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Article

Evaluation of 99 Pesticide Residues in Major Agricultural Products from the Western Highlands Zone of Cameroon Using QuEChERS Method Extraction and LC-MS/MS and GC-ECD Analyses

by
Joseph H. Y. Galani
1,2,*,
Michael Houbraken
2,
Abukari Wumbei
2,
Joseph F. Djeugap
3,
Daniel Fotio
4 and
Pieter Spanoghe
2
1
Department of Agriculture and Veterinary Medicine, Université des Montagnes, P.O. Box 208, Bangangté, Cameroon
2
Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
3
Department of Plant Protection, Faculty of Agronomy and Agricultural Sciences, University of Dschang, P.O. Box 222, Dschang, Cameroon
4
Inter-States Pesticides Committee of Central Africa, P.O. Box 16344, Yaounde, Cameroon
*
Author to whom correspondence should be addressed.
Foods 2018, 7(11), 184; https://doi.org/10.3390/foods7110184
Submission received: 17 October 2018 / Revised: 5 November 2018 / Accepted: 6 November 2018 / Published: 7 November 2018
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Abstract

:
There is no information available on pesticide residue levels in major food commodities harvested in Cameroon, especially from the western highlands region, the food basket of the country. Hence, this study evaluated the residues of 99 pesticides in 72 samples of 12 agricultural products collected in the region, using QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method extraction, and analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography with electron capture detection (GC-ECD). This method was suitable for detecting the targeted compounds: For 81 pesticides by LC-MS/MS, the limit of quantification (LOQ) was between 0.0004 and 0.0537 mg/kg; and for 18 halogenated pesticides by GC-ECD, it ranged from 0.0012 to 0.2180 mg/kg. The residues of 62 pesticides, including 12 banned compounds, were found in the samples. Insecticides (39.7%) were the most prevalent group, with all the samples containing at least one pesticide. Twenty-one pesticides (34.4%) exceeded their European Union maximum residue limits (MRLs) and 22 pesticides (34.4%) were found in all 6 sampling locations. Malathion and p,p′-DDT were the most distributed pesticides, found in almost all the samples and sampling sites. Food items with the highest rates of positive results were chili pepper (23.2%), white pepper (20.2%), kidney beans (17.3%), and soybeans (17.2%). Samples with residues above their MRLs represented 38% of all the positive analyses; chili pepper (6.4%) and kidney beans (5.5%) were found to have the most residues above their MRLs. The most critical food commodities were kidney beans, soybeans, chili pepper, and maize. This data presents scientific evidence that investigation into continuous monitoring and good regulation of pesticide usage in Cameroon is needed, and paves the way for health risks analysis.

Graphical Abstract

1. Introduction

To protect crops against pests and pathogens, over a thousand crop protection products from a broad range of classes are widely used worldwide in various combinations, at different stages of cultivation, and during postharvest storage. However, besides the unwanted side effects on the environment, direct toxicity to users, and development of resistance by pathogens and pests associated to the use of certain pesticides, pesticide residues that remain in the food supply could pose a risk for human health because of their potential sub-acute and chronic toxicity [1]. Pesticide residues can be found in different food items, such as dairy products, cereals, fruits, vegetables, and cash crops, and obsolete pesticides have been documented as one of the major problems in Africa [2].
In Cameroon, pesticides are largely used by farmers and traders to protect their growing plants and products in the field and during storage [3,4,5,6,7]. Despite the numerous advantages of pesticide use in agriculture, there is a need for scientific evaluation and control of these products. It is known that pesticide residues can be found in all environmental compartments, but the highest risk for consumers is through consumption of residues in food [8]. In developing countries like Cameroon, there is an increased concern in the dietary risk linked to increased use of crop protection products. However, only minimal emphasis have been put on assessing how the growing use of pesticides can impact food safety. Ten years ago, Gimou et al. [9] revealed low dietary exposure to pesticide residues in the capital city, Yaoundé. But more recently it was found that 75% of maize, cowpea, and millet samples from northern Cameroon contained pesticide residues above the maximum residue limits (MRLs) [10], and high amounts of organophosphorous pesticide residues were found in stored cowpea and two by-products [5], revealing a potential human dietary risk related to consumption of these grains.
Other information demonstrates a high possible exposure of consumers due to intensive utilization and limited knowledge about pesticide use in the country. Recent studies [4] suggested that the male farmers of Djutitsa in West Cameroon are exposed to agro-pesticides due to improper personal protective equipment (PPE), and this exposure may impair their reproductive function. Inappropriate use of pesticides by Cameroonian farmers has been documented in many studies from different parts of the country [4,7,10,11,12,13]. This was because farmers did not receive sufficient training on pesticide application and proper assistance from agricultural extension agents [7], which could result in high levels of pesticide residues in local foods. In December 2016, the Cameroon Government prohibited the importation, commercialization, and use of metalaxyl-based pesticides, which were intensively used in cocoa to control black pod disease. This measure was justified by the rejection of Cameroonian cocoa on the international market, due to the presence of metalaxyl residues beyond the 0.1 mg/kg MRL [14] in cocoa beans originating from Cameroon [15]. Additionally, a high number and various types of obsolete pesticides accumulated over the years were found in the country [16], and because of limited control of pesticide usage, they could be a source of severe acute or chronic pollution [9]. Due to their cheap price on the black market and limited control measures, they can be illegally used on crops and produce [17].
The most common food items produced in Cameroon include cocoa, coffee, palm oil, maize, beans, cassava, groundnuts, plantains, and bananas. Other items like soybeans, chili pepper, Egusi seeds, white pepper, and Bambara nuts largely produced and consumed in Cameroon are also found in markets of neighboring countries, which mostly depend on Cameroon for their food supply. The western highlands of Cameroon, from where the vast majority of these agricultural products originate, is considered as the food basket of the country, and by extension, of the whole Central African region [18]. There is no study investigating pesticide contamination levels of agricultural products from this high production region of Cameroon, hence this work was planned.
Among the various methods of pesticide residue determination in food items, the QuEChERS method (Quick, Easy, Cheap, Effective, Rugged, and Safe) with a dispersive solid-phase extraction (d-SPE) clean-up has been documented to give a better recovery compared to classical techniques. The QuEChERS method has the advantages of high recovery, high sample throughput, low solvent and glassware usage, less labor and bench space, lower reagent costs, ruggedness, and low worker exposure [19,20]. Thus, analytical chemists now prefer to use the QuEChERS method with a d-SPE clean-up when required; it is streamlined and effective for analysis of diverse residues in food matrices [2,21,22]. The present study analyzes and validates the residues of 99 pesticides in 72 samples of 12 agricultural products collected in the western highlands of Cameroon, using the QuEChERS method as the extraction and clean-up technique, and liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography with electron capture detection (GC-ECD) for detection.

