrin
21.10
Cyhalothrin lambda-
19.77
Cypermethrin
21.48
Cyproconazole
18.35
Cyprodinil
16.78
d
Ac ce p
DDD pp`-
17.95
te
DDD op`-
18.51
DDE pp`-
17.65
DDT op`-
18.35
DDT pp`-
18.89
Deltamethrin
23.43
Demeton-S- methyl
11.63
Diazinon
13.41
CE (V) 40 25 15 15 15 15 10 15 20 5 10 30 30 5 15 10 30 35 25 35 5 18 8 10 20 20 15 30 25 20 25 30 40 40 20 20 30 20 20 30 10 20 25 10 7 15 5 20
ip t
Chlozolinate
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 20 20 20 20 20 20 20 20 20 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 50 50 50 10 10 10 10 10
cr
18.40
Product ion (m/z) 166.9 221a 269.2a 205.1 147.1 145a 266.1a 238.1 222.1 116 109a 79 77.1 127.1a 91.1 161 152.1a 127.1 152.1 127.1 127a 125a 82 224a 208 165 199.1 165.1a 165 199.1 165.1a 176 176.1a 175.1 165 199.1 165.1a 165 199.1 165.1a 172a 93 152 79a 60 179a 179.2 137.2
us
Chlorthiophos
Precursor ion (m/z) 300.9 299a 324.9a 268.7 188.1 186a 349a 349 266 243 243a 243 226.9 163a 163 197 181.1a 181.1 181.1 181.1 162.9a 222a 222 225a 224 237 235 235a 237 235 235a 248 246a 246 237 235 235a 237 235 235a 253a 253 181 142a 88 304a 179.1 179.1
an
RT (min)
M
Compound(s)
26 Page 26 of 42
16.17
Dichlorvos
5.58
Diclofop methyl
19.09
Dicofol
16.55
Dieldrin
17.83
Difenoconazole
23.61
Dimethachlor
15.12
Dimethoate
14.21
d
Ac ce p
Diphenylamine
18.33
te
Diniconazole
11.52
Disulfoton-sulfoxide Endosulfan alpha-
17.37
Endosulfan beta-
18.70
Endosulfan-sulfate
19.30
Endrin
18.29
EPN
19.79
Epoxiconazole I
19.12
CE (V) 15 25 10 25 20 30 8 15 5 15 15 12 18 30 30 30 16 15 16 20 15 30 10 10 20 15 10 15 15 10 30 35 15 15 5 15 10 25 5 20 40 20 35 35 15 25 10 35
ip t
Dichlofluanid
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
14.68
Product ion (m/z) 136 100a 223a 205 77a 51.1 123 93 79a 253 162a 139 111a 75 193a 191 267 265a 204 202 105.1a 77.1 87 111 47a 234 232 232a 168a 167 78.9 63.1 205.9a 204 159 204 159a 125 253 236.9a 116.9 245 193a 190.9 110 77.1a 138.1a 111.1
us
Dichlofenthion
Precursor ion (m/z) 171 171a 279a 279 123a 123 224 185 109a 340 253a 250 139a 139 262.8a 262.8 325 323a 267 265 134.1a 134.1 229 143 125a 270 270 268a 169a 169 125 125 240.9a 238.8 195 238.8 195a 195 387 271.9a 271.9 281 262.9a 262.9 157 157a 191.9a 191.9
an
Dichlobenil
RT (min) 7.32
M
Compound(s)
27 Page 27 of 42
16.06
Ethoprophos
11.16
Ethoxyquin
12.74
Etofenprox
21.68
Etoxazole
19.39
Etridiazole
8.18
Fenarimol
20.69
Fenazaquin
19.72
Fenbuconazole
21.84
d
Ac ce p
Fenoxaprop-P-ethyl
16.10
te
Fenitrothion
20.59
Fenoxycarb
19.73
Fenpropathrin
19.44
Fenpropidin
14.35
Fenpropimorph
15.01
Fenvalerate
22.37
Fluazifop-p-butyl
17.61
Flucythrinate
21.41
Fluquinconazole
21.30
Flusilazole
17.94
CE (V) 10 35 5 25 5 15 15 15 15 30 30 10 10 15 20 8 5 15 10 15 35 5 20 5 10 20 5 9 9 10 15 15 35 22 5 10 10 15 15 35 10 15 15 10 30 15 20 20
ip t
Ethofumesate
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
18.36
Product ion (m/z) 138.1a 111.1 175 129a 207.3a 161.1 97.1a 80.9 174.3a 158.9 145.4 163 135a 107 270 176a 182.9a 139.9 107a 111 75 145.2a 117 129a 102 109a 260 288.1a 119.1 186a 109 210a 89 116 70a 55 110 70a 119 89.1 125a 238 91a 157.1a 107.1 313.2 298.2a 165a
us
Ethion
Precursor ion (m/z) 191.9a 191.9 231 231a 285.9a 285.9 158a 158 201.8a 201.8 201.8 376 163a 163 300 204a 210.9a 182.9 219a 139 139 160a 160 198a 129 277.1a 276.8 360.8a 287.8 255a 186 265a 265 209 98a 97 128 128a 225 167.1 167a 282 282a 199.1a 199.1 339.9 339.9a 233a
an
Epoxiconazole II
RT (min) 19.58
M
Compound(s)
28 Page 28 of 42
Fluvalinate tau-
21.96
Formothion I 15.6301
HCH beta-
14.82
HCH delta
15.42
HCH gamma- (Lindane)
14.01
Heptachlor
14.94
Heptachlor-endo-Epoxide (trans-)
16.86
d 16.72
Ac ce p
te
Heptachlor-exo-Epoxide (cis-) Heptenophos
an
12.75
M
HCH alpha-
10.55
Hexachlorobenzene (HCB)
12.36
Hexaconazole
17.59
Hexazinone
19.67
Imazalil
17.76
Iprobenfos
14.47
Iprodione
19.43
Isofenphos
16.65
Isofenphos-methyl
16.53
Product ion (m/z) 152 200.1 55.2 141.1a 77 79 47a 79a 47 183 145a 109 183 145a 109 109 145a 183a 145 109 239 236.8a 116.9 155 118.9a 281.9 262.8a 89.1a 63.2 248.8 213.9a 159 159 172a 85.1 71.1a 175 173a 109 122 91.1a 245 56 159 124a 185 121a 121a
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 20 20 20 10 10 20 20 25 25 10 10 10
us
Formothion II
Precursor ion (m/z) 233 250.1 250 208.9a 208.9 125.1 125.1a 125.1a 125.1 219 181a 181 219 181a 181 181 180.9a 219a 181 181 274 271.9a 271.9 183 183a 352.9 352.9a 124a 124 283.9 283.9a 256 214 213.9a 171.1 171.1a 217 214.9a 173 204 204a 314 314 187 187a 213.1 213.1a 199a
CE (V) 20 20 20 15 25 5 15 5 15 10 15 25 10 15 30 30 12 10 15 30 20 25 25 25 25 20 25 15 35 25 35 16 16 20 15 15 4 4 25 10 10 10 15 15 25 5 20 15
ip t
RT (min)
cr
Compound(s)
29 Page 29 of 42
18.06
Malathion
16.01
Mefenpyr-diethyl
19.29
Mepronil
18.70
Metalaxyl
15.65
Metazachlor
17.06
Methacrifos
8.90
Methidathion
17.73
Methiocarb
9.90
Metribuzin
15.75
d
Ac ce p
Mirex
8.39
te
Mevinphos
20.27
Myclobutanil
18.15
Napropamide
17.72
Nuarimol
19.32
Oxadiaxyl
19.10
Oxadiazon
17.40
Oxyfluorfen
17.63
Paraoxon-ethyl
15.83
Parathion-ethyl
16.31
CE (V) 15 2 10 10 5 5 15 20 25 25 15 15 20 5 20 25 8 4 5 15 10 10 15 20 5 10 15 15 25 40 6 14 5 5 5 15 5 15 13 30 15 15 5 20 20 10 10 10
ip t
Kresoxim-methyl
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
18.18
Product ion (m/z) 65 204 118a 131 116a 117.1 99a 190a 188.2 163.2 91a 65 162 132a 132.1a 117 180 180a 85a 58.1 153a 109 89 82.1a 110 109a 95 237a 235 116.9 152 125a 128 72a 139 139a 132a 117 175 76.1 112a 300 252 196a 102.1 81a 109a 81
us
Isoprothiolane
Precursor ion (m/z) 199 290 290a 206 206a 173.1 173.1a 252.8a 252.8 252.8 119a 119 206 206a 209a 133 240 208a 145a 145 168a 153 198.05 198.05a 198 127a 127 271.9a 271.9 271.9 179 179a 271 128a 314 235a 163a 163 301.9 175 174.9a 361 252.05 252.05a 149 108.9a 291a 291
an
RT (min)
M
Compound(s)
30 Page 30 of 42
15.68
PCB 101
17.16
PCB 118
18.19
PCB 138
18.88
PCB 153
18.37
PCB 180
19.64
Pendimethalin
d
Ac ce p
Pentachlorobenzene
16.86
te
Penconazole
16.65 9.18
Permethrin
20.59
Phenthoate
17.14
Ortho-Phenylphenol (2-phenylphenol) (OPP)
9.55
Phorate
12.16
Phosalone
20.27
Phosmet
20.19
Piperonyl-butoxide
18.84
CE (V) 15 30 15 27 12 27 23 23 25 12 27 27 25 27 27 25 15 27 25 12 27 22 12 27 15 25 10 20 15 25 40 15 25 30 20 10 30 30 35 5 5 5 15 30 20 15 30 15
ip t
PCB 052
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
15.02
Product ion (m/z) 109.1a 79.1 186a 150 255 220 220 220a 255.9 291 256a 254 255.9 256a 254 289.9 325 290a 289.9 325 290a 323.9 359 324a 192a 157 162.1a 161.2 213 214.9a 142 155.1 115.2 77.1a 125 121a 169.1 141.2 115a 75.2 47a 138 111 75.1a 133 105 77a 145.2
us
PCB 028
Precursor ion (m/z) 263a 263 258a 258 292 292 291.9 289.9a 327.9 325.9 325.9a 325.9 327.9 325.9a 325.9 361.8 359.8 359.8a 361.8 359.8 359.8a 395.8 393.7 393.7a 248a 248 252.1a 252.1 250 249.9a 249.9 183.1 183 183a 274 274a 170 170 169a 260 75a 182 182 182a 160 160 160a 175.9
an
Parathion-methyl
RT (min) 15.69
M
Compound(s)
31 Page 31 of 42
16.19
Pirimiphos-methyl
15.62
Procymidone
17.06
Profenofos
17.74
Profluralin
12.04
Promecarb I
7.063
Promecarb II
13.091
Prometon
13.08
Propachlor
d
Ac ce p
Propargite
15.53
te
Prometryn
11.08
18.90
Propazine
13.40
Propiconazol
18.94
Prosulfocarb
15.47
Prothiofos
17.46
Pyrazofos
20.38
Pyridaben
20.89
Pyridaphenthion
19.78
CE (V) 20 25 10 15 5 35 10 5 25 10 10 40 10 10 30 35 20 20 8 10 8 10 5 10 10 10 5 20 15 25 5 20 25 10 12 5 20 15 5 30 10 15 10 10 20 40 5 15
ip t
Pirimiphos-ethyl
D Well (ms) 10 10 10 10 10 10 10 10 10 15 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
15.00
Product ion (m/z) 117.1 103.1a 166a 96 318.2 109.1a 290 180 125a 255 96.1a 67.1 255 267 188.1 63.1a 199.2a 54.9 135a 115 135 115a 168a 112.1 183.9a 111.2 92.1 77.1a 107.1a 77.1 172a 104 94.5 173a 69 43.1a 41.1 239 239a 63.1 204 149.1 193a 132 117a 97 199.2a 108.2
us
Pirimicarb
Precursor ion (m/z) 175.9 175.9a 238a 166 333.1 318a 305 305 290.1a 283 283a 283 283 337 337 208a 318a 318 150a 135 150 135a 210a 210 241.2a 241.2 120.1 120.1a 135.1a 135.1 214.1a 214.1 214.1 259a 259 128a 128 309 267a 162 232 221.1 221a 147 147a 340.1 340a 340
an
RT (min)
M
Compound(s)
32 Page 32 of 42
Pyrimethanil
14.20
Pyriproxyfen
20.13
Quinalphos
17.11
Quintozene
13.72
Simazine
13.74
Spiroxamine I
14.21
M
Spiroxamine II
11.86
Tebuconazole
19.15
Ac ce p
Tecnazene
19.49
te
Tebufenpyrad
d
Sulfotep
10.90
Tefluthrin
12.60
Terbufos
13.16
Terbumeton
13.47
Terbuthylazine
13.76
Terbutryn
15.81
Tetraconazole
16.22
Tetradifon
20.20
Tetramethrin I 19.6222 Tetramethrin II
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
CE (V) 10 25 10 25 25 25 25 25 8 18 15 25 10 20 30 10 5 10 10 10 10 15 25 25 25 5 20 20 25 20 20 10 25 5 10 10 20 10 5 15 30 25 10 10 15 10 25 10
ip t
17.36
Product ion (m/z) 227 99.9a 136 99.9a 198a 156 118 118 96a 78 129 91.1 118a 237a 119 172.1a 91 72.2a 43 72.2a 43 174 146a 127 125a 276 171a 143 83a 137 127.1a 174.9 128.9a 154.2a 112.2 132 104a 170.2 170.1a 218a 204 164 159 159 201a 106.9 77.1a 106.9
cr
Pyrifenox II
Precursor ion (m/z) 261.8 171a 171 171a 199a 198 198 198 136a 136 157 146.1 146a 295a 237 201.05a 186 100a 100 100a 100 322.1 322.1a 252 250a 333 333a 202.9 202.9a 177.1 177.1a 231 231a 169a 169 214.1 214.1a 241.2 185a 336a 336 336 355.7 353.7 229a 164 164a 164
us
Pyrifenox I
RT (min) 17.36
an
Compound(s)
33 Page 33 of 42
12.92
Tolclofos-methyl
15.59
Tolylfluanid
16.99
Triadimefon
16.10
Triadimenol
16.93
Triazophos
19.10
Trifloxystrobin
18.64
Triflumizole
16.70
Vinclozolin
760 761 762 763
d
Ac ce p
Triticonazole
10.24
te
Trifluralin
20.21
14.99
CE (V) 25 20 20 20 20 6 15 25 10 15 30 5 15 10 15 20 5 5 10 8 15 20 6 10 30 25 10 5 15 15 20
ip t
Thiometon
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
16.14
Product ion (m/z) 77.1 100 72a 72 47 60a 250a 93 137 91a 65 181a 127 111 70 65a 162a 134 106 130 116.1 89.1a 73 179.2a 143.9 160 264a 217.2a 182.2 172a 145
us
Thiobencarb
Precursor ion (m/z) 164 257 257a 100 125 88a 265a 265 238 137a 137 208a 208 181 168 128a 257a 161 161 222 131 116a 278 206a 206 306.1 306a 235a 235 212a 212
an
RT (min)
M
Compound(s)
RT, retention time (min); CE, collision energy (volt); D well, D well time in ms a transitions for quantifier peaks; remaining transitions are the qualifier peaks
764
34 Page 34 of 42
ip t
765
us
cr
766
M
an
767
769
770
Ac ce p
te
d
768
771
35 Page 35 of 42
772 773 Table 2: Regression equation parameters and differences obtained for both neat standard and matrix matched
775
calibration curves
an
us
cr
ip t
774
Compound number
Acrinathrin Alachlor Ametryn Atraton Atrazine Azinphos-ethyl Azinphos-methyl Benalaxyl Bendiocarb Bifenthrin Bitertanol Boscalid Bromophos-ethyl Bromophos-methyl Bromopropylate Bromuconazole I Bromuconazole II Bupirimate Buprofezin Butachlor Butralin Cadusafos Chlordane cis- (alpha) Chlordane trans- (gamma) Chlorfenapyr Chlorfenvinphos Chlorfenvinphos
