The present study developed and validated the liquid chromatography-tandem mass spectroscopy (LC-MS/MS) based highly sensitive, rapid and reproducible analytical method for estimating trace level residue of 39 major multiclass pesticides. A total of 480 samples of solanaceous crops (chilli, bell pepper, tomato and brinjal) and 240 from fruit crops (pomegranate and grapes) were collected from local markets of Kalyana Karnataka region for the study. The results indicated, the developed method was linear and showed excellent correlation coefficient (R2) of 0.998–0.999 in solvent and 0.993–0.999 in all the matrices analyzed. No significant matrix effect was observed for all pesticides in different matrices and within the range of ± 20%. The recovery percentage at all three levels (0.01, 0.05 and 0.1 mg/kg) ranged from 71.28-113.98% with a relative standard deviation of less than 10% for all the matrices evaluated. Measurement uncertainty values estimated at 50 µg/kg spiking level were found lower than 14 µg/kg for all tested pesticides in different matrices. Occurrence of 17 pesticides in all the tested vegetable and fruits samples which include 11 insecticides and 6 fungicides. Imidacloprid, carbendazim, acephate, profenofos, chlorantraniliprole, dimethoate, and difenoconazole were the commonly detected pesticides with 81.67, 76.67, 30.83, 21.67, 43.33 and 86.67% contamination in green chilli, bell pepper, tomato, brinjal, pomegranate and grapes, respectively. The dietary health risk assessment revealed safe for both adults and children as the hazard quotient (HQ) and hazard index (HI) values were less than 1 and would not cause any hazardous risk upon consumption.
Research Article
Simultaneous determination of multi-class pesticide residues in solanaceous vegetables and selected fruits using LC-MS/MS and potential impact on consumer health with measurement uncertainty
https://doi.org/10.21203/rs.3.rs-5199827/v1
This work is licensed under a CC BY 4.0 License
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Multiresidue
Validation
QuEChERS
LC-MS/MS
Hazard index
Measurement uncertainty
Fruits and vegetables are known as powerhouse of nutrition which are essential for a healthy life. Green chilli (Capsicum frutescens L.), bell pepper (Capsicum annuum L.), tomato (Solanum lycopersicum L.), brinjal (Solanum melongena L.) are the major vegetables and the pomegranate (Punica granatum L.) and grapes (Vitis vinifera L.) are the important horticulture crops grown for local consumption as well as export purposes in Kalyan Karnataka region. India is the leading producer of vegetables and fruits, 11.21 million metric tonnes of fruits and 209.39 million metric tonnes of vegetables produced from 7.15 mha and 11.24 mha, area respectively. India is the leader in area and production of solanaceous vegetables (brinjal 12777 MT, 736000 ha; tomato 20573 MT, 812000 ha; chilli green 3851 MT, 364000 ha; bell pepper 514 MT, 33000 ha). Similarly, India is also a major exporter of fresh grapes and pomegranates to the world. Grapes (2500000 Tonnes from 162000 ha), Pomegranate (2315000 MT from 261000 ha) (NHB 2023).
Various constraints are affecting the mentioned vegetables and fruit crops production. Among those insect pests and diseases are the important constraints causing significant yield losses. Tomato, chilli and bell pepper crops were ravaged by major pests viz., thrips, mites, pinworm, and fruit borers accounting for the yield loss to the tune of 20–60% (Dhandapani et al. 2003; Jamuna et al. 2021). In brinjal the fruit and shoot borer and mites are major pests causing 20–80% crop losses (Patial et al. 2008; Raju et al. 2007). Similarly, fruit crops like pomegranate and grapes were majorly infested by thrips, and Anar butterfly. The fungal diseases like downy mildew (54%), powdery mildew and viral diseases are major biotic constraints causing severe yield loss in grapes (Koledenkova et al. 2012). Similarly, bacterial blight is the nightmare (40–85% severity) of pomegranate cultivating farmers in this region (Jamadar et al. 2011). To manage all these pests and diseases farmers are extensively using a wide range of pesticides with a variety of chemical groups from conventional to recently discovered novel molecules, which in turn increase the pesticide load, their residue, and input cost several folds (Ananda et al. 2009; Abd-Ella 2015).
There are various reasons associated with the pesticide residue accumulation on crop produce namely the lack of knowledge about label claim pesticide usage, lack of awareness about the safety period for harvest and most farmers harvest their crop before the safety period of sprayed pesticides, it may be due to the immediate need and market demand. This kind of wrong usage and unawareness of the safety period may lead to the deposition and accumulation of pesticide residues on the treated crops and their final produce, later this residue enters the human body (Rathod et al. 2021). The vegetables and fruits selected for the study are the most regularly used ones in this region and other parts of the country. Most of these vegetables are used for salads and half-baked food recipes. When this kind of pesticide residue contaminated vegetables consumed, it resulted in various abnormalities in human beings and other non-target organisms. Therefore, knowledge of the nature of chemicals and the amount of pesticides (residues) leftover on harvested produce is essential for food quality control authorities to analyze, certify its safety, and assess its health risks to human beings and other non-target animals.
The available literature reveals, there is a meager research effort has been made for analytical methods showing multi-class analysis of pesticide residues in fruit and vegetable samples in India. Szpyrka et al. 2014 reported 36.6% of tested fruits and vegetables are contaminated with pesticides with 1.8% residues exceeding MRL using GC with ECD and NPD detector. Qin et al. 2015 reported 25.49% of the samples were contaminated with pesticide residues less than or equal to MRLs and 4.94% of samples had residues greater than their MRLs using GC-MS. Kumari and John (2019) reported the presence of methyl parathion and triazophos residues in vegetables and fruits. Recently, residues of acephate (25-37.5% contamination), chlorpyrifos, and dichlorvos were reported in carrots from Karnataka, and chlorpyriphos residue was found above MRL of 0.2 mg/kg (Gowda et al. 2020). In Korea, Yi et al. 2020 analyzed 283 pesticides in 96 different vegetables and only 1.4% of samples were contaminated with residues exceeding MRLs. Likewise, another study of 81 pesticides by Galani et al. 2020 detected residues above MRLs in few agricultural products. Jiang et al. 2021 found a detection rate of 28.43% pesticides in vegetables with the maximum risk factor of six pesticides viz., carbofuran, omethoate, phorate, dicofol, dimethoate, and dichlorvos using GC-MS.
The pesticide residues in grapes were detected in several studies (Arora et al. 2008; Mohapatra et al. 2011, Reddy et al. 2021, Mahdavi et al. 2022). In pomegranate, a method using LC-MS/MS for simultaneous analysis of 74 pesticides in pomegranate (Naik et al. 2022), GC-ion trap for multi-residue analysis of 50 pesticides in grape, pomegranate, and mango (Savant et al. 2010); 82 pesticides in pomegranate, apple, and orange (Banerjee et al. 2008); LC-MS/MS method for determination of imidacloprid, indoxacarb and thiamethoxam in pomegranate (Mohapatra et al. 2019); buprofezin, dimethoate and imidacloprid 76–109% recovery (Utture et al. 2012).
