- Email: [email protected]
Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC−DAD
Accepted Manuscript Short communication Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC−DAD Eiki Watanabe, Yuso Kobara, Koji Baba, Heesoo Eun PII: DOI: Reference:
S0308-8146(13)01955-9 http://dx.doi.org/10.1016/j.foodchem.2013.12.075 FOCH 15192
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
18 September 2013 20 December 2013 21 December 2013
Please cite this article as: Watanabe, E., Kobara, Y., Baba, K., Eun, H., Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC−DAD, Food Chemistry (2014), doi: http://dx.doi.org/10.1016/ j.foodchem.2013.12.075
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
Aqueous acetonitrile extraction for pesticide residue analysis
2
in agricultural products with HPLC−DAD
3 4
Eiki Watanabe *, Yuso Kobara, Koji Baba, and Heesoo Eun
5 6
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki 305-8604, Japan
7 8
*Corresponding author. Tel/fax: +81 29 838 8306, e-mail: [email protected]
Page 1 of 23
9
Abstract
10
To reduce hazardous organic solvent consumption during sample preparation
11
procedures as much as possible, an extraction method of smallest feasible sample
12
volume (5 g) using aqueous acetonitrile (MeCN) was developed to extract pesticide
13
residues from agricultural samples prior to HPLC−DAD determination. Extraction with
14
MeCN/water (1:1, v/v), and adjustment of the MeCN concentration by diluting with
15
water after extraction recovered successfully most pesticides showing various
16
physicochemical properties. The matrix effects of tested samples on the proposed
17
method developed herein were generally negligibly-small. The average recoveries were
18
in the range 70−120% for all pesticides with the coefficient of variation values below
19
20%. The reduction rate of organic solvents used for the proposed sample preparation
20
method was up to approximately 60% compared with the Japanese authorized official
21
method for pesticide residue analyses. These results demonstrate the feasibility of the
22
proposed method for pesticides with diverse properties.
23 24
Keywords: aqueous acetonitrile extraction; pesticide residues; HPLC−DAD; matrix
25
effect; agricultural samples
Page 2 of 23
26
1. Introduction
27
One of the trends in pesticide residue analysis is the development of rapid, highly
28
sensitive, and highly accurate methodologies that can reliably identify and quantify the
29
analytes in complicated matrices at trace levels. Presently, high-performance liquid
30
chromatography (HPLC) or gas chromatography (GC) coupled with mass spectrometry
31
(MS) and/or tandem MS (MS/MS) seems to be the techniques of first choice for
32
pesticide residue analysis in food commodities (Hiemstra & de Kok, 2007; Payá et al.,
33
2007; Frenich, Vidal, Pastor-Montoro & Romero-González, 2008; Romero-González,
34
Frenich, Vidal, Prestes & Grio, 2011). On the other hand, the analytical performance of
35
the conventional HPLC coupled with diode array detector (DAD) is inferior compared
36
with LC─MS/MS. Therefore, thorough sample preparation procedures are indispensable
37
to determine pesticide residues at trace levels with the conventional HPLC that obtain
38
quantitative information according to retention times of target pesticides (Seccia,
39
Fidente, Montesano & Morrica, 2008). To propose a practical analytical methodology
40
for pesticide residues, it is necessary to consider (1) speed-up and simplification of
41
analytical procedures, (2) analytical cost (maintenance of analytical instruments and
42
reagents), and (3) environmental impact and influence on health of analysts by
43
consumption of a large amount of toxic organic solvent (Frenich et al., 2008). We have
44
recently reported an environmentally friendly sample preparation method using water as
45
an extractant for hydrophilic pesticides in agricultural samples with conventional HPLC
46
(Watanabe, Kobara, Baba & Eun, 2013). Although the proposed method (about 50 mL
47
of organic solvent per sample) contributes to organic solvent-saving, the applicability of
48
the method has been limited to only hydrophilic pesticides. Therefore, we decided to
49
conduct the study for the aim of (1) clarifying the technical limitation by applying the
Page 3 of 23
50
method to more hydrophobic pesticides, (2) improving the recoveries by using aqueous
51
acetonitrile (MeCN), and (3) assessing the developed method by analyzing artificially
52
spiked and real agricultural samples.
53 54
2. Experimental
55
2.1. Chemicals and reagents
56
Certified standards of pesticides were purchased from Wako Pure Chemical Industries
57
Ltd. (Osaka, Japan), Kanto Chemical Co., Inc. (Tokyo, Japan), and Dr. Ehrenstorfer
58
(Augsburg, Germany). Pesticide analysis-grade and HPLC-grade organic solvents were
59
obtained from Wako Pure Chemical Industries Ltd.. Water used for HPLC was prepared
60
directly in the laboratory with a Milli-Q water purification system (Millipore Corp.,
61
Bedford, MA). Cartridges used for SPE were Oasis HLB (225 mg; Waters, Milford,
62
MA) and Envi-Carb/LC-NH2 (500 mg + 500 mg/6 mL; Supelco, Bellefonte, PA).
63
Individual standard pesticide stock solutions (1,000 µg mL-1) were prepared in MeCN
64
and stored at 4°C in the dark. They were stable over a period of at least six months.
65
Standard working solution was daily prepared by appropriate dilution of each stock
66
solution with MeCN. The standard working solution of each pesticide was used as
67
spiking solution and to prepare the calibration standard solutions, at the concentration
68
levels between 0.01 and 2 µg mL-1.
69 70
2.2. Preparation of artificially spiked samples
71
Tomatoes, green peppers, and spinaches were obtained from local grocery stores. Each
72
vegetable sample was placed in a food cutter and chopped thoroughly until
73
homogeneous. In all cases the chopped samples were spiked with a spiking solution of
Page 4 of 23
74
the test analytes in MeCN such that the concentrations in the sample were 0.1, 0.5, and
75
1.0 mg kg-1. The spiked samples were allowed to stand for 30 min before extraction.
76 77
2.3. Preparation of agricultural samples with field-incurred residues
78
Three kinds of agricultural samples were grown in a plastic greenhouse on arable land
79
of the National Institute for Agro-Environmental Sciences. Each sample in the
80
harvesting stage was sprayed with mixed several pesticide formulations diluted with
81
water according to the manufacturer's labels using a handy sprayer, and then was
82
harvested at 1, 3, and 7 days after spraying. After harvesting, the residue samples were
83
placed in 500 mL of glass jars and frozen at −20°C until extraction.
84 85
2.4. Sample extraction
86
2.4.1. Optimized sample preparation
87
To 5 g of spiked or real sample, 15 mL of MeCN/water (1:1, v/v) was added and
88
extracted for 3 min with a high-speed Polytron PT2100 homogenizer (Kinematica,
89
Lucerne, Switzerland). The mixture was centrifuged (10 min, 10,000 rpm), and the
90
supernatant was passed through a cellulose filter paper on a Büchner funnel with suction.
91
The solid residue in the tube was extracted again with 10 mL of MeCN/water (1:1, v/v),
92
and then the mixture was centrifuged and filtered. After the combined sample extract
93
was diluted with 8 mL of water, the diluted extract was percolated through an Oasis
94
HLB cartridge preconditioned with 6 mL of methanol (MeOH) and 6 mL of ultra-pure
95
water. The cartridge was rinsed with 5 mL of ultra-pure water and vacuum-dried for 10
96
min to remove excess water. Finally, the retained pesticides were eluted with 10 mL of
97
MeOH and the eluate was concentrated to a final volume of about 1 mL under reduced
Page 5 of 23
98
pressure. The residue was reconstituted in 2 mL of MeCN/toluene (3:1, v/v) and the
99
solution was applied to an Envi-Carb/LC-NH2 cartridge preconditioned with 10 mL of
100
MeCN/toluene (3:1, v/v). The retained pesticides were eluted with 20 mL (40 mL if
101
necessary) of MeCN/toluene (3:1, v/v). The eluate was concentrated under reduced
102
pressure and evaporated under a gentle nitrogen stream at 50°C. The residue was
103
reconstituted in 1 mL of MeCN and syringe filtered using a 0.45 µm PTFE filter
104
(Millipore, Billerica, MA) into an autosampler vial.
105 106
2.4.2. Reference multiresidue pesticide analytical method authorized in Japan
107
Using the Japanese authorized official method (JMHLW, 2006) selected as the
108
reference method in this study, we confirmed equivalency of the analytical results
109
obtained from the proposed sample preparation method.
110
To 20 g of sample, 50 mL of MeCN was added and extracted for 3 min with a high-
111
speed homogenizer. The mixture was filtrated with suction, and the solid residue on the
112
funnel was extracted again with 20 mL of MeCN. Both extracts were accurately made
113
up to 100 mL with MeCN in a volumetric flask, and then 20 mL aliquots of the extract,
114
equivalent to 4 g of sample, was mixed with 10 g of sodium chloride and 20 mL of
115
0.5M phosphate buffer (pH 7.0). The mixture was vigorously shaken for 5 min, and
116
stood for about 10 min. After the aqueous phase was discarded, the MeCN phase was
117
anhydrated, filtrated, and then concentrated. The residue was similarly cleaned-up with
118
an Envi-Carb/LC-NH2 cartridge as described above.