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Ac ce p
te
d
M
Compound(s)
Matrix matched calibration curves Slope Intercept R2
Neat standard calibration c Slope Intercept
4.84 10.43 0.53 0.75 2.45 7.05 2.60 11.12 2.50 37.28 12.70 4.52 3.91 5.80 10.72 4.03 3.95 4.38 16.37 1.60 1.78 54.17 1.89 1.89 9.61 4.06 4.06
6.24 10.40 0.57 0.84 2.19 10.25 5.93 12.24 2.07 39.29 16.92 5.21 4.38 6.28 11.81 5.28 5.25 4.60 17.05 1.79 2.06 54.94 1.96 1.96 11.02 5.06 5.06
-1.32 -1.43 -0.11 -0.23 -0.47 -2.10 -1.08 -1.07 -0.33 -3.40 -2.49 -1.00 -0.68 -1.21 -1.40 -0.76 -0.93 -0.59 -1.35 -0.28 -0.46 -7.78 -0.24 -0.24 -1.14 -0.89 -0.89
0.99 1.00 1.00 0.99 1.00 0.99 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-0.72 -0.56 -0.04 -0.10 -0.01 -1.18 -1.02 0.26 -0.33 0.86 -1.66 -0.78 -0.21 -0.39 0.12 -0.41 -0.65 -0.39 -0.21 -0.12 -0.49 -1.99 -0.15 -0.15 -0.01 -0.56 -0.56
36 Page 36 of 42
Compound number
Neat standard calibration c 22.80 0.41 10.73 -0.29 4.36 -0.31 5.57 -0.25 6.83 -0.19 5.36 -0.10 2.71 -0.14 3.15 -0.51 25.55 -1.25 15.97 -2.13 2.06 -0.19 11.22 -1.50 17.74 -0.67 21.20 -1.39 1.30 -0.49 35.13 1.04 61.84 -2.02 10.64 -0.10 61.84 -2.02 4.92 -1.10 18.37 -1.30 2.00 -0.22 22.21 -0.41 13.21 -0.66 18.88 -1.56 3.29 0.00 4.21 -0.14 14.40 -4.10 1.24 -0.13 4.65 -0.07 18.13 -0.63 11.11 -0.90 4.94 -0.03 28.19 -0.55 0.50 0.01 1.68 -0.10 0.54 -0.04 1.77 -0.10 1.25 -0.13 8.68 -1.02 18.07 -1.78 14.98 -0.50 4.50 -0.32 11.16 -0.49 3.59 -0.77
d
M
an
us
cr
ip t
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
te
Ac ce p
Chlorobenzilate Chlorpropham Chlorpyrifos Chlorpyrifos-methyl Chlorthal-dimethyl Chlorthiophos Chlozolinate Clodinafop-propargyl ester Cyanophos Cyfluthrin Cyhalothrin lambdaCypermethrin Cyproconazole Cyprodinil DDD op`DDD pp`DDE pp`DDT op`DDT pp`Deltamethrin Demeton-S- methyl Diazinon Dichlobenil Dichlofenthion Dichlofluanid Dichlorvos Diclofop methyl Dicofol Dieldrin Difenoconazole Dimethachlor Dimethoate Diniconazole Diphenylamine Disulfoton-sulfoxide EPN Endosulfan alphaEndosulfan betaEndosulfan-sulfate Endrin Epoxiconazole II Ethion Ethofumesate Ethoprophos Ethoxyquin
Matrix matched calibration curves 20.28 -2.47 1.00 10.13 -2.52 1.00 4.16 -0.66 1.00 5.10 -0.77 1.00 6.88 -0.76 1.00 4.62 -0.69 1.00 2.48 -0.36 1.00 2.30 -0.62 0.99 21.99 -4.80 1.00 13.87 -3.08 1.00 1.64 -0.37 1.00 9.53 -2.19 1.00 14.65 -2.45 1.00 18.36 -3.82 1.00 1.41 -0.29 1.00 30.78 0.08 1.00 63.73 -7.47 1.00 10.05 -0.73 1.00 63.73 -7.47 1.00 3.09 -0.92 0.99 15.64 -4.83 0.99 1.97 -0.32 1.00 19.56 -5.34 0.99 12.49 -1.68 1.00 17.77 -3.89 1.00 2.26 -0.64 0.99 3.91 -0.42 1.00 20.68 -5.67 1.00 1.22 -0.22 1.00 3.92 -0.08 1.00 17.30 -2.33 1.00 6.87 -1.49 0.99 3.56 -0.71 1.00 27.31 -7.58 0.99 0.37 -0.04 1.00 1.64 -0.16 1.00 0.50 -0.07 1.00 1.56 -0.27 1.00 1.22 -0.21 1.00 6.46 -1.58 1.00 15.62 -3.18 1.00 6.62 -0.17 1.00 3.99 -0.53 1.00 9.83 -1.60 1.00 4.34 -0.52 1.00
37 Page 37 of 42
Compound number
Neat standard calibration c 49.41 -2.25 3.83 -0.19 2.18 -0.74 11.31 -0.68 28.48 0.05 31.80 -5.46 3.25 -0.66 3.63 -0.30 2.90 -0.16 1.86 -0.09 14.78 -0.99 27.12 -0.84 7.30 -1.08 7.23 -0.07 5.84 -0.84 4.92 -0.52 3.81 -0.23 0.92 0.02 7.33 0.11 2.73 -0.26 7.29 -0.25 11.44 -0.47 11.44 -0.47 6.02 -0.33 8.30 -0.77 1.11 -0.08 1.11 -0.08 14.49 0.02 8.49 -0.02 1.00 -0.09 33.58 -2.17 2.87 -0.49 39.32 -3.72 2.89 -0.13 13.72 -1.03 18.94 -1.16 3.78 -0.17 4.26 -0.24 7.37 -0.19 5.05 -0.09 87.79 4.20 0.65 -0.05 8.86 -0.69 7.48 0.04 25.67 -1.67
d
M
an
us
cr
ip t
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
te
Ac ce p
Etofenprox Etoxazole Etridiazole Fenarimol Fenazaquin Fenbuconazole Fenitrothion Fenoxaprop-P-ethyl Fenoxycarb Fenpropathrin Fenpropidin Fenpropimorph Fenvalerate Fluazifop-p-butyl Flucythrinate Fluquinconazole Flusilazole Fluvalinate tauFormothion I Formothion II HCH alphaHCH betaHCH deltaHCH gamma- (Lindane) Heptachlor Heptachlor-endo-Epoxide (trans-) Heptachlor-exo-Epoxide (cis-) Heptenophos Hexachlorobenzene (HCB) Hexaconazole Hexazinone Imazalil Iprobenfos Iprodione Isofenphos Isofenphos-methyl Isoprothiolane Kresoxim-methyl Malathion Mefenpyr-diethyl Mepronil Metalaxyl Metazachlor Methacrifos Methidathion
Matrix matched calibration curves 47.78 -5.62 1.00 3.50 -0.49 1.00 2.79 -0.90 0.99 9.67 -1.65 1.00 26.39 -3.29 1.00 32.41 -8.23 1.00 2.52 -0.73 0.99 3.10 -0.97 0.99 2.43 -0.31 1.00 1.70 -0.33 1.00 14.24 -1.67 1.00 26.62 -2.72 1.00 5.97 -1.40 1.00 6.33 -0.89 1.00 4.94 -1.24 0.99 4.40 -0.89 1.00 3.30 -0.48 1.00 0.97 -0.02 1.00 6.87 -0.83 1.00 2.03 -0.53 0.99 7.39 -1.01 1.00 12.18 -2.11 1.00 12.18 -2.11 1.00 5.65 -0.99 1.00 9.64 -1.81 1.00 1.17 -0.17 1.00 1.17 -0.17 1.00 11.76 -1.55 1.00 8.77 -1.02 1.00 0.77 -0.30 0.97 28.01 -5.19 1.00 3.55 -0.53 1.00 34.20 -7.01 1.00 2.13 -0.42 1.00 12.69 -2.14 1.00 17.95 -2.64 1.00 3.46 -0.48 1.00 3.90 -0.65 1.00 4.75 -0.33 1.00 4.35 -0.55 1.00 68.62 -11.95 1.00 0.61 -0.13 1.00 8.00 -1.27 1.00 7.18 -1.57 1.00 18.29 -4.35 1.00
38 Page 38 of 42
Compound number
Neat standard calibration c 3.51 -0.39 5.02 -1.01 18.14 0.44 10.30 -0.66 18.14 -0.76 14.06 -0.53 5.10 -0.13 8.29 -0.21 16.58 -0.09 8.30 -1.44 5.02 -0.71 3.11 -0.71 4.39 -0.81 9.30 -0.11 20.68 0.84 7.50 -0.17 9.95 -0.10 6.72 0.02 6.72 0.02 6.01 -0.12 6.21 -0.48 4.10 -0.95 8.96 -0.22 3.99 -0.18 4.03 -0.19 6.35 -0.65 21.19 0.04
14.79 9.07 18.43 17.05 8.53 1.61 5.08 4.39 2.68 2.52 19.87 19.87 10.62 21.74 22.55 5.57 3.92 3.11
15.16 13.59 27.86 20.33 9.15 1.69 5.26 5.09 3.17 2.62 23.69 23.69 11.51 23.69 21.84 5.82 4.45 3.98
d
M
an
us
cr
ip t
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
te
Ac ce p
Methiocarb Metribuzin Mevinphos Mirex Myclobutanil Napropamide Nuarimol Oxadiaxyl Oxadiazon Oxyfluorfen PCB 028 PCB 052 PCB 101 PCB 118 PCB 138 PCB 153 PCB 180 Paraoxon-ethyl Parathion-ethyl Parathion-methyl Penconazole Pendimethalin Pentachlorobenzene Permethrin Permethrin Phenthoate Phenylphenol ortho- (2phenylphenol) (OPP) Phorate Phosalone Phosmet Piperonyl-butoxide Pirimicarb Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenofos Profluralin Promecarb I Promecarb II Prometon Propachlor Propargite Propazine Propiconazol Propiconazole II
Matrix matched calibration curves 4.51 -0.75 1.00 5.14 -1.23 1.00 13.53 -2.75 1.00 9.17 -0.39 1.00 15.87 -1.92 1.00 11.90 -1.92 1.00 4.69 -0.54 1.00 6.94 -0.99 1.00 16.44 -1.21 1.00 6.22 -1.53 1.00 2.82 -0.99 0.99 2.44 -0.71 0.99 3.25 -0.95 0.99 8.37 -1.47 1.00 20.31 -1.85 1.00 6.96 -0.69 1.00 9.12 -0.87 1.00 6.43 -0.58 1.00 6.43 -0.58 1.00 5.65 -0.45 1.00 5.36 -0.96 1.00 3.54 -1.01 0.99 9.11 -0.92 1.00 3.62 -0.57 1.00 1.70 0.42 0.99 5.72 -1.23 1.00 17.88 -3.29 1.00
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
-2.15 -2.45 -6.08 -1.17 -1.30 -0.31 -0.83 -0.70 -0.52 -0.59 -3.58 -3.58 -1.67 -2.34 0.05 -0.84 -0.57 -0.31
1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-0.68 -1.06 -2.48 0.37 -0.52 -0.20 -0.41 -0.08 -0.19 -0.49 -0.12 -0.12 -0.76 -0.04 3.52 -0.47 -0.25 -0.12
39 Page 39 of 42
Compound number
776
Neat standard calibration c 7.27 -0.14 14.03 -0.98 45.74 -2.76 3.10 -0.11 1.19 -0.06 1.91 -0.09 15.71 -0.21 16.62 -0.25 22.14 -2.14 2.08 -0.27 1.54 -0.10 11.11 -0.11 11.11 -0.11 4.48 -0.21 5.13 -0.20 8.29 -0.07 1.56 -0.11 22.68 -0.50 13.99 -1.73 12.52 -0.61 10.94 -0.54 14.07 -0.84 2.83 -0.24 3.15 -0.14 18.70 0.79 18.70 0.79 0.95 -0.07 25.20 -1.03 14.85 -0.78 11.82 -1.36 9.16 -0.51 5.95 -0.36 2.36 -0.15 6.68 -0.22 15.33 -0.22 2.21 -0.16 8.44 -1.20 1.96 -0.26 3.86 -0.25
d
M
an
us
cr
ip t
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201
te
Ac ce p
Prothiofos Pyrazofos Pyridaben Pyridaphenthion Pyrifenox I Pyrifenox II Pyrimethanil Pyriproxyfen Quinalphos Quintozene Simazine Spiroxamine Spiroxamine II Sulfotep Tebuconazole Tebufenpyrad Tecnazene Tefluthrin Terbufos Terbumeton Terbuthylazine Terbutryn Tetraconazole Tetradifon Tetramethrin Tetramethrin II Thiobencarb Thiometon Tolclofos-methyl Tolylfluanid Triadimefon Triadimenol Triadimenol Triazophos Trifloxystrobin Triflumizole Trifluralin Triticonazole Vinclozolin
Matrix matched calibration curves 6.97 -0.84 1.00 10.95 -2.75 1.00 38.62 -7.31 1.00 2.33 -0.68 0.99 1.15 -0.19 1.00 1.74 -0.23 1.00 10.86 -0.32 1.00 14.71 -1.94 1.00 18.69 -3.39 1.00 2.11 -0.46 1.00 1.32 -0.26 1.00 12.32 -1.25 1.00 12.32 -1.25 1.00 4.51 -0.57 1.00 4.36 -0.66 1.00 7.54 -0.98 1.00 1.40 -0.18 1.00 22.97 -2.39 1.00 12.48 -2.46 1.00 12.41 -1.95 1.00 10.54 -1.57 1.00 13.19 -1.92 1.00 2.52 -0.49 1.00 2.78 -0.35 1.00 16.34 -1.99 1.00 16.34 -1.99 1.00 0.82 -0.14 1.00 25.79 -3.70 1.00 14.41 -2.12 1.00 10.99 -2.65 1.00 8.67 -1.34 1.00 4.33 -0.75 1.00 1.69 -0.29 1.00 4.93 -1.14 1.00 14.74 -1.61 1.00 2.22 -0.35 1.00 8.02 -1.69 1.00 1.39 -0.30 1.00 3.72 -0.61 1.00
Linear regression equation, y= ax + b; a, slope; b, intercept; y, relative response; x, relative concentration (ng mL-1)
40 Page 40 of 42
ip t
777
us
cr
778
M
an
779
781 782 783
Ac ce p
te
d
780
Highlights
784
•
Modified QuEChERS protocol coupled to GC-MS/MS was developed and validated
785
•
200 pesticide residues (> 50 families) were analyzed in honey in a less than 31 min
786
•
Streamlined quantification approach using neat standard calibration was utilized
787
•
Neat standard calibration in conjunction with correction for matrix effect was employed
788
•
Assay protocol is applicable for regulatory monitoring of pesticide residues in honey 41 Page 41 of 42
789 790
us
cr
ip t
791
Ac ce p
te
d
M
an
792
42 Page 42 of 42
S0021-9673(15)01712-4 http://dx.doi.org/doi:10.1016/j.chroma.2015.11.068 CHROMA 357082
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
2-10-2015 6-11-2015 22-11-2015
Please cite this article as: A.H. Shendy, M.A. Al-Ghobashy, M.N. Mohammed, S.A.G. Alla, H.M. Lotfy, Simultaneous Determination of 200 Pesticide Residues in Honey using Gas Chromatography-Tandem Mass Spectrometry in Conjunction with Streamlined Quantification Approach, Journal of Chromatography A (2015), http://dx.doi.org/10.1016/j.chroma.2015.11.068 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Simultaneous Determination of 200 Pesticide Residues in Honey using Gas
2
Chromatography-Tandem
3
Streamlined Quantification Approach
Mass
Spectrometry
in
Conjunction
with
ip t
4 5
Amr H. Shendya, Medhat A. Al-Ghobashyb,c,*, Moustapha N. Mohammeda, Sohair A. Gad Allaa
6
and Hayam M. Lotfyb,d
cr
7 8
Central Laboratory of Residue Analysis of Pesticides and Heavy Metals in Food, Agricultural Research
us
a
10
Center, Ministry of Agriculture and Land Reclamation, Giza, Egypt
12
c
13
d
Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt
an
11
b
Bioanalysis Research Group, Faculty of Pharmacy, Cairo University, Cairo, Egypt Pharmaceutical Chemistry Department, Faculty of Pharmaceutical Sciences and Pharmaceutical
14
Industries, Future University, Cairo, Egypt
15
M
9
16
*
17
Dr. Medhat A. Al-Ghobashy, Analytical Chemistry Department, Faculty of Pharmacy, Cairo
18
University, Cairo 11562, Egypt.
