Open Access

Determination of pesticide residues in honey: a preliminary study from two of Africa’s largest honey producers

International Journal of Food Contamination20163:14

DOI: 10.1186/s40550-016-0036-4

Received: 30 March 2016

Accepted: 18 August 2016

Published: 2 September 2016

Abstract

Background

The presence of pollutants in honey can influence honey bee colony performance and devalue its use for human consumption. Using liquid chromatography tandem mass spectrometry (LC-MS/MS), various clean-up methods were evaluated for efficient determination of multiclass pesticide contaminants in honey. The selected clean-up method was optimized and validated and then applied to perform a preliminary study of commercial honey samples from Africa.

Results

The most efficient method was primary-secondary amine (PSA) sorbent which was significantly different from the others (P <0.05; average recovery ~94 %) and was applied to analyze 96 pesticide residues in 28 retail honey samples from Kenya and Ethiopia. From our preliminary data, a total of 17 pesticide residues were detected at ~10-fold below maximum residue limit (MRL) established for food products except for malathion which was detected at almost 2-fold above its acceptable MRL.

Conclusions

A highly efficient approach for determining pesticide residues in honey with good recoveries was developed. All residue contaminants were detected at levels well below their acceptable MRLs except malathion suggesting that the retail honey analyzed is safe for human consumption. Although PSA clean-up method was selected as the most efficient for cleaning honey samples, omitting the clean-up step was the most economical approach with potential applicability in the food industry.

Keywords

Pesticide residues Honey bees Liquid chromatography-tandem mass spectrometry (LC-MS/MS) Honey Method development

Background

The recent sudden decline of honey bee colonies is of global concern not only because of pollination services they provide in food production process, but also due to honey production among other benefits. While there are multiple variables, including poor nutrition, pests, diseases, and loss of natural bee habitat, negatively affecting bee health, it is becoming increasingly clear that the widespread use of pesticides on agricultural crops is a major factor (Vanengelsdorp and Meixner 2010; Gill et al. 2012; Brodschneider and Crailsheim 2010). As such, to preserve honey bee health which is inextricably integrated with human health and to preserve the quality of bee by-products especially honey, requires regular monitoring using rigorous analytical methods to confirm product quality (Muli et al. 2014; Kujawski and Namiesnik 2008).

Honey is composed of over 300 compounds, mostly carbohydrates (>75 %) and water (~18 %), with minor components comprising of proteins, amino acids, vitamins, antioxidants, minerals, essential oils, sterols, pigments, phospholipids, and organic acids (Bogdanov et al. 2008; Kujawski and Namiesnik 2008). Whereas these diverse ranges of compounds make it a nutrient rich food commodity, they also make it a highly complex analytical matrix especially when analysing the presence of trace compounds such as toxins, pesticide residues and other environmental pollutants (Kujawski and Namiesnik 2008). The presence of pesticide residues and other contaminants in honey can have adverse health effects on bees and humans, decrease the quality of honey and devalue its beneficial properties (Bogdanov et al. 2008). Typically, pesticide residues in honey occurs when bees in search for food, visit crops that have been treated with various agro-chemicals and/or when beekeepers use chemicals to control bee pests or diseases (Bogdanov 2006). So far, several researchers have reported various residues of pesticides in honey at varying concentrations (De Pinho, et al. 2010; Irani 2009; Barganska et al. 2013; Blasco et al. 2011; Garcia-Chao et al. 2010; Herrera et al. 2005; Rissato et al. 2007; Weist et al. 2011; Fontana et al. 2010; Kujawski and Namiesnik 2011; Wang et al. 2010; Campillo et al. 2006; Choudhary and Sharma 2008; Martel et al. 2007; Erdogˇrul 2007; Blasco et al. 2003) confirming the need to constantly monitor the presence of pesticide residues in honey to assess any potential health risk and to ensure that its quality, whether as food or as a therapeutic, is not compromised. However, to date, only few studies have been carried out to monitor pesticide residues in honey produced from Africa (Eissa et al. 2014). A recent study conducted in Kenya in 2010 detected four pesticides from beeswax and bee bread at very low concentrations (Muli et al. 2014). However, the cumulative levels and presence of pesticides in hive products over time can pose health problems for both honeybees and humans. Therefore there is the need to develop highly sensitive and selective analytical techniques that have the ability to analyze multiple pesticides simultaneously in hive products.

Since honey is a complex analytical matrix, it is often necessary to clean-up the sample prior to instrumental analysis (Kujawski and Namiesnik 2008). This facilitates removal of matrix co-extractives that could result in enhancement or suppression of the signal of the targeted analytes during analysis (Ferrer et al. 2011; Kittlaus et al. 2011; Kruve et al. 2008). Conversely, this clean-up step is usually the most expensive, time consuming and laborious sample preparation step with the highest probability of introducing errors on recovery and method repeatability. Conventional extraction/clean-up methods such as liquid-liquid (LLE) or solid-phase extractions (SPE), require large volumes of organic solvents and usually target pesticides from a single chemical class (Fontana et al. 2010; Fernández and Simal 1991; Wang et al. 2010; Martel et al. 2007). Recently, extensive research has been geared towards finding more economical and environmental friendly methods that can yield good recoveries for a diverse range of pesticides. For instance, a recent study compared four different methods for extracting 12 organophosphates and carbamates from honey and concluded that the choice of the method depends on the targeted analytes (Blasco, et al. 2011). In another example (Kujawski et al. 2014), two methods; solid supported liquid-liquid extraction(SLE) and a modified Quick, Easy, Cheap, Effective and Safe (QuEChERS) method for multiresidue analysis were compared using extraction efficiencies for determination of 30 LC-amenable pesticides in honey at their MRLs. These authors concluded that in terms of recovery (ranged from 34 to 96 %) the methods had no significant difference but in terms of costs and time, the modified QuEChERS was better (Kujawski et al. 2014). In this study, an ultra-high performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was employed to analyze multiclass chemical contaminants in African honey at parts per billion (ppb) levels. Four different clean-up methods including PSA plus graphitized carbon (GCB), PSA plus C18, PSA alone, and a no clean-up approach were investigated using 96 LC-amenable pesticides to determine their applicability in a multiclass residue analysis in honey by comparing their recoveries. The method was validated and applied to conduct a preliminary study of pesticide residues in commercial honey samples obtained from Kenya and Ethiopia which are among the major producers of honey in Africa. Previous data on honey production in Africa indicates that Ethiopia is the largest producer with an estimate of 41,233 tons of honey followed by Tanzania at 28,678 tons and Kenya at 25,000 tons in 2004- 2006 (FAOSTAT). To the best of our knowledge, this is the first in-depth multiclass pesticide residue analysis of commercial honey from Africa. These results provide some insights in the safety of honey from Africa and some baseline information for future studies on other components of the hive matrix in relation to honey bee colony losses.

Methods

Chemicals and reagents

All pesticide standards were of high purity (>94 %) and were obtained from Sigma-Aldrich (Chemie GmbH, Germany) and Dr Ehrenstorfer (Augsburg, Germany) and were stored according to manufacturer’s recommendations until use. Pesticide stock solutions were prepared in acetonitrile at 1 μg/mL and stored in amber screw-capped glass vials at −20 °C.

