Advertisement

SN Applied Sciences

, 2:188 | Cite as

Validation of a modified QuEChERS method to determine multiclass multipesticide residues in apple, banana and guava using GC–MS and LC–MS/MS and its application in real sample analysis

  • Swagata Mandal
  • Rajlakshmi Poi
  • Inul Ansary
  • Dipak Kumar Hazra
  • Sudip Bhattacharyya
  • Rajib KarmakarEmail author
Research Article
  • 71 Downloads
Part of the following topical collections:
  1. Chemistry: Applied Separation Science 2019

Abstract

A modified QuEChERS method was developed for the determination of 64 pesticides in apple, guava andbanana using GC–MS and LC–MS/MS. Out of sixty-four pesticides, 39 (32 insecticides + 7 herbicides) were analysed by GC–MS and others were analysed in LC–MS/MS. The efficiency of methanol, ethyl-acetate and acetonitrile as extracting solvents were checked. Among these, acetonitrile gave the highest recovery. d-SPE clean-up was done for apple and guava using PSA, whereas PSA + GCB was used for banana to remove heteropolysaccharides and carotenoids. The method was validated as per SANTE/11813/2017 guidelines. The LOD was in the range of 0.002–0.04 mg kg−1 and LOQ was 0.005–0.11 mg kg−1. Average recovery ranged from 80 to 120% with RSD ≤ 20% and RPD % of precision ≤ 20%. The expanded uncertainty was ≤ 20%. The validated method was applied to check the real samples of apple, guava and banana collected from markets of four different districts of West Bengal, India.

Keywords

QuEChERS Pesticides Apple Guava Banana LC/MS/MS GC/MS 

1 Introduction

The nutritional intake from fruits and vegetables is higher among urban population than that of rural population. Along with the urbanisation, people are likely to increase their calorie intake at a higher pace through fruits and vegetables [1]. Apple, banana, guava are the most produced and consumed fruits around the world. India was the highest producer of guava and banana and fifth highest producer of apple around the world [2].During 2018–2019, India exported fruits and vegetables worth Rs. 10236.93 crores/1,469.33 USD Millions which comprised of fruits worth Rs. 4817.35 crores/692.01 USD Millions and vegetables worth Rs. 5419.48 crores/777.25 USD Millions [3]. During cultivation in Indian climaticcondition, fruits suffer a lot of problem due to attack of pest and diseases that would make yield reduction as well as downturn the food quality. The use of pesticidesthereforeis becoming necessary and the residues of which are coming in food matrices with an elevated amount.

The wide-range use of pesticides and their highly persistent nature are the major affair for the availability of the residue in environment and food stuffs. The presence of residue in food with high level to the consumers is the thing of global concern. Due to this fact, these pesticidesmust be documented and precisely scanned.Several methods have been reported for the analysis of multiclass pesticide residues in fruit matrices, associating soxhlet extraction [4], liquid–liquid extraction (LLE) [5], matrix solid phase dispersion (MSPD) [6], microwave assisted extraction (MAE) [7], accelerated solvent extraction (ASE) [8], ultrasound extraction [9] and solid phase extraction (SPE) [10, 11].These methods have been used with limited success. To optimize and detect the pesticides, therefore a simple, rapid and cost-effective method is needed. Anastassiadesand his co-workers [12] developed a multi residue method for the analysis of pesticide residue from food samples, known as QuEChERS. Considering the fact, a modified QuEChERSmethod [13] have been developed to detect residues of multipesticides using gas chromatography coupled with single quadrupole and liquid chromatography associated with triple quadrupole, without any hint on cleaning up of banana matrix. A total of sixty-four pesticides were chosen based on the report that apple, guava and banana growing farmers are usually spraying these pesticides in India. It is important to monitor residue levels of these pesticides in these raw consumable fruits. Based on the polarities of the pesticidesGC-MS and LC–MS/MS were chosen. Out of 64 pesticides, 39 (= 31 insecticides including OCs, OPs and synthetic pyrethroids + 1 tetronic acid insecticide spiromesifen + 7 herbicides) pesticides were analysed with GC–MS and 25 pesticides were analysed with LC–MS/MS. The purpose of the study is to developmulti-class, multi-residue method based on QuEChERS for the analysis of above-mentioned pesticides which are frequently used in apple, guava and banana orchards. Also, the method shall assist to detect and determine the quantities of pesticide loads for monitoring of apple, guava and banana fruit meant for export.