19
E-mail: [email protected]
21 22 23 24 25 26
d
te
Ac ce p
20
Correspondence:
27 28 29 30 31 1 Page 1 of 42
32 Abstract
34
A sensitive, accurate and reliable multi-class GC-MS/MS assay protocol for quantification and
35
confirmation of 200 common agricultural pesticides in honey was developed and validated
36
according to EU guidelines. A modified extraction procedure, based on QuEChERS method
37
(quick, easy, cheap, effective, rugged and safe) was employed. Mass spectrophotometric
38
conditions were individually optimized for each analyte to achieve maximum sensitivity and
39
selectivity in MRM mode. The use of at least two reactions for each compound allowed
40
simultaneous identification and quantification in a single run. The pesticides under investigation
41
were separated in less than 31 min using the Ultra-inert capillary column (DB-35MS). For all
42
analytes, neat standard calibration curves in conjunction with correction for matrix effect were
43
successfully employed. The detection limits of the assay ranged from 1.00 to 3.00 ng mL-1 for
44
the studied pesticides. The developed assay was linear over concentration range of 10.00 –
45
500.00 ng mL-1, with correlation coefficient of more than 0.996. At the LOQ, 81 % of the studied
46
pesticides were efficiently recovered in the range of 70.00-120.00 %, with CV % less than 15.00
47
% while 99.3 % compounds had mean percentage recovery of 60.00-140.00 %, with CV% less
48
than 21.00 % (N=18, over three different days). The proposed assay was successfully applied for
49
the analysis of the studied pesticide residues in one PT sample and 64 commercial honey
50
samples collected over one year from different districts around Egypt. Results revealed that only
51
one honey sample out of the 64 analyzed samples was contaminated with tau-Fluvalinate (10.00
52
µg Kg-1). This wide scope assay protocol is applicable for monitoring pesticide residues in honey
53
by national regulatory authorities and accredited labs; that should help ensure safety of such
54
widely used product.
56 57
cr
us
an
M
d
te
Ac ce p
55
ip t
33
58 59 60
Keywords
61
GC-MS/MS, honey; Pesticide residues; QuEChERS; Multi-class-multi-residue; Egyptian honey
62 2 Page 2 of 42
63 1.0 Introduction
65
Honey and bee products have the image of being natural, healthy and free of contaminants
66
although in many places they are produced in polluted environment [1-5]. Owing to the
67
extensive utilization and usual persistence in the environment, honey may get contaminated by
68
pesticides [1, 6-9]. It has been reported that pesticide residues can cause genetic mutations,
69
cellular degradation in addition to several public health problems [10-12]. This may occur
70
through direct contamination from beekeeping practices as well as indirect contamination from
71
environmental sources [13-15]. Acaricides, fungicides, insecticides and many other toxic
72
substances are used inside beehive colonies to control bee diseases especially varroaptosis and
73
ascospheriosis carrying the risk of direct contamination of honey and other hive products [1, 10,
74
16, 17]. On the other hand, the indirect contamination from environment occurs because of the
75
widespread use and extensive distribution of pesticides that helped introduce their residues into
76
honey by bees that have been fed on contaminated blossom [1, 4, 7, 11, 18, 19].
77
Reportedly, more than 150 different pesticides have been detected in colony samples [20]. In
78
general, the frequently detected pesticide residues are often from varroacides that have the ability
79
to migrate and accumulate in beeswax, pollen, and bee bread [21, 22]. Organophosphorus
80
(OPPs) and carbamates have almost replaced organochlorine pesticides (OCPs). However, owing
81
to the persistent nature of OCPs, they are still within the scope of recently developed analysis
82
procedures [19, 23]. According to EU regulations [24], honey is considered not suitable for
83
human consumption if residues are beyond the maximum residue levels (MRL) that are usually
84
in the range of 10.0 to 50.0 ng g-1 [11]. Based on the EU directive 96/23/EC (Annex Ι) [25] for
85
imported honey from the developing countries, many pesticide residue groups have been
86
identified as highly desirable to be monitored in honey samples. Thus, OCPs, OPPs, carbamates,
87
pyrethroids and polychlorinated biphenyls (PCBs) should be monitored in honey samples.
cr
us
an
M
d
te
Ac ce p
88
ip t
64
89
Determination of pesticides in honey at trace levels is a challenging task due to its complex
90
composition and particularly the presence of waxes and pigments. Conventional extraction
91
protocols using organic solvents followed by subsequent cleanup procedures prior to GC
92
determination have been a common practice [4, 7, 26-29]. The drawbacks of this traditional
93
approach are limited scope, large amounts of toxic solvents, prolonged analysis time and the 3 Page 3 of 42
need for large volume glassware. QuEChERS (quick, easy, cheap, effective, rugged and safe)
95
method on the other hand is based on liquid–liquid partitioning with acetonitrile followed by a
96
cleanup step via dispersive solid phase extraction (d-SPE) using primary secondary amine (PSA)
97
[30]. Coupling of QuEChERS protocol to GC-MS enabled multi-class, multi-residue analysis
98
over short analysis time. Determination of pesticide residues in honeybees using GC-MS/MS
99
have been previously reported [31]. To the best of our knowledge, very few studies were
100
reported for the simultaneous determination of pesticide residues in honey samples using
101
QuEChERS protocol coupled to tandem mass spectrometry. Paradise et al [32] reported the
102
simultaneous determination for 22 insecticides of three chemical families in honey using
103
QuEChERS/GC-MS/MS. The percentage recovery, correlation coefficient and LOD / LOQ were
104
63.00-139.00 % (CV% <25), 0.96-0.98 and 0.07-0.20 ng g-1 / 0.20-0.50 ng g-1; respectively. In
105
another study, Wiest et al [33] reported the determination of 80 environmental contaminants in
106
honey using a modified QuEChERS coupled to LC-MS/MS and GC-ToF. The percentage
107
recoveries and correlation coefficients using GC-ToF were 60.00-120.00 (CV% >25) and
108
>0.990; respectively while LOD and LOQ were 1.10- 47.50 ng g-1 and 10.80-128.00 ng g-1,
109
respectively. In both studies, a strong matrix effect was obtained and thus matrix matched
110
calibration was essential [32, 33].
te
111
d
M
an
us
cr
ip t
94
In the current study, QuEChERS method was revisited, modified accordingly and implemented
113
for the simultaneous determination of 200 pesticide residues, belonging to more than 50
114
functional and chemical classes (Table S1) and residues in honey samples using GC-MS/MS. A
115
streamlined quantification approach employing neat standard calibration curves in conjunction
116
with correction for matrix effect was used. The validation parameters have been evaluated for
117
each of the studied compounds according to EU guidelines [34-36]. This wide scope assay
118
protocol is applicable for monitoring pesticide residues in honey by national regulatory
119
authorities and accredited labs. The applicability of the developed protocol for the routine
120
monitoring of locally produced honey was investigated. This work is part of the national
121
initiative for developing a monitoring program for pesticide residues in Egyptian honey that
122
should help locally produced products to penetrate international markets.
123
2. Materials and methods
124
2.1 Chemicals, reagents and standard solutions
Ac ce p
112
4 Page 4 of 42
Pesticide reference standards were obtained from Dr. Ehrenstorfer GmbH (Germany), 99.00%
126
purity. An overview of the physicochemical properties of the studied compounds are
127
summarized in Table S1. Ethyl acetate, hexane, acetone, and acetonitrile of residue analysis
128
grade, were purchased from Sigma-Aldrich (USA). The QuEChERS kits (part no. 5682-5650)
129
with salt packets containing 4.00 g anhydrous magnesium sulfate, 1.00 g sodium chloride, 1.00 g
130
sodium citrate and 0.50 g sodium hydrogen citrate sesquihydrate, and 15 mL centrifuge tubes
131
with 150.00 mg anhydrous magnesium sulfate and 25.00 mg PSA for d-SPE (part no. 5982-
132
5021) were purchased from Agilent Technologies (USA). Stock solutions (1000 µg mL-1) of
133
each pesticide standard were prepared by dissolving 0.10 g of each pesticide in 100 mL toluene.
134
Working mixture standard solution of the studied pesticides (2.50 µg mL-1, each) was prepared
135
by diluting suitable aliquot of the stock solutions with toluene, and used to fortify honey
136
samples. A set of calibration standard solutions 10.00 - 500.00 ng mL-1 was prepared in
137
hexane/acetone (9:1 v/v). Stock standards and working solutions were stored at -20±2oC and 4-8
138
o
139
system (Millipore, Germany) with a resistivity of at least 18.2 MΩ.cm at 25oC and TOC below 5
140
ppb.
141
2.2 Instrumentation and analysis conditions
142
Analysis was carried out using an Agilent 7980A Gas Chromatography system equipped with
143
tandem mass spectrometer 7000B Quadrupole (Agilent Technologies, USA). Mass Hunter
144
software was employed for instrument control and data acquisition/processing (Agilent
145
Technologies, USA). NIST 08 mass spectral library, ver. 2.0f (Agilent P/N G1033A) was used
146
for confirmation of the studied compounds as well as identification of co-extractives.
147
Chromatographic separations were accomplished using the DB-35MS Ultra-inert capillary
148
column (20 m length x 0.18 mm id x 0.25 µm) that was obtained from Agilent Technologies
149
(USA). The GC oven temperature was programmed to initially be held at 70°C for 1.3 min then
150
increased to 135°C at 50°C min-1 (held for 0 min), and raised to 200°C at the rate of 6°C min-1
151
(held for 0 min), then increased from 200 to 310°C at 16°C min-1 (held for 8.2 min). The
152
injection volume was 1 µL and detection was achieved using EI source (-70 eV). Samples were
153
injected in a splitless mode and ultra-high purity helium (> 99.999%) was used as both the
154
carrier gas at flow rate of 0.7 mL min-1 and quench gas at 2.25 mL min-1, and nitrogen served as
155
the collision gas at 1.5 mL min-1. Injector temperature, transfer line temperature, ion source
an
us
cr
ip t
125
Ac ce p
te
d
M
C away from direct light, respectively. Ultra-pure water was obtained using a MilliQ UF-Plus
5 Page 5 of 42
temperature and quadrupole temperature were 250°C, 285°C, 280°C and 150°C, respectively.
157
The filament current (35 μA) was switched off during a solvent delay time of 6 min. Acquisition
158
was performed in MRM mode in which one MRM was used for quantification (quantifier peak)
159
and the others were used for confirmation (qualifier peaks). The MS/MS transitions and optimal
160
operational conditions used for analysis are summarized in Table 1. The correct identification of
161
the studied pesticides was based on the tR and the ion ratio of the qualifiers to quantifiers,
162
compared to that obtained via analysis of neat standard solutions. Regular maintenance was
163
carried out, where the liner (uni-taper) was replaced daily to avoid liner priming. In addition, 2 cm
164
from the front part of the column was trimmed to remove any accumulated non-volatile components
165
after about 500 injections.
166
2.3 Sample preparation
167
Initially, various conditions used for sample preparation as per QuEChERS protocol were
168
evaluated and optimized, if necessary. Locally produced organic honey samples were obtained
169
from GlobalG. AP-certified producers and used as blank samples. Aliquots of 5.00 ± 0.02 g were
170
accurately weighed into 50 mL polypropylene centrifuge tubes. Suitable volumes of the studied
171
pesticides working mixture standard solution were added to final concentration of 50.00 µg kg-1
172
of each compound. Spiked blank samples were vortex mixed for 30 s and stored away from light
173
at room temperature for 10 min. A modified QuEChERS protocol was optimized and applied,
174
briefly, deionized water (10.0 mL) was added to the sample then vortex mixed and incubated in a
175
water bath at 40oC until complete homogeneity was obtained. Acetonitrile (10 mL) was then
176
added and the content was shaken for 1 min using a mechanical shaker. The QuEChERS salt kit
177
was added and immediately shaken for further 1 min and subsequently centrifuged at 15,000 xg
178
at 4 – 8°C for 5 min. Thereafter, the whole acetonitrile fraction was transferred into a 15 mL d-
179
SPE polypropylene tube. The tube was shaken for 1 min and centrifuged for 2 min at 15,000 xg
180
using a cooling centrifuge. Finally, 2.00 mL of the supernatant was transferred into 50 mL round
181
bottom glass flask and evaporated under vacuum at 40 °C till complete dryness. The residue was
182
then reconstituted into 2.00 mL of hexane/acetone (9:1 v/v) containing 100.00 µg L-1 Aldrin
183
(Injection standard). Samples were then ultra-sonicated and filtered through a disposable 0.45
184
µm PTFE membrane filter into an amber glass vial and subjected to GC-MS/MS analysis.
185
2.4 Calibration and validation
Ac ce p
te
d
M
an
us
cr
ip t
156
6 Page 6 of 42
A set of calibration standard solutions of 10.00 - 500.00 ng mL-1 was prepared in hexane/acetone
187
(9:1 v/v) containing 100.00 ng mL-1 of Aldrin, and analyzed as described. The neat standard
188
calibration curves were constructed by plotting the relative concentration (ng mL-1) versus
189
instrument relative response and correlation coefficients were estimated. In order to determine
190
the matrix effect; calibration curves were constructed using the conventional matrix matched
191
standards and results were compared to those of the neat calibration curves. In-house validation
192
was performed according to the criteria and recommendations of the European guidelines [34-
193
36]. The performance requirements for the assay were as follows: (1) Range: the MRL for each
194
analyte in the matrix should be encompassed inside the range, (2) Linearity: the correlation
195
coefficient should be ≥ 0.990, and the CV % of the response for each calibration point should be
196
˂ 20.0 %, (3) Precision: expressed as reproducibility (inter-day precision) should be < 20%, (4)
197
Trueness/Accuracy: expressed as mean recovery, shall be in the range of 70.00 – 120.00 %, (5)
198
Limit of quantification (LOQ): should be ≤ the lowest MRL and should comply with
199
requirements (3 - 4) and (6) Uncertainty values must comply with the EU requirements of a
200
maximum uncertainty of 50%. Uncertainty was calculated following the guidance of
201
EURACHEM [35, 37]. Blank honey samples (organic honey samples from GlobalG AP-certified
202
producers) were fortified at three levels: 10.00, 50.00 and 100.00 µg Kg-1. At each level the
203
analysis was performed on three different days using six replicates / day in order to verify the
204
validation parameters.