LC-MS/MS instrumentation

An Agilent 1290 ultra high performance liquid chromatography (UHPLC) series coupled to a 6490 model triple quadrupole mass spectrometer (Agilent technologies) with an ifunnel JetStream electrospray source operating in the positive ionization mode was applied using dynamic multi-reaction monitoring (DMRM) software features. The electrospray ionization settings were gas temperature, 120 °C; gas flow, 15 L/min; nebuliser gas, 30 psi; sheath gas temperature, 375 °C; sheath gas flow, 12 L/min; capillary voltage, 3500 V; nozzle voltage, 300 V. The ifunnel parameters were high pressure RF 150 V and low pressure RF 60 V. Nitrogen was used both as a nebuliser and as the collision gas. Mass Hunter Data Acquisition; Qualitative and Quantitative analysis software (Agilent Technologies, Palo Alto, CA, v.B.06 and v.B.07) were used for method development, data acquisition and data processing for all the analyses.

The chromatographic separation was performed on a Rapid Resolution reverse phase column-C18 1.8 μm, 2.1 × 150 mm column (Agilent Technologies). The mobile phases comprised of 100 % water in 5 mM ammonium formate containing 0.1 % formic acid for solvent A and acetonitrile in 5 mM ammonium formate containing 0.1 % formic acid for solvent B. A gradient elution at a flow rate of 0.4 mL/min was used.

Optimization of LC-MS/MS parameters

Pesticide standard solutions, individually or as mixes, were used for method development and instrument parameters optimization. To ensure that the maximum sensitivity for identification and quantification of the targeted pesticides is obtained, careful optimization of all MS parameters was performed by infusing the standard solutions directly into the MS followed by infusion through the column to establish their respective retention times (RT). The parameters optimised included collision energy (CE), gas temperature; gas flow, sheath gas temperature and flow, high and low pressure radio-frequency. Table 1 demonstrates the parameters developed and optimised for the 96 pesticide residues targeted in this study.
Table 1

Instrumental parameters of the MS/MS detector and retention times (RT) of the 96 pesticides standard mixture used for method development

Compound name

RT (min)

Parent ion (m/z)

aTrans1

CE1(V)

aTrans2

CE2(V)

Omethoate

2.72

214

125

20

109

25

Acetamiprid

2.84

223

126

20

90

35

Acephate

2.84

184

143

5

125

15

Propamocarb

3.19

189

144

5

102

15

Oxamyl

3.58

237

90

0

72

15

Methomyl

3.84

163

106

5

88

0

Thiamethoxam

3.95

292

211

5

181

20

Monocrotophos

3.95

224

193

0

127

10

Aldicarb

3.98

208

116

0

89

10

Imidacloprid

4.42

256

209

10

175

15

Thiabendazol

4.45

202

175

25

131

35

Cymiazole

4.70

219

171

25

144

35

Dimethoate

4.82

230

199

0

125

20

Thiacloprid

5.13

253

126

20

90

40

Propagite

5.25

368

231

5

175

10

Aldicarb fragment

5.43

116

89

4

70

4

Pirimicarb

5.90

239

182

10

72

20

Dichlorvos

6.13

221

109

12

79

24

Carbofuran

6.36

222

165

5

123

20

Nicosulfuron

6.40

411

213

12

182

16

Metsulfuron-methyl

6.51

382

199

20

167

15

Metribuzin

6.54

215

187

15

84

20

Malathion

6.64

331

126

5

99

10

Carbaryl

6.93

202

145

0

127

25

Fosthiazole

7.16

284

228

5

104

20

Thiodicarb

7.16

355

108

10

88

10

Amidosulfuron

7.22

370

261

10

218

20

DEET

7.75

192

119

16

91

32

Molinate

7.75

188

126

25

98

12

Tribenuron-methyl

7.87

396

155

5

  

Metalaxyl

7.89

280

220

10

160

20

Flutriafol

8.01

302

70

15

123

30

Diuron

8.02

233

72

20

72

20

Isoxafluote

8.08

360

251

20

220

35

Methidathion

8.46

303

145

0

85

15

Flazasulfuron

8.73

408

182

15

  

Fenobucarb

8.79

208

152

5

95

10

Azoxystrobin

9.01

404

372

10

344

25

Linuron

9.19

249

182

10

160

15

Fludioxonil

9.30

247

169

32

126

32

Promecarb

9.64

208

151

0

  

Bosclid

9.67

343

271

28

307

12

Triadimefon

10.01

294

197

10

69

20

Bromuconazole

10.02

378

159

35

70

20

Bifenazate

10.09

301

170

15

  

Cyproconazole

10.16

292

70

15

125

35

Fluquinconazole

10.27

376

349

16

307

24

Iprovalicarb

10.27

321

203

0

119

20

Triadimenol

10.36

296

70

5

99

10

Flufenacet

10.38

364

194

5

152

15

Bupirimate

10.42

317

166

20

108

25

Tetraconazole

10.45

372

159

30

70

20

Ethoprophos

10.48

243

131

15

97

30

Epoxyconazol

10.65

330

121

20

101

45

Cyazofamid

10.68

325

261

5

108

10

Cyprodinil

10.81

226

93

40

77

45

Fenbuconazole

10.85

337

125

35

70

15

Metolachlor

10.94

284

252

10

176

20

Fenamiphos

10.95

304

217

20

202

35

Flusilazole

10.97

316

247

15

165

25

Picoxystrobin

11.05

368

205

0

145

20

Tebufenozid

11.10

353

297

0

133

15

Diflubenzuron

11.17

311

158

10

141

35

Rotenone

11.24

395

213

20

192

20

Fipronil

11.25

435

330

12

250

28

Kresoxim-methyl

11.53

314

267

0

222

10

Tebuconazole

11.53

308

125

40

70

20

Procymidon

11.64

284

67

12

256

28

Benalaxyl

11.71

326

294

5

148

15

Diazinon

11.71

305

169

20

153

20

Coumaphos

11.76

363

307

16

227

28

Prochloraz

11.76

376

308

5

266

10

Chlorfenvinphos

11.77

359

170

40

155

8

Hexaconazole

11.93

314

159

30

70

15

Pyraclostrobin

12.04

388

194

5

163

20

Clofentezin

12.06

303

138

10

102

40

Pirimiphos-methyl

12.21

306

164

20

108

30

Spinosyn A

12.23

732

142

30

98

45

Metconazole

12.30

320

125

40

  