2 Materials and methods

2.1 CRMs, chemicals and apparatus

All CRMs of 64 pesticideswith purity ≥ 98% were purchased from Dr.Ehrenstorfer (Augsburg, Germany). Individual mother stock solutions (500 mgL−1) of 39 GC amenable pesticides were prepared with hexane–toluene (1:1) mixture and that of 25 LC amenable pesticides were prepared in acetonitrile.Intermediate stock solution (100 mg L−1)were prepared respectively in the same solvents, by appropriate dilution from the mother stock solution and were stored at 4 °C.To prepare 10 mg L −1 mixture of 64 pesticides, the required volumes of intermediate stock solution of pesticides were added and hexene–toluene mixture, acetonitrile were evaporated in Tarbo-vap.The final volume was made with acetonitrile. Theworking standard solutions of 0.01, 0.05, 0.1, 0.5 and 1 mg L−1prepared in acetonitrilefrom 10 mg L−1 for LC–MS/MS. Acetonitrile from the respective working standard solutionswere evaporated up and reconstituted in hexane for GC–MS. Matrix-matched standards of 0.01, 0.05, 0.1, 0.5 and 1.0 mg L−1prepared by evaporating appropriate volumes of a standardmixturesolution and diluting withrespectivematrix of apple, guava and bananain hexane and acetonitrile respectively for use in GC–MS and LC–MS/MS. The working standard solutions were also stored at 4 °C.Analytical grade NaCl and anhydrous MgSO4 were obtained from Merck, Darmstadt, Germany. The MgSO4 was baked for 5 h at 500 °C in a muffle furnace to remove phthalates. Primary Secondary Amine (PSA) and Graphitized Carbon Black (GCB) sorbent were purchased from Agilent Technology, USA.Ammoniumformate buffer (Bio-ultra ≥ 99.0%) was procured from Fluka Milan, Italy. AnalyticalLC-MS grade acetonitrile, hexane, acetone, water, methanol and toluene were obtained from J.T. Baker, Avantor, USA. High precision calibrated analytical balance (Sartorius AG, Göttingen, Germany) was used for weighing the CRMs, reagents and samples accurately. A Robot Coupe Blixer 6V.V (7L) Vincennes, France was used to comminute the fruit samples. A vortex mixer (Spinix, Tarsons, Kolkata, India), Rotospin (Tarsons, Kolkata, India), Silent Crusher (Heidolph, Schwabach, Germany), Centrifuge (Super Spin R-V/FM Plasto Crafts, Mumbai, India) and Turbo Vap evaporator (Caliper Life Sciences, Hopkinton, Massachusetts, USA) were used for sample preparation. A solvent filtration unit (Borosil, India) and micropipettes (Boeco, Germany) of 1000 µl, 5000 µl and 10 ml, were used.

2.2 Selection of pesticides and fruits

Apple, guava and banana were selected for the study based on their export potential. All these sixty-four pesticides (50 insecticides, 8 fungicides and 6 herbicides) were selected for our study based on the report that farmers are using these pesticides in apple, guava and banana in India. Therefore, it is necessary to monitor their residue levels in raw consumable fruits. Apple (Malus domesticavar. Golden delicious), Banana (Musaacuminatevar.Singapuri) and Guava (Psidium guajavavar.Khaja) were randomly collected from the untreated control plots of research trials. These fruits were screened and confirmed that no any pesticidewas present before using these in the method developingprocess. Reals samples of apple, guava and banana were collected from the markets of Kolkata, Howrah, Hooghly and Burdwan districts of west Bengal, India. The samples were well comminuted, and placed in glass bottles, and stored at − 20 °C.

2.3 Instrumentation

GCMS-QP 2010 Plus (Shimadzu Corporation, Kyoto, Japan) with a mass selective detector (MSD, single quadrupole) and a Capillary column DB-5MS J&W 30 m × 0.25 mm id × 0.25 µm (Agilent, USA,) was used for confirmation and quantification study. GC–MS operated under following GC conditions:Initial temperature 40 °C for 1 min, then increased @25 °C min−1 to 130 °C and @12° Cmin−1 to180 °C and again increased @3°Cmin−1 to 280 °C, then hold for 7 min. Injector temperature was 250 °C. Carrier gas used was Helium (purity 99.999%). Ion source temperature was 250 °C. Interface temperature was 280 °C. The instrument operated in the spit mode with split ratio 1:10. Purge flow was 3 ml min−1. Injection volume was 2 µL. MS condition were as follows: delay solvent was 6 min; electron impact ionization voltage was 70 eV; scan rate was 0.50 s−1; scanned mass ranged 50–500 m/z. All samples wereanalysed in the Selected Ion Monitoring (SIM) mode. Retention times, selected monitoring ions used for the identification and confirmation are depicted in Table 1.The LC–MS/MS analysis was carried out using an API-3200 LC–MS/MS system (AB Sciex, Vaughan, Canada) hyphenated to a Waters UPLC (USA) controlled by Analyst 1.5 software. The chromatographic separation was carried out injecting 10 µL onto a reverse phase Zorbax SB-C18 (4.6 mm × 150mm × 5 µm) column (Agilent Technologies, USA) maintained at 35 °C with mobile phase flow rate of 0.35 ml min−1. The mobile phase was composed of (A) 5 mM ammonium formate in methanol and (B) 5 mM ammonium formatein water. The gradient elution programme was as follows: A (95%) B (5%) at the initial time (0 min), A (95%) B (5%) (at 1.70 min), A (50%) B (50%) (at 4.90 min), A (10%) B (90%) (at 9.90 min), A (10%) B (90%) (at 11.50 min), A (95%) B (5%) (at 13.21 min), A (95%) B (5%) (at 14 min). Total run time was 14 min. The mass spectrometric analysis of all 25 pesticides performed were estimated in positiveelectrospray ionization mode [ESI (+ve)] with dwell time of 30 ms. Here using two abundant precursor/products were used for transition of ion in MS/MS analysis for the construction of MRM. The MS source condition was: The ion source temperature was set at 500 °C, ion spray voltage was 5500 V in positivemode.Curtain gas of 30 psi, collisionally activated dissociation gas (CAD)of 5 psi, nebulizer gas (GS1) of 40 psi, heater gas of (GS2) 40 psi were used. The specific mass spectrometric parameters of 25 compounds were given in Table 2. The identification and quantification were performed in samples and standards in accordance with the confirmation criteria of the EC guidelines.
Table 1

Optimization of different parameters of 39 pesticide CRMs in GC–MS

Pesticide

GC–MS, SIM (min.)

Retention

Time (min.)