205
2.5 Application to commercial honey samples and PT sample
206
In order to ensure the reliability of the test results, the following samples were included with
207
every analysis batch: (1) blank extract, (2) reagent blank and (3) a spiked blank sample at 50.00
208
ng g-1. Calibration curves prepared and constructed in the same batch were employed in all
209
determinations. One point matrix matched standard (50.00 ng g-1) and an equivalent neat
210
standard were prepared and analyzed. Results were used to estimate the matrix effect and correct
211
for minor variability in test results. In routine analysis, recovery values between 60.00 and
212
140.00% were accepted in agreement to European guidelines [36]. The optimized assay protocol
213
was applied for the analysis of 64 commercial honey samples that were obtained from local
214
market in addition to a spiked strawberry sample, a proficiency testing sample (PT sample) for
215
pesticide residues (round 19178, 2014).
216
3. Results and discussion
Ac ce p
te
d
M
an
us
cr
ip t
186
7 Page 7 of 42
In this work, the analysis strategy was designed to detect and quantify as many pesticides as
218
possible in a single run. With more than 1000 pesticides being used world-wide, residue analysis
219
using LC-MS/MS and GC-MS/MS have become the gold standard. Several criteria were taken
220
into account while selecting which pesticides are to be included: (1) pesticides registered for crop
221
protection by local authorities, (2) surveying the literature for the commonly analyzed
222
compounds, especially those previously detected in Egyptian honey, (3) persistence of pesticides
223
in the environment and bioaccumulation, (4) international requirements for safety and suitability
224
for human use of honeybee products (CAC, EU, USA and EU-RASFF notifications) and (5)
225
amenability for GC-MS/MS analysis. It has been previously reported that a number of pesticide
226
classes are commonly used in Egypt such as OCPs, OPPs, carbamates, ureas, anilides and
227
pyrethroids [38-40]. In this study, we originally selected 200 pesticides of different classes:
228
OCPs, OPPs, synthetic pyrethroids, carbamates, fungicides, and herbicides. Physicochemical
229
properties of the selected pesticides along with their functional and chemical classification are
230
summarized in Table S1.
231
3.1 Instrumentation and analysis conditions
232
3.1.1 MS/MS optimization
233
Initially, optimum precursor ion for each of the studied compounds was identified to establish
234
the optimized MS/MS detection method. Thus, appropriate concentration of each analyte was
235
prepared and injected individually in full scan mode in the range of 50 - 550 m/z using EI
236
ionization mode at -70 eV. In general, the optimal precursor ions shall meet the criteria of the
237
most intense ion with the highest m/z, afterwards, several product ion scan methods were
238
created, each with different collision energy in order to find the optimum product ions. Within
239
each method, groups of approximately 10 pesticides were concurrently analyzed, and the tested
240
collision energies were 5 - 40 V with 5 V step increase. Fine optimization for some of the studied
241
compounds was carried out via slight increase or decrease of the collision energy in order to
242
obtain intense ions. As a consequence, the most intense ion was selected as the quantifier ion
243
while the less intense ones were used as qualifier ions. This yielded four identification points, 1
244
for the precursor ion and 1.5 for each product ion, in agreement with EU Guidelines [34, 36].
245
The MRM method was built and the D-well time was set at 10 ms for each transition. After
246
analyzing the obtained results, further optimization was required for the less sensitive
247
compounds. Consequently, the relevant segment was adjusted, whereas D-well time was
Ac ce p
te
d
M
an
us
cr
ip t
217
8 Page 8 of 42
increased for the less sensitive analytes such as deltamethrin by decreasing the D-well time for
249
analytes with intense signals. MS/MS transitions and optimal operational conditions for the
250
studied compounds are summarized in Table 1.
251
3.1.2 GC optimization
252
GC separation was optimized for all the studied pesticides that were from different chemical
253
classes. Two columns; DB-35MS (20 m length x 0.18 mm id x 0.25 µm) and HP-5MS (30 m
254
length x 0.25 mm id x 0.25 µm) were tested at constant flow rates of the carrier gas at 0.70, 1.20
255
and 1.80 mL min-1. Initially, separation for the studied compounds was achieved at a flow rate of
256
1.8 mL min-1 using HP-5MS column in conjunction with 30 segments and scan times. At such
257
conditions, the whole run time was relatively long (45 min) with asymmetric peaks. On the other
258
hand, when DB-35MS column was used at a flow rate of 1.8 mL min-1, the peaks eluted with
259
poor resolution. At such conditions, analysis was divided into ten segments, including too many
260
transitions and scan times of more than 1s. Efficient separation along with sufficient time
261
segments/scan times was obtained at flow rates of 0.70 and 1.2 mL min-1. Nevertheless, the flow
262
rate of 0.70 mL min-1 helped reduce the whole run time to less than 31 min with symmetrical
263
peaks when compared to HP-5MS column (tR of the last eluting peak, difenoconazole, was 23.6
264
min). As ultra-trace concentrations of the studied compounds were to be detected; splitless
265
injection mode for the final extracts was used for optimum sensitivity. Both of the injection
266
solvent type and volume; acetonitrile, hexane, acetone and hexane/acetone mixtures were tested
267
at different injection volumes (0.50, 1.00 and 2.00 µL), respectively. In contrast to the previously
268
published protocol [30], acetonitrile extract was not directly injected to the GC-MS/MS. This
269
could be attributed to the large expansion volume of acetonitrile upon injection under high inlet
270
temperatures that may result in irreproducible chromatograms, especially in routine analysis
271
[30]. Results showed that optimal sensitivity and peak symmetry for the studied compounds were
272
successfully accomplished upon conducting analyses using 1.00 µL of hexane/acetone (9:1 v/v)
273
as reconstitution solvent at the end of extraction step.
Ac ce p
te
d
M
an
us
cr
ip t
248
274 275
It should be noted that post-run, post-column backflush was used between each injection. The
276
use of backflush helped remove any high-boiling point matrix component that could be still
277
remaining in the column at the end of each run. In general, we recommend that after the injection
9 Page 9 of 42
of 25 samples, one point matrix matched standards of all analytes at concentration level of 50.00
279
µg Kg-1, each is injected to monitor possible deviation of analyte responses.
280
3.2 Sample preparation
281
Owing to the complexity of honey matrix that contains sugars, enzymes, proteins as well as other
282
minor components such as lipids and waxes, sample clean-up is generally employed [41].
283
QuEChERS method [30] has been in use for sample preparation prior to analysis of multi-class
284
residues of veterinary drugs in honey and milk samples [41-44] in addition to, pesticide residues
285
in various matrices including honey [5, 11, 38, 45-49]. Originally, QuEChERS method involved
286
an extraction step with acetonitrile followed by liquid – liquid partitioning with citrate buffer and
287
phase out separation using MgSO4 and NaCl salts. The obtained extracts were purified using d-
288
SPE with PSA and a large volume of the acetonitrile extract was directly injected to GC-MS
289
instrument [30]. Recent trends in sample preparation involves the development of multi-residue,
290
multi-class assays. Reduction in sample size, extraction time and extensive clean-up, thus solvent
291
consumption have always been the objectives [30, 50]. Accordingly, all relevant parameters for
292
efficient sample preparation were considered during the optimization of this extraction protocol,
293
as will be discussed in detail.
294
3.2.1 Sample size
295
Initially, reducing sample size without affecting the statistical reliability of the results was tested
296
in order to help improve the efficiency of the proposed assay and reduce interference from
297
matrix components. Different analytical portions of samples (1.00, 5.00, 10.00, 15.00 and 20.00
298
g) were taken from a bulk of 2.00 kg of honey samples spiked at 100.00 µg kg-1 with the target
299
compounds and analyzed in triplicate. Results showed good homogeneity with variability lower
300
than 20.00% for all compounds for sample sizes more than 1.00 g. As a result, we opted a sample
301
size of 5.00 g with the aim of reducing reagent consumption and time required for the
302
methodology. Although it has been previously reported that a subsample size of 10.00 – 15.00 g
303
is sufficiently representative [30, 50], our results showed the suitability of 5.00 g as the proposed
304
analytical portion for typical residue assays. This was in agreement to the recently published
305
report that recommended 2.00 – 5.00 g subsample for residue analysis [51].
306
3.2.2 Extraction and reconstitution solvents
307
Selecting the proper solvent for extraction depends on availability, cost, safety to the analyst and
308
the environment, in addition to, extraction efficiency with lowest possible matrix components. It
Ac ce p
te
d
M
an
us
cr
ip t
278
10 Page 10 of 42
has been previously reported that ethyl acetate would be the solvent of choice for extraction as it
310
provided good recoveries for a wide number of pesticides with different properties. In addition,
311
ethyl acetate is less polluting than chlorinated solvents, although it does also co-extract matrix
312
components [50]. Initially, the effect of extraction solvent type; ethyl acetate and acetonitrile in
313
conjunction with the effect of the PSA sorbent was investigated. In the absence of the PSA, the
314
overall mean percentage recoveries were 64.73% and 69.02% upon extraction with acetonitrile
315
and ethyl acetate, respectively. On the other hand, in the presence of the PSA, results indicated
316
that the overall mean percentage recovery for all of the studied compounds were relatively higher
317
than those obtained in absence of PSA (79.45% and 86.94% for acetonitrile and ethyl acetate,
318
respectively). Statistical analysis using one way ANOVA showed a significant difference
319
between the studied solvents either in the presence or absence of PSA (Table S2 and S3). Based
320
on the obtained results, it could be concluded that: (1) ethyl acetate was relatively more efficient
321
than acetonitrile in the absence or presence of PSA, (2) higher recoveries were obtained in the
322
presence of PSA regardless of solvent type, (3) extraction using ethyl acetate/PSA resulted in
323
relatively larger number of compounds recovered within the range of 70 – 120% when compared
324
to acetonitrile/PSA, however, almost all compounds were recovered using acetonitrile within the
325
range of 60 – 140% (Table S3 and Fig. S1, Raw data are provided in supplementary materials)
326
and (4) acetonitrile in conjunction with PSA was found capable of efficient extraction for the
327
compounds of concern with lowest possible co-extracts. This was concluded via comparing the
328
obtained chromatograms visually and by the aid of the NIST library. Thus, (acetonitrile/PSA)
329
was adopted in agreement with the original QuEChERS method as it provided efficient
330
extraction for almost all the studied pesticides with minimal amount of co-extractives. In the
331
current study, acetonitrile extract was not directly injected to the GC-MS/MS as discussed
332
earlier. Alternately, extracts were centrifuged at 15,000 xg, 4-8oC in order to enable further
333
sample clean-up via removal of solidified lipids and waxes. Evaporation under vacuum was
334
carried out at 40 oC till complete dryness using a rotary evaporator. At such conditions, studied
335
compounds were found to be not affected by either evaporation or degradation. In all future
336
experiments, 2.00 mL hexane/acetone (9:1 v/v) were used for reconstitution of evaporated
337
extracts. This volume was found appropriate in order to avoid long evaporation time.
338
3.2.3 Removal efficiency for matrix components
Ac ce p
te
d
M
an
us
cr
ip t
309
11 Page 11 of 42
Co-extracted non-volatile matrix components may cause serious problems in routine trace
340
analysis of pesticide residues [12, 52, 53]. Cleanliness of the final extracts should help improve
341
the selectivity of analysis. The use of acetonitrile in conjunction with PSA allowed for exclusion
342
of larger amount of co-extracted compounds such as enzymes, proteins, sugars, waxes and lipids
343
when compared to ethyl acetate. Co-extractives removal efficiency was estimated on the basis of
344
the weight of the remaining residue after evaporation of the extracts obtained from 5.00 g spiked
345
samples. Results showed that the residue obtained upon extraction of honey samples using ethyl
346
acetate/PSA and acetonitrile/PSA were ~55 mg g-1 and ~30 mg g-1, respectively. This conclusion
347
was further verified via inspection of the chromatograms of reconstituted residues using NIST
348
library as discussed above.
349
3.3 Confirmation and quantification
350
For all analytes under investigation, identification and quantification were based on four criteria:
351
(1) stability of chromatographic retention time, (2) matching between the retention time of
352
analytes in spiked samples and neat standards, (3) presence of the two or more relevant
353
transitions from the selected precursors with S/N>3 and (4) stability of the ion ratio (quantifier
354
and qualifier peaks), in accordance with the recommendations listed in CD 2002/657/EC and
355
SANCO/12571/2013
356
throughout the study in order to ensure that they were within the acceptable ranges [34, 36].
357
Matrix matched calibration is the most commonly adopted approach to compensate for signal
358
suppression/enhancement experienced during MS/MS analysis [41, 44, 54]. In the current study,
359
quantification was accomplished using a set of neat standard calibration curves via external
360
standardization approach. Results were corrected using one point matrix matched standard.
361
Assay validation and application to spiked honey samples, commercial samples and PT sample
362
were then carried out in order to verify the applicability of this approach.
363
3.3.1 Linearity and working range
364
A set of neat standard mixtures of all compounds was prepared in the presence of 100.00 ng mL-1
365
of the Aldrin. Analysis was carried out and the results were used to construct the neat standard
366
calibration curves. For all studied pesticides, the response was linear over the concentration
367
range (10.00 – 500.00 ng mL-1) with correlation coefficients more than 0.996 for all compounds.
368
The calibration data for the studied compounds are summarized in Table 2. A calibration level of
369
concentration 50.00 ng mL-1 of each compound was injected twenty times. The obtained results
d
M
an
us
cr
ip t
339
Ac ce p
te
[34, 36]. The ion ratio for each analyte was regularly monitored
12 Page 12 of 42
exhibited CV% values for the retention times of 0.001% - 0.764%, while for relative peak
371
responses the CV% ranged from 0.148% to 8.255%. Therefore, the results achieved could
372
indicate the stability of the chromatographic and mass conditions for the developed assay.
373
The matrix effect on instrument response was then investigated in order to reveal possible signal
374
suppression or enhancement [55]. Blank honey samples were extracted and fortified with
375
appropriate aliquots of studied compounds (10.00 – 500.00 ng mL-1). Matrix matched calibration
376
curves were constructed and regression equation parameters were compared to those obtained
377
using the neat standard calibration curves (Table 2). Results showed that both of the neat
378
standard and matrix matched calibration curves were linear with acceptable correlation
379
coefficients but with apparent slope differences.
us
cr
ip t
370
an
380
A set of six replicates of spiked honey samples (100.00 µg Kg-1) were analyzed and the
382
concentration was determined using both neat standard calibration and matrix-matched
383
calibration curves. Results showed acceptable mean percentage recoveries upon using matrix
384
matched calibration. On the other hand, results obtained using the neat standard calibration were
385
significantly lower than those obtained using the matrix matched calibration, as shown in Table
386
S4. Such results along with slope differences shown in Table 2, indicated matrix effect as will be
387
discussed in detail.