2. Materials and Methods

2.1. Reagents

Analytical grade reagents of above 99% purity were used in the experiments. UPLC-grade acetonitrile and hexane were procured from VWR Chemicals (Leuven, Belgium). Anhydrous magnesium sulphate, disodium hydrogen sesquihydrate, trisodium citrate dehydrate, sodium chloride, and the pesticide active ingredient standards were purchased from Sigma–Aldrich (Bornem, Belgium). Fifteen-milliliter d-SPE tubes were obtained from Waters (Zellik, Belgium). Water was produced locally though a Milli-Q purification system.

2.2. Samples Collection

A total of 72 dried samples were collected in March 2017 from local markets of 6 major towns in the West Region of Cameroon: Bafang, Bangangté, Bafoussam, Dschang, Foumban, and Mbouda (Figure 1). They belonged to 12 agricultural products; namely, 8 groundnut samples, 6 soybean, 10 kidney bean, 6 black bean, 7 cowpea, 6 chili pepper, 7 Egusi seeds, 4 coffee beans, 2 cocoa beans, 11 maize, 2 white pepper, and 3 Bambara nuts. Interviews of the wholesalers during sample collection revealed that each food batch was made of a pool of small lots originating from different farmers of the neighboring villages. Therefore, they were considered as representative samples. Approximately 200 g of each sample was collected in a hard paper envelope within polyethylene plastic bag, sealed, labelled, transported to the Laboratory of Crop Protection Chemistry at Ghent University, Belgium, and kept at 20 °C until extraction and analysis.

2.3. Pesticides Extraction

Extraction and clean-up were performed using the QuEChERS method commonly used in the multi-residue analysis of food matrices. Each sample (approx. 50 g) was ground to powder using a household mill equipped with a stainless steel knife (Krups, Fleurus, Belgium). Each time the grinder was thoroughly washed to avoid cross-contamination between samples. Precisely 5 g of powder was weighed into a 50 mL Teflon capped centrifuge tube, 5 mL of Milli-Q water followed by 15 mL of acetonitrile was added, and the mixture was vigorously shaken for 1 min. A mixture of disodium hydrogen citrate sesquihydrate (0.75 g), trisodium citrate dihydrate (1.5 g), sodium chloride (1.5 g), and anhydrous magnesium sulphate (6 g) was added to the extract in the tube, which was agitated for 3 min at 300 rpm on a shaker (Edmund Bühler, Hechingen, Germany). The sample was centrifuged for 5 min at 10,000 rpm (Eppendorf, Leipzig, Germany) and the supernatant was collected. Samples of groundnuts, chili pepper, coffee, cocoa, and white pepper required clean-up to remove any organic acids, polar pigments, and other compounds that could interfere with the analysis. For clean-up, 8 mL of the supernatant was pipetted into a 15 mL d-SPE tube packed with 300 mg primary secondary amines (PSA), 900 mg MgSO4, and 150 mg octadecyl (C18). The content of the tube was then shaken for 1 min, centrifuged for 5 min at 3000 rpm, and the supernatant collected. For LC-MS/MS analysis, 1 mL of the supernatant was transferred into a 10 mL flask and the volume was made up to 10 mL with Milli-Q water. After mixing, 2 mL of the diluted solution was sampled into a screw cap autosampler vial for chromatography analysis. For GC-ECD analysis, 5 mL of supernatant was taken into a 10 mL flask and acetonitrile was evaporated (Heidolph Instruments, Schwabach, Germany) at 40 °C until dryness. Acetonitrile was replaced by 5 mL of hexane, and 2 mL of the extract was sampled into a crimp top autosampler vial for analysis.

2.4. Sample Analysis

The selection of potential active ingredients to be screened was based on the list of registered agricultural pesticides authorized in Cameroon by the Ministry of Agriculture and Rural Development, commonly used on the sample crops [23]. For different chemical classes of pesticides, LC-MS/MS and GC-ECD were separately used.