Time (min.)

m/z for confirmation with ion ratio

Start

End

Target (T)

Q1 (%Q1/T)

Q2 (%Q2/T)

4-Br-2Cl- phenol

8.96

8.44

9.14

208

172 (42.09%)

170 (32.58%)

Trifluralin

13.61

13.574

13.79

306

43 (44.72%)

264 (40.28%)

Phorate

14.31

14.30

14.43

75

121 (50.28%)

260 (43.29%)

Alpha-HCH

14.59

14.43

14.71

181

183 (93.37%)

219 (51.04%)

Dimethoate

14.96

14.81

15.12

87

93 (51.23%)

125 (42.30%)

Atrazine

15.35

15.08

15.59

200

215 (97.23%)

58 (76.68%)

Beta-HCH

15.52

15.49

15.87

181

183 (68.78%)

219 (95.78%)

Lindane

15.76

15.59

15.87

181

183 (96.48%)

219(54.23%)

Phosphamedon

16.03

15.91

16.13

127

72 (40.28%)

264 (40.89%)

Chlorothalonil

16.38

15.87

16.43

266

264 (76.28%)

268 (50.23%)

Delta-HCH

16.92

16.83

17.04

181

183 (92.49%)

219(57.85%)

Dimethachlor

17.76

17.69

17.86

134

197 (40.23%)

77 (52.91%)

Parathion-methyl

18.31

18.25

18.45

263

109 (42.21%)

125 (35.23%)

Alachlor

18.30

18.22

18.45

45

160 (42.28%)

188 (26.22%)

Heptachlor

18.71

18.65

18.84

100

272 (42.28%)

274 (30.56%)

Malathion

19.80

19.71

19.92

125

127 (65.59%)

93 (47.54%)

Chlorpyriphos

20.14

19.92

20.23

97

197 (63.28%)

199 (41.56%)

Aldrin

20.36

20.23

20.50

66

263 (57.63%)

91 (22.73%)

Pendimethylene

21.77

21.50

21.90

252

162 (18.93%)

181 (20.43%)

Quinalphos

22.85

21.70

22.96

146

118 (65.00%)

156 (61%)

OP-DDD

23.72

23.62

23.80

235

165 (76.43%)

237 (53.85%)

Butachlor

23.87

23.70

24.02

57

176 (68.89%)

160 (76.31%)

Alpha-endo

24.18

24.02

24.46

241

195 (80.25%)

159 (22.91%)

PP-DDD

24.91

24.56

25.05

235

165 (56.28%)

237 (32.50%)

Profenophos

25.20

25.10

25.35

337

97 (68.11%)

139 (66.97%)

PP-DDE

25.39

25.20

24.56

246

318 (75.76%)

248 (54.81%)

OP-DDT

25.75

25.58

25.82

235

165 (40.12%)

237 (32.20%)

Beta-Endosulfan

27.42

27.25

27.52

241

195 (65.23%)

159 (40.21%)

Ethion

27.77

27.62

27.80

231

97 (60.28%)

153 (50.78%)

PP-DDT

27.77

27.62

27.80

235

165 (52.28%)

237 (61.12%)

Endo sulphate

29.67

29.52

29.95

272

274 (53.37%)

239 (50.21%)

Spiromesifen

31.70

31.55

31.95

272

99 (52.23%)

273 (36.16%)

Bifenthrin

32.91

32.74

33.12

181

166 (53.27%)

165 (62.74%)

Fenpropathrin

33.45

33.20

33.70

97

181 (96.97%)

265 (35.12%)

Lamda-cyhalothrin

36.23

36.14

36.30

181

197 (95.70%)

208 (58.77%)

Beta-cyfluthrin

41.24

41.07

41.40

163

206 (65.23%)

226 (54.15%)

Cypermethrin

42.24

42.10

42.30

181

163 (75.86%)

127 (47.59%)

Fenvalerate

44.76

44.60

44.92

125

167 (57.88%)

225 (44.54%)

Deltamethrin

47.87

47.72

47.99

181

253 (72.62%)

77 (69.84%)

C.R.M., certified reference materials; R.T., retention time; SIM., selected ion monitoring

Table 2

Optimization of instrument acquisition parameters of 25 pesticide CRMs in LC–MS/MS

Pesticides

RT (min)

Precursor ion (m/z)

First Transition quantification

Second transition identification

Product ion (m/z)

DP(V)

CE(V)

Product ion (m/z)

DP(V)

CE(V)