388
3.3.2 Matrix effect and quantification
389
In literature, it has been reported that slope differences between matrix matched and neat
390
standard calibration curves of more than ±10% limit indicate matrix effect [54]. Later on, a more
391
detailed discussion of the correlation between slope differences and matrix effect was presented:
392
(1) ±20.00% indicates mild or tolerable matrix effect, (2) more than ±20.00 % up to ±50.00 %
393
represent medium matrix effect, while (3) differences of ±50.00% or more represent strong
394
matrix effect [45]. Recently, it has been reported that matrix effect would be considered minimal
395
and neat standard calibration curves could be applicable if slope differences were within ±30%
396
[56].
Ac ce p
te
d
M
381
397 398
In this study, slope difference percent was calculated for each compound (Table 2 and Fig. 1,
399
Raw data are provided in supplementary materials). Results demonstrated that only 93
400
compounds showed no matrix effect (within ±10% limit [54]). According to Camino-Sanchez et 13 Page 13 of 42
al. [45], 132 of the studied compounds presented mild suppression ranging from -19.91 % to -
402
0.87 % and 25 compounds showed mild enhancement at range of +0.34 % to +16.09 %. Medium
403
matrix suppression ranging from -20.30 % to -43.77 % was noticed for 35 compound, while 6
404
compounds presented medium matrix enhancement ranging from +20.69 % to +43.57 %. It
405
should be noted that only 3 compounds experienced severe matrix suppression ranging from -
406
55.79 % to -57.90 %. On the other hand, according to Nie et al. [56], 188 of the studied
407
compounds (~94.00%) showed slope difference percent within ±30%. These results might suggest
408
that using matrix matched calibrations was not critical in our case. However, ANOVA results for
409
comparison between the recoveries obtained from the neat standard and matrix matched
410
calibration curves revealed significant differences between the two approaches (Table S4).
411
Here, we suggest that for accurate quantification in multi-residue, multi-class assays of wide
412
scope, neat standard calibration curves could be used only after correction for matrix effect. In
413
order to estimate a correction factor for the matrix effect, a set of extracted blank honey samples
414
were fortified at four concentration levels (10.00, 50.00, 100.00 and 500.00 µg Kg-1, 6 replicates
415
each) of the studied compounds. Analysis was carried out as described and mean percentage
416
recoveries (N = 24) and CV% were calculated using the neat standard calibration curve for each
417
compound (Fig. S2, Raw data are provided in supplementary materials). Results indicated a
418
homogenous matrix effect with an overall suppression in the mean percentage recovery for all
419
compounds throughout the linear concentration range of each of the studied compounds.
cr
us
an
M
d
te
Ac ce p
420
ip t
401
421
Based on the obtained results, a correction factor was calculated for each compound, as
422
previously suggested for analysis of nitrofuran metabolites and nitroimidazoles in honey using
423
LC-MS/MS by our research group [41]. One point matrix matched standard was prepared at
424
50.00 µg Kg-1 and analysis results using neat standard calibration approach were used to estimate
425
the correction factor. The following mathematical equation was applied for the determination of
426
analyte concentration in samples after correction for the matrix effect:
427
14 Page 14 of 42
ip t cr
428 429 Cs, analyte concentration in sample (mg Kg-1)
431
Ci, found analyte concentration determined from calibration curve (mg Kg-1)
432
Vext, total volume of extraction solvent (mL)
433
Vevp, aliquot volume taken for evaporation (mL)
434
Vf, final volume after reconstitution of evaporated aliquot (mL)
435
W, sample weight (g)
436
Cmtx (labeled), labeled concentration of one point matrix matched standard (mg Kg-1)
437
Cmtx (found), found concentration of one point matrix matched standard (mg Kg-1)
438
Cmtx (labeled) / Cmtx (found), matrix effect correction factor
439
In order to extrapolate and verify the applicability of the proposed method of calculation for GC-
440
MS/MS analysis of pesticide residues, spiked honey samples (n=6) at concentration of 100.00 µg
441
Kg-1 of each compound were prepared and analyzed. The concentrations were determined using
442
the corresponding neat standard calibration curves and results were corrected as described.
443
Results were compared to those obtained using the matrix matched calibration curves.
444
Significant difference (one-way ANOVA) between the results obtained using the neat standard
445
calibration curve and the matrix matched calibration curve confirmed the need for correction for
446
matrix effect. On the other hand, no significant difference between the corrected results and
447
those obtained using the conventional matrix matched calibration. A summary of the obtained
448
results and the statistical analysis were summarized in Table S4. These results confirmed the
449
applicability of one point matrix matched calibration for streamlined quantification of pesticide
450
residues using GC-MS/MS.
451
3.4 Validation procedure
Ac ce p
te
d
M
an
us
430
15 Page 15 of 42
Assay validation was carried out in accordance with the procedures outlined in EU regulations
453
[34, 36]. The ruggedness of the assay was demonstrated on an ongoing basis through its use for
454
routine analysis of honey samples. It is worth mentioning that during validation for the optimized
455
assay protocol, great concern was given to isomeric compounds that may partially co-elute
456
together. Among isomeric compounds that gave the same ion transitions are DDT o,p- and DDD
457
p,p- and partially co-eluted. Although they could be distinguished individually by their tR
458
difference of 9.60s (DDT o,p- 18.35 min and DDD p,p- 18.51 min), it was opted to integrate and
459
quantify such compounds as one peak. For pyrethroids that formed multiple chromatographic
460
peaks (cyfluthrin, cypermethrin, permethrin and flucythrinate), all peaks were integrated together
461
and expressed as sum of isomers.
462
3.4.1 Specificity
463
A specificity study was conducted in order to verify the absence of potential interfering
464
compounds at the retention time of the studied analytes. The assay was applied for analysis of
465
twenty blank honey samples (organic honey samples from GlobalG AP-certified producers) of
466
different origins / matrix composition (viscosity, pigment content, pollen grain contents, etc).
467
Representative honey samples were fortified with all analytes at 50.00 µg Kg-1 and analysis was
468
carried out as described. No interfering peaks were detected in the region of interest for all
469
analytes as shown in the TIC of blank and fortified honey samples at 50.00 µg Kg-1 (Fig. 2).
cr
us an
M
d
te
Ac ce p
470
ip t
452
471
3.4.2 Accuracy and precision
472
The accuracy and precision of the assay were measured at 10.00, 50.00 and 100.00 µg Kg-1 for
473
each analyte (6 replicates/day, over three days). Results indicated acceptable performance of the
474
assay for all analytes over the studied validation levels. The percentage recoveries were in the
475
range of 51.13 – 126.55% with CV% of 0.43% - 20.53%, respectively. These results were in
476
agreement to the requirements of EU guidelines [34, 36] regarding the CV% for repeated
477
analysis of spiked or incurred material. The majority of the studied pesticides exhibited mean
478
percentage recoveries within the range of 70.00 – 120.00 % (Fig. 3, Raw data are provided in
479
supplementary materials). The long-term stability of analytes in the extracts and robustness of
480
the developed assay were evaluated by repeated injections (50 injections) of one point matrix
481
matched standards at concentration level of 50.00 µg Kg-1. Results demonstrated the stability of
482
responses after repeated injection. 16 Page 16 of 42
3.4.3 Limits of detection and quantification
484
In the current study, estimating the LOQ for each compound would be a commonly inaccurate
485
process using MS/MS due to non-existent electronic noise and/or highly variable chemical noise
486
that depends on the matrix [57, 58]. As a result, the fit-for-purpose approach was followed to
487
determine the lowest calibration level of the proposed assay via injecting such calibration level.
488
For all studied compounds, it was found that the lowest concentrations that would be accurately
489
quantified ranged from 5.00 – 10.00 µg Kg-1. The LOQ was established at the concentration that
490
would provide a minimum S/N ratio of 10. Therefore, the LOQ for all analytes was compiled at
491
10.00 µg Kg-1. This value comply with the MRLs established in EU Regulation (EC) No.
492
396/2005 and its subsequent modifications [24]. For correct identification and accurate
493
quantification, the LOQ level was validated according to the quality criteria and requirement of
494
accuracy and precision initially established for the method. According to EURACHEM
495
guidelines [35]; it was normally sufficient to provide an approximate value for the LOD (the
496
level at which detection of the analyte becomes problematic). Thus, we can, however, estimate
497
that it is between 1.00 and 3.00 µg Kg-1 for the studied compounds at S/N of 3.
M
an
us
cr
ip t
483
d
498
501
Ac ce p
500
te
499
502
3.4.4 Measurement uncertainty (MU)
503
Uncertainty was calculated following the guidance of EURACHEM according to the document
504
“Eurachem/CITAC Guide CG4: Quantifying uncertainty in analytical measurement” [37]. The
505
calculated expanded uncertainty of the assay was 47.00%. This result was in agreement to the
506
European requirements for a maximum uncertainty of 50%.
507
3.5 Application of assay protocol
508
Throughout routine analysis, the following internal quality control (IQC) was carried out: (1)
509
blank extract; in order to identify false positives caused by any contamination during the
510
extraction procedure or by the presence of interfering compounds, (2) reagent blank; in order to
511
reveal any possibility of false positive due to contamination in the equipment or reagents and (3)
512
a spiked blank sample at 50.00 µg Kg-1 to assess the extraction efficiency.
513
5.1 Application to commercial honey samples 17 Page 17 of 42
In order to demonstrate the applicability of the optimized assay protocol as well as the correction
515
factor calculation, 64 commercial honey samples of different botanical origin were obtained
516
from local market of different governorates during the period of 2013 till the mid of 2014. The
517
validated assay protocol was then applied for the detection and determination of the studied
518
analytes. Results showed that all studied compounds were not detected in amounts above the
519
LOD or LOQ of the employed assay and were deemed suitable for human use except for one
520
sample that was found contaminated by tau-fluvalinate at concentration level of 10.00 µg Kg-1.
521
This result bears non associated heath risk to the consumer, since the MRL of tau-fluvalinate in
522
honey is 50.00 µg Kg-1 according to EU guidelines; 396/2005/EC [24]. It has been previously
523
reported that tau-fluvalinate is a pesticide with a high persistence in honey and its level does not
524
decrease with time, even during honey blending process or after storage at 35oC in the dark for 8
525
months [13]. Thus, repeated analysis for the naturally incurred honey sample was carried out
526
over six months. Results obtained for the repeated analysis of the tau-fluvalinate contaminated
527
sample further confirmed the validity of the assay for residues in honey samples.
528
3.5.2 Application to PT sample
529
Further verification of assay performance and calculation approach was carried out through
530
analysis of a PT sample (round 19178), as part of the Food Analysis Performance Assessment
531
Scheme (FAPAS). From a list of 220 pesticide residues, participating laboratories had to identify
532
and quantify those present in the PT of spiked strawberry sample. The assay was carried out as
533
described and results were calculated using the proposed calculation method. Results of the
534
optimized assay confirmed the presence of bromuconazole, parathion-ethyl, pirimiphos-methyl
535
and tebuconazole residues in the sample. Results reported in the FAPAS report (round 19178)
536
revealed that the z-score for the obtained results was found within the acceptable range |z| < 2
537
(Table S5). Satisfactory z-scores indicated the applicability of the proposed assay protocol for
538
the determination of the studied compounds and the possible applicability to other matrices.
539
4. Conclusion
540
An accurate and sensitive GC-MS/MS assay was developed and validated for the simultaneous
541
determination of 200 pesticide residues from different chemical classes in honey samples.
542
Analytes were extracted using modified QuEChERS protocol coupled to GC-MS/MS analysis.
543
Neat standard calibration curves were successfully employed in conjunction with correction for
544
matrix effect. Assay validation was carried out as per EU guidelines. The applicability of the
Ac ce p
te
d
M
an
us
cr
ip t
514
18 Page 18 of 42
545
method for the determination of the studied compounds was verified using spiked honey samples
546
and naturally incurred honey sample. The assay was deemed suitable for the regulatory
547
monitoring of pesticide residues in honey.
548
ip t
549 550
cr
551 552
us
553 554
an
555 556 557
M
558 559
563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579
te
562
Ac ce p
561
d
560
References
[1] S. Panseri, A. Catalano, A. Giorgi, F. Arioli, A. Procopio, D. Britti, L.M. Chiesa, Occurrence of pesticide residues in Italian honey from different areas in relation to its potential contamination sources, Food Control, 38 (2014) 150-156. [2] A. Giorgi, M. Madeo, J. Baumgartner, G.C. Lozzia, The relationships between phenolic content, pollen diversity, physicochemical information and radical scavenging activity in honey, Molecules, 16 (2011) 336-347. [3] A. Wilczynska, P. Przybylowski, Residues of organochlorine pesticides in Polish honeys, Apiacta, 42 (2007) 16-24. [4] G.P. de Pinho, A.A. Neves, M.E.L.R. de Queiroz, F.O. Silverio, Optimization of the liquidliquid extraction method and low temperature purification (LLE-LTP) for pesticide residue analysis in honey samples by gas chromatography, Food Control, 21 (2010) 1307-1311. [5] F.M. Malhat, M.N. Haggag, N.M. Loutfy, M.A.M. Osman, M.T. Ahmed, Residues of organochlorine and synthetic pyrethroid pesticides in honey, an indicator of ambient environment, a pilot study, Chemosphere, 120 (2015) 457-461.
19 Page 19 of 42
te
d
M
an
us
cr
ip t
[6] D. Debayle, G. Dessalces, M.F. Grenier-Loustalot, Multi-residue analysis of traces of pesticides and antibiotics in honey by HPLC-MS-MS, Anal. Bioanal. Chem., 391 (2008) 1011-1020. [7] B. Albero, C. Sanchez-Brunete, J.L. Tadeo, Analysis of pesticides in honey by solid-phase extraction and gas chromatography-mass spectrometry, J. Agric. Food Chem., 52 (2004) 5828-5835. [8] H. Yavuz, G.O. Guler, A. Aktumsek, Y.S. Cakmak, H. Ozparlak, Determination of some organochlorine pesticide residues in honeys from Konya, Turkey, Environ Monit Assess, 168 (2010) 277-283. [9] J. Regueiro, O. López-Fernández, R. Rial-Otero, B. Cancho-Grande, J. Simal-Gándara, A Review on the fermentation of foods and the residues of pesticides-biotransformation of pesticides and effects on fermentation and food quality, Crit. Rev. Food Sci. Nutr., 55 (2015) 839-863. [10] N. Al-Waili, K. Salom, A. Al-Ghamdi, M.J. Ansari, Antibiotic, pesticide, and microbial contaminants of honey: human health hazards, Sci. World J., 2012 (2012). [11] Z. Barganska, M. Slebioda, J. Namiesnik, Pesticide residues levels in honey from apiaries located of Northern Poland, Food Control, 31 (2013) 196-201. [12] R. González-Rodríguez, R. Rial-Otero, B. Cancho-Grande, C. Gonzalez-Barreiro, J. SimalGándara, A review on the fate of pesticides during the processes within the food-production chain, Crit. Rev. Food Sci. Nutr. , 51 (2011) 99-114. [13] R. Rial-Otero, E.M. Gaspar, I. Moura, J.L. Capelo, Chromatographic-based methods for pesticide determination in honey: An overview, Talanta, 71 (2007) 503-514. [14] J.F. Anderson, M.A. Wojtas, Honey bees (Hymenoptera: Apidae) contaminated with pesticides and polychlorinated biphenyls, J. Econ. Entomol., 79 (1986) 1200-1205. [15] M.W. Kujawski, E. Pinteaux, J. Namiesnik, Application of dispersive liquid-liquid microextraction for the determination of selected organochlorine pesticides in honey by gas chromatography-mass spectrometry, Eur. Food Res. Technol., 234 (2012) 223-230. [16] E. Korta, A. Bakkali, L.A. Berrueta, B. Gallo, F. Vicente, Study of an accelerated solvent extraction procedure for the determination of acaricide residues in honey by highperformance liquid chromatography-diode array detector, J. Food Protect. , 65 (2002) 161166. [17] G. Amendola, P. Pelosi, R. Dommarco, Solid-phase extraction for multi-residue analysis of pesticides in honey, J. Environ. Sci. Health: Part B, 46 (2010) 24-34. [18] S. Bogdanov, Contaminants of bee products1, Apidologie, 37 (2006) 1-18. [19] I. Mukherjee, Determination of pesticide residues in honey samples, Bull. Environ. Contam. Toxicol. , 83 (2009) 818-821. [20] C.A. Mullin, M. Frazier, J.L. Frazier, S. Ashcraft, R. Simonds, J.S. Pettis, High levels of miticides and agrochemicals in North American apiaries: implications for honey bee health, PLoS one, 5 (2010) e9754. [21] M. Frazier, C. Mullin, J. Frazier, S. Ashcraft, What have pesticides got to do with it?, Am. Bee J., 148 (2008) 521-524. [22] A.-C. Martel, S. Zeggane, C. Aurieres, P. Drajnudel, J.-P. Faucon, M. Aubert, Acaricide residues in honey and wax after treatment of honey bee colonies with Apivar or Asuntol 50, Apidologie, 38 (2007) 534-544.
