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Article

Multi-Residue Analysis of Pesticide Residues in Crude Pollens by UPLC-MS/MS

by
Zhou Tong
1,
Yan-Can Wu
1,2,
Qiong-Qiong Liu
1,
Yan-Hong Shi
1,
Li-Jun Zhou
1,
Zhen-Yu Liu
1,
Lin-Sheng Yu
1 and
Hai-Qun Cao
1,*
1
School of Plant Protection, Anhui Agricultural University, Hefei 230036, China
2
Hefei Testing and Inspection Center for Agricultural Products Quality, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Molecules 2016, 21(12), 1652; https://doi.org/10.3390/molecules21121652
Submission received: 6 November 2016 / Revised: 24 November 2016 / Accepted: 24 November 2016 / Published: 1 December 2016
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Abstract

:
A multi-residue method for the determination of 54 pesticide residues in pollens has been developed and validated. The proposed method was applied to the analysis of 48 crude pollen samples collected from eight provinces of China. The recovery of analytes ranged from 60% to 136% with relative standard deviations (RSDs) below 30%. Of the 54 targeted compounds, 19 pesticides were detected. The major detection rates of each compound were 77.1% for carbendazim, 58.3% for fenpropathrin, 56.3% for chlorpyrifos, 50.0% for fluvalinate, 31.3% for chlorbenzuron, and 29.2% for triadimefon in crude pollen samples. The maximum values of each pesticide were 4516 ng/g for carbendazim, 162.8 ng/g for fenpropathrin, 176.6 ng/g for chlorpyrifos, 316.2 ng/g for fluvalinate, 437.2 ng/g for chlorbenzuron, 79.00 ng/g for triadimefon, and so on. This study provides basis for the research on the risks to honeybee health.

Graphical Abstract

1. Introduction

Honeybee, belonging to the insect order Hymenoptera [1], is an important pollinator for natural ecosystems and agricultural crops [2]. Today, more than 75% of crop species worldwide—including oil crops, fruits, and vegetables—benefit from insect pollination. Farmers generally rely on the honeybee for providing food production, worth approximately 200 billion U.S. dollars [3]. Meanwhile, a great deal of hive products produced by the honeybee supply humans with various sources of nutrition.
However, a very recent phenomenon of colony collapse disorder (CCD), involving the sudden and massive disappearance of bee colonies around the world, is worrisome [4]. Within the past several years, due to global decline in honeybee population, honeybee health is a matter of public concern [4,5,6]. Since 2006, a large number of colonies have vanished. This phenomenon first emerged in North America. In Europe, the disappearance of the major honeybee colonies occurring in the vicinity of fields sprayed with pesticides was reported in 2012 [7]. CCD was reported in China in 2007 and rapidly spread nationwide. In addition, since 2007, CCD has been suspected to be occurring in Taiwan [8]. According to statistics, the number of colonies have reduced from 7.5 million in the 1990s to 6.8 million. The cause of CCD remains unknown, but there is an agreement among investigators that the possible cause of CCD could be several interacting factors [9]. The leading hypothesis for CCD links sublethal exposure to pesticides and other environmental factors, including parasitic infections and habitat loss, to honeybee losses and pollinator declines in general [10,11,12]. It is noteworthy that the pesticide-related hypothesis has received considerable attention since the emergence of CCD in 2006 [13]. Honeybees are exposed to pesticides in two ways, including foraging and contacting. Honeybees forage in an extensive range, leading them to contact contaminated food containing pollen, nectar, water, beebread, and so on. Consequently, it is particularly essential to investigate pesticide residues in pollens.
To date, a few large multi-residue methods have been developed for the determination of pesticide residues in pollens [9,14,15,16,17,18,19,20]. For example, in 2014, more than 115 pesticides were analyzed in bee pollen by LC-ESI-MS/MS. A sensitive and efficient method for routine pesticide multi-residue analysis in pollen was reported in 2015. The QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was designed and successfully used for the detection of 80 environmental contaminants in pollens, analysed by LC-MS and GC-MS. Two other multi-residue methods, zirconium-based sorbents (Z-Sep) and gel permeation chromatography (GPC), determined by GC-MS, were used in the analysis of 18 pesticides in pollen in 2015. Applications of these methods resulted in crucial information about the magnitude of pesticide contamination in those pollens, particularly in North America and Europe. As a large agricultural country, China faces a high risk of threat to honeybee health because of the wide application of pesticides used for plant protection. However, up to now, there has been no single report on the level of pesticide residue in crude pollens gathered in China. The present study aimed to analyze 48 crude pollen samples, which were collected from eight provinces of China. It provides a basis for studying the risk to honeybee health.