Bitertenol

12.38

338

269

0

70

0

Chlorpyrifos-methyl

12.41

322

290

10

125

25

Trifloxystrobin

12.78

409

186

10

145

45

Spinosyn D

12.88

747

142

35

98

55

Ipconazole

12.97

334

125

45

70

25

Indoxacarb

12.99

528

203

45

150

20

Novaluron

13.32

493

158

20

141

45

Buprofezin

13.45

306

201

5

116

10

Profenofos

13.48

375

347

5

305

15

Ethion

13.93

385

199

4

143

20

Temephos

14.02

467

419

20

125

44

Chlorpyrifos

14.08

350

200

15

198

15

Pyriproxyfen

14.17

322

185

20

96

10

Lufenuron

14.19

511

158

20

141

45

Hexythiazox

14.46

353

228

10

168

25

Fenazaquin

15.35

307

161

10

57

25

Pyridaben

15.44

365

309

10

147

25

Bifenthrin

16.47

440

181

5

166

20

Etofenprox

16.57

394

177

10

107

45

aTransition ions used to quantify and qualify the targeted analytes

Data analysis

Targeted analytes were identified by monitoring two transition ions where possible, for each analyte as recommended by SANCO guidelines for LC-MS/MS analysis (SANCO/12571/2013). The most dominant transition ion was used for quantification whereas the second most intense ion as a qualifier for confirmation purposes. Calibration standard solutions were prepared at seven calibration levels covering a concentration range of 0.1 to 100 parts per billion (ppb), including the zero point. The resulting calibration curve was used to determine the instrument’s limit of reporting (LOR) and limits of detection (LOD). These were set as calibration standard concentrations producing signal to noise ratio of 3 and 10 respectively. The LOR was set as the minimum concentration that could be quantified with acceptable accuracy and precision. The LC-MS/MS system’s linearity was evaluated by assessing the signal responses of the calibration standards.

Sample preparation

Prior analysis of a honey sample, obtained from the local organic farmer from Kenya, was performed to ensure that it did not contain any of the studied compounds. This sample was selected as a blank during method development for spiking, preparing matrix matched calibration curves and recovery purposes. Samples were prepared following the QuEChERS method (Anastassiades et al. 2003) with some modifications. Briefly, 5 g of this sample was weighed into a 50 ml falcon tube and 10 ml of water were added and the mixture homogenized. Acetonitrile (10 ml) plus a mixture of salts (4 g magnesium sulphate, 1 g sodium chloride, 1 g of trisodium citrate dehydrate and 0.5 g of disodium hydrogen citrate sesquihydrate) were added and the samples were vortexed for 1 min and centrifuged at 4200 rpm for 5 min. Aliquots of the supernatant were transferred to separate eppendorf tubes and subjected to either no clean-up or to various QuEChERS clean-up methods. A portion of 1 mL of the final solution was then transferred to an auto-sampler vial for LC-MS/MS analysis.

Extraction efficiency

A series of spiked samples were used to assess extraction efficiency of the method. These samples were prepared as follows: blank honey samples fortified at 10 times LOQ (10 ng/g) were dissolved in appropriate amounts of water and homogenized. Extractions of the spiked residues were performed following QuEChERS methods. Honey samples were spiked with a mixture of pesticide residues possessing different physic-chemical properties. After extraction, aliquots of the extract were subjected to three QuEChERS clean-up methods (PSA plus GCB or PSA plus C18 or PSA alone). Figure 1 represents a schematic diagram illustrating the workflow that was employed during method development. Extraction efficiencies of these clean-up methods were compared to extraction efficiencies of no clean-up methods to evaluate which of those methods will be best suited for our analysis. Instead, these samples were subjected to high centrifugation (12,000 rpm held at 4 °C) for 10 min and filtered through 0.22 μm PTFE filters on a Samplicity system (Merck Millipore, Germany). Each test was replicated three times.
Fig. 1

Schematic diagram representing sample preparation workflow

Matrix effects

The effect of matrix co-extractives was performed by assessing ion suppression or enhancement effects of signals from chromatograms of matrix matched standard solutions compared to spiked extracts at the same concentration levels as per DG SANCO guidelines for LC-MS/MS analysis (SANCO/12571/2013). These were prepared using the extract of blank matrix (honey) covering a target analyte concentration range of 0.1 to 100 ng/g. Detection and quantification limits of the method were determined as described previously.

Validation of the analytical procedure

Analytes to be validated were spiked into the blank honey sample at LOR (1 ng/g) and at the lowest MRL level (0.01 mg/kg or 10 ng/g). Analysis was performed as described previously. The recoveries and precision of the extraction method were determined as the average of five replicates. The method linearity was evaluated by assessing the signal responses of the targeted analytes from matrix-matched calibration solutions prepared by spiking blank extracts at seven concentration levels, from 0.1 to 100 ng/g, including the zero point or the blank. The method precision was expressed as percent relative standard deviation (%RSD) of the intra-day and inter-day analyses (n = 5). Blank matrices along with reagent blank were run during validation to ensure minimal risk of interferences, guarantee specificity of the method and to check for potential solvent contamination.

Application to real samples

The developed method was applied to conduct a preliminary study on chemical contaminants present in commercial honey in Africa. Ethiopia and Kenya were selected for this study as they are among the major producers of honey in Africa. From each country, 14 commercial honey samples were collected from local markets/farmers. These samples consisted of five honey samples from stingless (Apis meliponina) and nine honey bee (Apis mellifera) samples from various regions in each country. A total of 28 samples were analyzed at the African Reference Laboratory for Bee Health, International Centre of Insect Physiology and Ecology (icipe), Duduville Campus, Nairobi, Kenya at two different seasons (November 2014 and July 2015). All samples were stored in their original packaging under the recommended conditions prior to use and were prepared as previously described. The same calibration curve described above was run at the end of the sample series to check the stability of the detector after data acquisition of the unknown samples.

Statistical analysis

Data were analyzed using R version 3.1.1 (R Core Team 2014). For each pesticide or compound, the four clean-up methods were compared using one-way Analysis of Variance (ANOVA) and the means separated using the Student-Newman-Kuels (SNK) test. All tests were performed at 5 % significance level. Means with the same letter across are not significantly different.

Results and discussion

LC-MS/MS analysis

In this study, the methods investigated were selected based on the known matrix interferences expected from honey. Since sugars constitute the greatest proportion of honey (>75 %), three of the four methods investigated included PSA, as it removes sugars, along other interferences. Samples were spiked with a mixture of 96 pesticide standards at the default MRL value (0.01 mg/kg) since it provided great recoveries with the best reproducibility across multiple analytes during method development. Figure 2 shows representative chromatograms of honey extract processed using the four clean-up methods. Although the chromatographic profiles appeared similar for the four clean-up methods, the lowest recoveries were obtained from pesticides subjected to PSA combined with GCB clean-up with recoveries ranging from 5 to 117 % (Table 2). The use of GCB was important in removing pigment in honey; however, it also resulted in significant analyte losses during sample clean-up which could potentially lead to false negative results. Out of the 96 pesticides evaluated, 51 pesticides had the lowest recoveries from this method compared to the other methods (Table 2). Additionally, more than 45 % of the pesticides subjected to this method did not meet the minimum recommended criteria (>70 %) as indicated in the Guidance document on analytical quality control and validation procedures for pesticide residues analysis in food and feed (SANCO/12571/2013). On the other hand, for most pesticides, the best recoveries were obtained when PSA was used as a clean-up method. When compared to PSA plus C18 clean-up method, there were significant (P <0.05) differences in more than 10 % of the pesticides evaluated. Results from this study also indicate that out of the 96 pesticides studied, only three pesticides, nicosulfuron (43 %), procymidon (58 %) and propamocarb (58 %), had recoveries that were below the acceptable limit when PSA was used alone. There was no significant (P <0.05) difference in recoveries for procymidon cleaned using C18 plus PSA (78 %) and PSA alone (58 %). Therefore, to improve recoveries for nicosulfuron and propamocarb, other alternatives must be considered. For instance, for nicosulfuron, based on the data provided in Table 2, the clean-up step can be omitted to yield 100 % recovery. This suggests that in the absence of clean-up resources, satisfactory information on levels of residue contamination in honey can still be achieved with minimal sample manipulations as found in other studies (Kujawski et al. 2014). Although omitting the clean-up step offers time savings in sample processing and is more economical, further precaution must be taken to avoid any potential clogging of the LC-MS system or eventual contamination of the MS ionization source. Based on the findings highlighted in Table 2, the use of PSA was selected as the best method for our analysis but was complemented with the no clean-up method to maximize on recoveries of all targeted pesticides.
Fig. 2