Ac ce p
580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
20 Page 20 of 42
te
d
M
an
us
cr
ip t
[23] C. Blasco, M. Fernandez, A. Pena, C. Lino, M.I. Silveira, G. Font, Y. Pico, Assessment of pesticide residues in honey samples from Portugal and Spain, J. Agric. Food Chem., 51 (2003) 8132-8138. [24] European Commission Regulation 2005 (396/2005/EC) on maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending Council Directive 91/414/EEC, http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2005:070:0001:0016:en:PDF [25] European Commission. 1996. Commission Directive 96/23/EC. Off. J. Eur. Comm., http://ec.europa.eu/food/food/chemicalsafety/residues/council_directive_96_23ec.pdf [26] J. Al-Rifai, N. Akeel, Determination of pesticide residues in imported and locally produced honey in Jordan, J. Apicult. Res., 36 (1997) 155-161. [27] M.R. Driss, M. Zafzouf, S. Sabbah, M.L. Bouguerra, Simplified procedure for organochlorine pesticides residue analysis in honey, Int. J. Environ. Anal. Chem. , 57 (1994) 63-71. [28] U. Menkissoglu-Spiroudi, G.C. Diamantidis, V.E. Georgiou, A.T. Thrasyvoulou, Determination of malathion, coumaphos, and fluvalinate residues in honey by gas chromatography with nitrogen-phosphorus or electron capture detectors, J. AOAC Int., 83 (2000) 178-182. [29] Z. Barganska, M. slebioda, J. Namiesnik, Determination of pesticide residues in honeybees using modified QUEChERS sample work-Up and liquid chromatography-tandem mass spectrometry, Molecules, 19 (2014) 2911-2924. [30] M. Anastassiades, S.J. Lehotay, D. Stajnbaher, F.J. Schenck, Fast and easy multiresidue method employing acetonitrile extraction/partitioning and dispersive solid-phase extraction for the determination of pesticide residues in produce, J. AOAC Int., 86 (2003) 412-431. [31] S. Walorczyk, B. Gnusowski, Development and validation of a multi-residue method for the determination of pesticides in honeybees using acetonitrile-based extraction and gas chromatography–tandem quadrupole mass spectrometry, J. Chromatogr. A, 1216 (2009) 6522-6531. [32] D. Paradis, G. Bérail, J.-M. Bonmatin, L.P. Belzunces, Sensitive analytical methods for 22 relevant insecticides of 3 chemical families in honey by GC-MS/MS and LC-MS/MS, Anal. Bioanal. Chem., 406 (2014) 621-633. [33] L. Wiest, A. Buleté, B. Giroud, C. Fratta, S. Amic, O. Lambert, H. Pouliquen, C. Arnaudguilhem, Multi-residue analysis of 80 environmental contaminants in honeys, honeybees and pollens by one extraction procedure followed by liquid and gas chromatography coupled with mass spectrometric detection, J. Chromatogr. A, 1218 (2011) 5743-5756. [34] European Commission. 2002. Commission Decision 2002/657/EC of August 12, 2002: Implementing Council Directive 96/23/EC (2002) Concerning the performance of analytical methods and the interpretation of results, Off. J. Eur. Comm. L221: 8–36, http://ec.europa.eu/food/food/chemicalsafety/residues/lab_analysis_en.htm [35] Eurachem guide 2014: The fitness for purpose of analytical methods-a laboratory guide to method validation and related topics, https://www.eurachem.org/images/stories/Guides/pdf/MV_guide_2nd_ed_EN.pdf [36] SANCO/12571/2013, Guidance document on analytical quality control and validation 385 procedures for pesticide residues analysis in food and feed, http://ec.europa.eu/food/plant/pesticides/guidance_documents/docs/qualcontrol_en.pdf
Ac ce p
624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669
21 Page 21 of 42
te
d
M
an
us
cr
ip t
[37] Eurachem/CITAC guide: Quantifying uncertainty in analytical measurement, 2012, (https://www.eurachem.org/images/stories/Guides/pdf/QUAM2012_P1.pdf) [38] Y. Al Naggar, G. Codling, A. Vogt, E. Naiem, M. Mona, A. Seif, J.P. Giesy, Organophosphorus insecticides in honey, pollen and bees (Apis mellifera L.) and their potential hazard to bee colonies in Egypt, Ecotoxicol. Environ. Saf., 114 (2015) 1-8. [39] M.I. Badawy, Use and impact of pesticides in Egypt, Int. J. Environ. Health Res., 8 (1998) 223-239. [40] S.A. Mansour, Pesticide exposure—Egyptian scene, Toxicology, 198 (2004) 91-115. [41] A.H. Shendy, M.A. Al-Ghobashy, S.A.G. Alla, H.M. Lotfy, Development and validation of a modified QuEChERS protocol coupled to LC-MS/MS for simultaneous determination of multi-class antibiotic residues in honey, Food Chem., 190 (2016) 982-989. [42] J. Wang, D. Leung, The challenges of developing a generic extraction procedure to analyze multi-class veterinary drug residues in milk and honey using ultra-high pressure liquid chromatography quadrupole time-of-flight mass spectrometry, Drug Test. Anal.,, 4 (2012) 103-111. [43] M.M. Aguilera-Luiz, J.L.M. Vidal, R. Romero-Gonzalez, A.G. Frenich, Multi-residue determination of veterinary drugs in milk by ultra-high-pressure liquid chromatographytandem mass spectrometry, J. Chromatogr., A, 1205 (2008) 10-16. [44] Z. Barganska, J. Namiesnik, M. Slebioda, Determination of antibiotic residues in honey, Trends Anal. Chem., 30 (2011) 1035-1041. [45] F.J. Camino-Sanchez, A. Zafra-Gomez, J. Ruiz-Garcia, R. Bermudez-Peinado, O. Ballesteros, A. Navalon, J.L. Vilchez, UNE-EN ISO/IEC 17025: 2005 accredited method for the determination of 121 pesticide residues in fruits and vegetables by gas chromatography-tandem mass spectrometry, J. Food Comp. Anal., 24 (2011) 427-440. [46] M. LeDoux, Analytical methods applied to the determination of pesticide residues in foods of animal origin. A review of the past two decades, J. Chromatogr., A, 1218 (2011) 10211036. [47] L. Zhang, S. Liu, X. Cui, C. Pan, A. Zhang, F. Chen, A review of sample preparation methods for the pesticide residue analysis in foods, Cent. Eur. J. Chem., 10 (2012) 900-925. [48] D. Lu, X. Qiu, C. Feng, Y. Lin, L. Xiong, Y. Wen, D. Wang, G. Wang, Simultaneous determination of 45 pesticides in fruit and vegetable using an improved QuEChERS method and on-line gel permeation chromatography-gas chromatography/mass spectrometer, J. Chromatogr., B, 895 (2012) 17-24. [49] X. Hou, M. Han, X. Dai, X. Yang, S. Yi, A multi-residue method for the determination of 124 pesticides in rice by modified QuEChERS extraction and gas chromatography-tandem mass spectrometry, Food Chem., 138 (2013) 1198-1205. [50] J.L. Vidal, F.J. Liebanas, M.J. Rodiguez, A.G. Frenich, J.L. Moreno, Validation of a gas chromatography/triple quadrupole mass spectrometry based method for the quantification of pesticides in food commodities, Rapid Commun. Mass Spectrom., 20 (2006) 365-375. [51] S.J. Lehotay, J.M. Cook, Sampling and sample processing in pesticide residue analysis, J. Agric. Food Chem., 63 (2015) 4395–4404. [52] T. Cajka, C. Sandy, V. Bachanova, L. Drabova, K. Kalachova, J. Pulkrabova, J. Hajslova, Streamlining sample preparation and gas chromatography-tandem mass spectrometry analysis of multiple pesticide residues in tea, Anal. Chim. Acta, 743 (2012) 51-60. [53] J. Hajslova, J. Zrostlikova, Matrix effects in (ultra) trace analysis of pesticide residues in food and biotic matrices, J. Chromatogr., A, 1000 (2003) 181-197.
Ac ce p
670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715
22 Page 22 of 42
us
cr
ip t
[54] B.K. Matuszewski, M.L. Constanzer, C.M. Chavez-Eng, Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS, Anal. Chem., 75 (2003) 3019-3030. [55] F. Gosetti, E. Mazzucco, D. Zampieri, M.C. Gennaro, Signal suppression/enhancement in high-performance liquid chromatography tandem mass spectrometry, J. Chromatogr. A, 1217 (2010) 3929-3937. [56] J. Nie, S. Miao, S.J. Lehotay, W.-T. Li, H. Zhou, X.-H. Mao, J.-W. Lu, L. Lan, S. Ji, Multiresidue analysis of pesticides in traditional Chinese medicines using gas chromatographynegative chemical ionization tandem mass spectrometry, Food Addit. Contam.: Part A, 32 (2015) 1287-1300. [57] U. Koesukwiwat, S.J. Lehotay, N. Leepipatpiboon, Fast, low-pressure gas chromatography triple quadrupole tandem mass spectrometry for analysis of 150 pesticide residues in fruits and vegetables, J. Chromatogr., A, 1218 (2011) 7039-7050. [58] D.N. Heller, S.J. Lehotay, P.A. Martos, W. Hammack, A.R. Fernndez-Alba, Issues in mass spectrometry between bench chemists and regulatory laboratory managers: Summary of the roundtable on mass spectrometry held at the 123rd AOAC International Annual Meeting, J. AOAC Int., 93 (2010) 1625-1632.
an
716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733
M
734 735 736
d
737
te
738
740 741 742 743
Ac ce p
739
Figure captions
744
Figure 1: A bar chart representing the slope difference percent for the studied pesticides in honey
745
samples between matrix matched and neat standard calibration curves representing matrix-
746
induced suppression/enhancement in GC-MS/MS response. Compound numbers are as indicated
747
in Table 2. Raw data and an equivalent plot but versus compound name are provided in
748
supplementary materials.
749 750
Figure 2: Typical total ion chromatogram for the studied pesticides in fortified honey sample at
751
50.00 µg Kg-1 in comparison to the corresponding blank sample. 23 Page 23 of 42
752
Figure 3: Results of the validation study carried out over three days, at three different
753
concentration levels (10.00, 50.00 and 100.00 µg Kg-1, 6 replicate each), showing the mean
754
percentage recovery (N = 18) of the studied pesticide residues in honey samples. Compound
755
numbers are as indicated in Table 2. Raw data is provided in supplementary materials.