2. Results and Discussion

2.1. Choice of Mobile Phase

Due to a wide spectrum of analyzed pesticides in a pollen matrix and great diversities between their physicochemical properties and acid–base properties, it was quite difficult to acquire a well-defined chromatographic peak and reliable liquid chromatography analysis. So that each compound could be subjected to maximum sensitivity in ultraperformance liquid chromatography (UPLC), the mobile phase composition was optimized. We conducted the test using five kinds of mobile phases for multi-residue analysis (Table 1). The results showed that the sensitivity to all compounds could be maximized at the type I of the mobile phase.

2.2. Validation of the Dispersive Solid-Phase Extraction (dSPE) Clean-Up

To analyze a wide range of compounds, the multi-residue QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was used for pollen samples. Analytes were extracted from the matrix by an organic solvent that was subsequently salted out from an aqueous matrix. Only a large number of proteins, aminophenols, vitamins, and lipids were present in the pollen samples, but some higher polar pigments also existed. Since the pollen matrix is complex, an additional purification step (dSPE) was necessarily used to reduce the presence of interfering substances. Initially, a dSPE step was based on primary and secondary amine-bonded silica (PSA). Since 2006, this step has been further developed. In 2006, Leandro et al. [21] used PSA and octadecyl-bonded silica (PSA/C18) instead of PSA-bonded silica to limit apolar interferences of the matrix. In 2010, Mullin et al. successfully adopted this method and coupled it with analysis using a dual-layer cartridge containing PSA and graphitized carbon black (GCB) to purify components from wax, pollen, bee, and beebread [22]. Here, we designed four compositions to determine the optimal clean-up conditions (Table 2) among the recoveries during this step while considering matrix effects. The test indicated that the most advantageous procedure for the dSPE clean-up is that of level B (Table 2).

2.3. Limits of Detection and Quantification

The method limit of detection (LOD) was defined as the lowest concentration tested in which the signal response was three times more than the background noise from the chromatogram in both transitions. The method limit of quantification (LOQ) was defined as the lowest concentration tested in which the signal response was 10 times more than the background noise from the chromatogram in the quantification transition. The ion ratio is established with the respective ratio of a standard [17]. Both LOD and LOQ values are shown in Table 3. The LOD values for all substances were below 0.5 ng/g, with the exception of aldicarb sulfoxide, which had an LOD value of 0.5291 ng/g.

2.4. Linearity

Linearity was evaluated by assessing the detector responses of the objective compounds from matrix-matched calibration solutions, prepared by spiking blank extracts at eight concentration levels. Since there is diversity in the signal responses between each pesticide, the range of concentrations was set at three levels. A range of eight points was used, from 5 to 200 ng/g, with the exception of 2.5–100 ng/g for carbendazim, phosphamidon, pyrimethanil, azoxystrobin, triadimefon, triazophos, and diazinon and 10–1000 ng/g for thiamethoxam, imidacloprid, iprodione, and fluvalinate. The linear ranges of all pesticides are presented in Table 4. Good linearity was observed in all cases, with correlation coefficients better than 0.9902. For all compounds studied, the signal response was linear over the range studies. Therefore, the method had a good linear relationship.

2.5. Matrix Effects

In this study, one of the aims was to apply the multi-residue method to a great quantity of samples to receive a summarization of environmental contamination, so standard addition calibration could not be used [23,24,25]. The matrix effect in the mass spectrometric analysis was calculated by comparing the peak areas of the standards in the mobile phase with those of the same quantities of standards, which were added to the spiked samples following the extraction. The response of each pesticide in the mobile phase was designated as the 100% response value. Table 4 shows the spectrum with matrix effects for each compound determined in pollen. The diversification with matrix effects was dependent on the physicochemical properties of the compound and the matrix. These data indicated that the concentrations of the major pesticides could be affected by the matrix effect. The external calibration curve using matrix-matched standards was an efficacious method to overcome the matrix effects when a great quantity of complex samples such as pollens are to be determined.

2.6. Recovery Studies

Recoveries and relative standard deviations (RSDs, measurement of precision) of the target substances were determined by spiking blank pollen samples with three different concentrations (all compounds were spiked at 5, 50, and 500 ng/g, with the exception of carbendazim, phosphamidon, pyrimethanil, azoxystrobin, triadimefon, triazophos, and diazinon, which were spiked at 2.5, 25, and 250 ng/g, and thiamethoxam, imidacloprid, iprodione, and fluvalinate were spiked at 10, 100, and 1000 ng/g) and then analyzing five replicates for three levels named low, medium, and high. Results are exhibited in Table 3. The method showed excellent performance since recoveries for the majority of the compounds were within the satisfactory range of 70%–120%. Only dichlorvos, azoxystrobin, triadimefon, chlorbenzuron, profenofos, and pyridaben had accuracies not in the acceptable range of 60%–136%. The RSD values in all cases were below 20%; in addition, when chlorbenzuron was spiked in the low range, the RSD value was more than 30%. Consequently, the procedure described in the Section 3.2 is an accurate, sensitive, and efficient method for multi-residue analysis in pollens.