119 120
2.5. HPLC−DAD analysis
Page 6 of 23
121
The HPLC system consisted of an Agilent 1100 series equipped with a quaternary pump,
122
an autosampler, a column oven, and a DAD. The chromatographic separation was
123
performed in a SunFire C18 column (250 mm × 4.6 mm, 5 µm particle size) in
124
combination with a SunFire C18 guard column (20 mm × 4.6 mm, 5 µm particle size)
125
(Waters). The column temperature was maintained at 20°C. The mobile phase consisted
126
of MeCN/water (70:30, v/v) at a flow rate of 0.7 mL min-1. A volume of 20 µL was
127
injected for both standard and sample solutions. The detection wavelengths were 230,
128
246, 258, and 280 nm.
129 130
2.6. Analytical performance of HPLC−DAD
131
The external standard procedure was used and calibration curves constructed by plotting
132
peak area (y) against concentration (x) using several concentration levels and following
133
linear regression analysis. A repeatability study at 1.0 µg mL-1 with three consecutive
134
injections for the same day (n = 3) in five different days (n = 15) was carried out. Table
135
1 shows the CV values obtained for both retention times and peak areas for all the
136
pesticides. As it can be observed, acceptable precision was obtained in all cases:
137
intraday CV values were below 2%, while interday CV values were below 5%,
138
respectively. Table 1 also shows the calibration parameters. As it can be seen,
139
coefficient of regression (r) were higher than 0.999 for all cases. The limits of detection
140
(LODs) estimated by a signal-to-noise ratio of 3 were in the range of 5 ng mL-1 and 20
141
ng mL-1.
142 143
2.7. Evaluation of matrix effects
Page 7 of 23
144
Matrix effect, expressed as a signal from the pesticide in matrix compared to the signal
145
in pure solvent (mobile phase), were tested in all matrices. A mixture of pesticides was
146
added into an aliquot of blank extract in mobile phase, producing a final concentration
147
of 0.1 mg kg-1 of each agricultural sample. The effect was evaluated according to a
148
method described by Stahnke et al. (2012).
149 150
3. Results and discussion
151
3.1. Limiting point of water-based extraction and applicability of aqueous MeCN-based
152
extraction
153
The suitability of the previously proposed water-based extraction (Watanabe et al.,
154
2013) was applied to the determination of the agricultural samples spiked with relatively
155
hydrophobic pesticides. Although the hydrophilic pesticides such as neonicotinoids can
156
be recovered quantitatively from tested agricultural samples (Watanabe et al., 2013), the
157
drastic decrease of the recoveries of the tested pesticides in this study were nearly as we
158
expected (Fig. 1-(a)).
159
We confirmed the elution profiles of two SPE cartridges to locate the factor of low
160
recoveries. The elution rates of all pesticides from Envi-Carb/LC-NH2 SPE cartridge
161
were higher than 84% (see Fig. S1 in the Supporting Information). On the other hand,
162
those of pesticides less than 1 mg L-1 of water solubility from Oasis HLB SPE cartridge
163
decreased gradually (Fig. S1). Therefore, the low recoveries of pesticides (more than 1
164
mg L-1 of water solubility) in the water-based extraction resulted from inadequate
165
extraction efficiency of water, and those of pesticides showing high hydrophobicity
166
(water solubility < 1 mg L-1) were attributed to the extraction efficiency of water
167
together with inadequate retention to Oasis HLB SPE cartridge.
Page 8 of 23
168
To improve the recoveries of pesticides considering the feasible smallest amount of
169
organic solvent consumption, mixture of MeCN and water (1:4, 2:3, and 1:1, v/v) as
170
extractant were studied. As shown in Fig. S2-(a) (see in the Supporting Information),
171
the recoveries improved significantly with increased MeCN concentrations. Moreover,
172
when using MeCN/water (1:1, v/v), the highest hydrophobic pesticides such as
173
pyridaben (water solubility = 0.012 mg L-1) were extractable quantitatively from all
174
tested samples. However, the recoveries of pesticides showing more than 1 mg L-1 of
175
water solubility fell drastically with MeCN concentration.
176
The elution profiles when applying each pesticide dissolved in mixture of MeCN and
177
water (1:4, 2:3, and 1:1, v/v) to Oasis HLB SPE cartridge are shown in Fig. S2-(b).
178
Results show that although water containing higher MeCN concentration can extract
179
hydrophobic pesticides, it causes low recoveries of relatively hydrophilic pesticides
180
such as myclobutanil. It was thought that the inadequate retention to the cartridge
181
participated in the low recoveries rather than the loss at extraction stage with
182
MeCN/water (1:1).
183
Using the findings, we improved the low retention to the cartridge by adjusting MeCN
184
concentration to about 40% (v/v), which showed best retention to the cartridge (Fig. S2-
185
(b)) with water after extraction with MeCN/water (1:1, v/v). As Fig. 1-(b) shows, the
186
combination of extraction with MeCN/water (1:1, v/v) and direct application of sample
187
extract to the SPE cartridge after adjustment of MeCN concentration was successful
188
because diverse pesticides recovers quantitatively from all tested samples.
189 190
3.2. Matrix effect
Page 9 of 23
191
An important issue in the method development of pesticide residue analysis is the
192
possible occurrence of matrix effect. Fig. 2 shows matrix effects of 28 pesticides in the
193
proposed method and the reference method. Most pesticides in the tested matrix extracts
194
showed no considerable signal suppression or enhancement (matrix effect within ±20%),
195
which it is likely to be an obstacle to accurate determination (Mol, Plaza-Bolaños,
196
Zomer, de Rijk, Stolker, & Mulder, 2008). Boscalid showed a signal enhancement (31%
197
in the proposed method; 26% in the reference method) only tomato samples. The degree
198
of matrix effect seems to vary slightly according to the kind of agricultural samples
199
(Payá et al., 2007; Mol et al., 2008; Romero-González et al., 2011). In the proposed
200
sample preparation method, pesticides that were analyzed without substantial matrix
201
effects (matrix effect with in ±10%, Fig. 2) were 68% of the whole. Therefore, it might
202
be inferred that the clean-up efficiency was superior to the reference method by which
203
pesticides showing matrix effect within ±10% were about 40% of the whole. The
204
representative chromatograms of real green pepper samples treated with six kinds of
205
pesticides are shown in Fig. 3.
206 207 208
3.3. Analysis of spiked samples and evaluation of validity of proposed method using real samples
209
The accuracy of the proposed method was estimated using recovery experiments
210
conducted at three concentration levels (Table 2). For all matrices, the results obtained
211
for all analytes were satisfactory, with recoveries of 70─120% and CV values below
212
20% (EC, 2009).
213
For the evaluation of analytical methods under development, it has been acknowledged
214
that recoveries of field-incurred analytes from environmental matrices are far more
Page 10 of 23
215
realistic than recoveries based on laboratory spiking into the sample matrices (Pylypiw,
216
Jr., Arsenault, Thetford & Mattina, 1997). Miniaturization of sample volume for
217
extraction can be one effective means to reduce organic solvent consumption during
218
sample preparation (Wan & Wong, 1996). The validity of small sample volume was
219
assessed using real samples treated with several pesticide formulations. The analytical
220
results obtained using the proposed method were compared with those obtained using
221
the reference method (JMHLW, 2006). The determined concentrations of pesticides in
222
samples prepared with the proposed method were equivalent with those determined
223
using the reference method (r > 0.98) (Fig. 4). These results strongly indicate that the
224
reduction in sample volume does not affect substantially the accuracy of the proposed
225
method. Moreover, they suggest the possibility of reducing organic solvent
226
consumption in the extraction stage by reducing the sample volume. The proposed
227
method (about 60 mL of organic solvent per sample) was possible to reduce the
228
consumption of organic solvent 60% in comparison with the reference method (about
229
150 mL per sample) (JMHLW, 2006).
230 231
4. Conclusions
232
The multiresidue method of extracting pesticides from agricultural samples using an
233
environmentally friendly extraction method with a small sample and MeCN/water (1:1,
234
v/v) for conventional HPLC−DAD analysis has been demonstrated. Incorporating
235
adjustment of MeCN concentration was the key factor for high extraction efficiency and
236
stable retention of analytes to the Oasis HLB SPE cartridge. The use of MeCN/water
237
(1:1, v/v) and miniaturization of the sample volume can contribute greatly to reduction
238
of the organic solvent consumption in sample preparation procedures for conventional
Page 11 of 23
239
HPLC−DAD used in this study. The proposed method is unsuitable for the extraction of
240
some highly hydrophobic pesticides.