Carbendazim

5.31

19

160

33

30

132

33

43

Imidacloprid

4.88

256

209

41

21

175

41

31

Thiacloprid

5.01

253

126

61

25

90

61

55

Carbofuran

5.32

222

165

61

23

123

61

29

Carbaryl

5.36

202

145

31

17

127

31

39

Triazophos

5.88

314

162

29

25

119

29

49

Monocrotophos

4.76

224

127

31

21

98

31

17

Spirotetramet

6.04

374.40

302.40

56

23

330.50

46

23

Tebuconazole

6.54

308

70

61

55

125

61

59

Hexaconazole

6.96

314

70

52

38

159

52

38

Anilophos

6.30

368

199

55

20

171

55

29

Thiamethoxam

4.73

292

211

46

19

181

46

31

Acetamiprid

4.88

223

126

60

27

56

60

35

Chlorfenvinphos

6.46

359

99

55

49

170

55

66

Propiconazole

6.96

342

159

30

33

69

30

40

Fenamidon

5.59

312

236

53

21

92

53

35

Phosphamidon

5.02

300

174

49

34

132

49

34

Dimethoate

4.90

230

171

30

21

199

30

14

Indoxacarb

6.21

528

203

81

56

249

81

25

Omethoate

4.64

214

125

45

35

109

45

42

Methomyl

4.92

163

106

34

70

88

34

17

Chorantraniliprole

5.34

482

451

68

28

283

68

25

Edifenphos

6.67

311

283

61

20

109

61

46

Thiodicarb

5.34

355.20

88

55

25

108

55

20

Trifloxystrobin

6.57

409

186

47

26

186

47

22

D.P. deculsting potential, E.P. entrance potential

2.4 Sample preparation

About 2 kg of fruit (apple, banana, guava) samples were collected from the untreated control plots of research trialsand chopped sample was homogenized in Robot Coupe Blixer @5000 rpm for 10 min. The well comminuted samples were placed in glass bottles (Tarson make) and stored at − 20 °C before analysis. For each substrate, 15 g homogenized sample was taken into three 50 ml centrifuge tubes to optimize the extraction procedure and 15 ml three different solvents i.e. methanol, ethyl-acetate and acetonitrile were added. Then vortexed for 1 min. The mixture was then homogenized by Silent Crusher @ 12,500 rpm for 1 min. 1.5 g of activated sodium chloride and 4 g anhydrous phthalate free MgSO4 were added to it. Reagents were thoroughly mixed by vortex mixer for 1 min and rotospinedfor 5 min and then centrifuged for 5 min @ 10,000 rpm speed. In the d-SPE clean-up step, 5 ml supernatant was transferred to each of the 15 ml centrifuge tubes containing 250 mg PSA sorbent, 25 mg GCB(for banana only) and 750 mg anhydrous MgSO4. The tube was capped, vortexed for 30 s and centrifuged for 5 min @ 5000 rpm speed. 1 ml supernatant was transferred into a vial for LC–MS/MS analysis and another portion was taken in atube and solvent was evaporated by Turbovap evaporator at 40 °C and dried under a gentle stream of pure nitrogen. Finally, the residue was reconstituted in 1 ml acetone and filtered through 0.2 μm ultipore nylon 6, 6 membranes for GC–MS analysis. The same method was applied to the fortified and real samples.

2.5 Validation as per Eurachemand SANTE guidelines

The developed analytical method was validated with linearity, precision, accuracy, LOD, LOQ and measurement of uncertainty as per the recommendations of Eurachem [14] and SANTE guidelines [15]. LOD and LOQ were determined based on signal to noise ratio (S: N) 3:1 and 10:1 respectively. The five-point (0.01-1.0 mg kg−1) calibration curve was prepared for checking linearity with the regression coefficient (R2) for both pure and matrix-matched standard mixtures.Recovery experiment in seven replicates was carried out by fortifying 15 gm homogenized fruit (apple, banana and guava) sample @ LOQ, 5 × LOQ and 10 × LOQ level (Table 3). Pesticides having similar LOQ as well as MRL values were fortified at LOQ/MRL level. Fortifications were done with the pure working standards. The quantification was done by matrix-matched standard to appraise the robustnessof the method.Theprecision of the method was evaluated by two steps (a) intra-day (repeatability) which is the analysis of the sample in five replicates in one day by one analyst and their value is calculated by percentage of relative standard deviation, (b) inter-day(reproducibility) analysis was done by two analysts with the same sample(five replicates)in two alternative day by following all the same experimental and instrumental conditions.The results of precision reproducibility are also calculated with the  %RSD. Precision actually asserted the trueness of the method by expressing the value as relative percentage deviation called  % RPD [16] @ LOQ level that can be represented by the following equation:
$${\text{Relative percentage deviation }}\left( {\% {\text{RPD}}} \right) \, = \, \left[ {\left( {{\text{Interday recovery}} - {\text{Intraday recovery}}} \right)/ \, \left( {{\text{Interday}}\;{\text{recovery}} + {\text{Intraday}}\;{\text{recovery}}} \right)/2} \right] \, \times 100$$
Table 3

Average  % recovery (± RSD) (n = 7) of 64 pesticides in apple, banana, guava fortified @ LOQ, 5 x LOQ and 10 x LOQ level along with  %ME and  %uncertainty (k = 2) when analysed in GC–MS and LC–MS/MS

Pesticides

Fortification

(mg kg-1) LOQ

Average % recovery ± RSD

Fortification 5xLOQ

(mg kg-1)