2.7. Real Sample

The multi-residue analytical method established above was applied to measure pesticide concentrations in 48 pollen samples collected from 11 apiaries in 8 provinces of China. The selected regions are characterized by agricultural events, and therefore they are prone to pollen polluted with pesticides. One hundred percent of the samples analyzed included at least one pesticide with the concentration ranging from 3.6 to 4516.4 ng/g. Of the 54 targeted compounds, 19 of them were detected. Table 5 presents the pesticides detected in 48 samples of pollen. The pesticides most commonly found in the pollens were: carbendazim (77.1%), fenpropathrin (58.3%), chlorpyrifos (56.3%), fluvalinate (50.0%), chlorbenzuron (31.3%), and triadimefon (29.2%). An emblematical chromatogram of a pollen sample including carbendazim, fenpropathrin, chlorpyrifos, and triadimefon is exhibited in Figure 1.
Since there are not any reported data about the pesticide residues in crude pollens in China, these results are interesting. As shown in Table 5, some neonicotinoid pesticides containing imidacloprid and acetamiprid were still found in pollens, but both detection rates and maximum values were of a relatively low range. This indicates that, with the increase of the reports related to the poisoning of honeybee with neonicotinoids, people use pesticides more and more cautiously during the flowering time of plants [2,3,26,27,28]. However, some pesticides highly toxic to honeybees were detected as before, such as fenpropathrin and chlorpyrifos. These would cause a huge impact on honeybees, including their behavior [29], enzyme activity [30,31,32], and so on. This suggests that the risk of pesticides highly toxic to honeybee health cannot be ignored. Thus, the investigators who focus on studying the honeybee’s health should accelerate the progress of similar researches. Meanwhile, as a compound with the highest detection rate, the maximum concentration of carbendazim reached 4516.4 ng/g. The possible reason for this is that, as a broad-spectrum pesticide for fungal disease management, carbendazim has been widely used to combat against some nectar plant diseases, including the disease caused by the fungus Sclerotinia sclerotiorum. Therefore, with the end of oilseed rape flowering, the detection rate of carbendazim also decreased. Currently, there are a few concerns [33] about the impact of fungicides on bees due to the absence of the acute lethal effect off fungicides on honeybees. Nevertheless, based on our results, a hypothesis that fungicides could bring some chronic effects on honeybees, including behavior, heredity, and so forth, should be proposed. The research studying how pesticides bring a high risk to honeybee health will be a long-term process.

3. Experimental Section

3.1. Chemicals and Standards

LC-grade acetonitrile and methanol were obtained from Tedia (Shanghai, China). Acetic acid, formic acid, magnesium sulfate anhydrous (MgSO4), and sodium acetate (NaOAc) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). C18, GCB, and PSA were obtained from Agilent Technologies (Santa Clara, CA, USA).
The compounds used in this study were selected following the requirement of Ministry of Agriculture of China (Beijing, China). All standard solutions of pesticides including methamidophos, methomyl, acephate, omethoate, carbendazim, methomyl, thiamethoxam, monocrotophos, imidacloprid, trichlorfon, dimethoate, acetamiprid, aldicarb, phosphamidon, dichlorvos, carbofuran, carbaryl, pyrimethanil, methidathion, phosmet, azoxystrobin, malathion, triadimefon, dimethomorph, triazophos, ethoprophos, iprodione, diflubenzuron, prochloraz, sulfotep, chlorbenzuron, fenthion, coumaphos, diazinon, phoxim, phorate, phosalone, difenoconazole, emamectin benzoate, profenofos, chlorpyrifos, fenpropathrin, pendimethalin, pyridaben, and fluvalinate (with purity equal than 1000 mg/L) were obtained from Agro-Environmental Protection Institute, Ministry of Agriculture of China. In addition, some pesticides with purity higher than or equal to 98.5%, including aldicarb-sulfoxide, aldicarb-sulfone, carbofuran-3-hydroxy, fenthion-sulfoxide, fenthion-sulfone, phorate-sulfoxide, phorate-sulfone, terbufos-sulfone, and terbufos-sulfoxide, were purchased from Dr. Ehrenstorfer GmbH (Augsburg, Germany). The standard stock solution of each compound at 100 mg/L was prepared in acetone or methanol, except carbendazim in dimethylformamide, and stored at −20 °C.