The present investigation is a first of its kind in the Kalyan Karnataka region to simultaneously detect different groups of pesticide residues in different vegetables and fruits in a short run time of 24 min. using advanced tools like LC-MS/MS-based techniques which are very crucial for detecting pesticide residues at incredibly low concentrations, lower than MRLs. Therefore, developing and validating a sensitive and reliable method to quantify 39 multi-class pesticides in fresh vegetables and fruits would facilitate effective monitoring and grading of samples for domestic consumption and export to meet the global food standard requirements.
Selection of pesticides, chemicals and reagents used
To reflect the frequently used pesticides on fruits and vegetables in these cities, 39 pesticides with large sales volumes were chosen for evaluation: 18 insecticides, 11 fungicides, 06 herbicides, 03 acaricides, and 01 rodenticides belonging to neonicotinoids, organophosphates, diamides, pyridine azomethines, chitin synthesis inhibitors, triazoles, phenyl amide, and phenoxy pyrazole. All the pesticides analyzed are amenable to LC-MS/MS analysis.
Certified reference materials (CRM) were procured from Dr. Ehrenstorfer (Augsburg, Germany) with traceability to ISO/IEC 17034 and purity (≥ 98.0%). The solvents viz., LiChrosolv methanol (Supelco) hypergrade (made in Germany) and ammonium formate (LiChropur™, ≥ 99.0%) for LC-MS were procured from Sigma Aldrich. HPLC & UHPLC grade acetonitrile (99.9%) and formic acid (90%; made in Netherland) was procured from J.T. Baker, Avantor Performance Material, India Private Limited. Anhydrous magnesium sulphate and sodium sulphate (analytical grade, > 99%)) were obtained from Hi-media, Laboratories (Mumbai, India). Magnesium sulphate was heated at 400°C in muffle furnace for four hours and stored in a desiccator before use. Agilent Technologies (USA) provided primary secondary amine (PSA, 40 µm). Water used in the analysis was obtained from a Millipore unit (18.2 MΩ).
Preparation of standard and calibration curves
The individual standard stock solution of 39 pesticides (1000 mg/L) was prepared in methanol. Intermediate (0.1 mg/L) solutions were prepared from individual stock solutions and working solutions (0.01 mg/L) were prepared from 0.1 mg/L and stored in glass volumetric flasks at − 20 ℃. Further, a standard linear solution ranging from 0.01 to 1.00 mg/L was prepared to construct the calibration curve. Matrix-match standards at the same concentration were prepared using control extracts of green chilli, bell pepper, tomato, brinjal, pomegranate and grapes obtained through the sample preparation procedure described below. µg/mL
Sample collection, storage, preparation, and extraction
Sample collection, storage, preparation, and extraction
The standardized method mentioned in the Pesticide Residue Analysis Manual (Sharma 2013) was employed to collect 480 samples of four vegetables (40 each) 240 samples of two fruits (40 each) from the local markets of three districts namely Raichur, Koppal and Bellary of Kalyan Karnataka region mainly. Each weigh 1 kg and is collected in clean 16 × 18 cm polythene bags. The samples were labelled, stored in icebox, transported to the laboratory and processed within 24 hours (mixer grinder to obtain a uniform particle size). The modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method was used for extraction and cleanup of the samples (Anastassiades et al. 2003).
Weighed 10.00 ± 0.1 g of sample (50 mL centrifuge tube) and 5 mL of distilled water was added (kept for 10 min.). Then 10 mL of ethyl acetate was added and vortexed for 30 sec. Homogenized the sample by using low volume homogenizer at 10000–13000 rpm for 2–3 min. 5 g of sodium sulphate (vortexed for 30 sec.) was added and centrifuged at 5000 rpm for 5 min at 10 ℃. Furthermore, 7 mL of the upper organic layer was transferred to a 15 mL centrifuge tube containing 0.175 g PSA and 1.00 g of MgSO4. Vortexed for 2–3 min and centrifuged the sample at 5000 rpm for 5 min. at 10 ℃. Afterward, 2 mL of supernatant was transferred into a test tube and evaporated using a nitrogen flash evaporator at 35 ºC to near dryness and later reconstituted the residue with 1 mL of methanol and filtered using 0.22 µm polytetrafluoroethylene (PTFE) nylon filter for LC-MS/MS analysis.
Instrument conditions
LC-MS/MS conditions
The Shimadzu Nexara X2 UHPLC with a triple quadrupole mass spectrometer (Shimadzu LCMS-8040) was employed for quantifying the pesticide residues of vegetables and fruits. The instrument was controlled with LabSolution® software, version 1.5. Chromatographic separation of pesticides (39) done with a high-performance reversed-phase C18 column with 2.0 mm i.d. × 150 mm length × 0.2 µm (shim-pack XR-ODS III) was used for rapid separation of the compounds at a column oven temperature of 40°C. The mobile phase gradients program of the binary pump was 60% A and 40% B at the beginning for 3 min. followed by 100% B up to 20 min. and 40% B and 60% A for the next 4 min. The LC and MS conditions of the instrument are mentioned in Table 1.
|
LC conditions (Nexera) |
|
|---|---|
|
Column |
Shimpack XR ODS C18 Column (2 mm i.d x 150 mm length x 0.2 µm) |
|
Flow Rate |
0.3mL/min |
|
Mobile Phase |
A: Water with 5 mM ammonium formate and 0.01% formic acid B: Methanol with 5 mM ammonium formate and 0.01% formic acid |
|
LC Program |
Binary gradient (24 min); Pressure (max.)-1300 bar; min-0 bar |
|
Oven Temp. |
40°C |
|
Injection Vol. |
2 µL |
|
MS Conditions (LCMS-8040) |
|
|
Interface |
ESI |
|
Ion source voltage |
450 V |
|
DL Temp. |
250°C |
|
Heat Block Temp. |
400°C |
|
Nebulizing Gas Flow |
3 L/min |
|
Drying Gas Flow |
15 L/min |
|
CID gas |
230kPa |
|
Mode |
MRM (positive compounds) |
Quality Control
The method was validated in control matrices before sample analysis using the SANTE guidelines (The European Commission document; SANTE/11312/2021) for parameters such as specificity, linearity, matrix effect, limit of quantification (LOQ), trueness (recovery), repeatability, reproducibility, and ion ratio.
Specificity was evaluated by spiking 39 mix pesticide standard solution to the blank samples of chilli, bell pepper, tomato, brinjal, pomegranate and grapes and injected (six replications) into the equipment to check for any interference in the region of interest, where the target retention time of the analyte is expected to elute.
Calibration curves of 6 points scale (0.01, 0.05, 0.1, 0.25, 0.5, and 1.0 mg/L) for both 39 mix solvent standard and matrix match standard solutions were generated by plotting with concentrations against the peak area obtained from LC-MS chromatogram. Correlation coefficient (R2) values were recorded for both solvent and matrix match standards.
Matrix effect (ME) is the interference due to the natural constituent of the samples. It is quantified by comparing the peak areas response for both solvent standard and matrix-matched standard. It was assessed using the below formula:
The sensitivity of the method was assessed with LOQ by spiking at the lowest level of pesticide mixture at which the analyte could able to meet the acceptable recovery, repeatability and reproducibility with relative standard deviation (RSD) ≤ 20%. To assess the trueness (by recovery experiment), blank samples were fortified with 39 mix pesticide standards at LOQ, 5×LOQ, and 10×LOQ with five replicates at each level separately.