Average % recovery ± RSD

Apple

Guava

Banana

Apple

Guava

Banana

GC-MS amenable pesticides

4-Br-2Cl- phenol

0.05

106±7.69

94±5.07

100±8.22

0.25

103±5.45

104±8.97

92±2.11

Trifluralin

0.05

99±0.47

111±7.93

99±16.88

0.25

88±1.56

97±6.02

96±3.51

Phorate

0.05*

90±0.99

94±3.28

106±7.39

0.25

107±8.59

97±8.57

100±5.79

alpha-HCH

0.01#

86±3.68

101±3.90

102±7.22

0.05

96±4.82

102±11.37

97±9.50

Dimethoate

0.05+

111±9.60

105±2.23

98±7.23

0.25

102±9.90

90±4.79

108±8.25

Atrazine

0.05

111±9.60

114±6.36

88±3.89

0.25

99±9.98

97±7.27

91±2.38

beta-HCH

0.01#

102±0.83

86±5.08

96±7.23

0.05

86±3.00

92±4.15

103±6.88

Lindane

0.01#

102±0.84

88±5.88

108±8.56

0.05

100±2.84

92±13.09

109±2.63

Phosphamidon

0.05$

102±1.80

103±9.87

92±14.34

0.25

110±13.62

95±8.29

108±2.85

Chlorothalonil

0.01

108±6.33

107±6.22

111±5.76

0.05

105±8.65

89±7.05

110±6.75

delta-HCH

0.01#

101±3.47

90±9.18

91±10.06

0.05

109±5.17

100±17.28

92±5.84

Dimethachlor

0.05

102±1.80

112±8.09

96±10.20

0.25

92±6.86

109±12.35

109±4.56

Alachlor

0.05

97±5.15

100±15.19

109±11.24

0.25

104±0.96

102±6.97

111±6.64

Parathion-methyl

0.05$

103±3.28

96±4.17

102±13.55

0.25

99±8.40

88±8.21

88±12.93

Heptachlor

0.05

96±3.30

90±9.23

108±7.56

0.25

103±2.98

94±10.96

103±4.24

Malathion

0.05δ

104±5.60

94±3.58

95±10.25

0.25

96±8.11

101±10.129

98±5.52

Chlorpyriphos

0.05β

101±1.34

87±3.21

104±9.80

0.25

102±6.62

98±5.30

98±2.28

Aldrin

0.05©

103±2.23

93±6.41

109±10.07

0.25

103±3.11

87±3.07

105±6.04

Pendimethylene

0.05

99±0.92

114±5.27

102±10.28

0.25

107±2.20

107±11.61

113±3.14

Quinalphos

0.05

105±4.58

114±7.24

98±4.46

0.25

104±10.27

106±10.86

97±4.60

op-DDD

0.01

100±1.55

100±7.02

101±6.13

0.05

89±3.67

109±11.81

105±10.48

Butachlor

0.05

100±1.06

109±11.24

109±7.51

0.25

103±1.86

102±6.55

86±2.54

alpha-endosulfan

0.01+

100±0.61

109±10.47

104±7.04

0.05

111±7.37

95.±6.16

96±5.51

pp-DDD

0.01£

96±4.63

98.±6.24

99±1.72

0.05

100±5.84

96.±2.26

117±2.06

Profenophos

0.05

109±6.33

94±5.55

103±12.38

0.25

90±8.74

95±12.43

97±4.91

pp-DDE

0.01£

99±0.67

109±6.18

110±7.82

0.05

100±8.31

116.±5.90

99±2.53

op-DDT

0.01£

105±13.45

115±6.53

111±12.86

0.05

101±12.36

114±6.516

113±8.05

beta-endosulfan

0.01+

96±4.63

96±4.52

90±5.89

0.05

100±5.84

93±3.75

96±7.52

Ethion

0.05+

101±3.09

98±2.26

104±10.12

0.25

103±2.42

105±13.19

85±4.84

pp-DDT

0.01£

107±13.91

85±5.43

87±9.75

0.05

113±4.27

94±8.95

108±5.59

Endosulfan sulphate

0.01+

99±9.25

115±6.99

90±4.25

0.05

102±6.30

92±10.78

99±8.23

Spiromesifen

0.1

106±2.57

88±7.87

96±10.14

0.5

103±5.61

97±15.41

109±1.52

Bifenthrin

0.1

102±3.30

88±4.43

103±15.13

0.5

106±4.44

87±8.09

110±1.38

Fenpropathrin

0.1

107±2.87

88±4.01

110±11.01

0.5

90±4.54

103±13.00

113±2.94

lamda cyhalothrin

0.1

103±2.23

93±7.21

92±10.90

0.5

101±4.20

87±4.03

89±1.47

beta cyfluthrin(I+II+III+IV)

0.1

98±2.57

91±12.80

105±9.08

0.5

94±4.76

91±8.77

107±6.12

alpha cypermethrin

0.1

103±2.22

95±5.05

102±13.62

0.5

83±2.08

107±7.75

118±2.00

Fenvalerate-(I+II)