3.2. Sample Preparation

We followed QuEChERS method and made some modifications. First, 1 g of pollen samples were weighed in a 50 mL centrifuge tube, and 4 mL of water was added into the tube. The tube was shaken to blend the pollen. Then, 2 g portions of glass beads and 10 mL of 1% acetic acid mixture in acetonitrile were added and vortexed for 2 min at room temperature and for 10 min at −20 °C. Second, 0.5 g of MgSO4 and 2 g of NaOAc were added into each tube. The mixture was immediately hand-shaken for 1 min and centrifuged at 3500 rpm for 5 min. Subsequently, 5 mL of the acetonitrile fraction was transferred to a 15 mL centrifuge tube containing 1.25 g of the salt kits level B, described in Section 2.2. The tube was shaken by hand for 1 min and then centrifuged at 3500 rpm for 3 min. Finally, 2.5 mL of the extract was sampled in a 10 mL glass cone and was evaporated at 30 °C until dried under a gentle stream of nitrogen. Five hundred microliters of methanol was added, while the methanol layer was filtered through a 0.22 μm filter membrane and introduced into an autosampler vial for UPLC-MS/MS analysis [4,34,35,36].

3.3. UPLC-MS/MS Analysis

The UPLC-MS/MS instrument consists of a Waters Acquity ultraperformance liquid chromatograph (UPLC) equipped with a 1.7 μm, 2.1 mm × 100 mm particle size and Acquity BEH (ethylene bridged hybrid) C18 column (Waters, Milford, MA, USA) coupled to a Waters Xevo TQ triple quadrupole mass spectrometer operated in the positive electrospray ionization mode. The LC was operated under gradient conditions with mobile phases of water/methanol (98:2) + 0.05% formic acid (A) and methanol + 0.05% formic acid (B) at 40 °C [37]. It was run at 0.45 mL/min starting with 5% component B during a 0.25 min period of the injection time. Then, the composition changed to 100% component B and was maintained until 8.5 min after running. Ultimately, the mobile phase B decreased to 5% in 8.51 min and was held for 10 min to achieve re-equilibration. The total running time of analysis was 10 min. The injection volume was 3 μL.
The MS source temperature was set at 150 °C with nitrogen flow rates of 50 and 900 L/h for the cone and desolvation gases, respectively. The desolvation temperature was 500 °C. Argon was used as the collision gas with a flow of 0.15 mL/min. The mass spectrometer was operated in the multiple reaction monitoring mode (MRM) for monitoring two precursor/products ion transitions for each analyte. The target ion transition with the highest intensity (primary ion transition) was used for quantitation, whereas the second target ion transition was used for confirmation. Further confirmation was obtained through a product ion scan (PIC) for each peak, which was matched to a reference spectrum for each analyte. The quantification and confirmation calculations were determined using the software Target Lynx 4.1 (Waters Corp, Milford, MA, USA) implemented in the instrument. Ion transitions, cone voltages, collision energies, and dwell times for the analytes were shown in Table 6 [16,38].

3.4. Sample Collection

The pollen samples were provided in March, April, May, June, and July of 2016 by individual apiaries located in eight regions of China. For each month, the samples were collected in several colonies of individual apiary and repacked to obtain one crisper per apiary. All pollen samples were stored at −20 °C until further use. Figure 2 presents the location of the region of the sample collected in China.

4. Conclusions

In this study, an accurate and efficient modified QuEChERS protocol was established for determining 54 pesticide residues in crude pollen samples by UPLC-MS/MS analysis. More than 19 different compounds were detected in the samples collected in China. Although the maximum value of several pesticides detected in pollen is slightly lower than the LC50 that is reported for honeybee, the accumulation of pesticides would still endanger honeybee heath. With the appearance of the higher detection rates of carbendazim, fenpropathrin, chlorpyrifos, fluvalinate, chlorbenzuron, and triadimefon, evaluating the risk to honeybee health in the process of spraying pesticides will rely on this study. Further research on pesticide residue in other honeybee food will be necessary to better assess the risk to honeybee health.

Acknowledgments

This work was supported by the Earmarked Fund for China Agriculture Research System (No. CARS-45-KXJ9).