The precision of the method was evaluated in terms of repeatability (same method, operator, and equipment) and reproducibility (same method, different operator, or different equipment) in five replicates at each fortification level (LOQ, 5×LOQ, and 10×LOQ) and % RSD for all the five replicates at each fortification level should be within ≤ 20%.
As per SANTE guidelines, a minimum of 2 product ions were selected along with the precursor ion during method optimization. The ion ratio was calculated based on the intensity of the quantifier and qualifier ions was used as a confirmatory parameter for analysing 39 pesticides in different selected matrices.
Estimation of measurement uncertainty
The Measurement Uncertainty (MU) describes the range of values around a reported result within which the true value of the measured quantity is expected to lie within a defined probability (confidence level). MU ranges take into consideration all potential sources of error. MU values were estimated using validation data based on the top-down approach (Nordest 2017, International Organization for Standardization ISO 11352 2012). Precision and trueness (as bias), were taken as the main contributors to measurement uncertainty.
The uncertainty evaluated was associated with precision and trueness (as bias), day-wise and analyst-wise, and was considered the main contributor to measurement uncertainty. The most widely used method for MU calculation is the ISO GUM approach (EURACHEM/CITACGuide 2014; Joint Committee for Guides in Metrology 2008). The combined uncertainty (u') of each pesticide was estimated at 50 µg/kg using the following formula:
$$\:u{\prime\:}=\:\sqrt{u{\prime\:}({bias)}^{2}+u{\prime\:}({precision)}^{2}}$$
Where, u'= combined measurement uncertainty
u'(bias) = uncertainty component for the bias
u'(precision) = uncertainty component for the precision
The uncertainty arising due to bias and precision was estimated from the recovery experiment, and % RSD of reproducibility, respectively. The expanded uncertainty (U') was calculated by multiplying the combined uncertainty with a coverage factor (k) of 2 at a confidence level of 95%.
U' = k × u'
Dietary risk assessment
To assess the risk level associated with the pesticide residues found in fruit and vegetable samples, the human dietary assessment was calculated based on the Estimated Daily Intake (EDI) and ADI (Acceptable Daily Intake) of insecticides. EDI was calculated by the average daily consumption of vegetables and fruits multiplied by the mean residual concentration (mg/kg) divided by the average Indian body weight (adult 60 kg; child 25 kg) (NIN 2010, NSSO 2014). For Hazard Quotient (HQ) analysis, EDI was compared with ADI (Acceptable Daily Intake) of insecticides.
HQs were calculated individually, then the sum of the HQs was calculated for the Hazard index (HI),
HI estimates the cumulative risk from exposure to multiple pesticides. The risk of human dietary consumption of insecticides is acceptable if the HQ and HI < 1, if found ≥ 1 it is not safe for consumption (Gad Alla et al 2015, Reffstrup et al. 2010 and Mahato et al. 2023).
Optimization of LC-MS/MS parameters
The method optimization involves three steps: Firstly, Q1 scan for all the compounds (CID gas off) at 0.5 mg/L. Secondly, MRM (multiple reaction monitoring) was performed (CID gas on) in positive mode for optimization of various parameters like checking for precursor ions, selection of product ions, and adjustment of collision energy (CE) and entrance voltage (EV) for the 39 pesticides. Thirdly, the optimized precursor and product ion transitions along with CE and EV were used for fixing the retention time (RT) by injecting 0.1 mg/L (2 µL injection volume) with a binary gradient program of 24 min. by connecting the C18 column (2 mm i.d ×150 mm ×0.2 µm) at 40 ℃ oven temperature. The LC-MS parameters with different mobile phases and MS conditions mentioned in Table 1 provided for proper ionization, separation and good peak shape of all the 39 pesticides (Fig. 1). The MRM transitions, optimized collision energies (CE) for the 39 pesticides, and RT are mentioned in Table 2. The first mass transition was used for quantification, while the second and third mass transitions were used to confirm the target pesticide in the matrices (Table 2).
|
Sl. No. |
Pesticides |
Retention time (min) |
Quantification |
Confirmation |
||||
|---|---|---|---|---|---|---|---|---|
|
Quantifier ion (m/z) |
CE (V) |
Qualifier-1 (m/z) |
CE (V) |
Qualifier-2 (m/z) |
CE (V) |
|||
|
1 |
Acephate |
1.442 |
183.90 > 143.0 |
11 |
183.90 > 49.15 |
23 |
183.90 > 95.05 |
26 |
|
2 |
Omethoate |
1.446 |
214.00 > 125.00 |
24 |
214.00 > 182.95 |
12 |
214.00 > 109.00 |
30 |
|
3 |
Pymetrozine |
1.685 |
218.10 > 105.00 |
22 |
218.10 > 78.10 |
44 |
218.10 > 51.15 |
54 |
|
4 |
Imidacloprid |
2.485 |
256.10 > 209.00 |
17 |
256.05 > 221.15 |
9 |
256.05 > 174.95 |
19 |
|
5 |
Dimethoate |
3.272 |
229.95 > 199.00 |
10 |
229.95 > 125.00 |
22 |
229.95 > 171.10 |
17 |
|
6 |
Carbendazim |
3.607 |
191.90 > 160.15 |
18 |
191.90 > 132.05 |
32 |
191.90 > 105.05 |
40 |
|
7 |
Thiacloprid |
3.987 |
253.00 > 126.05 |
23 |
253..00 > 99.10 |
48 |
253.00 > 90.10 |
40 |
|
8 |
Phosphomidon |
6.663 |
300.00 > 174.05 |
14 |
300.00 > 127.10 |
28 |
300.00 > 132.10 |
24 |
|
9 |
Bendiocarb |
7.920 |
224.00 > 167.10 |
11 |
224.00 > 109.00 |
19 |
224.00 > 81.10 |
35 |
|
10 |
Carbofuran |
7.939 |
222.10 > 165.00 |
12 |
222.10 > 123.15 |
23 |
222.10 > 55.05 |
29 |
|
11 |
Metribuzin |
7.903 |
215.10 > 187.10 |
20 |
215.10 > 49.10 |
29 |
215.10 > 58.05 |
27 |
|
12 |
Carbaryl |
8.845 |
202.10 > 145.10 |
12 |
202.10 > 127.00 |
28 |
202.10 > 117.10 |
24 |
|
13 |
Metalaxyl |
10.503 |
280.10 > 220.00 |
15 |
280.10 > 192.05 |
18 |
280.10 > 248.15 |
11 |
|
14 |
Isoproturon |
10.463 |
207.00 > 72.05 |
23 |
207.00 > 46.20 |
18 |
207.00 > 165.10 |
15 |
|
15 |
Chlorantraniliprole |
11.348 |
483.90 > 452.90 |
17 |
483.90 > 285.90 |
16 |
483.90 > 177.05 |
50 |
|
16 |
Coumatetryl |
12.343 |
293.10 > 175.10 |
24 |
293.10 > 91.15 |
36 |
293.10 > 107.05 |
35 |
|
17 |