0.1

101±1.83

89±3.72

103±10.88

0.5

98±1.81

91.51±11.89

102±8.19

Deltamethrin

0.1

105±3.18

103±6.67

90±8.06

0.5

84±3.89

88.23±8.73

111±2.55

LC-MS/MS amenable pesticides

Carbendazim

0.01µ

85±8.57

97±11.34

92±1.92

0.05

96±14.42

99±7.93

108±1.80

Imidacloprid

0.01

91±4.88

108.±12.00

93±6.05

0.05

105±8.72

90±4.77

86±2.95

Thiacloprid

0.01

86±7.52

95±10.35

93±7.90

0.05

94±16.19

91±3.46

91±2.13

Carbofuran

0.01©

86±6.36

109±14.22

109±5.80

0.05

94±16.20

111±5.21

97±3.93

Carbaryl

0.01

90±8.62

106±11.43

87±3.73

0.05

90±5.93

115±5.78

116±0.43

Triazophos

0.01

88±5.23

111±3.78

86±4.41

0.05

92±10.57

112.±8.97

106±2.94

Monocrotophos

0.01#

86±3.90

92±9.16

102±13.38

0.05

90±14.82

100±1.99

97±3.51

Spirotetramet

0.01

99±3.15

109±7.75

105±4.11

0.05

102±3.35

106±5.14

118±1.93

Tebuconazole

0.01

97±3.50

104±5.87

97±7.74

0.05

104±7.08

110±4.39

118±0.78

Hexaconazole

0.01

110±10.13

101±9.02

115±8.59

0.05

119±1.80

109±8.31

103±5.01

Anilophos

0.01

90±4.75

102±12.08

96±3.51

0.05

88±3.79

116±2.71

111±0.55

Thiaomethoxam

0.01

93±6.89

107±10.79

101±7.52

0.05

94±1.82

98±7.58

108±3.44

Acetamiprid

0.01

89±4.83

96±5.09

104±4.52

0.05

92±5.35

85±5.22

112±2.38

Chlorfenvinphos

0.01

89±11.31

109±9.62

109±3.47

0.05

114±0.61

98±5.51

98±0.66

Propiconazole

0.01

88±8.23

94±6.13

100±12.69

0.05

85±9.20

111±3.33

99±4.33

Fenamidon

0.01

101±9.52

107±15.98

100±7.92

0.05

110±14.05

92±3.49

85±5.70

Phosphamidon

0.01

85±3.46

87±9.81

85±2.82

0.05

102±14.10

98±5.97

91±0.93

Dimethoate

0.01

98±8.85

95.±8.19

95±4.91

0.05

105±15.63

99±4.42

108±0.67

Indoxacarb

0.01

97±4.39

85±7.90

90±17.73

0.05

101±3.13

86±2.78

117±3.64

Omethoate

0.01

90±8.03

104±14.53

91±10.58

0.05

92±3.01

82±2.61

88±7.12

Methomyl

0.01

107±6.93

97±13.51

94±7.50

0.05

100±4.0

89±5.40

108±5.19

Chorantraniliprole

0.01

110±3.03

108±10.74

108±9.85

0.05

108±7.02

87±6.46

95±4.39

Edifenphos

0.01

98±4.51

99±7.61

103±4.16

0.05

85±2.30

98±13.46

110±1.81

Thiodicarb

0.01

111±1.93

106±14.25

104±8.27

0.05

111±1.30

118±1.83

103±7.49

Trifloxystrobin

0.01

101±1.42

95±1.77

94±1.87

0.05

83±3.25

98±2.26

99±3.67

Pesticides

Fortification 10xLOQ

(mg kg−1)

Average % recovery ± RSD

%ME

U (k=2) (%)