Example of total ion chromatograms (TIC) of 96 pesticides extracted from spiked honey sample at 10 ng/g level and cleaned up using (a) No clean-up (b) PSA only (c) PSA+C18 (d) PSA+GCB

Table 2

Percentage recoveries (±SD) of 96 pesticides subjected to either QuEChERS clean-up methods or no clean-up

 

% recovery at 10LOR (10 ng/g) ± SD

Compound name

GCB+PSA

C18+PSA

PSA

No clean-up

Acephate

72.8 ± 0.8(b)

85.1 ± 0.6(a)

76.1 ± 0.8(ab)

52.5 ± 0.7(c)

Acetamiprid

98.1 ± 0.6(a)

99.8 ± 0.03(a)

99.6 ± 0.3(a)

74.8 ± 0.0(b)

Aldicarb fragment

104.5 ± 0.4(a)

100.5 ± 0.3(b)

97.9 ± 0.2(b)

70.6 ± 0.1(c)

Amidosulfuron

87.0 ± 0.8(b)

74.3 ± 0.4(c)

89.5 ± 0.1(b)

94.3 ± 0.2(a)

Azoxystrobin

77.0 ± 0.7(b)

108.8 ± 0.8(a)

106.5 ± 0.8(a)

101.8 ± 0.5(a)

Benalaxyl

88.9 ± 0.6(a)

97.3 ± 1.0(a)

97.5 ± 0.4(a)

97.7 ± 0.6(a)

Bifenazate

23.7 ± 0.8(b)

117.5 ± 0.3(a)

111.6 ± 1.2(a)

121.6 ± 0.4(a)

Bifenthrin

45.7 ± 1.1(b)

92.5 ± 0.9(a)

79.8 ± 0.8(a)

90.2 ± 0.02(a)

Bitertanol

88.9 ± 0.6(b)

105.6 ± 0.4(a)

99.4 ± 0.01(a)

100.6 ± 0.7(a,b)

Bosclid (Nicobifen)

39.6 ± 1.0(b)

113.1 ± 1.3(a)

106.3 ± 0.4(a)

115.3 ± 0.7(a)

Bromuconazole

85.2 ± 1.6(b)

96.9 ± 0.1(ab)

103.0 ± 0.4(a)

92.3 ± 0.4(ab)

Bupirimate

61.3 ± 0.6(b)

104.6 ± 0.1(a)

102.5 ± 0.6(a)

110.7 ± 1.4(a)

Buprofezin

84.6 ± 0.5(c)

104.0 ± 0.4(ab)

106.9 ± 0.4(a)

102.9 ± 0.8(b)

Carbaryl

98.2 ± 1.2(a)

110.9 ± 0.9(a)

102.5 ± 0.2(a)

72.6 ± 0.1(b)

Carbofuran

108.3 ± 0.8(b)

120.4 ± 0.9(a)

119.9 ± 0.1(ab)

64.1 ± 0.6(c)

Chlorfenvinphos

78.1 ± 0.7(c)

93.6 ± 0.1(b)

103.6 ± 0.2(a)

94.9 ± 0.7(b)

Chlorpyrifos

21.4 ± 0.6(b)

93.4 ± 0.6(a)

94.2 ± 0.3(a)

87.6 ± 1.0(a)

Chlorpyrifos-methyl

26.4 ± 0.8(c)

105.3 ± 0.5(a)

99.5 ± 0.1(a)

95.5 ± 0.4(b)

Clofentezin

6.2 ± 0.7(b)

97.3 ± 0.3(a)

98.5 ± 0.3(a)

91.1 ± 0.8(a)

Coumaphos

5.4 ± 0.4(b)

102.6 ± 1.3(a)

105.2 ± 0.5(a)

109.2 ± 0.5(a)

Cyazofamid

79.1 ± 0.2(c)

102.2 ± 0.1(a)

100.6 ± 0.2(a)

92.3 ± 0.2(b)

Cymiazol

56.0 ± 0.9(c)

92.2 ± 0.4(a)

89.5 ± 0.1(a)

75.2 ± 0.5(a)

Cyproconazole

100.3 ± 0.2(b)

87.8 ± 1.0(b)

106.8 ± 0.2(a)

92.3 ± 0.5(b)

Cyprodinil

10.5 ± 0.6(c)

90.8 ± 0.2(b)

104.5 ± 0.4(a)

105.0 ± 1.0(ab)

DEET

109.1 ± 0.4(a)

100.4 ± 1.2(a)

96.0 ± 0.0(a)

83.5 ± 0.5(b)

Diazinon

81.4 ± 0.01(b)

98.7 ± 0.3(a)

99.0 ± 0.3(a)

99.4 ± 1.1(a)

Dichlorvos

107.0 ± 0.8(a)

97.3 ± 0.6(b)

99.4 ± 0.3(b)

85.7 ± 0.2(c)

Diflubenzuron

18.9 ± 4.8(b)

101.9 ± 0.3(a)

106.3 ± 1(a)

104.8 ± 0.6(a)

Dimethoate

99.2 ± 1.0(a)

101.7 ± 0.2(a)

94.3 ± 0.3(a)

62.8 ± 0.1(b)

Diuron

33.0 ± 0.8(c)

100.7 ± 0.6(a)

108.1 ± 0.2(a)

92.1 ± 0.4(b)

Epoxyconazol

38.8 ± 2.6(b)

91.2 ± 0.2(a)

96.4 ± 1.1(a)

89.9 ± 0.6(a)

Ethion

78.7 ± 0.2(b)

98.3 ± 0.1(a)

103.0 ± 0.5(a)

95.1 ± 0.1(a)

Ethoprophos

87.7 ± 0.9(a)

94.1 ± 0.4(a)

98.3 ± 1.0(a)

90.8 ± 0.7(a)

Etofenprox

24.2 ± 0.5(b)

98.5 ± 0.4(a)

99.5 ± 0.1(a)

92.2 ± 0.0(a)

Fenamiphos

56.4 ± 1.0(b)

107.8 ± 0.2(a)

111.6 ± 0.3(a)

107.8 ± 0.5(a)

Fenazaquin

9.9 ± 1.7(d)

93.5 ± 0.5(b)

98.4 ± 0.1(a)

89.4 ± 0.1(c)

Fenbuconazole

40.5 ± 1.2(b)