Triademenol |
13.181 |
296.10 > 70.05 |
12 |
296.10 > 43.15 |
47 |
296.10 > 99.10 |
18 |
|
18 |
Paclobutrazol |
12.542 |
294.10 > 70.10 |
23 |
294.10 > 125.05 |
37 |
294.25 > 43.15 |
50 |
|
19 |
Methoxyfenozide |
12.632 |
369.20 > 149.15 |
18 |
369.20 > 91.15 |
9 |
369.20 > 133.05 |
24 |
|
20 |
Triadimefon |
12.742 |
294.10 > 69.00 |
23 |
294.10 > 196.95 |
17 |
294.10 > 225.10 |
14 |
|
21 |
Triazophos |
12.997 |
314.10 > 162.05 |
19 |
314.10 > 119.15 |
35 |
314.10 > 97.05 |
37 |
|
22 |
Tetraconazole |
13.396 |
372.00 > 159.00 |
34 |
372.00 > 70.10 |
25 |
372.00 > 150.10 |
35 |
|
23 |
Metolachlor |
13.666 |
284.10 > 252.05 |
15 |
284.10 > 176.10 |
27 |
284.10 > 134.10 |
34 |
|
24 |
Quinalphos |
14.244 |
299.10 > 163.00 |
22 |
299.10 > 147.05 |
22 |
299.10 > 119.00 |
44 |
|
25 |
Penconazole |
14.376 |
284.10 > 70.05 |
18 |
284.10 > 158.95 |
31 |
284.10 > 267.20 |
8 |
|
26 |
Spinosad |
14.568 |
732.20 > 142.15 |
35 |
732.20 > 98.25 |
54 |
732.20 > 99.15 |
52 |
|
27 |
Benalaxyl |
14.492 |
326.20 > 148.10 |
23 |
326.20 > 91.05 |
12 |
326.20 > 208.10 |
16 |
|
28 |
Hexaconazole |
14.886 |
314.10 > 70.00 |
22 |
314.10 > 159.00 |
10 |
314.10 > 45.10 |
39 |
|
29 |
Bitertenol |
14.992 |
338.20 > 269.20 |
10 |
338.20 > 99.10 |
17 |
338.20 > 70.15 |
11 |
|
30 |
Phosalone |
14.970 |
368.00 > 182.00 |
16 |
368.00 > 111.00 |
43 |
368.00 > 138.10 |
32 |
|
31 |
Thiobencarb |
15.225 |
258.00 > 124.95 |
20 |
258.00 > 100.05 |
13 |
258.00 > 89.15 |
49 |
|
32 |
Difenconazole |
15.359 |
406.10 > 250.90 |
28 |
406.10 > 111.05 |
55 |
406.10 > 188.00 |
47 |
|
33 |
Emamectin benzoate |
15.812 |
886.60 > 158.20 |
42 |
886.60 > 126.15 |
48 |
886.60 > 82.10 |
50 |
|
34 |
Pretilachlor |
15.716 |
312.20 > 252.05 |
16 |
312.20 > 176.05 |
29 |
312.20 > 132.1 |
40 |
|
35 |
Profenofos |
16.138 |
374.90 > 304.80 |
21 |
374.90 > 346.95 |
14 |
374.90 > 128.15 |
47 |
|
36 |
Buprofezin |
16.460 |
306.20 > 201.05 |
11 |
306.20 > 57.00 |
30 |
306.20 > 116.10 |
16 |
|
37 |
Hexythiazox |
16.913 |
352.90 > 228.00 |
15 |
352.90 > 168.15 |
28 |
352.90 > 116.15 |
43 |
|
38 |
Pendimethalin |
17.127 |
282.20 > 212.05 |
11 |
282.20 > 199.95 |
8 |
282.20 > 194.00 |
20 |
|
39 |
Fenpyroximate |
17.509 |
422.00 > 366.20 |
16 |
422.00 > 138.20 |
35 |
422.00 > 215.15 |
27 |
Method performance
The multi-residue analytical method was developed and validated using SANTE/11312/2021 guidelines and residues of 39 pesticides in different matrices were detected, confirmed and quantified through triple quadrupole LC-MS/MS in MRM (Multiple Reaction Monitoring) mode. The method linearity ranged from 0.01 to 1.00 mg/L concentration for all the matrices analysed and the calibration curves were linear for green chilli, bell pepper, tomato, brinjal, pomegranate, and grapes extract and showed good linearity with correlation regression (R2) value ranging from 0.993–0.999 for all the 39 analytes tested (Table S1- S6). The LOQ was in the range of 2.02–10.58, 2.37–12.59, 2.47–12.34, 2.25–10.24, 3.26–12.34 and 2.45–12.69 µg/kg for green chilli, bell pepper, brinjal, tomato pomegranate and grapes, respectively. The percent recovery at all three levels (0.01, 0.05, 0.1 mg/kg) are within the acceptable limit of 70–120% and ranges from 72.09-113.98% in green chilli, 73.56-112.86% in bell pepper, 72.25-111.25% in brinjal, 72.15-111.15% in tomato, 72.34-105.26% in pomegranate and 71.28-103.54% in grapes with % RSD of less than ≤ 20%. The Matrix effect was found less than ± 20% for all the matrices which is below the maximum threshold limit of 20% signal suppression or enhancement (SANTE 2021). The ion ratio was found within the acceptable limit of ± 30% at all spiked concentrations. The precision in terms of repeatability (intra-day) and reproducibility (inter-day) at 0.05 mg/kg gives a satisfactory recovery within 70–120% and RSD of less than ≤ 20% as per SANTE, 2021 (Table S1-S6). The residue data generated in this study ensures reliable and authentic results because the laboratory participates in various National Proficiency Testing (PT) programs conducted by National Institute of Plant Health Management (NIPHM), Hyderabad for different food commodities and a satisfactory z score was obtained. Regular monthly Internal Quality Control (IQC) activities were conducted for at least 5% of the samples including intra-laboratory testing (with different analysts), replicate testing, retesting, and alternate instrument testing in different vegetables and fruits. Use of high-purity CRMs for calibrating the equipment as per the laboratory’s calibration programme and intermediate check for performance assurance of equipment.
Assessment of measurement Uncertainty analysis
To assess the trueness and precision of the procedure, the degree of uncertainty related to the residue measurements was evaluated. MU reflects the inherent variability in a measurement process. The combined uncertainty (u') was assessed by considering the factors independent of each other and a coverage factor of 2 was multiplied with combined uncertainty resulting in expanded uncertainty at a confidence level of 95%. The expanded uncertainty (U') estimated at 50 µg/kg and it ranged from (±) 7.02–12.96, 6.02–12.32, 5.12–12.98, 6.46–12.50, 7.10-12.78, 5.80-13.16 µg/kg in green chilli, bell pepper, brinjal, tomato, pomegranate, and grapes, respectively (Table S1-S6). In the present study, 39 multi-class pesticides recorded uncertainty measurement values in the range of (±) 5.12 to 13.16 µg/kg (10.24–26.32%) at a fortification level of 50 µg/kg. Similar results were reported previously in tomatoes where the measurement uncertainties values were found below the default limit of 50% (Shrestha et al. 2024), in okra measurement uncertainties were in the range of 1.81 to 12.91 µg/kg assessed at 50 µg/kg. (Pallavi et al. 2023) in pomegranate, it ranged from 4.02 to 16.12 µg/kg (Naik et al. 2022), in pigeon pea found in the range of 3.42 to 12.76 µg/kg (Harischandra et al. 2021a) in rice grain found lower than 20 µg/kg (Harischandra et al. 2021b) and in groundnut Saha et al. 2014 recorded expanded uncertainties were < 11%.