Apple

Guava

Banana

Apple

Guava

Banana

Apple

Guava

Banana

GC-MS amenable pesticides

4-Br-2Cl- phenol

0.5

90±7.34

88±6.64

101±15.18

− 12.89

15.32

− 19.04

9.25

2.83

5.53

Trifluralin

0.5

101±10.80

92±2.22

89± 7.13

− 0.46

15.27

− 19.77

15.28

5.25

2.22

Phorate

0.5

99±8.19

94±5.82

87± 5.05

15.31

19.74

− 18.24

18.74

9.28

9.26

alpha-HCH

0.1

84±4.56

103±10.29

100± 2.29

1.50

− 19.90

− 21.32

15.26

2.26

11.28

Dimethoate

0.5

103±2.31

98±7.25

107± 4.29

5.16

− 14.52

4.23

17.28

8.55

12.03

Atrazine

0.5

108±6.45

108±6.88

91± 6.71

− 18.65

− 16.63

10.59

8.25

10.25

17.12

beta-HCH

0.1

92±9.57

96±4.11

109± 7.28

20.77

− 20.10

− 17.92

9.78

8.14

19.52

Lindane

0.1

96±10.64

91±7.10

112± 3.86

1.90

− 16.83

− 13.25

4.29

2.29

20.25

Phosphamidon

0.5

95±5.13

106±2.23

87± 7.12

− 17.00

10.27

− 7.24

14.88

6.22

16.46

Chlorothalonil

0.1

91±4.21

98±7.08

101± 4.06

22.65

− 20.59

− 14.71

10.29

9.88

4.56

delta-HCH

0.1

99±7.17

85±6.53

93± 7.00

− 12.05

− 18.41

− 20.70

13.50

5.27

7.89

Dimethachlor

0.5

91±3.29

114±4.90

103± 3.25

5.30

8.51

4.34

18.75

6.99

8.90

Alachlor

0.5

91±1.25

108±2.59

111± 6.32

21.95

19.45

− 19.07

23.54

2.17

2.55

Parathion-methyl

0.5

91±4.41

90±5.10

98± 1.18

19.91

19.37

12.60

11.48

2.23

1.55

Heptachlor

0.5

99±5.77

105±4.85

107± 3.93

− 18.75

− 20.21

− 4.95

22.80

9.87

4.22

Malathion

0.5

95±4.52

110±13.25

116± 2.02

0.69

19.27

13.82

15.87

8.59

2.44

Chlorpyriphos

0.5

103±8.27

87±9.15

107± 6.56

− 16.59

5.36

2.50

25.84

4.58

2.99

Aldrin

0.5

109±5.31

96±17.31

106± 1.79

− 13.44

− 18.67

20.00

7.29

18.20

14.84

Pendimethylene

0.5

98±6.62

109±4.18

90± 3.88

17.65

18.47

− 5.37

8.41

3.18

4.02

Quinalphos

0.5

108±6.97

90±6.28

83±2.02

− 17.90

− 12.75

7.64

10.20

8.25

9.99

op-DDD

0.1

102±9.31

98±4.61

105± 8.55

− 15.79

14.70

− 18.64

19.50

11.47

8.27

Butachlor

0.5

110±8.87

116±9.68

82± 1.50

21.47

7.85

− 19.90

17.47

19.25

11.12

alpha-endosulfan

0.5

90±10.27

97±7.58

103± 5.32

− 20.17

− 16.65

− 16.73

19.50

21.58

18.29

pp-DDD

0.1

94±8.91

104±5.23

111± 6.22

10.23

12.23

− 8.46

7.88

10.56

4.25

Profenophos

0.5

87±5.58

94±11.76

108± 7.32

− 14.63

1.95

− 20.85

23.22

6.20

9.26

pp-DDE

0.1

81±4.973

86±5.91

108± 6.50

− 11.46

− 20.35

− 18.55

9.53

6.22

7.14

op-DDT

0.1

102±3.18

107±11.14

93± 9.80

− 17.42

− 18.53

− 1.75

17.43

5.62

2.63

beta-endosulfan

0.1

94±8.91

112±6.46

94± 7.24

13.33

− 14.28

13.71

17.84

3.22

1.58

Ethion

0.5

111±5.20

111±6.28

113± 3.14

− 18.82

− 17.78

− 16.22

12.45

5.55

6.27

pp-DDT

0.1

108±5.32

104±10.26

87± 3.00

3.53

− 15.36

− 33.99

18.44

2.23

9.25

Endosulfan sulphate

0.1

96±11.06

109±12.51

106±10.28

− 16.54

6.46

16.25

16.11

6.58

4.25

Spiromesifen

1.0

103±4.06

106±5.89

111± 1.70

18.81

17.47

− 19.36

4.28

9.25

6.80

Bifenthrin

1.0

104±9.73

90±8.20

98± 2.77

− 20.42

− 19.91

18.35

8.47

18.52

22.25

Fenpropathrin

1.0

96±3.06

88±7.36

112± 0.59

− 1.25

− 3.46

6.86

9.14

14.44

7.52

lamda cyhalothrin

1.0

102±6.96

95±9.80

81± 2.68

19.69

20.26

2.44

15.88

19.22

9.60

beta cyfluthrin(I+II+III+IV)

1.0

99±5.33

90±5.48

114± 3.14

− 18.07

19.59

− 0.58

18.46

13.20

19.65

alpha cypermethrin

1.0

98±12.19

100±9.26

104.± 7.34

− 7.22

− 18.68

− 0.38

12.80

7.52

5.26

Fenvalerate-(I+II)

1.0

93±5.92

86±4.18

116± 3.05

21.91

− 14.17

− 12.94

10.55

9.88

2.03

Deltamethrin

1.0

85±5.09

111±7.90

104± 1.07

7.37

− 16.44

− 10.20

26.85

2.33

5.69

LC-MS/MS amenable pesticides

Carbendazim

0.1

82±2.90

84±3.94

94±3.20

− 11.26

18.92

− 9.07

5.87

11.23

9.20

Imidacloprid

0.1

85±6.08

112±8.47

90±10.03

− 13.77

− 17.85

− 10.55

2.26

5.60

3.84

Thiacloprid

0.1

96±11.18

100±9.70

82±3.87

− 9.56

− 17.96

9.82

16.20

9.20

8.22

Carbofuran

0.1

83±6.80

112±6.19

87±7.45

− 8.96

4.28

25.69

2.20

7.14

5.82

Carbaryl

0.1

88±10.21

118±2.10

88.±4.62

1.84

13.82

− 17.90

8.44

12.55

3.26

Triazophos

0.1

91±14.97

118±4.10

89±12.20

10.96

− 6.31

− 20.80

17.22

5.45

8.28

Monocrotophos

0.1

80±10.71

90±7.75

95±12.79

− 5.18

5.91

− 16.09

16.33

10.22

9.25

Spirotetramet

0.1

89±7.66

111±5.30

93±15.61

− 5.38

− 17.04

3.73

13.20

3.22

5.60

Tebuconazole

0.1

90±1.00

118±1.93

89±4.95

7.27

13.94

3.93

4.59

2.20

5.88

Hexaconazole

0.1

92±8.59

107±5.54

98±17.75

14.19

− 19.01

− 14.29

5.55

9.85

2.22

Anilophos

0.1

82±3.26

115±5.83

87±8.77

− 20.34

13.64

14.07

6.90

5.45

2.54

Thiaomethoxam

0.1

106±2.58

117±1.98

87±4.24

5.55

8.00

− 19.35

7.15

11.27

19.55

Acetamiprid

0.1

81±3.81

93±11.70

88±5.20

− 8.70

− 3.38

16.58

4.87

18.50

10.55

Chlorfenvinphos

0.1

101±13.76

116±3.65

94±12.96

− 19.26

− 6.71

19.11

2.22

6.57

9.80

Propiconazole

0.1

93±17.79

95±4.31

82±8.80

− 0.14

− 9.91

18.59

5.54

6.23

15.20

Fenamidon

0.1

83±16.48

108±8.39

79±8.21

6.96

− 12.24

− 20.25

17.45

20.12

19.52

Phosphamidon

0.1

81±0.24

88±6.11

87±6.44

− 19.95

13.60

− 12.71

13.22

7.80

4.52

Dimethoate

0.1

84±8.91

103±10.65

87±4.02

− 16.32

18.84

− 19.59

2.79

5.56

2.20

Indoxacarb

0.1

100±15.32

99±7.94

90±17.73

8.30

− 11.67

2.50

11.45

19.52

14.27

Omethoate

0.1

86±1.45

89±13.38

84±6.16

− 17.40

17.76

− 20.24

17.45

10.25

8.44

Methomyl

0.1

97.4±6.0

94.±13.54

86±2.39

18.90

12.28

15.23

5.94

7.52

5.56

Chorantraniliprole

0.1

99±3.17

107±7.51

83±8.42

10.12

− 5.03

11.63

18.45

14.22

5.54

Edifenphos

0.1

95±13.07

112±2.29

95±6.66

− 9.03

18.81

− 20.65

10.32

17.45

12.52

Thiodicarb

0.1

117±2.6

110±11.75

114±18.33

14.80

− 19.35

7.23

11.28

16.25

19.89

Trifloxystrobin

0.1

91±10.92

97±1.82

86±2.74

− 19.76

23.37

20.33

12.23

10.55

12.45

MRL a0.05 mg kg−1, b1.0 mg kg−1, + 2.0 mg kg−1, c0.2 mg kg−1, d4.0 mg kg−1, e0.5 mg kg−1, f0.1 mg kg−1, g3.5 mg kg−1, h1.0 (apple) 1.5 (banana) mg kg−1, i5.0 (apple and guava), 1.0 (banana) mg kg−1