107.8 ± 0.3(a)

109.1 ± 0.9(a)

107.0 ± 0.3(a)

Fenobucarb

94.7 ± 2.0(b)

80.6 ± 0.4(c)

101.6 ± 0.1(a)

90.4 ± 0.2(b)

Fipronil

107.9 ± 1.0(a)

111.4 ± 0.1(a)

108.3 ± 0.3(a)

111.1 ± 0.4(a)

Flazasulfuron

81.1 ± 1.5(b)

46.7 ± 0.5(d)

70.3 ± 0.3(c)

96.7 ± 0.4(a)

Fludioxonil

34.1 ± 2.9(b

105.9 ± 0.5(a)

104.7 ± 0.1(a)

110.8 ± 0.5(a)

Flufenacet

102.8 ± 1.9(a)

117.5 ± 0.9(a)

103.8 ± 0.7(a)

100.9 ± 0.5(a)

Fluquinconazole

43.6 ± 2.0(b)

92.7 ± 1.1(a)

99.3 ± 0.9(a)

90.5 ± 0.9(a)

Flusilazole

97.8 ± 0.8(b)

117.4 ± 0.7(a)

108.3 ± 0.8(ab)

98.6 ± 0.5(ab)

Flutriafol

94.3 ± 0.2(b)

97.9 ± 0.4(ab)

101.3 ± 0.1(a)

96.8 ± 0.5(ab)

Fosthiazate

101.8 ± 0.2(a)

107.1 ± 0.9(a)

103.6 ± 0.1(a)

70.1 ± 0.1(b)

Hexaconazole

90.0 ± 1.0(b)

99.3 ± 1.1(b)

110.5 ± 0.5(a)

97.9 ± 0.1(b)

Hexythiazox

77.2 ± 0.6(c)

99.8 ± 0.1(a)

99.6 ± 0.2(a)

94.1 ± 0.4(b)

Imidacloprid

80.3 ± 0.2(a)

88.3 ± 0.2(a)

87.6 ± 0.3(a)

66.4 ± 0.3(b)

Indoxacarb

56.4 ± 1.6(b)

103.0 ± 0.5(a)

102.1 ± 0.1(a)

96.1 ± 0.3(a)

Ipconazole

57.7 ± 0.9(b)

103.7 ± 0.2(a)

102.7 ± 0.1(a)

98.9 ± 0.5(a)

Iprovalicarb

58.6 ± 6.5(a)

95.6 ± 0.1(a)

99.1 ± 1.3(a)

74.5 ± 1.2(a)

Isoxaflutole

99.0 ± 0.3(a)

98.9 ± 0.5(a)

105.6 ± 0.4(a)

120.8 ± 2.0(a)

Kresoxim-methyl

74.7 ± 0.2(a)

96.0 ± 1.1(a)

93.1 ± 0.3(a)

92.3 ± 0.8(a)

Linuron

39.7 ± 3.4(b)

103.3 ± 0.03(a)

107.9 ± 0.5(a)

97.5 ± 0.1(a)

Lufenuron

5.9 ± 3.2(d)

105.1 ± 0.4(a)

98.6 ± 0.2(b)

95.4 ± 0.2(c)

Malathion

102.9 ± 0.2(a)

113.6 ± 0.2(a)

109.4 ± 0.3(a)

98.1 ± 0.1(a)

Metalaxyl

100.3 ± 0.1(a)

102.8 ± 0.4(a)

108.1 ± 0.2(a)

99.3 ± 0.5(a)

Metconazole

56.8 ± 1.7(b)

101.8 ± 0.8(a)

109.9 ± 0.1(a)

101.2 ± 0.2(a)

Methidathion

76.4 ± 0.7(b)

98.2 ± 0.4(a)

99.8 ± 0.5(a)

77.7 ± 0.2(b)

Methomyl

63.9 ± 7.9(a)

111.1 ± 0.6(a)

105.6 ± 0.3(a)

86.0 ± 0.4(a)

Metolachlor

88.6 ± 0.3(a)

100.2 ± 0.1(a)

97.7 ± 0.3(a)

98.4 ± 0.9(a)

Metribuzin

106.3 ± 0.1(a)

103.9 ± 0.5(a)

106.0 ± 0.5(a)

48.7 ± 0.3(b)

Metsulfuron-methyl

72.7 ± 1.7(b)

46.6 ± 0.7(c)

72.8 ± 0.5(b)

122.1 ± 0.4(a)

Monocrotophos

86.4 ± 0.1(a)

98.4 ± 0.2(a)

86.3 ± 0.3(a)

14.7 ± 7.1(b)

Nicosulfuron

43.9 ± 2.5(b)

19.0 ± 1.4(c)

42.6 ± 0.3(b)

100.6 ± 2.1(a)

Novaluron

16.5 ± 2.5(c)

104.4 ± 0.2(a)

107.4 ± 0.3(a)

92.1 ± 0.6(b)

Omethoat

88.0 ± 0.2(b)

90.6 ± 0.2(a)

86.4 ± 0.3(b)

83.5 ± 0.1(b)

Oxamyl

90.3 ± 0.1(a)

94.7 ± 0.0(a)

94.4 ± 0.1(a)

67.8 ± 0.2(b)

Picoxystrobin

76.7 ± 0.1(b)

94.3 ± 0.5(a)

92.6 ± 1.1(a)

79.1 ± 0.3(b)

Pirimicarb

37.1 ± 2.2(c)

103.6 ± 1.1(a)

102.3 ± 0.5(a)

81.2 ± 0.5(b)

Pirimiphos-methyl

44.6 ± 1.1(b)

99.2 ± 1.1(a)

99.1 ± 0.2(a)

91.2 ± 0.2(a)

Prochloraz

34.1 ± 3.7(b)

106.5 ± 0.4(a)

111.4 ± 0.4(a)

103.6 ± 0.1(a)

Procymidon

54.1 ± 1.0(a)

78.8 ± 0.7(a)

58.3 ± 1.7(a)

66.5 ± 0.7(a)

Profenofos

31.7 ± 2.6(b)

95.9 ± 0.7(a)

97.2 ± 0.2(a)

89.1 ± 0.4(a)

Promecarb

106.6 ± 0.4(a)

107.8 ± 0.1(a)

102.6 ± 0.2(a)

95.1 ± 0.1(a)

Propamocarb

75.9 ± 0.0(a)

49.3 ± 0.7(c)

57.9 ± 0.2(b)

73.4 ± 0.1(a)

Propargit

70.1 ± 1.4(ab)

98.7 ± 0.4(a)

99.4 ± 0.7(a)

64.2 ± 0.2(ab)

Pyraclostrobin

5.2 ± 0.9(c)

111.8 ± 0.0(a)

106.7 ± 0.2(a)

100.3 ± 0.3(b)

Pyridaben

53.1 ± 2.0(b)

100.0 ± 0.8(a)

102.6 ± 0.3(a)

89.9 ± 1.3(a)

Pyriproxyfen

36.9 ± 2.6(b)

98.1 ± 0.5(a)

97.9 ± 0.3(a)

92.3 ± 0.1(a)

Rotenone

46.7 ± 2.7(b)