2.4.1. Liquid Chromatography Tandem Mass Spectrometry

Eighty-one compounds were analyzed by LC-MS/MS (Table 1) following a protocol adapted from Houbraken et al. [24]. The equipment consisted of a Waters Acquity UPLC module coupled to a Waters Xevo TQD Tandem triple quadrupole mass spectrometer equipped with an electrospray ionization (ESI) interface (Waters, Zellik, Belgium). Separation was carried out through a HSS T3 column (100 mm × 2.1 mm, 1.8 µm) maintained at 40 °C. The injection volume was 10 μL, and mobile phase A consisted of 10 mM ammonium acetate solution in water, while mobile phase B was acetonitrile with 0.1% formic acid. The flow rate was set at 0.4 mL/min with a run time of 10 min. The separation started with an initial gradient of 98% mobile phase A for 0.25 min, followed by a linear gradient to 98% mobile phase B from 0.25 to 7 min, which was maintained for 1 min. Then, a linear gradient was used to 98% mobile phase A and the column was reconditioned for 1 min. Except for fludioxonil and 2,4-dichlorophenoxyacetic acid (2,4-D), which were analyzed in negative ion mode, analyses of all the other pesticides were performed in positive ion mode. The ESI capillary needle was maintained at +2 kV, the source temperature at 150 °C, the desolvation temperature at 600 °C, cone gas flow at 50 L/h, and desolvation gas flow at 1000 L/h. The analytes were monitored and quantified using multiple reaction monitoring (MRM). Optimization of the MS/MS conditions, identification of the parent and product ions, as well as the selection of the cone and collision voltages, were performed with direct infusion of their individual standard solutions prepared at 1 mg/mL in acetonitrile/water (10/90). Two different m/z transitions were selected for each analyte, one for quantification (QIT) and one for confirmation (CIT). The dwell time was calculated automatically. MassLynx 4.1 software (Waters) was used for the LC-MS/MS system control and data acquisition and analysis.

2.4.2. Gas Liquid Chromatography with Electron Capture Detection

Eighteen halogenated compounds (Table 2) were analyzed using an Agilent 6890N Network gas chromatograph with an auto-sampler, coupled to an electron capture detector (Agilent Technologies, Diegem, Belgium). The protocol was adapted from Amulen et al. [25]. Separation was performed on a HP-5MS (5% phenyl methyl siloxane) capillary column (30 m × 0.25 mm, 0.25 μm). The operating conditions were as follows: The column was initially set at a temperature of 80 °C, then increased at a rate of 30 °C/min to 205 °C and held for 4 min. It was further increased at a rate of 20 °C/min to 290 °C and held constant for 8 min, followed by an increase at a rate of 50 °C/min to 325 °C. The temperature of the injector and detector were maintained at 280 °C and 300 °C, respectively. Helium was used as a carrier gas at a flow rate of 1.1 mL/min, and the injections were made in the split mode with a split ratio of 52.7:1. The Agilent GC ChemStation version Rev. A.10.02 software was used for system control and data acquisition and analysis.

2.5. Method Validation

Validation of the analysis was performed as recommended in Document No. SANTE/11945/2015 [26]. The validation parameters were linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision. Eight replicates of samples obtained from organic agriculture markets in Belgium were spiked at 0.01 mg/kg with pesticide standards. The spiked samples were left for 1 h to allow pesticide absorption into samples before being subjected to the extraction, clean-up process, and analysis as described previously. The LOD together with the LOQ were calculated by multiplying the standard deviation of the detected pesticide concentrations from the replicates by 2.99 and 10, respectively. The accuracy (average recovery) was calculated by dividing the recovered concentrations by spiked concentration, and precision (relative standard deviation of within-laboratory reproducibility analyses, %RSD) was obtained by dividing the standard deviation by the average concentration. To determine linearity and calculate pesticide content in samples, five different concentrations of the pesticide standards stock solution (0.1, 0.05, 0.01, 0.005, 0.001 mg/L) were prepared by dilution with acetonitrile/water (10/90) to make a calibration curve.

2.6. Data Analysis

Descriptive statistics depicting the frequencies of occurrence and the distribution of quantified pesticides in the analyzed agricultural products and the sampling locations were generated. The quantified pesticides were compared with the maximum residue limits (MRLs) of the European Union regulations. As the number of samples was not equal for all the food items, the contamination rate (the relative number of positive samples), the pesticide rate (relative number of pesticides quantified), as well as the above MRLs rate (relative number of samples above the MRLs values) for each food item were calculated.

3. Results

3.1. Method Validation

Five attributes of the extraction and analysis methods were validated: Accuracy (percentage recovery), precision (%RSD), limit of detection (LOD), limit of quantification (LOQ), and linearity. The obtained recoveries data and validation parameters of the 2 analysis methods are presented in Supplementary Material Table S1. The linearity of analysis of 81 pesticides by LC-MS/MS ranged between 0.9993 and 0.9999 (Table 1). The recovery varied greatly among the pesticides, and for a given pesticide, among the food commodities, with the median between 11.5% and 227% for methsulfuron-methyl and spiroxamine, respectively. In general, high recoveries (>120) were mostly found in groundnuts, soybeans, beans, and cocoa, while low values (<70) were mostly obtained in maize, white pepper, Egusi seeds, and coffee. The %RSD showed consistent precision with only 30 out of 720 (4.1%) of the %RSD values above the 20% acceptable threshold. The LOD ranged between 0.0001 and 0.0161 mg/kg, and the LOQ between 0.0004 and 0.0537 mg/kg.
Analysis of 18 halogenated pesticides by GC-ECD showed linearity between 0.9987 and 0.9999 (Table 2). Large variations were also observed in recoveries, with median values that ranged between 87.8 and 170.8% for Aldrin and o,p′-DDT, respectively. In general, beans and cocoa showed high recovery values, while low values were found in groundnuts and chili pepper. The %RSD showed that the analyses were precise; only 3 out of 153 (1.9%) of the %RSD values were above the 20% limit. The LOD varied from 0.0004 to 0.0652 mg/kg, and the LOQ from 0.0012 to 0.2180 mg/kg.