Author Contributions

Hai-Qun Cao, Yan-Hong Shi, and Lin-Sheng Yu conceived and designed the study. Zhou Tong, Yan-Can Wu, and Qiong-Qiong Liu performed the experiments. Yan-Can Wu and Zhou Tong analyzed data. Zhou Tong wrote the manuscript. Zhen-Yu Liu, Hai-Qun Cao and Li-Jun Zhou edited and revised the manuscript. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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  • Sample Availability: Samples of the compounds are available from the authors.
Molecules 21 01652 g001 550
Figure 1. An example of the extracted Quantification ion (MRM1) chromatograms of a pollen sample, indicating the presence of (A) carbendazim; (B) fenpropathrin; (C) chlorpyrifos; and (D) triadimefon.
Figure 1. An example of the extracted Quantification ion (MRM1) chromatograms of a pollen sample, indicating the presence of (A) carbendazim; (B) fenpropathrin; (C) chlorpyrifos; and (D) triadimefon.
Molecules 21 01652 g001
Molecules 21 01652 g002 550
Figure 2. Location of the region of the sample collected in China: A—Jilin Province; B—Shanxi Province; C—Shandong Province; D—Henan Province; E—Anhui Province; F—Hubei Province; G—Chongqing Province; H—Hainan Province.
Figure 2. Location of the region of the sample collected in China: A—Jilin Province; B—Shanxi Province; C—Shandong Province; D—Henan Province; E—Anhui Province; F—Hubei Province; G—Chongqing Province; H—Hainan Province.
Molecules 21 01652 g002
Table
Table 1. The composition of mobile phases.
Table 1. The composition of mobile phases.
Type Mobile Phase AMobile Phase B
Iwater/methanol (98:2) + 0.05% formic acidmethanol + 0.05% formic acid
II0.1% formic acidmethanol
III0.1% formic acidacetonitrile
IV0.05% formic acid + 5 mmol/L ammonium acetatemethanol
V0.05% formic acid + 5 mmol/L ammonium acetateacetonitrile
Table
Table 2. Four levels of the composition for dispersive solid-phase extraction (dSPE) clean-up.
Table 2. Four levels of the composition for dispersive solid-phase extraction (dSPE) clean-up.
LevelPSAC18GCBMgSO4
A50 mg50 mg0 mg150 mg
B50 mg50 mg3.75 mg150 mg
C50 mg50 mg7.5 mg150 mg
D50 mg50 mg15 mg150 mg
PSA (primary secondary amine), C18 (octadecyl-bonded silica), GCB (graphitized carbon black).
Table
Table 3. Limit of determination and quantification (LOD and LOQ) of the method, recovery of analytes, and repeatability (relative standard deviations, RSD) obtained in pollens.
Table 3. Limit of determination and quantification (LOD and LOQ) of the method, recovery of analytes, and repeatability (relative standard deviations, RSD) obtained in pollens.
CompoundLOD (ng/g)LOQ (ng/g)Recovery (%)RSD (%)
Low (ng/g)Medium (ng/g)High (ng/g)Low (ng/g)Medium (ng/g)High (ng/g)
Methamidophos0.05560.166710385.189.15.951.961.70
Acephate0.26910.807212387.791.212.51.391.52
Omethoate0.13830.414911289.693.53.751.791.93
Aldicarb-sulfoxide0.52911.587312093.397.53.333.012.02
Aldicarb-sulfone0.13430.403010898.498.13.700.812.05
Carbendazim0.10640.319110993.393.18.455.512.63
Methomyl0.03370.101011189.396.55.524.231.33
Thiamethoxam0.00280.00841251031014.631.780.71
Monocrotophos0.00510.015411290.996.93.572.212.68
Imidacloprid0.08090.242792.785.696.28.991.403.50
Trichlorfon0.12650.379412186.998.03.813.723.67
Dimethoate0.03660.109810587.292.49.561.832.84
Carbofuran-3-hydroxy0.03440.103294.790.199.36.451.852.03
Acetamiprid0.01140.034310386.997.75.951.412.25
Aldicarb0.04320.129593.386.198.76.554.581.30
Phosphamidon0.00370.011211786.995.53.942.132.56
Dichlorvos0.24830.745065.369.778.48.667.2613.60
Carbofuran0.00600.017988.087.595.64.554.321.26
Fenthion-sulfoxide0.02020.060510489.396.43.851.031.66