101.2 ± 0.5(a)

100.1 ± 0.5(a)

96.5 ± 0.1(a)

Spinosyn A

12.5 ± 2.6(d)

98.3 ± 0.3(b)

109.2 ± 0.7(a)

87.0 ± 0.5(c)

Spinosyn D

9.3 ± 3.2(c)

92.0 ± 0.1(b)

101.6 ± 0.4(a)

90.5 ± 0.3(b)

Tebuconazole

59.7 ± 1.6(b)

99.5 ± 0.7(a)

114.4 ± 0.6(a)

111.4 ± 0.4(a)

Tebufenozid

100.9 ± 0.5(b)

112.0 ± 0.4(a)

120.4 ± 1.5(a)

98.6 ± 0.4(b)

Temephos

25.4 ± 3.0(c)

105.9 ± 0.4(a)

100.7 ± 0.2(ab)

98.0 ± 0.6(b)

Tetraconazole

92.5 ± 0.6(c)

108.2 ± 1.0(a)

102.4 ± 0.5(ab)

93.4 ± 0.6(bc)

Thiabendazol

17.2 ± 1.6(d)

82.2 ± 0.4(a)

77.3 ± 0.1(b)

57.9 ± 0.2(c)

Thiacloprid

85.3 ± 1.2(a)

99.5 ± 0.6(a)

94.7 ± 0.0(a)

61.0 ± 0.3(b)

Thiamethoxam

95.6 ± 0.6(a)

101.2 ± 0.1(a)

99.7 ± 0.2(a)

59.7 ± 0.1(b)

Thiodicarb

43.3 ± 2.4(c)

101.4 ± 0.6(a)

103.5 ± 0.5(a)

76.8 ± 0.3(b)

Triadimenol

92.8 ± 0.1(b)

108.6 ± 1.0(a)

111.3 ± 0.03(a)

97.9 ± 0.6(a)

Triadimefon

117.7 ± 0.3(a)

107.4 ± 0.8(a)

115.7 ± 0.1(a)

110.6 ± 0.7(a)

Tribenuron-methyl

64.2 ± 1.7(b)

73.3 ± 0.1(a)

81.5 ± 0.1(a)

63.4 ± 0.4(b)

Trifloxystrobin

60.1 ± 1.7(b)

101.2 ± 0.2(a)

103.8 ± 0.1(a)

93.7 ± 0.1(a)

*For each pesticide, mean recoveries with the same letter are not significantly different

Analytes eluted in 17 min followed by a short high-organic rinse to maintain the column and also in avoiding matrix carryover into the next sample. Elution of the remaining matrix material during subsequent analysis can cause unexpected matrix effects resulting in significant ionization inefficiencies. Matrix effects may either result to signal enhancement leading to recoveries >100 % or signal suppression resulting in poor recoveries. Aside from polar pesticides, other pesticides were well distributed across the elution window facilitating proper scan rate for scheduled MRM methods of targeted analytes as shown in Fig. 3. This figure illustrates an example of MRM chromatogram of the 96 pesticides targeted in this study that were extracted from spiked honey after PSA clean up. From this chromatogram, each colored peak represent a unique pesticide identified based on the MRM transition ions. A detailed summary indicating the identity of each peak shown in Fig. 3 and their corresponding retention times along with their molecular masses are provided in Table 1.
Fig. 3

Representative example of MRM chromatogram of 96 pesticides extracted from a spiked honey sample at 10 ng/g level and cleaned up using PSA only

Validation of the selected method

The developed method was validated following the guidelines provided in the Guidance document on analytical quality control and validation procedures for pesticide residues analysis in food and feed (SANCO/12571/2013). To meet these guidelines, the method was validated in terms of recovery, linearity, LOQ, matrix effects, intra-day and inter-day precision. The mean recovery values used in this study were within the range of 70–120 %, with an associated repeatability, RSD <20 %, for all compounds within the scope of the method. Matrix-matched calibration standards were used to calculate recoveries as this helped in compensating for any matrix effects arising from matrix interferences or co-extractives that can change the ionization efficiency of an analyte causing signal suppression or enhancement leading to poor recoveries. This could have an adverse effect on the quality of the data and can erroneously result in false positive or negative results. It is therefore imperative for any LC-MS/MS method to give acceptable quantitative results; matrix effects must be considered (Ferrer et al. 2011; Kittlaus et al. 2011).

Table 3 shows the list of pesticides validated and demonstrates the summarized recovery results along with the linearity of the validated analytes. This table illustrates recoveries obtained at LOR using PSA and no clean-up approach. Percent recovery values for these analytes were calculated using matrix-matched calibration curves. The LOR for the method was determined as the lowest spike level of the validation meeting these method performance acceptability criteria. Although the LOD and LOR varied depending on the pesticides in question, most compounds could be detected at 0.1 and quantified below 1 ng/g. Overall, the LOD and the LOR was set at 0.5 and 1 ng/g, respectively. From this study, approximately 10 % of the studied compounds had poor recoveries from either method but there was tremendous improvement on recoveries when both methods were combined. In this case, all pesticides, except for two (fluquinconazole −68 % and propamocarb - 63 %) had good recoveries which were well within the recommended limits provided in SANCO/12575/2013 document. It is worth noting that pesticides with good recoveries had good reproducibility (RSD <20 %) whereas those with poor recoveries were characterized by poor reproducibility. As a result, during recovery studies, blank matrix was fortified at 10 times the LOR since it gave the best reproducibility for all studied compounds. The method linearity was evaluated by assessing the signal responses of the targeted analytes from matrix-matched calibration solutions prepared in blank extracts at seven concentration levels. The developed method was proven satisfactory with linear chromatographic response for the tested pesticides, ranging from 0.1 to 100 ng g−1. Majority of the correlation coefficients (R2) was higher or equal to 0.995, see Table 3.
Table 3

Extraction efficiencies of validated pesticides spiked at LOR, precision in terms of RSD (n = 5) and coefficients of determination for the investigated pesticides

 

% recovery at LOR (1 ng/g)

 

No clean-up

PSA

Compound name

% recovery

% RSD, n = 5

% recovery

% RSD, n = 5

R^2

Acephate

70.1

2.6

97.4

4.7

0.9989

Acetamiprid

82.6

3.2

115.0

3.9

0.9982

Aldicarb fragment

73.0

0.9

102.8

1.5

0.9476

Amidosulfuron

87.1

1.2

54.2

24.3

0.9990

Azoxystrobin

84.5

4.5

90.8

4.9

0.9986

Benalaxyl

87.6

3.3

87.5

1.5

0.9989

Bifenazate

89.6

2.4

96.3

4.3

0.9919

Bifenthrin

91.3

6.5

99.0

5.0

0.9996

Bitertanol

85.7

0.4

89.9

0.1

0.9982

Bosclid (Nicobifen)