3.2. Pesticide Residues in Food Samples

After validation of the QuEChERS method, 99 pesticide residues were screened in 72 samples of 12 agricultural products. The results of the quantified pesticides are available in Supplementary Material Table S2, and the pesticide distributions are summarized in Table 3. All of the 18 halogenated compounds analyzed by GC-ECD could be quantified in the samples, against 44 of the 81 compounds analyzed by LC-MS/MS. Residues of 62 pesticides were found in the samples: 39.7% were insecticides, 30.9% were fungicides, herbicides represented 16.2%, acaricides 8.8%, and nematicides 4.4%. Twelve banned compounds were found in the samples, among which there was 1 herbicide (alachlor), 3 insecticide/acaricides (malathion, α-Endosulfan, and β-Endosulfan), and 8 insecticides (β-HCH, aldrin, carbaryl, carbofuran, diazinon, dieldrin, heptachlor, and propoxur).
The distribution of the pesticides by sampling location showed that 22 pesticides (34.4%) were found in all 6 locations, while 15 pesticides (24.2%) could be found at only one sampling site. The repartition based on food commodities showed that 12 compounds, representing 19.3% of the pesticides quantified in the samples, were found in all 12 food items; except for malathion, 11 of them are halogenated compounds. However, 17 compounds (27.7%) could be quantified in only 1 food commodity. Considering individual pesticides, malathion was the most distributed pesticide as it was found in 70 samples, followed by p,p′-DDT, which was quantified in 69 samples. Thirteen individual halogenated pesticides were found in above 40 samples. Fifteen pesticides were quantified in only 1 sample. Concentration-wise, the single pesticides with the highest quantified concentrations were malathion in kidney beans (5.5 mg/kg), hexachlorobenzene in kidney beans (3.1 mg/kg), chlorotoluron in soybeans (1.5 mg/kg), cypermethrin in kidney beans (0.9 mg/kg), captan in chili pepper (0.8 mg/kg), and methoxychlor in maize (0.8 mg/kg). In general, malathion was found at all 12 sampling locations, in almost all the 72 samples, in all 12 food commodities, and with the highest concentrations. Whereas isoproturon was found at only 1 sampling location, in a single black bean sample, and with the lowest concentration (0.0004 mg/kg).
The distribution of quantified pesticides in the 12 food items (Table 4) demonstrates that all samples contained one or more of the 62 quantified pesticides. Chili pepper showed the highest relative number of positive samples, with a contamination rate of 23.2%, followed by white pepper (20.2%), kidney beans (17.3%), and soybeans (17.2%). White pepper, with a pesticide rate of 14.6%, contained the highest relative number of pesticides per sample, followed by cocoa (8.1%) and Bambara nuts (8.1%).
In total, 21 pesticides (34.4%) were found above their existing European Union MRL values (Supplementary Material Table S2, Table 3). Malathion was found above its MRL in 49 samples, followed by aldrin (30 samples), hexachlorobenzene (25 samples), alachlor (22 samples), and β-HCH (21 samples). Except for Bambara nuts and Egusi seeds, which do not have established MRL values yet, all the food items had residues above the threshold limit fixed by the European Union. Samples with residues above MRLs represented 38% of all the positive analyses, and were found in all 6 sampling locations. Chili pepper showed the highest above MRL rate (6.4%), followed by kidney beans (5.5%), while the lowest rate (2.1%) was found in groundnuts.