Carbaryl0.10870.326186.789.198.15.332.891.25
Fenthion-sulfone0.03690.110888.086.498.14.550.931.93
Pyrimethanil0.01450.0435nd82.189.9-1.121.36
Phorate-sulfoxide0.02440.073111290.996.57.142.542.09
Phorate-sulfone0.02410.072298.786.194.92.343.520.64
Methidathion0.00950.028696.088.41008.334.321.20
Phosmet0.03430.102994.785.390.82.443.5510.42
Terbufos-sulfone0.07590.227711293.698.06.193.082.45
Terbufos-sulfoxide0.02430.072910989.994.52.110.510.65
Azoxystrobin0.00620.018613191.797.19.352.661.90
Malathion0.06100.182910185.098.52.281.442.00
Triadimefon0.00290.008813389.691.23.461.791.52
Dimethomorph0.00490.014770.785.393.66.540.541.48
Triazophos0.00820.024611293.31017.146.491.82
Ethoprophos0.01770.053090.783.595.39.183.083.81
Iprodione0.06110.183390.085.591.413.68.663.83
Diflubenzuron0.00450.013681.381.992.511.45.381.75
Procholraz0.01660.049912096.393.72.036.452.71
Sulfotep0.01950.058589.387.293.95.172.434.94
Chlorbenzuron0.02260.067868.089.992.330.66.061.52
Fenthion0.05140.154286.791.788.313.32.190.94
Coumaphos0.00300.009088.01031009.097.104.39
Diazinon0.01760.052910186.490.74.563.212.70
Phoxim0.01350.040692.083.795.34.359.183.57
Phorate0.01540.046272.078.785.69.620.593.99
Phosalone0.01580.047597.386.795.68.553.243.16
Difenoconazole0.02420.072610485.984.43.852.344.52
Emamectin benzoate0.00080.002585.381.176.57.161.141.09
Profenofos0.01890.056812792.884.84.828.311.89
Terbufos0.14140.424197.377.186.48.555.121.85
Chlorpyrifos0.06380.191410374.781.912.53.762.78
Fenpropathrin0.04330.130092.075.580.07.531.624.77
Pendimethalin0.02360.070885.382.490.17.162.571.56
Pyridaben0.00700.021113685.384.85.881.081.89
Fluvalinate0.00680.020398.776.866.01.179.162.12
Table
Table 4. Matrix effects (ME), retention time (tR), linear range, linear regression equation, and linearity.
Table 4. Matrix effects (ME), retention time (tR), linear range, linear regression equation, and linearity.
CompoundME (%)tR (min)Linear Range (ng/g)Linear Regression EquationLinearity
Methamidophos−61.175–200Y = 140.5X + 99.140.9984
Acephate01.505–200Y = 61.56X − 69.020.9990
Omethoate−211.745–200Y = 266.0X + 94.550.9963
Aldicarb-sulfoxide−51.925–200Y = 38.60X + 39.180.9954
Aldicarb-sulfone−102.105–200Y = 112.0X + 38.230.9994
Carbendazim−102.262.5–100Y = 941.6X + 11.030.9993
Methomyl32.385–200Y = 98.08X − 28.380.9997
Thiamethoxam−242.5410–400Y = 66.00X + 134.20.9975
Monocrotophos−202.675–200Y = 1022X + 889.60.9967
Imidacloprid−133.0410–400Y = 71.91X − 48.570.9996
Trichlorfon−803.265–200Y = 138.7X + 12.850.9991
Dimethoate−763.295–200Y = 154.1X + 79.950.9986
Carbofuran-3-hydroxy−803.355–200Y = 179.0X + 101.90.9991
Acetamiprid−503.375–200Y = 645.1X + 279.10.9986
Aldicarb−243.975–200Y = 578.5X + 620.20.9954
Phosphamidon154.332.5–100Y = 207.4X − 73.140.9996
Dichlorvos−14.485–200Y = 232.7X − 24.550.9998
Carbofuran−384.585–200Y = 854.1X − 164.80.9995
Fenthion-sulfoxide−514.765–200Y = 780.0X + 66.080.9999
Carbaryl−304.805–200Y = 139.9X + 21.770.9985
Fenthion-sulfone−154.915–200Y = 165.1X − 26.610.9991
Pyrimethanil−255.032.5–100Y = 1810X − 63.220.9999
Phorate-sulfoxide−275.055–200Y = 938.3X + 576.20.9990
Phorate-sulfone−245.145–200Y = 302.9X − 5.8270.9998
Methidathion−45.395–200Y = 93.08X + 9.2930.9998
Phosmet−505.525–200Y = 106.4X + 64.630.9976
Terbufos-sulfone165.625–200Y = 96.91X − 66.940.9975
Terbufos-sulfoxide205.645–200Y = 192.5X − 80.410.9983
Azoxystrobin255.672.5–100Y = 363.1X + 48.650.9990
Malathion135.895–200Y = 151.5X − 111.60.9952
Triadimefon155.992.5–100Y = 310.0X − 135.00.9955
Dimethomorph−206.015–200Y = 168.5X − 169.40.9903
Triazophos166.052.5–100Y = 747.9X + 45.030.9995
Ethoprophos−16.195–200Y = 305.2X − 87.030.9992