241 242
Acknowledgement
243
We sincerely express our gratitude to Mr. Takahiro Ara and Mr. Hiroshi Yamaguchi
244
(National Institute for Agro-Environmental Sciences) for support in preparation of real-
245
world agricultural samples.
246 247
References
248
EC. Method Validation and Quality Control Procedures for Pesticide Residues Analysis
249
in
Food
and
Feed,
2009.
URL
250
http://ec.europa.eu/food/plant/protection/resources/qualcontrol_en.pdf#search=%27
251
No.%20SANCO/10684/2009%27. Accessed 6 September 2013.
252
Frenich, A.G., Vidal, J.L.M., Pastor-Montoro, E., & Romero-González, R. (2008).
253
High-throughput determination of pesticide residues in food commodities by use of
254
ultra-performance liquid chromatography−tandem mass spectrometry. Analytical
255
and Bioanalytical Chemistry, 390, 947−959.
256
Hiemstra, M. & de Kok, A. (2007). Comprehensive multi-residue method for the target
257
analysis of pesticides in crops using liquid chromatography−tandem mass
258
spectrometry. Journal of Chromatography A, 1154, 3−25.
259
JMHLW (Japanese Ministry of Health, Labour and Welfare). Analytical Methods for
260
Residual Compositional Substances of Agricultural Chemicals, Feed Additives, and
261
Veterinary
Drugs
in
Food,
2006.
URL
Page 12 of 23
262
http://www.mhlw.go.jp/english/topics/foodsafety/positivelist060228/dl/060526-
263
1a.pdf. Accessed 6 September 2013.
264
Mol, H.G.J., Plaza-Bolaños, P., Zomer, P., de Rijk, T.C., Stolker, A.A.M., & Mulder,
265
P.P.J. (2008). Toward a generic extraction method for simultaneous determination
266
of pesticides, mycotoxins, plant toxins, and veterinary drugs in feed and food
267
matrixes. Analytical Chemistry, 80, 9450−9459.
268
Payá, P., Anastassiades, M., Mack, D., Sigalova, I., Tasdelen, B., Oliva, J., & Barba, A.
269
(2007). Analysis of pesticide residues using the Quick Easy Cheap Effective Rugged
270
and Safe (QuEChERS) pesticide multiresidue method in combination with gas and
271
liquid chromatography and tandem mass spectrometric detection. Analytical and
272
Bioanalytical Chemistry, 389, 1697−1714.
273
Pylypiw, Jr., H.M., Arsenault, T.L., Thetford, C.M., & Mattina, M.J.I. (1997).
274
Suitability of microwave-assisted extraction for multiresidue pesticide analysis of
275
produce, Journal of Agricultural and Food Chemistry, 45, 3522−3528.
276
Romero-González, R., Frenich, A.G., Vidal, J.L.M., Prestes, O.D., & Grio, S.L. (2011).
277
Simultaneous determination of pesticides, biopesticides and mycotoxins in organic
278
products applying a qucik, easy, cheap, effective, rugged and safe extraction
279
procedure and ultra-high performance liquid chromatography−tandem mass
280
spectrometry. Journal of Chromatography A, 1218, 1477−1485.
281
Seccia, S., Fidente, P., Montesano, D., & Morrica, P. (2008). Determination of
282
neonicotinoid insecticides residues in bovine milk samples by solid-phase extraction
283
clean-up and liquid chromatography with diode-array detection. Journal of
284
Chromatography A, 1214, 115−120.
Page 13 of 23
285
Stahnke, H., Kittlaus, S., Kempe, G., & Alder, L. (2012). Reduction of matrix effects in
286
liquid chromatography−electrospray ionization−mass spectrometry by dilution of
287
sample extracts: How much dilution is needed? Analytical Chemistry, 84,
288
1474−1482.
289 290
Wan, H.B. & Wong, M.K. (1996). Minimization of solvent consumption in pesticide residue analysis. Journal of Chromatography A, 754, 43−47.
291
Watanabe, E., Kobara, Y., Baba, K., & Eun, H. (2013). Reduction of hazardous organic
292
solvent in sample preparation for hydrophilic pesticide residues in agricultural
293
products with conventional liquid chromatography. Journal of Agricultural and
294
Food Chemistry, 61, 4792−4798.
295
Page 14 of 23
296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311
Figure captions Figure 1. Recoveries (n = 3) of water-based extraction (a) and the proposed extraction method (b) in agricultural samples at the level of 1 mg kg-1. The values inside brackets show water solubility (mg L-1) for each pesticide. Figure 2. Matrix effects in the proposed method (a) and the reference method (b). Figure 3. Representative HPLC chromatograms of the proposed method (a) and of the reference method (b) of real green pepper samples harvested at 7 days after spraying: 1, azoxystrobin; 2, myclobutanil; 3, cyazofamid; 4, lufenuron; 5, flufenoxuron; and 6, hexythiazox. Figure 4. Comparison of the analytical results between the proposed method and the reference method. Each symbol is the following: (□), cyazofamid; (∆), flufenoxuron (common to all samples); (○), azoxystrobin; (◊), lufenuron (common to tomato and green pepper samples); (●), myclobutanil; (■), hexythiazox (green pepper samples only); (●), phenthoate (spinach samples only). Each point is the average of individual quintuplicate determinations. The dotted line corresponds to a perfect correlation (y = x).
Page 15 of 23
312
Figure 1 (a) water-based extraction method
(b) proposed method 120
■ tomato □ green pepper ■ spinach
■ tomato □ green pepper ■ spinach
110
110
100
100
90
90
80 70 60
Average recovery (%)
Average recovery (%)
120
80 70 60
50
50
40
40
30
30
20
20
10
10
0
0
313
Page 16 of 23
Figure 2
Number of pesticides (%)
60
60
(a) proposed method ■ tomato □ green pepper ■ spinach
(b) reference method ■ tomato □ green pepper ■ spinach
50
50
40
40
Number of pesticides (%)
314
30
20
10
20
10
0
0 medium (-40~-20%)
315
30
minor no significant no significant (-20~-10%) (-10~0%) (0~10%)
minor (10~20%)
medium (20~40%)
not significant signal suppression
signal enhancement
medium (-40~-20%)
minor no significant no significant (-20~-10%) (-10~0%) (0~10%)
minor (10~20%)
medium (20~40%)
not significant signal suppression
signal enhancement
Page 17 of 23
Figure 3
(b) reference method
(a) proposed method
10
10
6 (0.15 mg kg-1)
9
6 (0.15 mg kg-1)
9 -1
8
4 (0.12 mg kg-1)
5
230 nm 3 (0.20 mg kg-1)
3 2 1
Absorbance (mAU)
7
6
4
5 (0.22 mg kg )
8
5 (0.23 mg kg-1)
7
4 (0.13 mg kg-1)
6 5
230 nm
4
3 (0.18 mg kg-1)
3 2 1
0
280 nm
-1
0
280 nm
-1
-2 0
5
10
15
20
25
30
35
-2 0
5
10
15
Time (min)
20
25
30
35
Time (min)
25
25 -1
1 (0.57 mg kg ) 20
20
15
2 (0.17 mg kg-1)
10 5 0
Absorbance (mAU)
Absorbance (mAU)
Absorbance (mAU)
316 317
1 (0.51 mg kg-1)
15
2 (0.14 mg kg-1) 10 5 0
7
8
9 Time (min)
10
7
8
9
10
Time (min)
Page 18 of 23
Figure 4 0.4
(a) tomato y = 1.2864x − 0.0243, r = 0.9847
Proposed method (mg kg-1)
0.3
0.2
0.1
0.0 0.0
0.1
0.2 Reference method (mg kg-1)
0.3
0.4
1.6
(b) green pepper y = 1.0954x − 0.0034, r = 0.9977 1.4
1.2 Proposed method (mg kg-1)
318 319 320
1.0
0.8
0.6
0.4
0.2
0.0 0.0
0.2
0.4
0.6 0.8 1.0 Reference method (mg kg-1)
1.2
1.4
1.6
Page 19 of 23
321 322 323
0.6
Proposed method (mg kg-1)
(c) spinach y = 1.0568x − 0.0027, r = 0.9971
0.4
0.2
0.0 0.0
0.2 0.4 Reference method (mg kg-1)
0.6
Page 20 of 23
1 2
Table 1. Results of the repeatability (expressed as %CV) obtained for the HPLC-DAD procedure (data given for 1.0 g mL-1) and calibration data for the selected 28 kinds of pesticides in the current work. Pesticide
Azoxystrobin Boscalid Bromopropylate Chlorfenapyr Chlorfluazuron Chromafenozide Cyazofamid Cyflufenamid Diethofencarb Diflubenzuron Etofenprox Famoxadone Fipronil Flubendiamide Flufenoxuron Hexythiazox Iprodione Isoxathion Kresoxim-methyl Lufenuron Myclobutanil Phenthoate Pyraclofos