4. Discussion

According to the European Commission, if the recoveries of pesticides are consistently high or low in replicate tests, this outcome is acceptable [26]. The method appeared to be suitable for detecting almost all the targeted compounds in all the food items, and adjustments were performed for recoveries lower than 70% or higher than 120%. Consistently low recoveries were found with apolar compounds, in cleaned-up samples, and in samples with high fat content like groundnuts, Egusi seeds, and cocoa beans. This can be attributed to losses during the extraction and clean-up steps with d-SPE tubes in the modified QuEChERS method [27]. These observations agree with the findings of Mekonen et al. [2] on similar food items. Conversely, very high or very low recovery values were also consistently obtained for polar pesticides, and in some samples which did not require clean-up. In LC-MS/MS analysis, this may be due to the presence of matrix components that coelute with the compounds of interest and can interfere with the ionization process in the mass spectrometer, causing ionization suppression or enhancement; this phenomenon is called the matrix effect [28]. To further understand these recovery differences and ensure a thorough quantitative analysis, the matrix effect of these food matrices on the screened pesticides must be investigated.
In this study, pesticides were detected in all the samples. In a similar preliminary study of pesticide residues in fruits at the market level in Accra, Ghana, pesticide residues were also found in all the 320 samples of pawpaw, tomato, and apple [29]. All 42 samples of staple food items from the Jimma zone in Ethiopia also contained 1 or more pesticide [2]. Additionally, our results of the 62 quantified pesticides in the food items agree with the findings of Winter [30], who reported a total of 77 individual pesticides detected from market basket samples analyzed by the U.S. Food and Drug Administration in 2004 and 2005. The report of Manfo et al. [4], who showed that 56 pesticides containing 25 active substances were used by farmers of Djutitsa, located in our sampling region of Cameroon, can justify our results. Moreover, Mahob et al. [6] found that 35 different chemicals were marketed in Cameroon for use in cocoa, among which there were 4 herbicides, 11 fungicides, and 20 insecticides. The great majority of farmers (96.8%) apply pesticides on their farms, while fungicides were used most often (61.8%), followed by insecticides (38.2%).
In a similar study on staple foods from Ethiopia, the main pesticides detected were DDT, endosulfan, cypermethrin, and permethrin at concentrations varying from 0.011 to 1.115 mg/kg [2]. In maize, cowpea, and millet samples from northern Cameroon, organochlorine and organophosphorus pesticides including β-endosulfan, lindane, pirimiphos-methyl, and malathion were detected more frequently [10]. Moreover, Sonchieu et al. [5] found that in cowpea samples from the markets of Ngaoundéré in northern Cameroon, residues of the organophosphorus pesticides dichlorvos, methyl-parathion, malathion, profenofos, diazinon, and chlorpyrifos were found in concentrations ranging from 0.02 to 5.4 mg/kg. The above results corroborate our findings of halogenated compounds as the most distributed pesticides in samples from the western highlands of Cameroon, and the pesticide concentrations we obtained are on par with all these studies.
Our analyses revealed that 34.4% of the pesticides were found above their existing MRL values and in 38% of the positive analyses. Our results are comparable to the findings of Bempah and Donkor [29], who reported 32.8% of fruit samples with residues above MRLs, and Mekonen et al. [2], in which one-third of samples were above the MRLs. However, Sonchieu et al. [10] obtained higher values, with 75% of samples containing pesticide residues above MRLs. These disparities could be due to the single food item (cowpea) used in their study.
We found 12 banned compounds in the samples, including DTT and its metabolites, as well as β-Hexachlorocyclohexane, a by-product of lindane. Similar results were reported in Ghana [29], in Ethiopia [2], and in samples from northern Cameroon [5,10,17]. This raises the concern of the presence of banned pesticides in foods in Africa, and particularly in Cameroon. This can result from environmental persistence of these pesticides, which could have returned into the food chain. However, in Cameroon, the availability of a huge stock of obsolete pesticides, coupled with their illegal use in agriculture, can justify the high distribution and concentration of banned pesticides in analyzed food samples. In 353 stores visited in the whole country, Tarla et al. [16] inventoried 210,047 kg and 309,521 L of obsolete pesticides, among which there was 4146 kg of persistent organic pollutants. Eight officially banned active ingredients were still being used in Cameroonian cocoa farms, and over 77% of farmers did not respect the official spray recommendations for chemicals [6]. Similarly, in Foumbot, one of our sampling locations, it was found that because of the absence of any formal training on pesticide application, and the lack of assistance from agricultural extension agents, farmers did not respect treatment frequencies [7]. Moreover, Sonchieu et al. [17] reported that the presence of high residues in northern Cameroon samples indicates the continuous use of banned pesticides in Cameroon acquired from black markets, because of their effectiveness and low price, to the detriment of consumer health.

5. Conclusions

It is critical to understand the occurrence of agrochemical contaminants in foods for assessing the health risk and preserving consumer health. This study validated a multi-residue method, and used it to screen 99 pesticides in 12 agricultural products from the food basket of Cameroon using LC-MS/MS and GC-ECD. We found that samples from all 6 locations and of all 12 food items were contaminated with one or more of the 63 pesticides quantified, among which 12 banned compounds were found. Halogenated pesticides, especially malathion, were highly distributed among the samples. Chili pepper and white pepper were the most contaminated food items. Twenty one pesticides were found above their European Union MRL values and represented 38% of the positive samples. These results pave the way for estimating the potential health risks associated with exposure to these pesticides in Cameroon. They also represent scientific evidence to create awareness on the necessity of good pesticide monitoring in Cameroon. There is an urgent need to develop strategies for lowering pesticide residues in food, and actions to be taken by regulatory authorities to manage the countries obsolete pesticide stock and regulate agrochemical usage in the country.

Supplementary Materials

The following are available online at https://www.mdpi.com/2304-8158/7/11/184/s1; Table S1: Recovery data and validation parameters of analysis of 99 pesticides in 12 food items from western highlands of Cameroon by LC-MS/MS and GC-ECD; Table S2: Analysis of 99 pesticide residues in 72 samples of 12 agricultural products from western highlands of Cameroon by LC-MS/MS and GC-ECD.

Author Contributions

Conceptualization, J.H.Y.G., M.H., J.F.D., D.F. and P.S.; Data curation, J.H.Y.G. and M.H.; Formal analysis, J.H.Y.G., M.H. and A.W.; Funding acquisition, J.H.Y.G. and P.S.; Investigation, J.H.Y.G.; Methodology, J.H.Y.G., M.H. and P.S.; Project administration, P.S.; Resources, J.F.D., D.F. and P.S.; Supervision, P.S.; Validation, J.H.Y.G., M.H., A.W. and P.S.; Writing—original draft, J.H.Y.G.; Writing—review & editing, J.H.Y.G., M.H., A.W., J.F.D. and D.F.

Funding

This research received no external funding.