Iprodione−466.3310–400Y = 55.78X − 106.20.9952
Diflubenzuron−66.345–200Y = 86.59X − 52.920.9941
Prochloraz−276.445–200Y = 587.4X − 606.30.9902
Sulfotep−336.445–200Y = 1144X − 10420.9955
Chlorbenzuron−276.485–200Y = 89.28X − 107.60.9940
Fenthion−666.515–200Y = 101.6X − 19.600.9983
Coumaphos−356.545–200Y = 63.55X − 95.860.9914
Diazinon96.552.5–100Y = 1220X − 596.60.9907
Phoxim−206.635–200Y = 49.86X + 5.4120.9969
Phorate−126.675–200Y = 33.04X − 65.920.9996
Phosalone−266.695–200Y = 49.75X − 34.820.9944
Difenoconazole−106.845–200Y = 520.7X − 417.00.9949
Emamectin benzoate−126.835–200Y = 1177X − 804.80.9931
Profenofos−257.055–200Y = 182.7X − 171.30.9935
Terbufos−347.105–200Y = 83.82X − 1.6360.9989
Chlorpyrifos−447.325–200Y = 327.6X + 67.460.9995
Fenpropathrin−467.325–200Y = 407.6X + 67.030.9999
Pendimethalin−267.345–200Y = 230.1X − 83.930.9987
Pyridaben−737.675–200Y = 1811X − 172.80.9984
Fluvalinate−777.7410–400Y = 472.3X + 334.30.9997
Table
Table 5. Several typical pesticides in pollen samples.
Table 5. Several typical pesticides in pollen samples.
CompoundPositive SampleTotal Number of SampleDetection Rate (%)Detected Concentration Ranges (ng/g)Max Value (ng/g)Central Values (ng/g)
carbendazim374877.13.200–4516451644.00
fenpropathrin284858.35.000–162.8162.821.70
chlorpyrifos274856.35.000–176.6176.623.60
fluvalinate274850.06.600–316.2316.233.00
chlorbenzuron154831.35.000–437.2437.227.00
triadimefon144829.22.600–79.0079.0019.70
acetamiprid8481.75.200–63.6063.608.300
imidacloprid7481.517.60–49.8049.8027.60
Table
Table 6. Ion transitions used for the quantification (MRM1) and confirmation (MRM2), dwell time, cone voltages, and collision energies for MS.
Table 6. Ion transitions used for the quantification (MRM1) and confirmation (MRM2), dwell time, cone voltages, and collision energies for MS.
CompoundTransitionsDwell Time (s)Cone Voltage (V)Collision Energy (eV)
MethamidophosQuantification ion 142 > 93.90.0501713
Confirmation ion 142 > 124.91713
AcephateQuantification ion 184.1 > 1430.03688
Confirmation ion 184.1 > 125.1818
OmethoateQuantification ion 214.1 > 125.10.0281622
Confirmation ion 214.1 > 183.11611
Aldicarb sulfoxideQuantification ion 207 > 890.0281314
Confirmation ion 207 > 1321310
Aldicarb sulfoneQuantification ion 223 > 860.0282214
Confirmation ion 223 > 1482210
CarbendazimQuantification ion 192.1 > 160.10.0282418
Confirmation ion 192.1 > 132.12428
MethomylQuantification ion 163 > 880.0281710
Confirmation ion 163 > 1061710
ThiamethoxamQuantification ion 292.1 > 210.90.0441812
Confirmation ion 292.1 > 1811824
MonocrotophosQuantification ion 224.1 > 127.10.0441516
Confirmation ion 224.1 > 98.11512
ImidaclopridQuantification ion 256.1 > 209.10.0282315
Confirmation ion 256.1 > 175.12320
TrichlorfonQuantification ion 257 > 1090.0282218
Confirmation ion 257 > 792230
DimethoateQuantification ion 230.1 > 1990.0281210
Confirmation ion 230.1 > 1251220
Carbofuran-3-hydroxyQuantification ion 238 > 1630.0282516
Confirmation ion 238 > 1812510
AcetamipridQuantification ion 223 > 1260.0282320
Confirmation ion 223 > 56.12315
AldicarbQuantification ion 212.8 > 88.90.0782016
Confirmation ion 212.8 > 115.92012
PhosphamidonQuantification ion 300.1 > 174.10.0281714
Confirmation ion 300.1 > 127.11725
DichlorvosQuantification ion 221 > 1090.0222322
Confirmation ion 221 > 792334
CarbofuranQuantification ion 222.1 > 165.10.0222516
Confirmation ion 222.1 > 1232516
Fenthion-sulfoxideQuantification ion 295 > 1090.0222932
Confirmation ion 295 > 2802918
CarbarylQuantification ion 202 > 1450.0221922
Confirmation ion 202 > 1171928
Fenthion-sulfoneQuantification ion 311 > 1250.0222922
Confirmation ion 311 > 1092928