Detection wavelength (nm) 230 230 230 230 230 230 280 230 246 258 230 230 230 230 230 230 230 258 230 230 230 230 258
Intraday precision (n = 3) tR
Interday precision (n = 15)
Peak area 0.05 0.1 0.05 0.1 0.1 0.02 0.06 0.1 0.1 0.1 0.1 0.05 0.06 0.1 0.2 0.09 0.05 0.1 0.1 0.1 0.06 0.1 0.1
0.6 1.1 0.9 0.8 1.1 0.9 1.0 0.6 0.6 0.8 1.3 0.6 1.2 0.7 1.5 1.4 0.4 1.0 0.9 1.9 1.3 0.6 0.2
tR
Calibration data
Peak area 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.3
1.4 1.3 2.6 3.9 2.2 2.4 3.1 2.3 1.8 2.4 2.5 1.8 2.9 1.8 4.1 3.0 3.0 1.1 1.0 2.7 3.9 2.8 1.2
Equation of calibration curve
Linearity (g mL-1)
y = 74.3x + 0.68 y = 46.8x + 0.10 y = 44.6x − 2.55 y = 28.5x + 0.31 y = 45.4x − 0.85 y = 30.4x − 1.30 y = 38.2x + 0.64 y = 31.4x + 1.19 y = 45.8x − 0.24 y = 27.4x − 0.20 y = 30.3x + 1.64 y = 48.6x − 1.85 y = 31.2x − 1.45 y = 25.9x + 0.94 y = 35.4x + 1.15 y = 47.5x + 1.19 y = 32.8x − 0.15 y = 128.9x + 2.52 y = 33.1x + 0.55 y = 43.0x − 0.85 y = 14.9x + 0.32 y = 23.6x + 0.99 y = 54.3x + 0.87
0.01−2 0.02−2 0.02−2 0.04−2 0.04−2 0.02−2 0.01−2 0.02−2 0.01−2 0.03−2 0.01−2 0.01−2 0.04−2 0.03−2 0.03−2 0.02−2 0.01−2 0.01−2 0.04−2 0.02−2 0.04−2 0.02−2 0.01−2
r 1.0000 1.0000 0.9996 0.9998 0.9998 1.0000 0.9999 0.9999 1.0000 1.0000 0.9998 0.9998 0.9998 0.9998 0.9999 0.9999 1.0000 1.0000 1.0000 0.9999 0.9995 0.9999 1.0000
LOD (ng mL-1) 5 10 10 20 20 10 5 10 5 15 5 5 20 15 15 10 5 5 20 10 20 10 5
Page 1 of 3
Pyridaben Pylidalyl Pyriproxyfen Teflubenzuron Trifloxystrobin
230 230 230 230 230
0.03 0.1 0.08 0.04 0.07
1.0 1.5 0.2 1.4 1.4
0.2 0.4 0.3 0.2 0.3
2.9 4.9 2.6 3.1 3.7
y = 32.5x + 1.57 y = 28.5x + 0.02 y = 55.0x + 0.90 y = 35.8x − 0.95 y = 34.5x − 0.30
0.01−2 0.02−2 0.02−2 0.01−2 0.02−2
1.0000 0.9997 1.0000 0.9998 0.9999
5 10 10 5 10
3 4
Page 2 of 3
5 6
Table 2. Recoveries and variations obtained for 10 pesticides artificially spiked in tomato, green pepper, and spinach samples and analyzed with HPLC−DAD. Average recovery (%) (CV) (n = 5 replicates) Tomato -1
Spiked level (mg kg ) Boscalid Cyflufenamid Diethofencarb Diflubenzuron Flubendiamide Flufenoxuron Isoxathion Kresoxim-methyl Phenthoate Pyraclofos
0.1 100 (8) 107 (3) 101 (8) 98 (3) 105 (11) 97 (4) 86 (2) 106 (4) 79 (2) 95 (3)
Green pepper
Spinach
0.5
1.0
0.1
0.5
1.0
0.1
101 (2) 99 (6) 82 (8) 94 (2) 104 (4) 92 (2) 89 (2) 95 (3) 85 (5) 94 (3)
109 (2) 101 (1) 76 (8) 92 (1) 100 (4) 92 (1) 88 (2) 97 (2) 84 (4) 94 (1)
87 (5) 85 (5) 71 (8) 96 (4) 100 (1) 91 (10) 77 (9) 93 (5) 87 (8) 94 (2)
91 (3) 90 (3) 72 (6) 97 (2) 96 (15) 96 (3) 86 (4) 92 (2) 81 (7) 96 (3)
92 (1) 94 (1) 75 (1) 93 (2) 95 (6) 93 (2) 90 (3) 94 (3) 89 (2) 95 (2)
105 (9) 89 (3) 110 (5) 94 (4) 119 (5) 93 (12) 95 (2) 91 (4) 105 (7) 88 (5)
0.5 99 (2) 89 (2) 77 (2) 94 (1) 101 (2) 105 (10) 89 (2) 91 (1) 85 (3) 96 (2)
1.0 97 (2) 93 (2) 79 (11) 95 (2) 98 (7) 96 (10) 90 (1) 92 (1) 88 (5) 97 (3)
7 8
Page 3 of 3
334 335 336 337 338 339 340 341 342 343 344 345 346 347
Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC-DAD: a trial of reduction of organic solvent consumption in sample preparation <Authors rel="nofollow"> Eiki Watanabe, Yuso Kobara, Koji Baba, and Heesoo Eun <Highlights> Development of an extraction method by aqueous acetonitrile for pesticide residue. Use of smallest feasible sample volume for extraction. Key point is adjustment of acetonitrile concentration after extraction. Most pesticides can be analyzed without substantial matrix effects. Reducing up to 60% of solvent consumption comparison with current official methods.<br />
<br />
Page 23 of 23<br />
<br />
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="modal fade" id="report" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog">
<div class="modal-content">
<form role="form" method="post" action="https://c.coek.info/report/aqueous-acetonitrile-extraction-for-pesticide-residue-analysis-in-agricultural-p" style="border: none;">
<div class="modal-header">
<button type="button" class="close" data-dismiss="modal" aria-hidden="true">×</button>
<h4 class="modal-title">Report "Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC−DAD"</h4>
</div>
<div class="modal-body">
<div class="form-group">
<label>Your name</label>
<input type="text" name="name" required="required" class="form-control" />
</div>
<div class="form-group">
<label>Email</label>
<input type="email" name="email" required="required" class="form-control" />
</div>
<div class="form-group">
<label>Reason</label>
<select name="reason" required="required" class="form-control">
<option value="">-Select Reason-</option>
<option value="pornographic" selected="selected">Pornographic</option>
<option value="defamatory">Defamatory</option>
<option value="illegal">Illegal/Unlawful</option>
<option value="spam">Spam</option>
<option value="others">Other Terms Of Service Violation</option>
<option value="copyright">File a copyright complaint</option>
</select>
</div>
<div class="form-group">
<label>Description</label>
<textarea name="description" required="required" rows="3" class="form-control" style="border: 1px solid #cccccc;"></textarea>
</div>
<div class="form-group">
<div style="display: inline-block;">
<div class="g-recaptcha" data-sitekey="6LciDjwUAAAAAMdLRL0FhiLgqyD9pO-Cr0MB-hVG"></div>
</div>
</div>
<script src='https://www.google.com/recaptcha/api.js'></script>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
<button type="submit" class="btn btn-primary">Send</button>
</div>
</form>
</div>
</div>
</div>
</div>
<!-- BEGIN FOOTER -->
<footer id="footer">
<div id="footer-top" class="container">
<div class="row">
<div class="block col-sm-4">
<a href="https://c.coek.info/"><img style="width: 50%;" src="https://c.coek.info/assets/img/kundoc_logo.png" alt="c.coek.info" /></a>
<br><br>
<p></p>
<hr />
<h3>Contact information</h3>
<p>
<strong>Joseph Rodriguez</strong><br />
<i class="fa fa-envelope-o"></i> <a href="mailto:info@c.coek.info">info@c.coek.info</a>.<br /><br />
<span><i class="fa fa-map-marker"></i> Address: </span>
<address>
Tiptrans, Ste #29578,<br />
Slevacska 476/2A,<br />
Rumburk,<br />
Ustecky / Usti nad Labem, 40801<br />
Czech Republic
</address>
</p>
</div>
<div class="block col-sm-4">
<h3>Helpful Links</h3>
<ul class="footer-links">
<li><a href="https://c.coek.info/about">About Us</a></li>
<li><a href="https://c.coek.info/contact">Contact Us</a></li>
<li><a href="https://c.coek.info/copyright">Copyright</a></li>
<li><a href="https://c.coek.info/privacy">Privacy Policy</a></li>
<li><a href="https://c.coek.info/term">Terms and Service</a></li>
<li><a href="https://c.coek.info/faq">FAQ</a></li>
<li><a href="https://c.coek.info/cookie_policy">Cookie Policy</a></li>
</ul>
</div>
<!-- BEGIN NEWSLETTER -->
<div class="block col-sm-4" data-animation-direction="from-bottom" data-animation-delay="200">
<form action="https://c.coek.info/newsletter" method="post">
<div id="newsletter" class="col-sm-12">
<h2 class="section-title">Subscribe our weekly<br><span>Newsletter</span></h2>
<div class="input-group">
<input type="text" placeholder="Enter your E-mail" name="newsletter_email" id="newsletter_email" class="form-control" />
<span class="input-group-btn">
<button class="btn btn-default" type="submit">Subscribe</button>
</span>
</div>
</div>
</form>
</div>
<!-- END NEWSLETTER -->
</div>
</div>
<!-- BEGIN COPYRIGHT -->
<div id="copyright">
<div class="container">
<div class="row">
<div class="col-sm-12">
Copyright © 2024 C.COEK.INFO. All rights reserved.