Acknowledgments

The authors are thankful to the Erasmus Mundus Action 2 CARIBU Project for sponsoring and supporting the study. The great laboratory assistance of Lilian Goeteyn is gratefully acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

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Foods 07 00184 g001 550
Figure 1. Map of the West Region of Cameroon showing the administrative divisions and major towns where samples were collected.
Figure 1. Map of the West Region of Cameroon showing the administrative divisions and major towns where samples were collected.
Foods 07 00184 g001
Table
Table 1. Parameters of acquisition method and linearity results of LC-MS/MS analysis of 81 pesticides.
Table 1. Parameters of acquisition method and linearity results of LC-MS/MS analysis of 81 pesticides.
Sr. No.AnalyteRetention Time (min)Parent Ion (m/z)Cone Voltage (eV)Ionization ModeDwell Time (s)Fragment Ion 1 (m/z)Collision Energy 1 (eV)Fragment Ion 2 (m/z)Collision Energy 2 (eV)Linearity
12,4-dichlorophenoxyacetic acid3.51160.750-0.07188.920124.9 *180.9998
2Acetamiprid2.7222334+0.01556.115126 *200.9998
3Ametryn3.11228.132+0.01368.136186.1 *180.9997
4Atrazine 2.4617430+0.03896 *20103.9200.9995
5Azoxystrobin4.2340422+0.01532930372 *150.9998
6Benalaxyl 4.96326.120+0.0649134148 *200.9998
7Bentazone3.32241.421+0.015107.226199.1 *120.9994
8Bitertanol4.74338.115+0.01570.1 *899.1160.9998
9Boscalid4.40342.935+0.013139.9 *20307200.9997
10Butachlor6.14312.220+0.06757.322238.2 *120.9998
11Cadusafos5.20271.122+0.01513122159 *160.9997
12Carbaryl3.3820222+0.0811728145 *220.9998
13Carbendazim2.28192.127+0.08132.12816.1 *180.9998
14Carbofuran3.22222.128+0.012123 *16165.1160.9989
15Chlorpyrifos6.31349.930+0.03797 *32198200.9994
16Chlorotoluron3.3621320+0.0372 *20140300.9999
17Cyanazine3.09241.135+0.039625214170.9998
18Cyflufenamid 5.73413.230+0.05220335295.1 *150.9999
19Cymoxanil2.7919917+0.01511118128 *80.9999
20Diazion5.2030531+0.0179635169 *220.9998
21Difenconazole5.2140640+0.015111.160251.1 *250.9993
22Dimethoate2.67230.118+0.01212520199 *100.9987
23Dimethomorph4.02388.135+0.01316530300.9 *200.9993
24Diuron3.5623328+0.01246.31472.1 *180.9987
25Epoxiconazole4.3333028+0.0310150121 *220.9997
26Ethoprophos4.34243.226+0.0129731131 *200.9989
27Fenamiphos4.30304.130+0.012202.136217.1 *240.9999
28Fenbuconazole4.6833732+0.01270.1 *20125360.9983
29Fenoxycarb4.74302.122+0.038820116.1 *110.9999
30Fenpropimorph3.44304.250+0.01557.230147.2 *280.9995
31Fludioxonil4.18246.850-0.013126 *30180280.9997
32Hexaconazole4.6931416+0.01370.1 *34159220.9998
33Hexythiazox6.3135324+0.136168.126228.1 *140.9996
34Imazalil3.0229734+0.0269 *22159220.9984
35Imidacloprid2.64256.134+0.038175.1 *20209.1150.9997
36Iprodione4.6833015+0.015244.7 *16288150.9992
37Isoproturon3.50207.334+0.03461672 *160.9997
38Kresoxim-methyl5.01314.118+0.01711612206 *70.9997
39Linuron4.10249.131+0.015159.9 *18181.8160.9997
40Malathion4.5533120+0.0139924127 *120.9997
41Metalaxyl3.44280.120+0.012192.117220.1 *130.9986
42Methiocarb4.0122622+0.01512122169 *100.9996
43Methomyl2.4016320+0.01788 *10106100.9998
44Metribuzin3.1021535+0.0128920131 *180.9988
45Metsulfuron methyl3.1538222+0.02167 *16198220.9997
46Monocrotophos2.40224.120+0.16398.112127.1 *160.9996
47Oxamyl2.3723715+0.16372 *1090100.9998
48Penconazole 4.6728428+0.05270.1 *16159340.9999
49Pendimethanil6.29282.220+0.02819418212.2 *100.9992
50Pirimicarb2.54239.128+0.0177228182.1 *150.9998
51Pirimiphos-methyl5.13306.130+0.052108.1 *32164.1220.9999
52Prochloraz4.1937616+0.01570.1 *34307.1160.9994
53Profenofos6.68372.936+0.017127.940302.6 *200.9998
54Propanil3.93217.934+0.01512722161.9 *160.9999
55Propazine3.95230.234+0.03146.124188.1 *180.9994
56Propiconazole4.8134240+0.0176922159 *340.9998
57Propoxur3.1921015+0.013111 *16168100.9997
58Pyrachlostrobin5.37388.125+0.01716325193.9 *120.9995
59Pyrazosulfuron-ethyl4.0741522+0.01282.945182 *200.9992
60Pyrimethanil3.3920045+0.0158224107 *240.9996
61Simazine3.0820234+0.039622124 *160.9993
62Spinosad A4.12732.650+0.01398.159142 *310.9997
63Spinosad D4.39746.545+0.01398.153142 *310.9999
64Spirodiclofen7.00411.125+0.10871.2 *13313130.9999
65Spiroxamine3.4729832+0.01310032144 *200.9990
66Tebuconazole4.5530840+0.01570.1 *22125400.9999
67Tebufenozide4.95353.113+0.05213320297.1 *80.9997
68Tebuthiuron2.9022930+0.01511616172 *180.9997
69Temephos6.30466.832+0.052125 *38418.9220.9988
70Terbufos6.1128912+0.01757.222103 *80.9997
71Terbutryn3.51242.134+0.0159128186.1 *200.9997
72Terbutylazine4.0123028+0.039628174 *160.9998
73Thiabendazole2.3620245+0.01313130175 *250.9997
74Thiacloprid2.8725325+0.07190.140126 *200.9998
75Thifensulfuron-methyl3.0838830+0.0155640167 *150.9997
76Thimetoxam2.4929222+0.03813222211.2 *120.9995
77Thiodicarb3.1735520+0.01587.9 *16107.9160.9998
78Tiofanate-methyl3.1234322+0.0159346151220.9997
79Triadimenol4.07296.115+0.01770.2 *1099.1150.9999
80Triazophos4.65314.125+0.012118.935161.9 *180.9997
81Trifloxystrobin5.7840928+0.07314540186 *160.9994
* Represents the transition used for quantification (QIT). LC-MS/MS: liquid chromatography tandem mass spectrometry.
Table
Table 2. Parameters and linearity results of GC-ECD analysis of 18 pesticides.
Table 2. Parameters and linearity results of GC-ECD analysis of 18 pesticides.
Sr. No.Retention Time (min)AnalyteLinearity
110.6Heptaclor0.9997
210.8β-HCH0.9996
311.4Chlorothalonil0.9992
412.05Alachlor0.9994
512.8Aldrin0.9993
613.5Hexachlorobenzene0.9999
713.6Captan0.9987
814.2α-Endosulfan0.9999
914.5p,p′-DDE0.9999
1014.7Dieldrin0.9999
1115.2Endrin0.9996
1215.3p,p′-DDD0.9998
1315.3β-Endosulfan0.9998