ip t
756 757
cr
758 759
Acrinathrin
RT (min) 19.48
Alachlor
15.18
d 15.62
te
Aldrin (ISTD)
15.66
Ac ce p
Ametryn
Precursor ion (m/z) 289a 208 181.1 188.1a 160.05 293 263a 262.9 227.15 227.15a 227.15 211a 211 200.1 200.1 200.1a 160.05 160.05a 160.05a 160.05 204a 148 166a 151 181.1a 181 170a 170 342
M
Compound(s)
an
us
Table 1: MS/MS transitions and optimal operational conditions used for pesticide residue analysis
Atraton
13.16
Atrazine
13.59
Azinphos-ethyl
21.03
Azinphos-methyl
20.78
Benalaxyl
18.85
Bendiocarb
5.65
Bifenthrin
19.00
Bitertanol
20.74
Boscalid
22.38
Product ion (m/z) 93a 181 152.1 160.1a 130 186 193a 190.9 170.1 152.1a 170.1 196a 169 122.1 103.9 94.1a 102 77.1a 77.1a 132.1 176a 105 151a 81.2 166.1a 165 141a 115 140
D Well (ms) 20 20 20 10 10 20 20 20 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
CE (V) 5 5 25 10 30 30 25 40 30 20 30 15 15 10 20 20 15 15 20 5 5 17 10 10 15 25 20 35 10
24 Page 24 of 42
16.51
Bromopropylate
19.42
Bromuconazole I
19.86
Bromuconazole II
20.26
Bupirimate
17.87
Buprofezin
17.70
Butachlor
16.94
Butralin
15.94
Cadusafos
11.60
d
Ac ce p
Chlordane trans- (gamma)
te
Chlordane cis- (alpha)
17.29
17.13
Chlorfenapyr
17.92
Chlorfenvinphos
16.57
Chlorobenzilate
18.10
Chlorpropham 11.34 Chlorpyrifos
16.03
Chlorpyrifos-methyl
15.32
Chlorthal-dimethyl
16.00
CE (V) 10 15 5 20 35 20 20 15 15 15 15 15 5 5 15 15 10 5 5 25 10 10 10 8 5 20 30 25 30 25 10 10 10 20 40 20 12 15 30 5 5 20 14 15 40 20 20 25
ip t
Bromophos-methyl
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 30 30 30 10 10 10 10 10 10
cr
16.97
Product ion (m/z) 112a 302.9a 331 315.9a 285.9 185 183 155a 173 145a 173a 145 208.2 193a 108 57a 104 188.3 160.3a 105 220.2a 190.2 174.1 131 130.9 97a 265.9 263.9a 266.1a 264.1 59 31a 29 159a 81 159 139a 111 75 171 127 65a 258a 168.9 107 270.9 93a 222.9
us
Bromophos-ethyl
Precursor ion (m/z) 140a 358.9a 358.7 330.9a 330.9 341 341 183a 294.8 172.9a 294.8a 172.9 316.1 273a 273 172a 105 236.8 236.8a 175.9 265.9a 265.9 265.9 159 158.7 158.7a 372.9 372.9a 372.7a 372.7 408 59a 59 267a 267 267 251a 139 139 213 171 127a 313.8a 196.9 196.9 286 286a 300.9
an
RT (min)
M
Compound(s)
25 Page 25 of 42
16.62
Clodinafop-propargyl ester
18.92
Cyanophos
14.44
Cyfluthrin
21.10
Cyhalothrin lambda-
19.77
Cypermethrin
21.48
Cyproconazole
18.35
Cyprodinil
16.78
d
Ac ce p
DDD pp`-
17.95
te
DDD op`-
18.51
DDE pp`-
17.65
DDT op`-
18.35
DDT pp`-
18.89
Deltamethrin
23.43
Demeton-S- methyl
11.63
Diazinon
13.41
CE (V) 40 25 15 15 15 15 10 15 20 5 10 30 30 5 15 10 30 35 25 35 5 18 8 10 20 20 15 30 25 20 25 30 40 40 20 20 30 20 20 30 10 20 25 10 7 15 5 20
ip t
Chlozolinate
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 20 20 20 20 20 20 20 20 20 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 50 50 50 10 10 10 10 10
cr
18.40
Product ion (m/z) 166.9 221a 269.2a 205.1 147.1 145a 266.1a 238.1 222.1 116 109a 79 77.1 127.1a 91.1 161 152.1a 127.1 152.1 127.1 127a 125a 82 224a 208 165 199.1 165.1a 165 199.1 165.1a 176 176.1a 175.1 165 199.1 165.1a 165 199.1 165.1a 172a 93 152 79a 60 179a 179.2 137.2
us
Chlorthiophos
Precursor ion (m/z) 300.9 299a 324.9a 268.7 188.1 186a 349a 349 266 243 243a 243 226.9 163a 163 197 181.1a 181.1 181.1 181.1 162.9a 222a 222 225a 224 237 235 235a 237 235 235a 248 246a 246 237 235 235a 237 235 235a 253a 253 181 142a 88 304a 179.1 179.1
an
RT (min)
M
Compound(s)
26 Page 26 of 42
16.17
Dichlorvos
5.58
Diclofop methyl
19.09
Dicofol
16.55
Dieldrin
17.83
Difenoconazole
23.61
Dimethachlor
15.12
Dimethoate
14.21
d
Ac ce p
Diphenylamine
18.33
te
Diniconazole
11.52
Disulfoton-sulfoxide Endosulfan alpha-
17.37
Endosulfan beta-
18.70
Endosulfan-sulfate
19.30
Endrin
18.29
EPN
19.79
Epoxiconazole I
19.12
CE (V) 15 25 10 25 20 30 8 15 5 15 15 12 18 30 30 30 16 15 16 20 15 30 10 10 20 15 10 15 15 10 30 35 15 15 5 15 10 25 5 20 40 20 35 35 15 25 10 35
ip t
Dichlofluanid
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
14.68
Product ion (m/z) 136 100a 223a 205 77a 51.1 123 93 79a 253 162a 139 111a 75 193a 191 267 265a 204 202 105.1a 77.1 87 111 47a 234 232 232a 168a 167 78.9 63.1 205.9a 204 159 204 159a 125 253 236.9a 116.9 245 193a 190.9 110 77.1a 138.1a 111.1
us
Dichlofenthion
Precursor ion (m/z) 171 171a 279a 279 123a 123 224 185 109a 340 253a 250 139a 139 262.8a 262.8 325 323a 267 265 134.1a 134.1 229 143 125a 270 270 268a 169a 169 125 125 240.9a 238.8 195 238.8 195a 195 387 271.9a 271.9 281 262.9a 262.9 157 157a 191.9a 191.9
an
Dichlobenil
RT (min) 7.32
M
Compound(s)
27 Page 27 of 42
16.06
Ethoprophos
11.16
Ethoxyquin
12.74
Etofenprox
21.68
Etoxazole
19.39
Etridiazole
8.18
Fenarimol
20.69
Fenazaquin
19.72
Fenbuconazole
21.84
d
Ac ce p
Fenoxaprop-P-ethyl
16.10
te
Fenitrothion
20.59
Fenoxycarb
19.73
Fenpropathrin
19.44
Fenpropidin
14.35
Fenpropimorph
15.01
Fenvalerate
22.37
Fluazifop-p-butyl
17.61
Flucythrinate
21.41
Fluquinconazole
21.30
Flusilazole
17.94
CE (V) 10 35 5 25 5 15 15 15 15 30 30 10 10 15 20 8 5 15 10 15 35 5 20 5 10 20 5 9 9 10 15 15 35 22 5 10 10 15 15 35 10 15 15 10 30 15 20 20
ip t
Ethofumesate
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
18.36
Product ion (m/z) 138.1a 111.1 175 129a 207.3a 161.1 97.1a 80.9 174.3a 158.9 145.4 163 135a 107 270 176a 182.9a 139.9 107a 111 75 145.2a 117 129a 102 109a 260 288.1a 119.1 186a 109 210a 89 116 70a 55 110 70a 119 89.1 125a 238 91a 157.1a 107.1 313.2 298.2a 165a
us
Ethion
Precursor ion (m/z) 191.9a 191.9 231 231a 285.9a 285.9 158a 158 201.8a 201.8 201.8 376 163a 163 300 204a 210.9a 182.9 219a 139 139 160a 160 198a 129 277.1a 276.8 360.8a 287.8 255a 186 265a 265 209 98a 97 128 128a 225 167.1 167a 282 282a 199.1a 199.1 339.9 339.9a 233a
an
Epoxiconazole II
RT (min) 19.58
M
Compound(s)
28 Page 28 of 42
Fluvalinate tau-
21.96
Formothion I 15.6301
HCH beta-
14.82
HCH delta
15.42
HCH gamma- (Lindane)
14.01
Heptachlor
14.94
Heptachlor-endo-Epoxide (trans-)
16.86
d 16.72
Ac ce p
te
Heptachlor-exo-Epoxide (cis-) Heptenophos
an
12.75
M
HCH alpha-
10.55
Hexachlorobenzene (HCB)
12.36
Hexaconazole
17.59
Hexazinone
19.67
Imazalil
17.76
Iprobenfos
14.47
Iprodione
19.43
Isofenphos
16.65
Isofenphos-methyl
16.53
Product ion (m/z) 152 200.1 55.2 141.1a 77 79 47a 79a 47 183 145a 109 183 145a 109 109 145a 183a 145 109 239 236.8a 116.9 155 118.9a 281.9 262.8a 89.1a 63.2 248.8 213.9a 159 159 172a 85.1 71.1a 175 173a 109 122 91.1a 245 56 159 124a 185 121a 121a
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 20 20 20 10 10 20 20 25 25 10 10 10
us
Formothion II
Precursor ion (m/z) 233 250.1 250 208.9a 208.9 125.1 125.1a 125.1a 125.1 219 181a 181 219 181a 181 181 180.9a 219a 181 181 274 271.9a 271.9 183 183a 352.9 352.9a 124a 124 283.9 283.9a 256 214 213.9a 171.1 171.1a 217 214.9a 173 204 204a 314 314 187 187a 213.1 213.1a 199a
CE (V) 20 20 20 15 25 5 15 5 15 10 15 25 10 15 30 30 12 10 15 30 20 25 25 25 25 20 25 15 35 25 35 16 16 20 15 15 4 4 25 10 10 10 15 15 25 5 20 15
ip t
RT (min)
cr
Compound(s)
29 Page 29 of 42
18.06
Malathion
16.01
Mefenpyr-diethyl
19.29
Mepronil
18.70
Metalaxyl
15.65
Metazachlor
17.06
Methacrifos
8.90
Methidathion
17.73
Methiocarb
9.90
Metribuzin
15.75
d
Ac ce p
Mirex
8.39
te
Mevinphos
20.27
Myclobutanil
18.15
Napropamide
17.72
Nuarimol
19.32
Oxadiaxyl
19.10
Oxadiazon
17.40
Oxyfluorfen
17.63
Paraoxon-ethyl
15.83
Parathion-ethyl
16.31
CE (V) 15 2 10 10 5 5 15 20 25 25 15 15 20 5 20 25 8 4 5 15 10 10 15 20 5 10 15 15 25 40 6 14 5 5 5 15 5 15 13 30 15 15 5 20 20 10 10 10
ip t
Kresoxim-methyl
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
18.18
Product ion (m/z) 65 204 118a 131 116a 117.1 99a 190a 188.2 163.2 91a 65 162 132a 132.1a 117 180 180a 85a 58.1 153a 109 89 82.1a 110 109a 95 237a 235 116.9 152 125a 128 72a 139 139a 132a 117 175 76.1 112a 300 252 196a 102.1 81a 109a 81
us
Isoprothiolane
Precursor ion (m/z) 199 290 290a 206 206a 173.1 173.1a 252.8a 252.8 252.8 119a 119 206 206a 209a 133 240 208a 145a 145 168a 153 198.05 198.05a 198 127a 127 271.9a 271.9 271.9 179 179a 271 128a 314 235a 163a 163 301.9 175 174.9a 361 252.05 252.05a 149 108.9a 291a 291
an
RT (min)
M
Compound(s)
30 Page 30 of 42
15.68
PCB 101
17.16
PCB 118
18.19
PCB 138
18.88
PCB 153
18.37
PCB 180
19.64
Pendimethalin
d
Ac ce p
Pentachlorobenzene
16.86
te
Penconazole
16.65 9.18
Permethrin
20.59
Phenthoate
17.14
Ortho-Phenylphenol (2-phenylphenol) (OPP)
9.55
Phorate
12.16
Phosalone
20.27
Phosmet
20.19
Piperonyl-butoxide
18.84
CE (V) 15 30 15 27 12 27 23 23 25 12 27 27 25 27 27 25 15 27 25 12 27 22 12 27 15 25 10 20 15 25 40 15 25 30 20 10 30 30 35 5 5 5 15 30 20 15 30 15
ip t
PCB 052
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
15.02
Product ion (m/z) 109.1a 79.1 186a 150 255 220 220 220a 255.9 291 256a 254 255.9 256a 254 289.9 325 290a 289.9 325 290a 323.9 359 324a 192a 157 162.1a 161.2 213 214.9a 142 155.1 115.2 77.1a 125 121a 169.1 141.2 115a 75.2 47a 138 111 75.1a 133 105 77a 145.2
us
PCB 028
Precursor ion (m/z) 263a 263 258a 258 292 292 291.9 289.9a 327.9 325.9 325.9a 325.9 327.9 325.9a 325.9 361.8 359.8 359.8a 361.8 359.8 359.8a 395.8 393.7 393.7a 248a 248 252.1a 252.1 250 249.9a 249.9 183.1 183 183a 274 274a 170 170 169a 260 75a 182 182 182a 160 160 160a 175.9
an
Parathion-methyl
RT (min) 15.69
M
Compound(s)
31 Page 31 of 42
16.19
Pirimiphos-methyl
15.62
Procymidone
17.06
Profenofos
17.74
Profluralin
12.04
Promecarb I
7.063
Promecarb II
13.091
Prometon
13.08
Propachlor
d
Ac ce p
Propargite
15.53
te
Prometryn
11.08
18.90
Propazine
13.40
Propiconazol
18.94
Prosulfocarb
15.47
Prothiofos
17.46
Pyrazofos
20.38
Pyridaben
20.89
Pyridaphenthion
19.78
CE (V) 20 25 10 15 5 35 10 5 25 10 10 40 10 10 30 35 20 20 8 10 8 10 5 10 10 10 5 20 15 25 5 20 25 10 12 5 20 15 5 30 10 15 10 10 20 40 5 15
ip t
Pirimiphos-ethyl
D Well (ms) 10 10 10 10 10 10 10 10 10 15 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
15.00
Product ion (m/z) 117.1 103.1a 166a 96 318.2 109.1a 290 180 125a 255 96.1a 67.1 255 267 188.1 63.1a 199.2a 54.9 135a 115 135 115a 168a 112.1 183.9a 111.2 92.1 77.1a 107.1a 77.1 172a 104 94.5 173a 69 43.1a 41.1 239 239a 63.1 204 149.1 193a 132 117a 97 199.2a 108.2
us
Pirimicarb
Precursor ion (m/z) 175.9 175.9a 238a 166 333.1 318a 305 305 290.1a 283 283a 283 283 337 337 208a 318a 318 150a 135 150 135a 210a 210 241.2a 241.2 120.1 120.1a 135.1a 135.1 214.1a 214.1 214.1 259a 259 128a 128 309 267a 162 232 221.1 221a 147 147a 340.1 340a 340
an
RT (min)
M
Compound(s)
32 Page 32 of 42
Pyrimethanil
14.20
Pyriproxyfen
20.13
Quinalphos
17.11
Quintozene
13.72
Simazine
13.74
Spiroxamine I
14.21
M
Spiroxamine II
11.86
Tebuconazole
19.15
Ac ce p
Tecnazene
19.49
te
Tebufenpyrad
d
Sulfotep
10.90
Tefluthrin
12.60
Terbufos
13.16
Terbumeton
13.47
Terbuthylazine
13.76
Terbutryn
15.81
Tetraconazole
16.22
Tetradifon
20.20
Tetramethrin I 19.6222 Tetramethrin II
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
CE (V) 10 25 10 25 25 25 25 25 8 18 15 25 10 20 30 10 5 10 10 10 10 15 25 25 25 5 20 20 25 20 20 10 25 5 10 10 20 10 5 15 30 25 10 10 15 10 25 10
ip t
17.36
Product ion (m/z) 227 99.9a 136 99.9a 198a 156 118 118 96a 78 129 91.1 118a 237a 119 172.1a 91 72.2a 43 72.2a 43 174 146a 127 125a 276 171a 143 83a 137 127.1a 174.9 128.9a 154.2a 112.2 132 104a 170.2 170.1a 218a 204 164 159 159 201a 106.9 77.1a 106.9
cr
Pyrifenox II