<!-- BEGIN SOCIAL NETWORKS -->
<ul class="social-networks">
<li><a href="#"><i class="fa fa-facebook"></i></a></li>
<li><a href="#"><i class="fa fa-twitter"></i></a></li>
<li><a href="#"><i class="fa fa-pinterest"></i></a></li>
<li><a href="#"><i class="fa fa-youtube"></i></a></li>
<li><a href="#"><i class="fa fa-rss"></i></a></li>
</ul>
<!-- END SOCIAL NETWORKS -->
</div>
</div>
</div>
</div>
<!-- END COPYRIGHT -->
</div>
<!-- END WRAPPER -->
</footer>
<!-- END FOOTER -->
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-Q76CSPQQQC"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-Q76CSPQQQC');
</script>
<script src="https://c.coek.info/assets/js/jquery-3.2.1.min.js"></script>
<script src="https://c.coek.info/assets/js/bootstrap.min.js"></script>
<script src="https://c.coek.info/assets/js/scripts.js?v=1"></script>
<script src="https://c.coek.info/assets/js/jquery-ui.min.js"></script>
<script>
$(function () {
$("#pdf_search").autocomplete({
source: function (request, response) {
$.ajax({
url: "https://c.coek.info/suggest",
dataType: "json",
data: {
term: request.term
},
success: function (data) {
response(data);
}
});
},
autoFill: true,
select: function (event, ui) {
$(this).val(ui.item.value);
$(this).parents("form").submit();
}
});
});
</script>
<!-- cookie policy -->
<div id="CCOEKINFO_cookie_box" style="z-index:99999; border-top: 1px solid #fefefe; background: #FFC107; width: 100%; position: fixed; padding: 5px 15px; text-align: center; left:0; bottom: 0;">
Our partners will collect data and use cookies for ad personalization and measurement. <a href="https://c.coek.info/cookie_policy" target="_blank">Learn how we and our ad partner Google, collect and use data</a>. <a href="#" class="btn btn-success" onclick="accept_CCOEKINFO_cookie_box();return false;">Agree & close</a>
</div>
<script>
function accept_CCOEKINFO_cookie_box() {
document.cookie = "CCOEKINFO_cookie_box_viewed=1;max-age=15768000;path=/";
hide_CCOEKINFO_cookie_box();
}
function hide_CCOEKINFO_cookie_box() {
var cb = document.getElementById('CCOEKINFO_cookie_box');
if (cb) {
cb.parentElement.removeChild(cb);
}
}
(function () {
var CCOEKINFO_cookie_box_viewed = (function (name) {
var matches = document.cookie.match(new RegExp("(?:^|; )" + name.replace(/([\.$?*|{}\(\)\[\]\\\/\+^])/g, '\\$1') + "=([^;]*)"));
return matches ? decodeURIComponent(matches[1]) : undefined;
})('CCOEKINFO_cookie_box_viewed');
if (CCOEKINFO_cookie_box_viewed) {
hide_CCOEKINFO_cookie_box();
}
})();
</script>
<!-- end cookie policy -->
</body>
</html>
<script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script>
S0308-8146(13)01955-9 http://dx.doi.org/10.1016/j.foodchem.2013.12.075 FOCH 15192
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
18 September 2013 20 December 2013 21 December 2013
Please cite this article as: Watanabe, E., Kobara, Y., Baba, K., Eun, H., Aqueous acetonitrile extraction for pesticide residue analysis in agricultural products with HPLC−DAD, Food Chemistry (2014), doi: http://dx.doi.org/10.1016/ j.foodchem.2013.12.075
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
Aqueous acetonitrile extraction for pesticide residue analysis
2
in agricultural products with HPLC−DAD
3 4
Eiki Watanabe *, Yuso Kobara, Koji Baba, and Heesoo Eun
5 6
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki 305-8604, Japan
7 8
*Corresponding author. Tel/fax: +81 29 838 8306, e-mail: [email protected]
Page 1 of 23
9
Abstract
10
To reduce hazardous organic solvent consumption during sample preparation
11
procedures as much as possible, an extraction method of smallest feasible sample
12
volume (5 g) using aqueous acetonitrile (MeCN) was developed to extract pesticide
13
residues from agricultural samples prior to HPLC−DAD determination. Extraction with
14
MeCN/water (1:1, v/v), and adjustment of the MeCN concentration by diluting with
15
water after extraction recovered successfully most pesticides showing various
16
physicochemical properties. The matrix effects of tested samples on the proposed
17
method developed herein were generally negligibly-small. The average recoveries were
18
in the range 70−120% for all pesticides with the coefficient of variation values below
19
20%. The reduction rate of organic solvents used for the proposed sample preparation
20
method was up to approximately 60% compared with the Japanese authorized official
21
method for pesticide residue analyses. These results demonstrate the feasibility of the
22
proposed method for pesticides with diverse properties.
23 24
Keywords: aqueous acetonitrile extraction; pesticide residues; HPLC−DAD; matrix
25
effect; agricultural samples
Page 2 of 23
26
1. Introduction
27
One of the trends in pesticide residue analysis is the development of rapid, highly
28
sensitive, and highly accurate methodologies that can reliably identify and quantify the
29
analytes in complicated matrices at trace levels. Presently, high-performance liquid
30
chromatography (HPLC) or gas chromatography (GC) coupled with mass spectrometry
31
(MS) and/or tandem MS (MS/MS) seems to be the techniques of first choice for
32
pesticide residue analysis in food commodities (Hiemstra & de Kok, 2007; Payá et al.,
33
2007; Frenich, Vidal, Pastor-Montoro & Romero-González, 2008; Romero-González,
34
Frenich, Vidal, Prestes & Grio, 2011). On the other hand, the analytical performance of
35
the conventional HPLC coupled with diode array detector (DAD) is inferior compared
36
with LC─MS/MS. Therefore, thorough sample preparation procedures are indispensable
37
to determine pesticide residues at trace levels with the conventional HPLC that obtain
38
quantitative information according to retention times of target pesticides (Seccia,
39
Fidente, Montesano & Morrica, 2008). To propose a practical analytical methodology
40
for pesticide residues, it is necessary to consider (1) speed-up and simplification of
41
analytical procedures, (2) analytical cost (maintenance of analytical instruments and
42
reagents), and (3) environmental impact and influence on health of analysts by
43
consumption of a large amount of toxic organic solvent (Frenich et al., 2008). We have
44
recently reported an environmentally friendly sample preparation method using water as
45
an extractant for hydrophilic pesticides in agricultural samples with conventional HPLC
46
(Watanabe, Kobara, Baba & Eun, 2013). Although the proposed method (about 50 mL
47
of organic solvent per sample) contributes to organic solvent-saving, the applicability of
48
the method has been limited to only hydrophilic pesticides. Therefore, we decided to
49
conduct the study for the aim of (1) clarifying the technical limitation by applying the
Page 3 of 23
50
method to more hydrophobic pesticides, (2) improving the recoveries by using aqueous
51
acetonitrile (MeCN), and (3) assessing the developed method by analyzing artificially
52
spiked and real agricultural samples.
53 54
2. Experimental
55
2.1. Chemicals and reagents
56
Certified standards of pesticides were purchased from Wako Pure Chemical Industries
57
Ltd. (Osaka, Japan), Kanto Chemical Co., Inc. (Tokyo, Japan), and Dr. Ehrenstorfer
58
(Augsburg, Germany). Pesticide analysis-grade and HPLC-grade organic solvents were
59
obtained from Wako Pure Chemical Industries Ltd.. Water used for HPLC was prepared
60
directly in the laboratory with a Milli-Q water purification system (Millipore Corp.,
61
Bedford, MA). Cartridges used for SPE were Oasis HLB (225 mg; Waters, Milford,
62
MA) and Envi-Carb/LC-NH2 (500 mg + 500 mg/6 mL; Supelco, Bellefonte, PA).
63
Individual standard pesticide stock solutions (1,000 µg mL-1) were prepared in MeCN
64
and stored at 4°C in the dark. They were stable over a period of at least six months.