Detection of pesticide residues in real samples
In the present investigation, 39 pesticides were screened in green chilli, bell pepper, brinjal, tomato, pomegranate and grapes using LC-MS/MS. In green chilli, 11 pesticides in 120 samples (81.67% contamination) were detected, and among these highest percent of neonicotinoid insecticide, imidacloprid residues (60%) followed by carbendazim (33.33%) were recorded (Table 3). The imidacloprid residues were detected in bell pepper (68.33%) followed by pomegranate (42%), tomato (18%), grapes (17%) and brinjal (16%) (Table 3). Carbendazim belongs to the benzimidazole group and was frequently detected fungicide in green chilli, bell pepper, tomato, brinjal, pomegranate and grapes with an average mean residue of 0.161, 0.082, 0.038, 0.046, 0.081, 0.213 mg/kg, respectively (Table 3).
In green chilli, wide varieties of pesticides belonging to different chemical groups were detected including organophosphates (acephate, dimethoate, profenofos) ranged between 0.012–0.085 mg/kg due to their lipophilicity, bioavailability, mobility and low cost with effective control of pests. Other groups include carbamate (carbofuran), diamide (chlorantraniliprole), triazole (difenoconazole), phenylamide (metalaxyl, benalaxyl), phenoxy pyrazole acaricide (fenpyroximate) ranging 0.026–0.124, 0.013–0.032, 0.015–0.146, 0.015–0.025, 0.018–0.039, 0.012–0.052 mg/kg, respectively (Table 3 and Fig. 2a). As per FSSAI the pesticides like carbendazim (36 samples), carbofuran (5 samples), dimethoate (14 samples) detected in green chilli samples were more than the MRL limit, whereas as per EU, 62 samples (Acephate 20, Benalaxyl 05, carbendazim 18, carbofuran 05, dimethoate 14, profenofos 10) crosses MRL values (Table 4).
Similarly, in bell pepper, 92 samples were contaminated with pesticides (76.67%) and among these imidacloprid (68.33%) acephate (54.17%), carbendazim (43.33%), dimethoate (20.83%), profenofos (15%) were frequently detected. The less detected carbofuran, chlorantraniliprole, difenoconazole, fenpyroximate, hexaconazole and omethoate were found in 9.17, 7.50, 12.50, 6.66, 10.00, 8.33% samples, respectively (Table 3 and Fig. 2b). As per FSSAI and EU, 53 samples (42 with carbendazim; 11 with carbofuran) and 72 samples of bell pepper (65 acephate, 11 carbofuran, 10 carbendazim, 25 dimethoate, 08 imidacloprid, 10 omethoate, 12 hexaconazole, 18 profenofos) were detected are above MRL, respectively (Table 4).
|
Pesticide |
Chilli |
Bell pepper |
Tomato |
Brinjal |
Pomegranate |
Grapes |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
No. of positive samples (%) |
Conc. Range |
Mean value (mg/kg) |
|
|
Acephate |
20 (16.67) |
0.020–0.085 |
0.048 |
65 (54.17) |
0.011–1.141 |
0.172 |
10 (8.33) |
0.013–0.027 |
0.020 |
12 (10) |
0.013–0.053 |
0.032 |
13 (10.83) |
0.016–0.056 |
0.035 |
11 (9.17) |
0.013–0.064 |
0.037 |
|
Benalaxyl |
5 (4.17) |
0.018–0.039 |
0.026 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
|
Buprofezin |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
5 (4.17) |
0.015–0.045 |
0.028 |
16 (13.33) |
0.014–1.381 |
0.423 |
|
Carbendazim |
40 (33.33) |
0.022–1.196 |
0.161 |
52 (43.33) |
0.014–0.184 |
0.082 |
15 (12.5) |
0.032–0.045 |
0.038 |
9 (7.5) |
0.012–0.075 |
0.046 |
55 (45.83) |
0.010–0.160 |
0.081 |
24 (20) |
0.093–0.802 |
0.213 |
|
Carbofuran |
8 (6.66) |
0.026–0.124 |
0.066 |
11 (9.17) |
0.011–0.032 |
0.021 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
|
Chlorantranlip-role |
12 (10) |
0.013–0.032 |
0.022 |
9 (7.5) |
0.011–0.043 |
0.024 |
8 (6.66) |
0.011–0.030 |
0.020 |
5 (4.16) |
0.011–0.029 |
0.022 |
- |
- |
- |
- |
- |
- |
|
Dimethoate |
14 (11.67) |
0.052–0.085 |
0.054 |
25 (20.83) |
0.012–0.072 |
0.040 |
7 (5.83) |
0.013–0.036 |
0.025 |
12 (10) |
0.015–0.064 |
0.037 |
- |
- |
- |
8 (6.67) |
0.012–0.192 |
0.141 |
|
Difenconazole |
12 (10) |
0.015–0.146 |
0.058 |
15 (12.5) |
0.019–0.086 |
0.050 |
- |
- |
- |
- |
- |
- |
16 (13.33) |
0.010–0.132 |
0.065 |
15 (12.5) |
0.061–0.112 |
0.081 |
|
Fenpyroximate |
4 (3.33) |
0.012–0.052 |
0.030 |
8 (6.66) |
0.051–0.077 |
0.055 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
|
Hexaconazole |
- |
- |
- |
12 (10) |
0.041–0.108 |
0.068 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
13 (10.83) |
0.014–0.060 |
0.040 |
|
Imidacloprid |
72 (60) |
0.011–0.849 |
0.122 |
82 (68.33) |
0.011–1.082 |
0.120 |
18 (15) |
0.010–0.058 |
0.035 |
16 (13.33) |
0.012–0.140 |
0.072 |
42 (35) |
0.011–0.065 |
0.041 |
17 (14.17) |
0.011–0.507 |
0.136 |
|
Metalaxyl |
4 (3.33) |
0.015–0.025 |
0.023 |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
10 (8.33) |
0.023–0.173 |
0.087 |
|
Omethoate |
- |
- |
10 (8.33) |
0.010–0.018 |
0.014 |
- |
- |
- |
2 (1.67) |
0.011–0.019 |
0.015 |
- |
- |
- |
2 (1.67) |
0.012–0.019 |
0.016 |
|
|
Profenofos |
10 (8.33) |
0.012–0.030 |
0.021 |
18 (15) |
0.050–0.322 |
0.122 |
15 (12.5) |
0.035–0.186 |
0.088 |
13 (10.83) |
0.016–0.048 |
0.029 |
10 (8.33) |
0.012–0.038 |
0.025 |
13 (10.83) |
0.025–0.175 |
0.075 |
|
Spinosad |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
5 (4.17) |
0.010–0.027 |
0.019 |
|
Tetraconazole |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
7 (5.83) |
0.028–0.128 |
0.076 |
|
Quinalphos |
- |
- |
- |
- |
- |
- |
5 (4.17) |
0.011–0.023 |
0.017 |
- |
- |
- |
- |
- |
- |
6 (5) |
0.012–0.032 |
0.022 |
| Conc.: Concentration; Concentration measured in mg/kg | ||||||||||||||||||
In tomato, 30.83% of samples were contaminated with pesticides, including imidacloprid, profenofos, and carbendazim in 15.00, 12.50 and 12.50% of samples, respectively. Residues of acephate (0.013–0.027 mg/kg), carbendazim (0.032–0.045 mg/kg), chlorantraniliprole (0.011–0.030 mg/kg), dimethoate (0.013–0.036 mg/kg), imidacloprid (0.010–0.058 mg/kg), profenofos (0.035–0.186 mg/kg) and quinalphos (0.011–0.023 mg/kg) were detected in tomatoes (Table 3 and Fig. 2c). As per FSSAI norms total of 15 samples of tomato were detected with carbendazim and were found above MRL, whereas as per EU norms 22 samples detected with acephate, dimethoate, quinalphos was above MRL (Table 4).