Precursor ion (m/z) 261.8 171a 171 171a 199a 198 198 198 136a 136 157 146.1 146a 295a 237 201.05a 186 100a 100 100a 100 322.1 322.1a 252 250a 333 333a 202.9 202.9a 177.1 177.1a 231 231a 169a 169 214.1 214.1a 241.2 185a 336a 336 336 355.7 353.7 229a 164 164a 164
us
Pyrifenox I
RT (min) 17.36
an
Compound(s)
33 Page 33 of 42
12.92
Tolclofos-methyl
15.59
Tolylfluanid
16.99
Triadimefon
16.10
Triadimenol
16.93
Triazophos
19.10
Trifloxystrobin
18.64
Triflumizole
16.70
Vinclozolin
760 761 762 763
d
Ac ce p
Triticonazole
10.24
te
Trifluralin
20.21
14.99
CE (V) 25 20 20 20 20 6 15 25 10 15 30 5 15 10 15 20 5 5 10 8 15 20 6 10 30 25 10 5 15 15 20
ip t
Thiometon
D Well (ms) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
cr
16.14
Product ion (m/z) 77.1 100 72a 72 47 60a 250a 93 137 91a 65 181a 127 111 70 65a 162a 134 106 130 116.1 89.1a 73 179.2a 143.9 160 264a 217.2a 182.2 172a 145
us
Thiobencarb
Precursor ion (m/z) 164 257 257a 100 125 88a 265a 265 238 137a 137 208a 208 181 168 128a 257a 161 161 222 131 116a 278 206a 206 306.1 306a 235a 235 212a 212
an
RT (min)
M
Compound(s)
RT, retention time (min); CE, collision energy (volt); D well, D well time in ms a transitions for quantifier peaks; remaining transitions are the qualifier peaks
764
34 Page 34 of 42
ip t
765
us
cr
766
M
an
767
769
770
Ac ce p
te
d
768
771
35 Page 35 of 42
772 773 Table 2: Regression equation parameters and differences obtained for both neat standard and matrix matched
775
calibration curves
an
us
cr
ip t
774
Compound number
Acrinathrin Alachlor Ametryn Atraton Atrazine Azinphos-ethyl Azinphos-methyl Benalaxyl Bendiocarb Bifenthrin Bitertanol Boscalid Bromophos-ethyl Bromophos-methyl Bromopropylate Bromuconazole I Bromuconazole II Bupirimate Buprofezin Butachlor Butralin Cadusafos Chlordane cis- (alpha) Chlordane trans- (gamma) Chlorfenapyr Chlorfenvinphos Chlorfenvinphos
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Ac ce p
te
d
M
Compound(s)
Matrix matched calibration curves Slope Intercept R2
Neat standard calibration c Slope Intercept
4.84 10.43 0.53 0.75 2.45 7.05 2.60 11.12 2.50 37.28 12.70 4.52 3.91 5.80 10.72 4.03 3.95 4.38 16.37 1.60 1.78 54.17 1.89 1.89 9.61 4.06 4.06
6.24 10.40 0.57 0.84 2.19 10.25 5.93 12.24 2.07 39.29 16.92 5.21 4.38 6.28 11.81 5.28 5.25 4.60 17.05 1.79 2.06 54.94 1.96 1.96 11.02 5.06 5.06
-1.32 -1.43 -0.11 -0.23 -0.47 -2.10 -1.08 -1.07 -0.33 -3.40 -2.49 -1.00 -0.68 -1.21 -1.40 -0.76 -0.93 -0.59 -1.35 -0.28 -0.46 -7.78 -0.24 -0.24 -1.14 -0.89 -0.89
0.99 1.00 1.00 0.99 1.00 0.99 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-0.72 -0.56 -0.04 -0.10 -0.01 -1.18 -1.02 0.26 -0.33 0.86 -1.66 -0.78 -0.21 -0.39 0.12 -0.41 -0.65 -0.39 -0.21 -0.12 -0.49 -1.99 -0.15 -0.15 -0.01 -0.56 -0.56
36 Page 36 of 42
Compound number
Neat standard calibration c 22.80 0.41 10.73 -0.29 4.36 -0.31 5.57 -0.25 6.83 -0.19 5.36 -0.10 2.71 -0.14 3.15 -0.51 25.55 -1.25 15.97 -2.13 2.06 -0.19 11.22 -1.50 17.74 -0.67 21.20 -1.39 1.30 -0.49 35.13 1.04 61.84 -2.02 10.64 -0.10 61.84 -2.02 4.92 -1.10 18.37 -1.30 2.00 -0.22 22.21 -0.41 13.21 -0.66 18.88 -1.56 3.29 0.00 4.21 -0.14 14.40 -4.10 1.24 -0.13 4.65 -0.07 18.13 -0.63 11.11 -0.90 4.94 -0.03 28.19 -0.55 0.50 0.01 1.68 -0.10 0.54 -0.04 1.77 -0.10 1.25 -0.13 8.68 -1.02 18.07 -1.78 14.98 -0.50 4.50 -0.32 11.16 -0.49 3.59 -0.77
d
M
an
us
cr
ip t
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
te
Ac ce p
Chlorobenzilate Chlorpropham Chlorpyrifos Chlorpyrifos-methyl Chlorthal-dimethyl Chlorthiophos Chlozolinate Clodinafop-propargyl ester Cyanophos Cyfluthrin Cyhalothrin lambdaCypermethrin Cyproconazole Cyprodinil DDD op`DDD pp`DDE pp`DDT op`DDT pp`Deltamethrin Demeton-S- methyl Diazinon Dichlobenil Dichlofenthion Dichlofluanid Dichlorvos Diclofop methyl Dicofol Dieldrin Difenoconazole Dimethachlor Dimethoate Diniconazole Diphenylamine Disulfoton-sulfoxide EPN Endosulfan alphaEndosulfan betaEndosulfan-sulfate Endrin Epoxiconazole II Ethion Ethofumesate Ethoprophos Ethoxyquin
Matrix matched calibration curves 20.28 -2.47 1.00 10.13 -2.52 1.00 4.16 -0.66 1.00 5.10 -0.77 1.00 6.88 -0.76 1.00 4.62 -0.69 1.00 2.48 -0.36 1.00 2.30 -0.62 0.99 21.99 -4.80 1.00 13.87 -3.08 1.00 1.64 -0.37 1.00 9.53 -2.19 1.00 14.65 -2.45 1.00 18.36 -3.82 1.00 1.41 -0.29 1.00 30.78 0.08 1.00 63.73 -7.47 1.00 10.05 -0.73 1.00 63.73 -7.47 1.00 3.09 -0.92 0.99 15.64 -4.83 0.99 1.97 -0.32 1.00 19.56 -5.34 0.99 12.49 -1.68 1.00 17.77 -3.89 1.00 2.26 -0.64 0.99 3.91 -0.42 1.00 20.68 -5.67 1.00 1.22 -0.22 1.00 3.92 -0.08 1.00 17.30 -2.33 1.00 6.87 -1.49 0.99 3.56 -0.71 1.00 27.31 -7.58 0.99 0.37 -0.04 1.00 1.64 -0.16 1.00 0.50 -0.07 1.00 1.56 -0.27 1.00 1.22 -0.21 1.00 6.46 -1.58 1.00 15.62 -3.18 1.00 6.62 -0.17 1.00 3.99 -0.53 1.00 9.83 -1.60 1.00 4.34 -0.52 1.00
37 Page 37 of 42
Compound number
Neat standard calibration c 49.41 -2.25 3.83 -0.19 2.18 -0.74 11.31 -0.68 28.48 0.05 31.80 -5.46 3.25 -0.66 3.63 -0.30 2.90 -0.16 1.86 -0.09 14.78 -0.99 27.12 -0.84 7.30 -1.08 7.23 -0.07 5.84 -0.84 4.92 -0.52 3.81 -0.23 0.92 0.02 7.33 0.11 2.73 -0.26 7.29 -0.25 11.44 -0.47 11.44 -0.47 6.02 -0.33 8.30 -0.77 1.11 -0.08 1.11 -0.08 14.49 0.02 8.49 -0.02 1.00 -0.09 33.58 -2.17 2.87 -0.49 39.32 -3.72 2.89 -0.13 13.72 -1.03 18.94 -1.16 3.78 -0.17 4.26 -0.24 7.37 -0.19 5.05 -0.09 87.79 4.20 0.65 -0.05 8.86 -0.69 7.48 0.04 25.67 -1.67
d
M
an
us
cr
ip t
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
te
Ac ce p
Etofenprox Etoxazole Etridiazole Fenarimol Fenazaquin Fenbuconazole Fenitrothion Fenoxaprop-P-ethyl Fenoxycarb Fenpropathrin Fenpropidin Fenpropimorph Fenvalerate Fluazifop-p-butyl Flucythrinate Fluquinconazole Flusilazole Fluvalinate tauFormothion I Formothion II HCH alphaHCH betaHCH deltaHCH gamma- (Lindane) Heptachlor Heptachlor-endo-Epoxide (trans-) Heptachlor-exo-Epoxide (cis-) Heptenophos Hexachlorobenzene (HCB) Hexaconazole Hexazinone Imazalil Iprobenfos Iprodione Isofenphos Isofenphos-methyl Isoprothiolane Kresoxim-methyl Malathion Mefenpyr-diethyl Mepronil Metalaxyl Metazachlor Methacrifos Methidathion
Matrix matched calibration curves 47.78 -5.62 1.00 3.50 -0.49 1.00 2.79 -0.90 0.99 9.67 -1.65 1.00 26.39 -3.29 1.00 32.41 -8.23 1.00 2.52 -0.73 0.99 3.10 -0.97 0.99 2.43 -0.31 1.00 1.70 -0.33 1.00 14.24 -1.67 1.00 26.62 -2.72 1.00 5.97 -1.40 1.00 6.33 -0.89 1.00 4.94 -1.24 0.99 4.40 -0.89 1.00 3.30 -0.48 1.00 0.97 -0.02 1.00 6.87 -0.83 1.00 2.03 -0.53 0.99 7.39 -1.01 1.00 12.18 -2.11 1.00 12.18 -2.11 1.00 5.65 -0.99 1.00 9.64 -1.81 1.00 1.17 -0.17 1.00 1.17 -0.17 1.00 11.76 -1.55 1.00 8.77 -1.02 1.00 0.77 -0.30 0.97 28.01 -5.19 1.00 3.55 -0.53 1.00 34.20 -7.01 1.00 2.13 -0.42 1.00 12.69 -2.14 1.00 17.95 -2.64 1.00 3.46 -0.48 1.00 3.90 -0.65 1.00 4.75 -0.33 1.00 4.35 -0.55 1.00 68.62 -11.95 1.00 0.61 -0.13 1.00 8.00 -1.27 1.00 7.18 -1.57 1.00 18.29 -4.35 1.00
38 Page 38 of 42
Compound number
Neat standard calibration c 3.51 -0.39 5.02 -1.01 18.14 0.44 10.30 -0.66 18.14 -0.76 14.06 -0.53 5.10 -0.13 8.29 -0.21 16.58 -0.09 8.30 -1.44 5.02 -0.71 3.11 -0.71 4.39 -0.81 9.30 -0.11 20.68 0.84 7.50 -0.17 9.95 -0.10 6.72 0.02 6.72 0.02 6.01 -0.12 6.21 -0.48 4.10 -0.95 8.96 -0.22 3.99 -0.18 4.03 -0.19 6.35 -0.65 21.19 0.04
14.79 9.07 18.43 17.05 8.53 1.61 5.08 4.39 2.68 2.52 19.87 19.87 10.62 21.74 22.55 5.57 3.92 3.11
15.16 13.59 27.86 20.33 9.15 1.69 5.26 5.09 3.17 2.62 23.69 23.69 11.51 23.69 21.84 5.82 4.45 3.98
d
M
an
us
cr
ip t
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
te
Ac ce p
Methiocarb Metribuzin Mevinphos Mirex Myclobutanil Napropamide Nuarimol Oxadiaxyl Oxadiazon Oxyfluorfen PCB 028 PCB 052 PCB 101 PCB 118 PCB 138 PCB 153 PCB 180 Paraoxon-ethyl Parathion-ethyl Parathion-methyl Penconazole Pendimethalin Pentachlorobenzene Permethrin Permethrin Phenthoate Phenylphenol ortho- (2phenylphenol) (OPP) Phorate Phosalone Phosmet Piperonyl-butoxide Pirimicarb Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenofos Profluralin Promecarb I Promecarb II Prometon Propachlor Propargite Propazine Propiconazol Propiconazole II
Matrix matched calibration curves 4.51 -0.75 1.00 5.14 -1.23 1.00 13.53 -2.75 1.00 9.17 -0.39 1.00 15.87 -1.92 1.00 11.90 -1.92 1.00 4.69 -0.54 1.00 6.94 -0.99 1.00 16.44 -1.21 1.00 6.22 -1.53 1.00 2.82 -0.99 0.99 2.44 -0.71 0.99 3.25 -0.95 0.99 8.37 -1.47 1.00 20.31 -1.85 1.00 6.96 -0.69 1.00 9.12 -0.87 1.00 6.43 -0.58 1.00 6.43 -0.58 1.00 5.65 -0.45 1.00 5.36 -0.96 1.00 3.54 -1.01 0.99 9.11 -0.92 1.00 3.62 -0.57 1.00 1.70 0.42 0.99 5.72 -1.23 1.00 17.88 -3.29 1.00
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
-2.15 -2.45 -6.08 -1.17 -1.30 -0.31 -0.83 -0.70 -0.52 -0.59 -3.58 -3.58 -1.67 -2.34 0.05 -0.84 -0.57 -0.31
1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-0.68 -1.06 -2.48 0.37 -0.52 -0.20 -0.41 -0.08 -0.19 -0.49 -0.12 -0.12 -0.76 -0.04 3.52 -0.47 -0.25 -0.12
39 Page 39 of 42
Compound number
776
Neat standard calibration c 7.27 -0.14 14.03 -0.98 45.74 -2.76 3.10 -0.11 1.19 -0.06 1.91 -0.09 15.71 -0.21 16.62 -0.25 22.14 -2.14 2.08 -0.27 1.54 -0.10 11.11 -0.11 11.11 -0.11 4.48 -0.21 5.13 -0.20 8.29 -0.07 1.56 -0.11 22.68 -0.50 13.99 -1.73 12.52 -0.61 10.94 -0.54 14.07 -0.84 2.83 -0.24 3.15 -0.14 18.70 0.79 18.70 0.79 0.95 -0.07 25.20 -1.03 14.85 -0.78 11.82 -1.36 9.16 -0.51 5.95 -0.36 2.36 -0.15 6.68 -0.22 15.33 -0.22 2.21 -0.16 8.44 -1.20 1.96 -0.26 3.86 -0.25
d
M
an
us
cr
ip t
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201
te
Ac ce p
Prothiofos Pyrazofos Pyridaben Pyridaphenthion Pyrifenox I Pyrifenox II Pyrimethanil Pyriproxyfen Quinalphos Quintozene Simazine Spiroxamine Spiroxamine II Sulfotep Tebuconazole Tebufenpyrad Tecnazene Tefluthrin Terbufos Terbumeton Terbuthylazine Terbutryn Tetraconazole Tetradifon Tetramethrin Tetramethrin II Thiobencarb Thiometon Tolclofos-methyl Tolylfluanid Triadimefon Triadimenol Triadimenol Triazophos Trifloxystrobin Triflumizole Trifluralin Triticonazole Vinclozolin
Matrix matched calibration curves 6.97 -0.84 1.00 10.95 -2.75 1.00 38.62 -7.31 1.00 2.33 -0.68 0.99 1.15 -0.19 1.00 1.74 -0.23 1.00 10.86 -0.32 1.00 14.71 -1.94 1.00 18.69 -3.39 1.00 2.11 -0.46 1.00 1.32 -0.26 1.00 12.32 -1.25 1.00 12.32 -1.25 1.00 4.51 -0.57 1.00 4.36 -0.66 1.00 7.54 -0.98 1.00 1.40 -0.18 1.00 22.97 -2.39 1.00 12.48 -2.46 1.00 12.41 -1.95 1.00 10.54 -1.57 1.00 13.19 -1.92 1.00 2.52 -0.49 1.00 2.78 -0.35 1.00 16.34 -1.99 1.00 16.34 -1.99 1.00 0.82 -0.14 1.00 25.79 -3.70 1.00 14.41 -2.12 1.00 10.99 -2.65 1.00 8.67 -1.34 1.00 4.33 -0.75 1.00 1.69 -0.29 1.00 4.93 -1.14 1.00 14.74 -1.61 1.00 2.22 -0.35 1.00 8.02 -1.69 1.00 1.39 -0.30 1.00 3.72 -0.61 1.00
Linear regression equation, y= ax + b; a, slope; b, intercept; y, relative response; x, relative concentration (ng mL-1)
40 Page 40 of 42
ip t
777
us
cr
778
M
an
779
781 782 783
Ac ce p
te
d
780
Highlights
784
•
Modified QuEChERS protocol coupled to GC-MS/MS was developed and validated
785
•
200 pesticide residues (> 50 families) were analyzed in honey in a less than 31 min
786
•
Streamlined quantification approach using neat standard calibration was utilized
787
•
Neat standard calibration in conjunction with correction for matrix effect was employed
788
•
Assay protocol is applicable for regulatory monitoring of pesticide residues in honey 41 Page 41 of 42
789 790
us
cr
ip t
791
Ac ce p
te
d
M
an
792
42 Page 42 of 42