65
Standard working solution was daily prepared by appropriate dilution of each stock
66
solution with MeCN. The standard working solution of each pesticide was used as
67
spiking solution and to prepare the calibration standard solutions, at the concentration
68
levels between 0.01 and 2 µg mL-1.
69 70
2.2. Preparation of artificially spiked samples
71
Tomatoes, green peppers, and spinaches were obtained from local grocery stores. Each
72
vegetable sample was placed in a food cutter and chopped thoroughly until
73
homogeneous. In all cases the chopped samples were spiked with a spiking solution of
Page 4 of 23
74
the test analytes in MeCN such that the concentrations in the sample were 0.1, 0.5, and
75
1.0 mg kg-1. The spiked samples were allowed to stand for 30 min before extraction.
76 77
2.3. Preparation of agricultural samples with field-incurred residues
78
Three kinds of agricultural samples were grown in a plastic greenhouse on arable land
79
of the National Institute for Agro-Environmental Sciences. Each sample in the
80
harvesting stage was sprayed with mixed several pesticide formulations diluted with
81
water according to the manufacturer's labels using a handy sprayer, and then was
82
harvested at 1, 3, and 7 days after spraying. After harvesting, the residue samples were
83
placed in 500 mL of glass jars and frozen at −20°C until extraction.
84 85
2.4. Sample extraction
86
2.4.1. Optimized sample preparation
87
To 5 g of spiked or real sample, 15 mL of MeCN/water (1:1, v/v) was added and
88
extracted for 3 min with a high-speed Polytron PT2100 homogenizer (Kinematica,
89
Lucerne, Switzerland). The mixture was centrifuged (10 min, 10,000 rpm), and the
90
supernatant was passed through a cellulose filter paper on a Büchner funnel with suction.
91
The solid residue in the tube was extracted again with 10 mL of MeCN/water (1:1, v/v),
92
and then the mixture was centrifuged and filtered. After the combined sample extract
93
was diluted with 8 mL of water, the diluted extract was percolated through an Oasis
94
HLB cartridge preconditioned with 6 mL of methanol (MeOH) and 6 mL of ultra-pure
95
water. The cartridge was rinsed with 5 mL of ultra-pure water and vacuum-dried for 10
96
min to remove excess water. Finally, the retained pesticides were eluted with 10 mL of
97
MeOH and the eluate was concentrated to a final volume of about 1 mL under reduced
Page 5 of 23
98
pressure. The residue was reconstituted in 2 mL of MeCN/toluene (3:1, v/v) and the
99
solution was applied to an Envi-Carb/LC-NH2 cartridge preconditioned with 10 mL of
100
MeCN/toluene (3:1, v/v). The retained pesticides were eluted with 20 mL (40 mL if
101
necessary) of MeCN/toluene (3:1, v/v). The eluate was concentrated under reduced
102
pressure and evaporated under a gentle nitrogen stream at 50°C. The residue was
103
reconstituted in 1 mL of MeCN and syringe filtered using a 0.45 µm PTFE filter
104
(Millipore, Billerica, MA) into an autosampler vial.
105 106
2.4.2. Reference multiresidue pesticide analytical method authorized in Japan
107
Using the Japanese authorized official method (JMHLW, 2006) selected as the
108
reference method in this study, we confirmed equivalency of the analytical results
109
obtained from the proposed sample preparation method.
110
To 20 g of sample, 50 mL of MeCN was added and extracted for 3 min with a high-
111
speed homogenizer. The mixture was filtrated with suction, and the solid residue on the
112
funnel was extracted again with 20 mL of MeCN. Both extracts were accurately made
113
up to 100 mL with MeCN in a volumetric flask, and then 20 mL aliquots of the extract,
114
equivalent to 4 g of sample, was mixed with 10 g of sodium chloride and 20 mL of
115
0.5M phosphate buffer (pH 7.0). The mixture was vigorously shaken for 5 min, and
116
stood for about 10 min. After the aqueous phase was discarded, the MeCN phase was
117
anhydrated, filtrated, and then concentrated. The residue was similarly cleaned-up with
118
an Envi-Carb/LC-NH2 cartridge as described above.
119 120
2.5. HPLC−DAD analysis
Page 6 of 23
121
The HPLC system consisted of an Agilent 1100 series equipped with a quaternary pump,
122
an autosampler, a column oven, and a DAD. The chromatographic separation was
123
performed in a SunFire C18 column (250 mm × 4.6 mm, 5 µm particle size) in
124
combination with a SunFire C18 guard column (20 mm × 4.6 mm, 5 µm particle size)
125
(Waters). The column temperature was maintained at 20°C. The mobile phase consisted
126
of MeCN/water (70:30, v/v) at a flow rate of 0.7 mL min-1. A volume of 20 µL was
127
injected for both standard and sample solutions. The detection wavelengths were 230,
128
246, 258, and 280 nm.
129 130
2.6. Analytical performance of HPLC−DAD
131
The external standard procedure was used and calibration curves constructed by plotting
132
peak area (y) against concentration (x) using several concentration levels and following
133
linear regression analysis. A repeatability study at 1.0 µg mL-1 with three consecutive
134
injections for the same day (n = 3) in five different days (n = 15) was carried out. Table
135
1 shows the CV values obtained for both retention times and peak areas for all the
136
pesticides. As it can be observed, acceptable precision was obtained in all cases:
137
intraday CV values were below 2%, while interday CV values were below 5%,
138
respectively. Table 1 also shows the calibration parameters. As it can be seen,
139
coefficient of regression (r) were higher than 0.999 for all cases. The limits of detection
140
(LODs) estimated by a signal-to-noise ratio of 3 were in the range of 5 ng mL-1 and 20
141
ng mL-1.
142 143
2.7. Evaluation of matrix effects
Page 7 of 23
144
Matrix effect, expressed as a signal from the pesticide in matrix compared to the signal
145
in pure solvent (mobile phase), were tested in all matrices. A mixture of pesticides was
146
added into an aliquot of blank extract in mobile phase, producing a final concentration
147
of 0.1 mg kg-1 of each agricultural sample. The effect was evaluated according to a
148
method described by Stahnke et al. (2012).
149 150
3. Results and discussion
151
3.1. Limiting point of water-based extraction and applicability of aqueous MeCN-based
152
extraction
153
The suitability of the previously proposed water-based extraction (Watanabe et al.,
154
2013) was applied to the determination of the agricultural samples spiked with relatively
155
hydrophobic pesticides. Although the hydrophilic pesticides such as neonicotinoids can
156
be recovered quantitatively from tested agricultural samples (Watanabe et al., 2013), the
157
drastic decrease of the recoveries of the tested pesticides in this study were nearly as we
158
expected (Fig. 1-(a)).
159
We confirmed the elution profiles of two SPE cartridges to locate the factor of low
160
recoveries. The elution rates of all pesticides from Envi-Carb/LC-NH2 SPE cartridge
161
were higher than 84% (see Fig. S1 in the Supporting Information). On the other hand,
162
those of pesticides less than 1 mg L-1 of water solubility from Oasis HLB SPE cartridge
163
decreased gradually (Fig. S1). Therefore, the low recoveries of pesticides (more than 1
164
mg L-1 of water solubility) in the water-based extraction resulted from inadequate
165
extraction efficiency of water, and those of pesticides showing high hydrophobicity
166
(water solubility < 1 mg L-1) were attributed to the extraction efficiency of water
167
together with inadequate retention to Oasis HLB SPE cartridge.
Page 8 of 23
168
To improve the recoveries of pesticides considering the feasible smallest amount of
169
organic solvent consumption, mixture of MeCN and water (1:4, 2:3, and 1:1, v/v) as
170
extractant were studied. As shown in Fig. S2-(a) (see in the Supporting Information),
171
the recoveries improved significantly with increased MeCN concentrations. Moreover,
172
when using MeCN/water (1:1, v/v), the highest hydrophobic pesticides such as
173
pyridaben (water solubility = 0.012 mg L-1) were extractable quantitatively from all
174
tested samples. However, the recoveries of pesticides showing more than 1 mg L-1 of
175
water solubility fell drastically with MeCN concentration.
176
The elution profiles when applying each pesticide dissolved in mixture of MeCN and
177
water (1:4, 2:3, and 1:1, v/v) to Oasis HLB SPE cartridge are shown in Fig. S2-(b).
178
Results show that although water containing higher MeCN concentration can extract
179
hydrophobic pesticides, it causes low recoveries of relatively hydrophilic pesticides
180
such as myclobutanil. It was thought that the inadequate retention to the cartridge
181
participated in the low recoveries rather than the loss at extraction stage with
182
MeCN/water (1:1).
183
Using the findings, we improved the low retention to the cartridge by adjusting MeCN
184
concentration to about 40% (v/v), which showed best retention to the cartridge (Fig. S2-
185
(b)) with water after extraction with MeCN/water (1:1, v/v). As Fig. 1-(b) shows, the
186
combination of extraction with MeCN/water (1:1, v/v) and direct application of sample
187
extract to the SPE cartridge after adjustment of MeCN concentration was successful
188
because diverse pesticides recovers quantitatively from all tested samples.