1415.5o,p′-DDT0.9999
1516.2p,p′-DDT0.9997
1617.4Bifenthrin0.9999
1717.6Methoxychlor0.9988
1824.2Cypermethrin0.9999
HCH = Hexachlorocyclohexane, DEE = Dichlorodiphenyldichloroethylene, DDD = Dichlorodiphenyldichloroethane, DDT = Dichlorodiphenyltrichloroethane.
Table
Table 3. Characteristics of the 62 pesticides analyzed by LC-MS/MS and GC-ECD (gas chromatography-electron capture detection) in 12 food items from the western highlands of Cameroon.
Table 3. Characteristics of the 62 pesticides analyzed by LC-MS/MS and GC-ECD (gas chromatography-electron capture detection) in 12 food items from the western highlands of Cameroon.
Sr. No.PesticideMethodApplicationBannedNumber of Positive LocationsNumber of Positive Food ItemsNumber of Positive SamplesLowest Value (mg/kg)Highest Value (mg/kg)Mean Value (mg/kg)Median (mg/kg)Number of Samples >MRLs
1AcetamipridLC-MS/MSInsecticideNo5460.00040.04000.00860.00250
2AlachlorGC-ECDHerbicideYes612480.00260.62750.06100.018622
3AldrinGC-ECDInsecticideYes612640.00120.46460.06630.022430
4AtrazineLC-MS/MSHerbicideNo611290.00060.00400.00180.00130
5AzoxystrobinLC-MS/MSFungicideNo1110.00110.00110.00110.00110
6BenalaxylLC-MS/MSFungicideNo2120.00460.02220.01340.01340
7BentazonLC-MS/MSHerbicideNo3450.00200.01280.00660.00620
8BifenthrinGC-ECDInsecticideNo2340.00560.03100.01390.00960
9BitertanolLC-MS/MSFungicideNo4340.00080.00930.00340.00170
10CadusafosLC-MS/MSInsecticide/NematicideNo4140.00890.62850.16840.01823
11CaptanGC-ECDFungicideNo612600.00970.85570.14670.039019
12CarbarylLC-MS/MSInsecticideYes2220.02970.07580.05280.05280
13CarbendazimLC-MS/MSFungicideNo1110.00140.00140.00140.00140
14CarbofuranLC-MS/MSInsecticideYes3230.00060.00270.00150.00110
15ChlorothalonilGC-ECDFungicideNo612480.00390.06830.01230.00922
16ChlorpyrifosLC-MS/MSInsecticideNo3560.00710.36670.11550.07203
17ChlorotoluronLC-MS/MSHerbicideNo67110.07091.55080.26090.108310
18CypermethrinGC-ECDInsecticideNo612520.00140.94490.06940.021311
19DiazinonLC-MS/MSInsecticideYes1110.00200.00200.00200.00200
20DieldrinGC-ECDInsecticideYes610440.00120.06040.00690.00295
21DifenconazoleLC-MS/MSFungicideNo63110.00090.00210.00140.00130
22DimethomorphLC-MS/MSFungicideNo1110.00070.00070.00070.00070
23EndrinGC-ECDInsecticideNo610360.00120.03370.00500.00283
24EpoxiconazoleLC-MS/MSFungicideNo66260.00040.01760.00320.00160
25FenamiphosLC-MS/MSNematicideNo1110.00140.00140.00140.00140
26FenbuconazoleLC-MS/MSFungicideNo3230.00090.00150.00110.00090
27FenoxycarbLC-MS/MSInsecticideNo1110.00130.00130.00130.00130
28FenpropimorfLC-MS/MSFungicideNo4370.00040.00130.00080.00050
29HeptaclorGC-ECDInsecticideYes67160.00120.12360.01820.00212
30HexachlorobenzeneGC-ECDFungicideNo612570.00143.08950.13620.021925
31HexaconazoleLC-MS/MSFungicideNo2220.00250.01210.00730.00730
32ImazalilLC-MS/MSFungicideNo1110.00320.00320.00320.00320
33ImidaclopridLC-MS/MSInsecticideNo2560.00080.01200.00450.00350
34IsoproturonLC-MS/MSHerbicideNo1110.00040.00040.00040.00040
35LinuronLC-MS/MSHerbicideNo2330.00240.10410.03670.00361
36MalathionLC-MS/MSInsecticide/AcaricideYes612700.00735.52690.95460.313749
37MetalaxylLC-MS/MSFungicideYes5790.00040.17360.02050.00071
38MethiocarbLC-MS/MSAcaricide/InsecticideNo3240.00150.00610.00430.00486
39MethoxychlorGC-ECDInsecticideNo55100.01600.81650.18210.04750
40MethribuzinLC-MS/MSHerbicideNo1110.00370.00370.00370.00370
41MonocrotophosLC-MS/MSAcaricideNo3330.00120.00790.00340.00120
42o,p′-DDTGC-ECDInsecticideNo610510.00130.01560.00410.00270
43p,p′-DDDGC-ECDInsecticideNo612420.00120.02410.00370.00220
44p,p′-DDEGC-ECDInsecticideNo612440.00130.02760.00510.00240
45p,p′-DDTGC-ECDInsecticideNo612690.00330.14660.02460.01467
46PenconazoleLC-MS/MSFungicideNo4270.00620.02140.01160.00980
47Pirimiphos-methylLC-MS/MSInsecticideNo611480.00040.27350.01390.00306
48PropazineLC-MS/MSHerbicideNo1110.00320.00320.00320.00320
49PropiconazoleLC-MS/MSFungicideNo2230.00040.00320.00170.00160
50PropoxurLC-MS/MSInsecticideYes2220.00060.00170.00110.00110
51PyrimethanilLC-MS/MSFungicideNo2440.00620.09290.03720.02481
52SimazineLC-MS/MSHerbicideNo1110.00050.00050.00050.00050
53TebuconazoleLC-MS/MSFungicideNo1110.00160.00160.00160.00160
54TebufenozideLC-MS/MSInsecticideNo5590.00040.00480.00140.00070
55TerbuthrynLC-MS/MSHerbicideNo1110.00410.00410.00410.00410
56TerbuthylazineLC-MS/MSHerbicideNo611410.00530.18780.04540.02758
57Thiofanate-methylLC-MS/MSFungicideNo3230.00130.01800.00700.00170
58TriazophosLC-MS/MSAcaricide/NematicideNo2220.00130.00200.00160.00160
59TrifloxystrobinLC-MS/MSFungicideNo1110.00120.00120.00120.00120
60α-EndosulfanGC-ECDInsecticide/AcaricideYes612630.00120.04150.00760.00490
61β-EndosulfanGC-ECDInsecticide/AcaricideYes1110.00170.00170.00170.00170
62β-HCHGC-ECDInsecticideYes612470.00120.13710.01530.006021
Table
Table 4. Distribution of quantified pesticides in the 12 food items from the western highlands of Cameroon.
Table 4. Distribution of quantified pesticides in the 12 food items from the western highlands of Cameroon.
Sr. No.Food ItemNumber of SamplesNumber of AnalysesNumber of QuantificationsContamination Rate (%)Number of Pesticides QuantifiedPesticides Rate (%)Number of Samples >MRLsAbove MRLs Rate (%)
1Bambara nuts32974414.8248.1NANA
2Black beans6594569.4223.7183.0
3Chili pepper659413823.2355.9386.4
4Cocoa21982713.6168.152.5
5Coffee43965714.4235.8123.0
6Cowpea769310715.4284.0324.6
7Egusi seeds769310314.9243.5NANA
8Groundnuts87928410.6202.5172.1
9Kidney beans1099017117.3313.1545.5
10Maize11108917616.2312.8312.8
11Soybeans659410217.2325.4193.2
12White pepper21984020.22914.694.5
NA: Not Applicable, because of no existing MRLs.