81.7

0.7

88.2

1.5

0.9977

Bromuconazole

117.3

11.5

102.3

9.3

0.9991

Bupirimate

87.6

1.7

95.6

4.1

0.9998

Buprofezin

81.9

9.0

86.1

4.3

0.9985

Carbaryl

83.8

1.8

118.3

1.3

0.9980

Carbofuran

67.8

3.1

115.8

5.1

0.9987

Chlorfenvinphos

95.0

5.7

103.0

1.1

1.0000

Chlorpyriphos

93.1

6.2

99.3

1.8

0.9996

Chlorpyriphos-methyl

82.9

16.9

92.7

8.6

0.9990

Clofentezin

93.8

1.8

94.0

1.9

0.9996

Coumaphos

70.3

4.7

75.9

3.3

0.9935

Cyazofamid

108.4

9.4

113.6

1.6

0.9992

Cymiazol

86.6

0.5

109.6

5.9

0.9987

Cyproconazole

84.8

0.9

89.2

4.1

0.9976

Cyprodinil

93.7

1.0

96.7

1.6

0.9995

DEET

68.5

3.3

96.1

4.0

0.9999

Diazinon

87.6

3.6

100.4

0.5

0.9999

Dichlorvos

72.8

15.4

101.4

10.9

0.9998

Diflubenzuron

95.6

0.4

99.5

3.9

0.9999

Dimethoate

67.5

2.9

108.7

0.3

0.9997

Diuron

55.9

0.7

98.4

4.7

0.9979

Epoxyconazol

100.4

8.4

100.7

0.4

1.0000

Ethion

74.9

7.1

82.8

8.9

0.9986

Ethoprophos

91.6

3.7

103.8

11.8

0.9998

Etofenprox

95.2

0.8

101.3

2.8

1.0000

Fenamiphos

100.2

8.9

103.8

2.0

0.9991

Fenazaquin

95.1

2.3

99.5

5.2

0.9999

Fenbuconazole

94.9

1.9

101.3

0.4

1.0000

Fenobucarb

83.0

5.1

96.7

0.3

0.9999

Fipronil

99.7

2.6

98.3

13.7

0.9982

Flazasulfuron

87.1

3.7

25.7

62.6

0.9995

Fludioxonil

75.6

5.3

62.2

1.8

0.9981

Flufenacet

82.2

14.1

79.1

17.0

0.9926

Fluquinconazole

68.3

6.9

68.3

1.7

0.9981

Flusilazole

79.5

4.7

90.9

6.1

0.9991

Flutriafol

93.5

4.9

93.9

1.8

0.9999

Fosthiazate

73.2

4.1

109.4

1.6

0.9998

Hexaconazole

94.6

0.3

98.8

0.9

1.0000

Hexythiazox

89.7

2.7

98.2

0.8

0.9997

Imidacloprid

69.3

6.0

101.4

3.4

0.9996

Indoxacarb

93.2

1.1

96.8

1.9

0.9998

Ipconazole

89.8

2.4

95.7

4.9

0.9996

Iprovalicarb

90.7

13.6

100.6

9.9

0.9988

Isoxaflutole

91.4

0.5

76.0

5.7

0.9951

Kresoxim-methyl

108.8

10.7

113.5

13.2

0.9962

Linuron

76.3

7.1

74.0

3.0

0.9958

Lufenuron

91.9

15.8

90.2

9.0

0.9990

Malathion

78.8

8.3

86.3

1.0

0.9972

Metalaxyl

83.1

1.3

91.4

0.7

0.9995

Metconazole

78.2

11.0

81.0

8.2

0.9984

Methidathion

72.7

8.2

89.5

2.6

0.9993

Methomyl

96.1

0.4

114.6

3.5

0.9996

Metolachlor

111.6

1.1

115.3

3.7

0.9984

Metribuzin

66.3

14.3

115.4

2.8

0.9976

Metsulfuron-methyl

121.6

0.5

53.5

28.1

0.9994

Monocrotophos

77.9

0.4

112.5

2.7

0.9968

Nicosulfuron

90.1

2.1

10.5

39.9

0.9994

Novaluron

94.9

0.2

95.5

0.6

0.9999

Omethoate

78.3

5.9

81.3

15.8

0.9995

Oxamyl

72.9

15.3

112.4

2.5

0.9995

Picoxystrobin

93.1

2.6

101.4

9.4

0.9992

Pirimicarb

87.0

2.5

113.1

0.4

0.9992

Pirimiphos-methyl

99.3

1.0

112.2

4.4

0.9999

Prochloraz

79.9

0.4

80.1

1.2

0.9978

Procymidon

79.9

6.0

111.7

10.9

0.9956

Profenofos

98.5

3.7

109.2

11.2

0.9994

Promecarb

85.1

1.3

87.7

0.2

0.9993

Propamocarb

62.8

0.0

29.7

83.9

0.9996

Propargit

25.2

52.7

76.5

19.8

0.9983

Pyraclostrobin

80.0

10.4

88.1

5.6

0.9972

Pyridaben

84.3

1.5

87.2

1.2

0.9998

Pyriproxyfen

92.7

4.4

100.4

1.0

1.0000

Rotenone

97.7

1.9

113.0

1.7

0.9985

Spinosyn A

92.0

0.8

95.9

0.8

0.9999

Spinosyn D

85.1

0.1

92.3

2.0

0.9997

Tebuconazole

92.1

4.0

97.1

0.1

0.9993

Tebufenozid

75.3

0.5

87.7

9.5

0.9958

Temephos

94.4

1.6

95.5

2.6

0.9999

Tetraconazole

83.1

6.1

108.6

3.1

0.9997

Thiabendazol

91.3

2.7

110.9

1.9

0.9946

Thiacloprid

67.7

0.4

108.2

1.8

0.9995

Thiamethoxam

55.4

5.5

100.0

1.6

1.0000

Thiodicarb

78.3

1.0

102.4

3.5

0.9996

Triadimenol

73.7

8.5

69.8

13.5

0.9903

Triadimefon

85.6

8.9

83.8

2.2

0.9948

Tribenuron-methyl

71.8

4.9

65.7

13.4

0.9997

Trifloxystrobin

94.7

3.1

100.6

0.6

0.9999

Application of the method to real samples

As a natural product manufactured by bees, honey is considered to be free from any extraneous material. However, chemical residues have been reported in honey by several investigators. The presence of these residues in honey has prompted the need for setting up monitoring programs to determine the proper assessment of human exposure to pesticides (Choudhary and Sharma 2008). Unfortunately, there is no homogeneity on MRLs as different national regulations have established their own maximum concentrations of pesticide residues permitted in honey. In the absence of MRLs set for honey in the two African countries studied, the European Union set MRLs were employed and where no MRL existed, it was presumed at 10 which is the default MRL for pesticides with no specific value set as recommended in Regulation(EC)No 396/2005.