In Brinjal, 21.67% samples were contaminated with mean residual concentration of most abundant imidacloprid, carbendazim, dimethoate, acephate, profenofos, chlorantraniliprole and omethoate were 0.072, 0.046, 0.037, 0.032, 0.029, 0.022 and 0.015 mg/kg, respectively (Table 3 and Fig. 2d). As per FSSAI five brinjal samples were detected with carbendazim above MRL (Table 4). whereas 12 samples were detected with acephate and were more than MRL as per EU (Table 4).
In case of pomegranate, 43.33% of samples were contaminated with pesticides, the residues of carbendazim were found to be the highest (45.83%) and ranged from 0.010–0.160 mg/kg followed by imidacloprid (35%), difenoconazole (13.33%) acephate (10.83%) and profenofos (8.33%) (Table 3 and Fig. 2e). As per EU 72 samples of pomegranate were reported more than the MRL values (Table 4).
In grapes, 13 pesticides were detected (86.67% contamination) ranging from 0.010–1.381 mg/kg. The frequently detected pesticides were carbendazim (0.213 mg/kg), imidacloprid (0.136 mg/kg), buprofezin (0.423 mg/kg), difenoconazole (0.081 mg/kg), hexaconazole (0.040), and profenofos (0.075 mg/kg) (Table 3 and Fig. 2f). As per the EU norms 65 samples of grapes were above MRL (acephate 11, buprofezin 16, Carbendazim 06, dimethoate 08, hexaconazole 13, omethoate 02, profenofos 13, hexaconazole 03, quinalphos 06), whereas only 11 samples with carbendazim and 3 samples with dimethoate were detected above MRL values as per FSSAI (Table 4).
The complete study of four solanaceous vegetables (chilli, bell pepper, brinjal and tomato) and two fruits (pomegranate and grape) with their percent contamination represented in Fig. 3 and revealed the occurrence of 17 pesticides in all the tested vegetable and fruits samples which include 11 insecticides and 6 fungicides, which exhibit farmers are using these pesticides during the crop growth period for management of various pests and diseases. Moreover, detection of these residues in vegetables and fruit crops might be due to, application timing of the pesticides near to harvest, tank mixing of more than one pesticide above the recommended dose, irrigation water and spray drift and not following proper pre-harvest interval (PHI) could also be one the reason for detection of residues above MRL at harvest. In the foregoing study, we also detected residues of few pesticides viz., benalaxyl and dimethoate in chilli; acephate, carbendazim, dimethoate and profenofos in tomato; acephate in brinjal; acephate, buprofezin, carbendazim, imidacloprid and profenofos in pomegranate; acephate, dimethoate, profenofos and quinalphos in grapes, which are non-crop labelled as per CIB&RC to use in these crops (Table 4).
This clearly indicates farmers are not consulting experts from SAUs/ICAR/State department of agriculture and depend completely upon pesticide dealers, field consultants of pesticide company and fellow farmers. The primary sources of pesticides for purchase by farmers were the agrochemical shops in the local and municipal markets. The major source of information for use of pesticides by farmers were based on notifications by radio broad casting, televisions, pamphlets and leaflets that were made available from agrochemical shops and also through sales representatives from various agrochemical companies and state agricultural consultants from government and SAUs. Majority of the farmers had only primary education and no formal education on pesticides. The level of education and illiteracy majorly contributed the poor awareness about the use of pesticides in different crops. Therefore, knowledge regarding pesticide residues and awareness of farmers towards Good Agricultural Practices (GAPs) with special reference to pesticides needs improvement. Lack of knowledge among the farmers about preventive and proper pesticide application with recommended dosage was also observed.
Present findings are in conformity with Sharma et al. 2022 analysed a total of 155 pesticides and frequently detected imidacloprid, acetamiprid, metalaxyl, cypermethrin and profenofos in okra, green chilli and cabbage. Pallavi et al. 2023 reported 73 pesticides in okra which includes imidacloprid, carbendazim, profenofos, fenpyroximate, acephate, hexaconazole, emamectin benzoate, triademenol and bifenthrin in market and field samples ranging from 0.01 to 0.016 mg/kg and 0.01 to 0.11 mg/kg, respectively. Rathod et al. 2021 detected 77 multiclass pesticides and their metabolites in tomato and bell pepper. Naik et al. 2022 reported 11 pesticide residues in pomegranate (acephate, thiamethoxam, imidacloprid, carbendazim, tebuconazole, difenoconazole, profenofos, quinalphos, novaluron, thiophanate methyl and acetamiprid). Imidacloprid residues in whole fruit of pomegranate were 0.056 mg/kg and 0.039 mg/kg (Utture et al. 2012). Balasubramani et al. 2019 quantified residues of 15 pesticides, majorly acetamiprid, fenazaquin and spinosad contaminated the chilli samples. Charan et al. 2010 found 46.43% of tomato and 50% of brinjal samples were contaminated with different pesticides. Pesticide residues were found in 376 samples (36.6% of tested samples) and 18 samples (1.8%), and residues exceeded MRL (Szpyrka et al. 2014). Similar reports of residues detected in fruits and vegetables were documented by Beena kumari 2007; Osman et al. 2010; Ananda and Somashekar 2012; Sheikh et al. 2013; Harinathareddy et al. 2014; Li Wei Hao et al. 2014; Al-Dabbas et al. 2014; Chourasiya et al. 2015; Bilehal et al. 2017; Jallow et al. 2017; Yu et al. 2018; Amjad et al. 2019; Diop et al. 2016.