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Galani, J.H.Y.; Houbraken, M.; Wumbei, A.; Djeugap, J.F.; Fotio, D.; Spanoghe, P. Evaluation of 99 Pesticide Residues in Major Agricultural Products from the Western Highlands Zone of Cameroon Using QuEChERS Method Extraction and LC-MS/MS and GC-ECD Analyses. Foods 2018, 7, 184. https://doi.org/10.3390/foods7110184

AMA Style

Galani JHY, Houbraken M, Wumbei A, Djeugap JF, Fotio D, Spanoghe P. Evaluation of 99 Pesticide Residues in Major Agricultural Products from the Western Highlands Zone of Cameroon Using QuEChERS Method Extraction and LC-MS/MS and GC-ECD Analyses. Foods. 2018; 7(11):184. https://doi.org/10.3390/foods7110184

Chicago/Turabian Style

Galani, Joseph H. Y., Michael Houbraken, Abukari Wumbei, Joseph F. Djeugap, Daniel Fotio, and Pieter Spanoghe. 2018. "Evaluation of 99 Pesticide Residues in Major Agricultural Products from the Western Highlands Zone of Cameroon Using QuEChERS Method Extraction and LC-MS/MS and GC-ECD Analyses" Foods 7, no. 11: 184. https://doi.org/10.3390/foods7110184

APA Style

Galani, J. H. Y., Houbraken, M., Wumbei, A., Djeugap, J. F., Fotio, D., & Spanoghe, P. (2018). Evaluation of 99 Pesticide Residues in Major Agricultural Products from the Western Highlands Zone of Cameroon Using QuEChERS Method Extraction and LC-MS/MS and GC-ECD Analyses. Foods, 7(11), 184. https://doi.org/10.3390/foods7110184

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