So far, there is little information that is currently available on chemical residues present in honey or hive products from most African countries (Muli et al. 2014; Eissa et al. 2014). Previous studies have shown that whereas in North America honey bees are exposed to at least 7 pesticides per food visit, this is not the case in Africa (Mullin et al. 2010). Results from a recent study carried out in Kenya detected less than four pesticides for the whole study duration at very minimal concentrations in honey bees and their hive products (Muli et al. 2014). In the current study, a preliminary analysis of pesticide residues in 28 honey samples obtained from local farmers’ markets and supermarkets from various regions in Kenya and Ethiopia during the period of November 2014 to July 2015 revealed the presence of 17 pesticide residues out of the 96 pesticides investigated. The concentrations for each detected pesticide were compared with the set MRL values. Table 4 indicates the summarized results obtained from the two countries. Our preliminary results show that, with the exception of malathion, an organophosphate that has multiple uses in Africa, no other pesticide was detected at a level higher than the set MRL levels. For most pesticides, the levels obtained were about 10-fold lower than the set MRL levels, with concentration levels at <100 ng/g. However, the maximum concentration detected for malathion was 0.092 mg/kg, a level that far exceeds its acceptable MRL of 0.05 mg/kg. Although this compound is quickly metabolized from the body and is known to be non-persistent in the environment, exposure to the levels detected (0.092 mg/kg) in this study over a long period could result in adverse health effects to both humans and honey bees. Thus, further investigation is required to determine its cumulative effects and whether there are any potential synergistic effects when other contaminants are present. Malathion is also considered to be highly toxic to honey bees with LD50 of 0.16 μg/bee (Allison 2011). It is worth noting that data from the present study does not reflect seasonality of pesticide present in honey samples obtained from the two countries. This would require in-depth systematic studies using large samples obtained directly from specific beekeeping sites over different seasons in the two countries. Follow up studies are underway to investigate how seasonality affects residues present in honey from various African countries.
Table 4

Detected pesticide residues in honey obtained from Kenya and Ethiopia

 

Identified pesticide residues

SampleID

ACTM

AF

CF

CAR

CHP

Cy

DEET

DDVP

DM

BPMC

HEX

Mal

Met

Metri

Rot

TBN

THIA

Kenya

 Taita

<LOQ

N/D

N/D

N/D

<LOQ

<LOQ

0.708

N/D

N/D

N/D

<LOQ

56.9

N/D

49.4

N/D

<LOQ

<LOQ

 VapA

<LOQ

1.37

<LOQ

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

92.3

1.81

N/D

N/D

N/D

<LOQ

 Cab

<LOQ

N/D

N/D

N/D

<LOQ

1.59

<LOQ

N/D

N/D

<LOQ

<LOQ

N/D

1.95

14.1

N/D

N/D

N/D

 Nak

N/D

<LOQ

N/D

<LOQ

N/D

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Ken

<LOQ

<LOQ

N/D

N/D

N/D

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Mwi

N/D

N/D

N/D

1.26

N/D

N/D

1.01

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Kak

<L/OQ

<LOQ

N/D

N/D

N/D

<LOQ

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

<LOQ

N/D

N/D

N/D

 ML

ND

N/D

N/D

<LOQ

N/D

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

34.0

N/D

N/D

N/D

 HR

<LOQ

N/D

N/D

2.87

N/D

<LOQ

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

70.4

N/D

N/D

N/D

 Gedi

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 K-B

N/D

N/D

N/D

<LOQ

N/D

N/D

1.37

2.58

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 K-M

N/D

N/D

N/D

N/D

N/D

N/D

<LOQ

<LOQ

<LOQ

N/D

N/D

N/D

N/D

<LOQ

N/D

N/D

N/D

 K-N

<LOQ

N/D

N/D

<LOQ

N/D

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 VapB

<LOQ

<LOQ

N/D

N/D

<LOQ

N/D

1.09

N/D

N/D

N/D

1.66

76.7

5.29

N/D

N/D

<LOQ

<LOQ

Ethiopia

 MB

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

9.52

N/D

N/D

N/D

 Tol

<LOQ

N/D

N/D

N/D

<LOQ

<LOQ

<LOQ

N/D

N/D

<LOQ

N/D

60.5

4.77

11.2

N/D

<LOQ

N/D

 Tig

<LOQ

<LOQ

N/D

N/D

<LOQ

<LOQ

<LOQ

N/D

N/D

N/D

<LOQ

15.3

N/D

N/D

N/D

N/D

N/D

 SapV

<LOQ

<LOQ

<LOQ

N/D

<LOQ

<LOQ

<LOQ

N/D

N/D

<LOQ

<LOQ

45.1

1.25

14.2

N/D

<LOQ

<LOQ

 E-1

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

2.60

N/D

N/D

N/D

 E-2

N/D

N/D

N/D

N/D

N/D

N/D

N/D

1.16

<LOQ

N/D

N/D

N/D

N/D

44.2

N/D

N/D

N/D

 E-3

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 E-4

ND

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

7.95

6.99

N/D

N/D

 E-5

ND

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 E-M1

<LOQ

<LOQ

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 E-H

<LOQ

N/D

N/D

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Tol2

N/D

N/D

1.10

N/D

N/D

N/D

N/D

3.46

<LOQ

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Tig2

N/D

N/D

N/D

N/D

N/D

N/D

4.98

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

N/D

 Sap S2

<LOQ

N/D

<LOQ

N/D

<LOQ

10.5

<LOQ

N/D

N/D

<LOQ

<LOQ

22.3

N/D

21.0

N/D

N/D

N/D

 MRL

50

10

10

50

*10

*10

*10

*10

*10

*10

*10

50

50

100

10

50

10

*Set at default MRL value; N/D not detected, <LOQ below the quantification limits

Identified pesticide residues: ACTM Acetamiprid, AF Aldicarb fragment, CF Carbofuran, CHP Chlopyrifos, Cy Cymiazole, DDVP Dichlorvos, DM Dimethoate, BPMC Fenobucarb, HEX Hexaconazole, Mal Malathion, Met Metalaxyl, Metri Metribuzin, Rot Rotenone, TBN Tebuconazole, THIA Thiomethoxam

Conclusion

A highly efficient approach for determining pesticide residues in honey with good recoveries was developed. This approach involved using a modified QuEChERS method along with or without any clean-up. The viability of this approach was demonstrated by using 96 pesticides. About 98 % of these pesticides investigated had recoveries that are well within the acceptable limits of 70–120 %. The methods were linear (>0.995) over the range tested (0.1–100 ng/g) with LOR for most pesticides at 1 ng/g or ppb. The applicability of the developed methods to real samples was tested by performing a preliminary study of commercial honey from Africa. A total of 17 pesticide residues were detected at levels 10-fold lower than their set MRL values except malathion which was detected at almost 2-fold higher than its set MRL. Overall, these results suggest that honey from these regions maybe safe for both bees and human consumption but further investigation is required to determine the cumulative effect of these pesticides. In-depth follow up studies using this method are underway to verify this observation in honey samples collected from different agro-ecological regions from various African countries.

Declarations

Acknowledgements

The authors would like to thank icipe management for their support, Daisy Salifu of icipe, for her support in statistical analysis, colleagues from African Reference Laboratory Bee Health (ARLBH) at icipe for their support, Beatrice Njuguna of ARLBH for providing the honeybee samples for analysis. This work has been supported financially by the European Union grant number DCI-FOOD-2013/313-659.

Authors’ contributions

JI designed the study and the experimental setting, performed the analytical work and wrote the manuscript. BT contributed in experimental design and critically revised the manuscript. SK edited and proofread the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
African Reference Laboratory for Bee Health, International Centre of Insect Physiology and Ecology (icipe)

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Copyright

© The Author(s). 2016