Risk assessment through dietary exposure and hazard quotient
The pesticide residue is the major culprit and important indicator and is considered as barrier for both the export and import of various agricultural commodities, we have several examples where most of the time the consignments will fail to meet this specified fixed residual limit requirements of importing countries and get rejections that in turn causes huge amount of losses to the exporting countries. Generally, if the food product exceeds the MRL limit causes health hazards to human being in the long run. Consumption of fresh fruits and vegetables were the primary route of pesticide intake through the diet (Claeys et al. 2011, Zhu et al. 2019). Dietary exposure is estimated through the average daily consumption, average body weight, and mean pesticide residue level in food (Sharma et al. 2022).
|
Pesticides |
MRL (mg/kg) |
No. Samples above MRL |
Non-crop labelled pesticides (✓) as per CIBRC, 2023 |
Mean value (mg/kg) |
EDI (mg/kg bodyweight) |
ADI (mg/kg body weight) |
Hazard Index (HQ) |
Hazard Index (HI) |
|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
FSSAI |
EU |
FSSAI |
EU |
Adult |
Children |
Adult |
Children |
Adult |
Children |
||||
|
Chilli |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
20 |
- |
0.048 |
0.0002 |
0.0003 |
0.03 |
0.0053 |
0.0096 |
0.4738 |
0.8530 |
|
Benalaxyl |
- |
0.01 |
- |
5 |
✓ |
0.026 |
0.0001 |
0.0002 |
0.07 |
0.0012 |
0.0022 |
||
|
Chlorantraniliprole |
- |
1.0 |
- |
0 |
- |
0.022 |
0.0001 |
0.0001 |
2.0 |
0.0000 |
0.0001 |
||
|
Carbendazim |
0.025 |
0.1 |
36 |
18 |
- |
0.161 |
0.0005 |
0.0010 |
0.03 |
0.0179 |
0.0322 |
||
|
Carbofuran |
0.005 |
0.002 |
05 |
05 |
- |
0.066 |
0.0002 |
0.0004 |
0.001 |
0.2200 |
0.3960 |
||
|
Difenoconazole |
- |
0.9 |
- |
0 |
- |
0.058 |
0.0002 |
0.0003 |
0.01 |
0.0193 |
0.0348 |
||
|
Dimethoate |
0.025 |
0.01 |
14 |
14 |
✓ |
0.054 |
0.0002 |
0.0003 |
0.001 |
0.1800 |
0.3240 |
||
|
Fenpyroximate |
- |
0.3 |
- |
0 |
- |
0.030 |
0.0001 |
0.0002 |
0.005 |
0.0200 |
0.0360 |
||
|
Imidacloprid |
- |
0.9 |
- |
0 |
- |
0.122 |
0.0004 |
0.0007 |
0.06 |
0.0068 |
0.0122 |
||
|
Metalaxyl |
- |
0.5 |
- |
0 |
- |
0.023 |
0.0001 |
0.0001 |
0.08 |
0.0010 |
0.0017 |
||
|
Profenofos |
- |
0.01 |
- |
10 |
- |
0.021 |
0.0001 |
0.0001 |
0.03 |
0.0023 |
0.0042 |
||
|
Bell pepper |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
65 |
✓ |
0.172 |
0.0006 |
0.0010 |
0.03 |
0.0191 |
0.0344 |
0.4672 |
0.8409 |
|
Carbendazim |
0.025 |
0.1 |
42 |
10 |
✓ |
0.082 |
0.0003 |
0.0005 |
0.03 |
0.0091 |
0.0164 |
||
|
Carbofuran |
0.005 |
0.002 |
11 |
11 |
✓ |
0.021 |
0.0001 |
0.0001 |
0.001 |
0.0700 |
0.1260 |
||
|
Chlorantraniliprole |
- |
1.0 |
- |
0 |
✓ |
0.024 |
0.0002 |
0.0001 |
2.0 |
0.0000 |
0.0001 |
||
|
Dimethoate |
0.1 |
0.01 |
0 |
25 |
✓ |
0.040 |
0.0001 |
0.0002 |
0.001 |
0.1333 |
0.2400 |
||
|
Difenoconazole |
- |
0.9 |
- |
0 |
✓ |
0.050 |
0.0002 |
0.0003 |
0.01 |
0.0167 |
0.0300 |
||
|
Fenpyroximate |
- |
0.3 |
- |
0 |
✓ |
0.055 |
0.0002 |
0.0003 |
0.005 |
0.0367 |
0.0660 |
||
|
Imidacloprid |
- |
0.9 |
- |
08 |
✓ |
0.120 |
0.0004 |
0.0007 |
0.06 |
0.0067 |
0.0120 |
||
|
Omethoate |
- |
0.01 |
- |
10 |
✓ |
0.014 |
0.0000 |
0.0001 |
0.0004 |
0.1167 |
0.2100 |
||
|
Hexaconazole |
- |
0.01 |
- |
12 |
✓ |
0.068 |
0.0002 |
0.0004 |
0.005 |
0.0453 |
0.0816 |
||
|
Profenofos |
- |
0.01 |
- |
18 |
✓ |
0.122 |
0.0004 |
0.0007 |
0.03 |
0.0136 |
0.0244 |
||
|
Tomato |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
10 |
✓ |
0.020 |
0.0001 |
0.0001 |
0.03 |
0.0022 |
0.0040 |
0.2147 |
0.3868 |
|
Carbendazim |
0.025 |
0.3 |
15 |
0 |
✓ |
0.038 |
0.0001 |
0.0002 |
0.03 |
0.0042 |
0.0076 |
||
|
Chlorantraniliprole |
- |
0.6 |
- |
0 |
- |
0.020 |
0.0001 |
0.0001 |
2.0 |
0.0000 |
0.0001 |
||
|
Dimethoate |
0.1 |
0.01 |
0 |
07 |
✓ |
0.025 |
0.0001 |
0.0002 |
0.001 |
0.0833 |
0.1500 |
||
|
Imidacloprid |
- |
0.3 |
- |
0 |
- |
0.035 |
0.0001 |
0.0002 |
0.06 |
0.0019 |
0.0035 |
||
|
Profenofos |
- |
10.0 |
- |
0 |
✓ |
0.088 |
0.0003 |
0.0005 |
0.03 |
0.0098 |
0.0176 |
||
|
Quinalphos |
- |
0.01 |
- |
05 |
- |
0.017 |
0.0001 |
0.0001 |
0.0005 |
0.1133 |
0.2040 |
||
|
Brinjal |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
12 |
✓ |
0.032 |
0.0001 |
0.0002 |
0.03 |
0.0036 |
0.0064 |
0.2642 |
0.4757 |
|
Carbendazim |
0.025 |
0.5 |
5 |
0 |
- |
0.046 |
0.0002 |
0.0003 |
0.03 |
0.0051 |
0.0092 |
||
|
Chlorantraniliprole |
- |
0.6 |
- |
0 |
- |
0.022 |
0.0001 |
0.0001 |
2.0 |
0.0000 |
0.0001 |
||
|
Dimethoate |
0.1 |
0.01 |
0 |
12 |
- |
0.037 |
0.0001 |
0.0002 |
0.001 |
0.1233 |
0.2220 |
||
|
Imidacloprid |
- |
0.3 |
- |
0 |
- |
0.072 |
0.0002 |
0.0004 |
0.06 |
0.0040 |
0.0072 |
||
|
Omethoate |
- |
0.01 |
- |
02 |
- |
0.015 |
0.0001 |
0.0001 |
0.0004 |
0.1250 |
0.2250 |
||
|
Profenofos |
- |
0.01 |
- |
13 |
- |
0.029 |
0.0001 |
0.0002 |
0.03 |
0.0032 |
0.0058 |
||
|
Pomegranate |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
13 |
✓ |
0.035 |
0.0001 |
0.0001 |
0.03 |
0.0019 |
0.0047 |
0.0267 |
0.0644 |
|
Buprofezin |
- |
0.01 |
- |
05 |
✓ |
0.038 |
0.0001 |
0.0002 |
0.009 |
0.0070 |
0.0169 |
||
|
Carbendazim |
0.25 |
0.1 |
0 |
22 |
✓ |
0.081 |
0.0001 |
0.0003 |
0.03 |
0.0045 |
0.0108 |
||
|
Difenoconazole |
- |
0.1 |
- |
03 |
- |
0.065 |
0.0001 |
0.0003 |
0.01 |
0.0108 |
0.0260 |
||
|
Imidacloprid |
- |
0.01 |
- |
42 |
✓ |
0.041 |
0.0001 |
0.0002 |
0.06 |
0.0011 |
0.0027 |
||
|
Profenofos |
- |
0.01 |
- |
10 |
✓ |
0.025 |
0.0000 |
0.0001 |
0.03 |
0.0014 |
0.0033 |
||
|
Grapes |
|||||||||||||
|
Acephate |
- |
0.01 |
- |
11 |
✓ |
0.037 |
0.0001 |
0.0001 |
0.03 |
0.0021 |
0.0049 |
0.1831 |
0.4393 |
|
Buprofezin |
- |
0.01 |
- |
16 |
- |
0.423 |
0.0007 |
0.0017 |
0.009 |
0.0783 |
0.1880 |
||
|
Carbendazim |
0.25 |
0.3 |
11 |
06 |
- |
0.213 |
0.0004 |
0.0009 |
0.03 |
0.0118 |
0.0284 |
||