PyrimethanilQuantification ion 200.2 > 1070.0224224
Confirmation ion 200.2 > 824224
Phorate-sulfoxideQuantification ion 277 > 96.90.0221532
Confirmation ion 277 > 1431520
Phorate-sulfoneQuantification ion 293 > 96.90.0221530
Confirmation ion 293 > 1151524
MethidathionQuantification ion 303 > 1450.0221010
Confirmation ion 303 > 85.11020
PhosmetQuantification ion 318 > 1600.0182014
Confirmation ion 340 > 214.13014
Terbufos-sulfoneQuantification ion 321.2 > 1710.0181912
Confirmation ion 321.2 > 971940
Terbufos-sulfoxideQuantification ion 305 > 1870.0181011
Confirmation ion 305 > 971040
AzoxystrobinQuantification ion 404 > 3720.0171715
Confirmation ion 404 > 3291730
MalathionQuantification ion 331 > 1270.0181812
Confirmation ion 331 > 791840
TriadimefonQuantification ion 294.1 > 197.20.0182215
Confirmation ion 294.1 > 69.32220
DimethomorphQuantification ion 388.1 > 300.90.0183020
Confirmation ion 388.1 > 1653030
TriazophosQuantification ion 314.1 > 161.90.0132218
Confirmation ion 314.1 > 118.92235
EthoprophosQuantification ion 243.2 > 1310.0081820
Confirmation ion 243.2 > 971831
IprodioneQuantification ion 330 > 244.70.0081216
Confirmation ion 330 > 2881215
DiflubenzuronQuantification ion 310.9 > 157.90.0082014
Confirmation ion 310.9 > 140.92036
ProcholrazQuantification ion 376 > 3080.0082015
Confirmation ion 376 > 2662015
SulfotepQuantification ion 323 > 970.0081732
Confirmation ion 323 > 1711715
ChlorbenzuronQuantification ion 309 > 155.90.0082226
Confirmation ion 309 > 138.82218
FenthionQuantification ion 279 > 168.90.0083018
Confirmation ion 279 > 1053028
CoumaphosQuantification ion 363.1 > 3070.0082116
Confirmation ion 363.1 > 2892124
DiazinonQuantification ion 305.1 > 1690.0082022
Confirmation ion 305.1 > 96.92035
PhoximQuantification ion 299 > 1290.0081213
Confirmation ion 299 > 153127
PhorateQuantification ion 261 > 970.0081428
Confirmation ion 261 > 751410
PhosaloneQuantification ion 367.9 > 181.90.0081214
Confirmation ion 367.9 > 110.91242
DifenoconazoleQuantification ion 406 > 251.10.0263725
Confirmation ion 406 > 111.13760
Emamectin benzoateQuantification ion 886.5 > 158.10.0222032
Confirmation ion 886.5 > 81.92064
ProfenofosQuantification ion 372.9 > 302.60.0222520
Confirmation ion 372.9 > 127.92540
TerbufosQuantification ion 289 > 1030.022128
Confirmation ion 289 > 57.21222
ChlorpyrifosQuantification ion 350 > 970.0222732
Confirmation ion 350 > 1982720
FenpropathrinQuantification ion 350.1 > 970.0221534
Confirmation ion 350.1 > 1251514
PendimethalinQuantification ion 252.2 > 212.20.0221210
Confirmation ion 252.2 > 194.11217
PyridabenQuantification ion 365.1 > 147.10.0221924
Confirmation ion 365.1 > 309.11912
FluvalinateQuantification ion 507 > 181.10.0221530
Confirmation ion 507 > 208.11512

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MDPI and ACS Style

Tong, Z.; Wu, Y.-C.; Liu, Q.-Q.; Shi, Y.-H.; Zhou, L.-J.; Liu, Z.-Y.; Yu, L.-S.; Cao, H.-Q. Multi-Residue Analysis of Pesticide Residues in Crude Pollens by UPLC-MS/MS. Molecules 2016, 21, 1652. https://doi.org/10.3390/molecules21121652

AMA Style

Tong Z, Wu Y-C, Liu Q-Q, Shi Y-H, Zhou L-J, Liu Z-Y, Yu L-S, Cao H-Q. Multi-Residue Analysis of Pesticide Residues in Crude Pollens by UPLC-MS/MS. Molecules. 2016; 21(12):1652. https://doi.org/10.3390/molecules21121652

Chicago/Turabian Style

Tong, Zhou, Yan-Can Wu, Qiong-Qiong Liu, Yan-Hong Shi, Li-Jun Zhou, Zhen-Yu Liu, Lin-Sheng Yu, and Hai-Qun Cao. 2016. "Multi-Residue Analysis of Pesticide Residues in Crude Pollens by UPLC-MS/MS" Molecules 21, no. 12: 1652. https://doi.org/10.3390/molecules21121652

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