189 190
3.2. Matrix effect
Page 9 of 23
191
An important issue in the method development of pesticide residue analysis is the
192
possible occurrence of matrix effect. Fig. 2 shows matrix effects of 28 pesticides in the
193
proposed method and the reference method. Most pesticides in the tested matrix extracts
194
showed no considerable signal suppression or enhancement (matrix effect within ±20%),
195
which it is likely to be an obstacle to accurate determination (Mol, Plaza-Bolaños,
196
Zomer, de Rijk, Stolker, & Mulder, 2008). Boscalid showed a signal enhancement (31%
197
in the proposed method; 26% in the reference method) only tomato samples. The degree
198
of matrix effect seems to vary slightly according to the kind of agricultural samples
199
(Payá et al., 2007; Mol et al., 2008; Romero-González et al., 2011). In the proposed
200
sample preparation method, pesticides that were analyzed without substantial matrix
201
effects (matrix effect with in ±10%, Fig. 2) were 68% of the whole. Therefore, it might
202
be inferred that the clean-up efficiency was superior to the reference method by which
203
pesticides showing matrix effect within ±10% were about 40% of the whole. The
204
representative chromatograms of real green pepper samples treated with six kinds of
205
pesticides are shown in Fig. 3.
206 207 208
3.3. Analysis of spiked samples and evaluation of validity of proposed method using real samples
209
The accuracy of the proposed method was estimated using recovery experiments
210
conducted at three concentration levels (Table 2). For all matrices, the results obtained
211
for all analytes were satisfactory, with recoveries of 70─120% and CV values below
212
20% (EC, 2009).
213
For the evaluation of analytical methods under development, it has been acknowledged
214
that recoveries of field-incurred analytes from environmental matrices are far more
Page 10 of 23
215
realistic than recoveries based on laboratory spiking into the sample matrices (Pylypiw,
216
Jr., Arsenault, Thetford & Mattina, 1997). Miniaturization of sample volume for
217
extraction can be one effective means to reduce organic solvent consumption during
218
sample preparation (Wan & Wong, 1996). The validity of small sample volume was
219
assessed using real samples treated with several pesticide formulations. The analytical
220
results obtained using the proposed method were compared with those obtained using
221
the reference method (JMHLW, 2006). The determined concentrations of pesticides in
222
samples prepared with the proposed method were equivalent with those determined
223
using the reference method (r > 0.98) (Fig. 4). These results strongly indicate that the
224
reduction in sample volume does not affect substantially the accuracy of the proposed
225
method. Moreover, they suggest the possibility of reducing organic solvent
226
consumption in the extraction stage by reducing the sample volume. The proposed
227
method (about 60 mL of organic solvent per sample) was possible to reduce the
228
consumption of organic solvent 60% in comparison with the reference method (about
229
150 mL per sample) (JMHLW, 2006).
230 231
4. Conclusions
232
The multiresidue method of extracting pesticides from agricultural samples using an
233
environmentally friendly extraction method with a small sample and MeCN/water (1:1,
234
v/v) for conventional HPLC−DAD analysis has been demonstrated. Incorporating
235
adjustment of MeCN concentration was the key factor for high extraction efficiency and
236
stable retention of analytes to the Oasis HLB SPE cartridge. The use of MeCN/water
237
(1:1, v/v) and miniaturization of the sample volume can contribute greatly to reduction
238
of the organic solvent consumption in sample preparation procedures for conventional
Page 11 of 23
239
HPLC−DAD used in this study. The proposed method is unsuitable for the extraction of
240
some highly hydrophobic pesticides.
241 242
Acknowledgement
243
We sincerely express our gratitude to Mr. Takahiro Ara and Mr. Hiroshi Yamaguchi
244
(National Institute for Agro-Environmental Sciences) for support in preparation of real-
245
world agricultural samples.
246 247
References
248
EC. Method Validation and Quality Control Procedures for Pesticide Residues Analysis
249
in
Food
and
Feed,
2009.
URL
250
http://ec.europa.eu/food/plant/protection/resources/qualcontrol_en.pdf#search=%27
251
No.%20SANCO/10684/2009%27. Accessed 6 September 2013.
252
Frenich, A.G., Vidal, J.L.M., Pastor-Montoro, E., & Romero-González, R. (2008).
253
High-throughput determination of pesticide residues in food commodities by use of
254
ultra-performance liquid chromatography−tandem mass spectrometry. Analytical
255
and Bioanalytical Chemistry, 390, 947−959.
256
Hiemstra, M. & de Kok, A. (2007). Comprehensive multi-residue method for the target
257
analysis of pesticides in crops using liquid chromatography−tandem mass
258
spectrometry. Journal of Chromatography A, 1154, 3−25.
259
JMHLW (Japanese Ministry of Health, Labour and Welfare). Analytical Methods for
260
Residual Compositional Substances of Agricultural Chemicals, Feed Additives, and
261
Veterinary
Drugs
in
Food,
2006.
URL
Page 12 of 23
262
http://www.mhlw.go.jp/english/topics/foodsafety/positivelist060228/dl/060526-
263
1a.pdf. Accessed 6 September 2013.
264
Mol, H.G.J., Plaza-Bolaños, P., Zomer, P., de Rijk, T.C., Stolker, A.A.M., & Mulder,
265
P.P.J. (2008). Toward a generic extraction method for simultaneous determination
266
of pesticides, mycotoxins, plant toxins, and veterinary drugs in feed and food
267
matrixes. Analytical Chemistry, 80, 9450−9459.
268
Payá, P., Anastassiades, M., Mack, D., Sigalova, I., Tasdelen, B., Oliva, J., & Barba, A.
269
(2007). Analysis of pesticide residues using the Quick Easy Cheap Effective Rugged
270
and Safe (QuEChERS) pesticide multiresidue method in combination with gas and
271
liquid chromatography and tandem mass spectrometric detection. Analytical and
272
Bioanalytical Chemistry, 389, 1697−1714.
273
Pylypiw, Jr., H.M., Arsenault, T.L., Thetford, C.M., & Mattina, M.J.I. (1997).
274
Suitability of microwave-assisted extraction for multiresidue pesticide analysis of
275
produce, Journal of Agricultural and Food Chemistry, 45, 3522−3528.
276
Romero-González, R., Frenich, A.G., Vidal, J.L.M., Prestes, O.D., & Grio, S.L. (2011).
277
Simultaneous determination of pesticides, biopesticides and mycotoxins in organic
278
products applying a qucik, easy, cheap, effective, rugged and safe extraction
279
procedure and ultra-high performance liquid chromatography−tandem mass
280
spectrometry. Journal of Chromatography A, 1218, 1477−1485.
281
Seccia, S., Fidente, P., Montesano, D., & Morrica, P. (2008). Determination of
282
neonicotinoid insecticides residues in bovine milk samples by solid-phase extraction
283
clean-up and liquid chromatography with diode-array detection. Journal of
284
Chromatography A, 1214, 115−120.
Page 13 of 23
285
Stahnke, H., Kittlaus, S., Kempe, G., & Alder, L. (2012). Reduction of matrix effects in
286
liquid chromatography−electrospray ionization−mass spectrometry by dilution of
287
sample extracts: How much dilution is needed? Analytical Chemistry, 84,
288
1474−1482.
289 290
Wan, H.B. & Wong, M.K. (1996). Minimization of solvent consumption in pesticide residue analysis. Journal of Chromatography A, 754, 43−47.
291
Watanabe, E., Kobara, Y., Baba, K., & Eun, H. (2013). Reduction of hazardous organic
292
solvent in sample preparation for hydrophilic pesticide residues in agricultural
293
products with conventional liquid chromatography. Journal of Agricultural and
294
Food Chemistry, 61, 4792−4798.
295
Page 14 of 23
296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311
Figure captions Figure 1. Recoveries (n = 3) of water-based extraction (a) and the proposed extraction method (b) in agricultural samples at the level of 1 mg kg-1. The values inside brackets show water solubility (mg L-1) for each pesticide. Figure 2. Matrix effects in the proposed method (a) and the reference method (b). Figure 3. Representative HPLC chromatograms of the proposed method (a) and of the reference method (b) of real green pepper samples harvested at 7 days after spraying: 1, azoxystrobin; 2, myclobutanil; 3, cyazofamid; 4, lufenuron; 5, flufenoxuron; and 6, hexythiazox. Figure 4. Comparison of the analytical results between the proposed method and the reference method. Each symbol is the following: (□), cyazofamid; (∆), flufenoxuron (common to all samples); (○), azoxystrobin; (◊), lufenuron (common to tomato and green pepper samples); (●), myclobutanil; (■), hexythiazox (green pepper samples only); (●), phenthoate (spinach samples only). Each point is the average of individual quintuplicate determinations. The dotted line corresponds to a perfect correlation (y = x).