|
Difenoconazole |
- |
3.0 |
- |
0 |
- |
0.081 |
0.0001 |
0.0003 |
0.01 |
0.0135 |
0.0324 |
||
|
Dimethoate |
0.1 |
0.01 |
03 |
08 |
✓ |
0.141 |
0.0002 |
0.0006 |
0.001 |
0.2350 |
0.5640 |
||
|
Hexaconazole |
- |
0.01 |
- |
13 |
- |
0.040 |
0.0001 |
0.0002 |
0.005 |
0.0133 |
0.0320 |
||
|
Imidacloprid |
- |
0.7 |
- |
0 |
- |
0.136 |
0.0002 |
0.0005 |
0.06 |
0.0038 |
0.0091 |
||
|
Metalaxyl |
- |
2.0 |
- |
0 |
- |
0.087 |
0.0001 |
0.0003 |
0.08 |
0.0018 |
0.0044 |
||
|
Omethoate |
- |
0.01 |
- |
02 |
✓ |
0.016 |
0.0000 |
0.0001 |
0.0004 |
0.0667 |
0.1600 |
||
|
Profenofos |
- |
0.01 |
- |
13 |
✓ |
0.075 |
0.0001 |
0.0003 |
0.03 |
0.0042 |
0.0100 |
||
|
Spinosad |
- |
0.5 |
- |
0 |
- |
0.019 |
0.0000 |
0.0001 |
0.02 |
0.0016 |
0.0038 |
||
|
Tetraconazole |
- |
0.07 |
- |
03 |
- |
0.076 |
0.0001 |
0.0003 |
0.004 |
0.0317 |
0.0760 |
||
|
Quinalphos |
- |
0.01 |
- |
06 |
✓ |
0.022 |
0.0000 |
0.0001 |
0.0005 |
0.0733 |
0.1760 |
||
| CIBRC: Central Insecticide Board and Registration Committee; EU: European Union; FSSAI: Food Safety and Standard Authority of India; EDI: Estimated Daily Intake | |||||||||||||
| ADI: Acceptable Daily Intake, Source: JMPR (2020) | |||||||||||||
According to recent studies, food commodities that have an HI ≥ 1 poses health risk to consumers, but those with an HI < 1 are considered as safe (Galani et al. 2020, Sharma et al. 2021, Mahato et al. 2023a). The current study, which assessed the dietary risk of pesticide residues in green chilli, bell pepper, tomato, brinjal, pomegranate, and grapes grown in the Kalyan, Karnataka, region of India, found that the levels of pesticide residues in these crops were HQ and HI is less than 1, meaning they do not pose a health risk to humans including adults and children’s at the present level of contamination and can be consumed safely without any risk (Table 4 and Fig. 4).
This study validated and developed multi-residue method for 39 pesticides by using highly sensitive equipment like LC-MS/MS for the analysis of 720 samples of vegetables and fruits from Kalyan Karnataka region which covers three districts including Raichur, Koppal and Bellary of India revealed the highest percentage of pesticides residues were found in grapes (86.67%), followed by chilli (81.67%), bell pepper (76.67%), pomegranate (43.33%), tomato (30.83%) and brinjal (21.67%). The frequently detected pesticides were carbendazim, imidacloprid, profenofos, acephate, dimethoate and chlorantraniliprole in chilli, bell pepper, tomato and brinjal; whereas acephate, buprofezin, carbendazim, difenoconazole, hexaconazole, imidacloprid, metalaxyl and profenofos in pomegranate and grapes indicated that farmers are using regularly these pesticides for the management of pests and diseases in these crops. Due to illiteracy and bad agricultural practices, residues of several non-crop labelled pesticides were also detected in vegetables and fruit samples not only affecting the quality but also threatening human health and the environment. Good Agricultural Practices (GAP) need to be applied when growing vegetable and fruit crops and controlling pests. Some of the socio-economic factors, such as education, training on pest management, regular information flow and extension services are the pre-requisite to improve rice farmer’s understandings about the pesticide usage pattern in different crops. In few samples the residues crossed the MRL values but dietary risk assessment study for both adults and children showed HQ and HI values < 1 indicates the food is safe for consumption without any health risk. Studies based on multi-residue analysis followed by assessment of HQ and HI will help the regulatory bodies, scientists, manufacturers, industrialists, and various other stakeholders to evaluate consumer safety and promote safe and judicious use of pesticides.
Acknowledgment
The authors are indebted to the Pesticide Residue and Food Quality Analysis Laboratory (PRFQAL), University of Agricultural Sciences, Raichur, Karnataka for laboratory research facility support and Dr. Timmanna, Sr. Scientist, ICAR- Central Research Institute for Dryland Agriculture (CRIDA), for reviewing the manuscript.
Author Contributions
PA: Conceptualization, Investigation, Visualization, Funding acquisition, Project administration, Resources and Supervision; JB: Conceptualization, Investigation, Visualization, Methodology, Validation, Data curation, Formal analysis, Writing-original draft and Writing-review and editing; SM: Conceptualization, Investigation, Visualization, Methodology, Validation, Data curation, Formal analysis, Writing-original draft and Writing-review and editing; SH: Conceptualization, Investigation, Visualization and Supervision: NK: Conceptualization, Investigation, Visualization and Supervision. SU: Investigation, Methodology, Validation and Formal analysis, Data curation. PK: Validation and Formal analysis. NP: Validation and Formal analysis. AG: Validation and Formal analysis. RSR: Validation and Formal analysis. MP: Validation and Formal analysis. All authors read and approved the final manuscript.
Compliance with Ethical Standards
Conflict of Interest: The author and co-authors declare no conflicts of interest.
Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors.
Informed Consent: Informed consent is not applicable in this study
Funding
The authors are thankful to the Rashtriya Krishi Vikas Yojana, Govt. of India for funding to conduct research work.
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declaration of competing interest
The author and co-authors declare that they have no potential conflict of interest that could have appeared to influence the work reported in this paper.
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The authors declare no competing interests.