Page 15 of 23
312
Figure 1 (a) water-based extraction method
(b) proposed method 120
■ tomato □ green pepper ■ spinach
■ tomato □ green pepper ■ spinach
110
110
100
100
90
90
80 70 60
Average recovery (%)
Average recovery (%)
120
80 70 60
50
50
40
40
30
30
20
20
10
10
0
0
313
Page 16 of 23
Figure 2
Number of pesticides (%)
60
60
(a) proposed method ■ tomato □ green pepper ■ spinach
(b) reference method ■ tomato □ green pepper ■ spinach
50
50
40
40
Number of pesticides (%)
314
30
20
10
20
10
0
0 medium (-40~-20%)
315
30
minor no significant no significant (-20~-10%) (-10~0%) (0~10%)
minor (10~20%)
medium (20~40%)
not significant signal suppression
signal enhancement
medium (-40~-20%)
minor no significant no significant (-20~-10%) (-10~0%) (0~10%)
minor (10~20%)
medium (20~40%)
not significant signal suppression
signal enhancement
Page 17 of 23
Figure 3
(b) reference method
(a) proposed method
10
10
6 (0.15 mg kg-1)
9
6 (0.15 mg kg-1)
9 -1
8
4 (0.12 mg kg-1)
5
230 nm 3 (0.20 mg kg-1)
3 2 1
Absorbance (mAU)
7
6
4
5 (0.22 mg kg )
8
5 (0.23 mg kg-1)
7
4 (0.13 mg kg-1)
6 5
230 nm
4
3 (0.18 mg kg-1)
3 2 1
0
280 nm
-1
0
280 nm
-1
-2 0
5
10
15
20
25
30
35
-2 0
5
10
15
Time (min)
20
25
30
35
Time (min)
25
25 -1
1 (0.57 mg kg ) 20
20
15
2 (0.17 mg kg-1)
10 5 0
Absorbance (mAU)
Absorbance (mAU)
Absorbance (mAU)
316 317
1 (0.51 mg kg-1)
15
2 (0.14 mg kg-1) 10 5 0
7
8
9 Time (min)
10
7
8
9
10
Time (min)
Page 18 of 23
Figure 4 0.4
(a) tomato y = 1.2864x − 0.0243, r = 0.9847
Proposed method (mg kg-1)
0.3
0.2
0.1
0.0 0.0
0.1
0.2 Reference method (mg kg-1)
0.3
0.4
1.6
(b) green pepper y = 1.0954x − 0.0034, r = 0.9977 1.4
1.2 Proposed method (mg kg-1)
318 319 320
1.0
0.8
0.6
0.4
0.2
0.0 0.0
0.2
0.4
0.6 0.8 1.0 Reference method (mg kg-1)
1.2
1.4
1.6
Page 19 of 23
321 322 323
0.6
Proposed method (mg kg-1)
(c) spinach y = 1.0568x − 0.0027, r = 0.9971
0.4
0.2
0.0 0.0
0.2 0.4 Reference method (mg kg-1)
0.6
Page 20 of 23
1 2
Table 1. Results of the repeatability (expressed as %CV) obtained for the HPLC-DAD procedure (data given for 1.0 g mL-1) and calibration data for the selected 28 kinds of pesticides in the current work. Pesticide
Azoxystrobin Boscalid Bromopropylate Chlorfenapyr Chlorfluazuron Chromafenozide Cyazofamid Cyflufenamid Diethofencarb Diflubenzuron Etofenprox Famoxadone Fipronil Flubendiamide Flufenoxuron Hexythiazox Iprodione Isoxathion Kresoxim-methyl Lufenuron Myclobutanil Phenthoate Pyraclofos
Detection wavelength (nm) 230 230 230 230 230 230 280 230 246 258 230 230 230 230 230 230 230 258 230 230 230 230 258
Intraday precision (n = 3) tR
Interday precision (n = 15)
Peak area 0.05 0.1 0.05 0.1 0.1 0.02 0.06 0.1 0.1 0.1 0.1 0.05 0.06 0.1 0.2 0.09 0.05 0.1 0.1 0.1 0.06 0.1 0.1
0.6 1.1 0.9 0.8 1.1 0.9 1.0 0.6 0.6 0.8 1.3 0.6 1.2 0.7 1.5 1.4 0.4 1.0 0.9 1.9 1.3 0.6 0.2
tR
Calibration data
Peak area 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.3
1.4 1.3 2.6 3.9 2.2 2.4 3.1 2.3 1.8 2.4 2.5 1.8 2.9 1.8 4.1 3.0 3.0 1.1 1.0 2.7 3.9 2.8 1.2
Equation of calibration curve
Linearity (g mL-1)
y = 74.3x + 0.68 y = 46.8x + 0.10 y = 44.6x − 2.55 y = 28.5x + 0.31 y = 45.4x − 0.85 y = 30.4x − 1.30 y = 38.2x + 0.64 y = 31.4x + 1.19 y = 45.8x − 0.24 y = 27.4x − 0.20 y = 30.3x + 1.64 y = 48.6x − 1.85 y = 31.2x − 1.45 y = 25.9x + 0.94 y = 35.4x + 1.15 y = 47.5x + 1.19 y = 32.8x − 0.15 y = 128.9x + 2.52 y = 33.1x + 0.55 y = 43.0x − 0.85 y = 14.9x + 0.32 y = 23.6x + 0.99 y = 54.3x + 0.87
0.01−2 0.02−2 0.02−2 0.04−2 0.04−2 0.02−2 0.01−2 0.02−2 0.01−2 0.03−2 0.01−2 0.01−2 0.04−2 0.03−2 0.03−2 0.02−2 0.01−2 0.01−2 0.04−2 0.02−2 0.04−2 0.02−2 0.01−2
r 1.0000 1.0000 0.9996 0.9998 0.9998 1.0000 0.9999 0.9999 1.0000 1.0000 0.9998 0.9998 0.9998 0.9998 0.9999 0.9999 1.0000 1.0000 1.0000 0.9999 0.9995 0.9999 1.0000
LOD (ng mL-1) 5 10 10 20 20 10 5 10 5 15 5 5 20 15 15 10 5 5 20 10 20 10 5
Page 1 of 3
Pyridaben Pylidalyl Pyriproxyfen Teflubenzuron Trifloxystrobin
230 230 230 230 230
0.03 0.1 0.08 0.04 0.07
1.0 1.5 0.2 1.4 1.4
0.2 0.4 0.3 0.2 0.3
2.9 4.9 2.6 3.1 3.7
y = 32.5x + 1.57 y = 28.5x + 0.02 y = 55.0x + 0.90 y = 35.8x − 0.95 y = 34.5x − 0.30
0.01−2 0.02−2 0.02−2 0.01−2 0.02−2
1.0000 0.9997 1.0000 0.9998 0.9999
5 10 10 5 10
3 4
Page 2 of 3
5 6
Table 2. Recoveries and variations obtained for 10 pesticides artificially spiked in tomato, green pepper, and spinach samples and analyzed with HPLC−DAD. Average recovery (%) (CV) (n = 5 replicates) Tomato -1
Spiked level (mg kg ) Boscalid Cyflufenamid Diethofencarb Diflubenzuron Flubendiamide Flufenoxuron Isoxathion Kresoxim-methyl Phenthoate Pyraclofos
0.1 100 (8) 107 (3) 101 (8) 98 (3) 105 (11) 97 (4) 86 (2) 106 (4) 79 (2) 95 (3)
Green pepper
Spinach
0.5
1.0
0.1
0.5
1.0
0.1
101 (2) 99 (6) 82 (8) 94 (2) 104 (4) 92 (2) 89 (2) 95 (3) 85 (5) 94 (3)
109 (2) 101 (1) 76 (8) 92 (1) 100 (4) 92 (1) 88 (2) 97 (2) 84 (4) 94 (1)
87 (5) 85 (5) 71 (8) 96 (4) 100 (1) 91 (10) 77 (9) 93 (5) 87 (8) 94 (2)
91 (3) 90 (3) 72 (6) 97 (2) 96 (15) 96 (3) 86 (4) 92 (2) 81 (7) 96 (3)
92 (1) 94 (1) 75 (1) 93 (2) 95 (6) 93 (2) 90 (3) 94 (3) 89 (2) 95 (2)
105 (9) 89 (3) 110 (5) 94 (4) 119 (5) 93 (12) 95 (2) 91 (4) 105 (7) 88 (5)
0.5 99 (2) 89 (2) 77 (2) 94 (1) 101 (2) 105 (10) 89 (2) 91 (1) 85 (3) 96 (2)
1.0 97 (2) 93 (2) 79 (11) 95 (2) 98 (7) 96 (10) 90 (1) 92 (1) 88 (5) 97 (3)
7 8
Page 3 of 3
334 335 336 337 338 339 340 341 342 343 344 345 346 347