Matrix effect can be evaluated by the following equation:
$$\% {\text{ME}} = \, \left( {{\text{F}} - 1} \right) \, \times 100$$
\({\text{F}} = {\text{S}}_{\text{matrix}} /{\text{S}}_{\text{standard}}\), where, \({\text{S}}_{\text{matrix}}\) represents peak area of the fortified extract and \({\text{S}}_{\text{standard}}\) states peak area of the pure standard. Positive value of  % ME indicates matrix enhancement and negative value will be matrix suppression. For the strong matrix effect  %ME > 50, 20 < %ME ≤ 50 having considered the medium matrix effect [17]. But currently  %ME ≤ 20 (enhancement or suppression) as per SANTEguideline is accepted.For the uncertainty measurement top down approach was used. The combined uncertainty that is associated with standard and sample was calculated as per the following equation:
$${\text{Uc = }}\sqrt {\left( {{\text{U}}_{ 1}^{ 2} {\text{ + U}}_{ 2}^{ 2} {\text{ + U}}_{ 3}^{ 2} {\text{ + U}}_{ 4}^{ 2} {\text{ + U}}_{ 5}^{ 2} {\text{ + U}}_{ 6}^{ 2} {\text{ + U}}_{ 7}^{ 2} {\text{ + U}}_{ 8}^{ 2} {\text{ + U}}_{ 9}^{ 2} {\text{ + U}}_{ 1 0}^{ 2} {\text{ + U}}_{ 1 1}^{ 2} } \right)}$$
Where, U1= recovery-accuracy, U2= recovery-precision, U3=purity of standard, U4=balance for standard preparation, U5=volumetric flask for stock solution, U6 = volumetric flask for working standard solution, U7= 1 ml pipette, U8=5 ml pipette, U9 = linearity of balance, U10 = uncertainty of seven points calibration, U11=uncertainty precision of instrument at LOQ. Expanded uncertainty (U) is twice of combined uncertainty at a confidence level of 95%.
$${\text{Total uncertainty }}\left( {\text{MU}} \right)\, = \,{\text{LOQ}}\, \times \,2{\text{ Uc}}$$

3 Results and Discussion

3.1 Standardization of Extractionand cleaning up step

Carneiro and his co-workers [13] reported extraction process by modified QuEChERS but they did not use any clean-up step for banana matrix. Jardimaand her team [18] used buffer QuEChERS in apple and guava samples. In thepresent study, extracting solvent was standardized, among three different extracting solvents i.e., methanol, acetonitrile and ethyl-acetate used based on their polarities. A comparative picture of average percent recovery using these extracting solvents which provided the representative data was presented in Fig. 1. Based on the data, acetonitrile was standardised as the extracting solvent because it gave the highest average percent recovery in all three matrices and for all the pesticides. The research works of [19, 20, 21] resulted the use of acetonitrile as good extracting solvent. Present studyusedPSAto absorb matrix co-extractives, MgSO4 to remove water and combination of PSA + GCB only for bananato remove heteropolysaccharide and carotenoids which areotherwisenot removed by PSA alone. Okihashiand his associates [22] reported the role of GCB in case of banana extract. Saito and his co-workers [23] also reported that the combination of GCB + PSA provided excellent clean-up for removal of matrix materials. In our study, using GCB as cleaning up agent for banana matrices showed promising result. Without using GCB, beta-endosulfan (191.49%), indoxacarb (124.77%), trifloxystrobin (142.59%) and Chorantraniliprole (81.81%) suffered very high matrix enhancement effect. GCB reduced these values to 13.71%, − 2.50%, 20.33% and 11.63% respectively. Some pesticides also suffered strong matrix suppression effects viz beta-HCH (− 86.11%), alachlor (− 81.40%), pp-DDT(− 30.09%) and beta-cyfluthrin (− 48.74%) which were reduced to − 17.92%, − 19.07%, − 17.39% and − 0.58% respectively. Comparison of matrix effect in presence and absence of GCB in banana matrix was depicted in Fig. 2.
Fig. 1

Comparison of average  % recovery of 64 pesticides using MeOH, EtOAC and MeCN as extracting solvents in case of a apple, b guava and c banana matrices fortified at LOQ level

Fig. 2

Comparison of  %ME of 64 pesticides while cleaning up with and without GCB in Banana matrix

3.2 Efficiency of the method

A total of 39 pesticides were analysed in GC–MS. On the other hand, 25 pesticides were analysed in LC–MS/MS. In GC–MS analysis, at LOQ level, 86–111%, 87–110%, 84–114% recoverywas found respectively for apple, banana, guavawith respective relative standard deviation (RSD) ranging from 0.47 to 13.45%, 3.89 to 16.88%, 2.23 to 12.80%.Irrespective of the pesticides and matrices, pp-DDT acquired the least recovery of 84.44% with RSD value 5.43%. In GC–MS analysis, instrument parameters are standardized to increase the sensitivity and selectivity of the instrument. Considering the GC injection mode, split mode has been chosen to avoid the overloaded peaks that reduced the separation efficiency of the column. The GC oven temperature programming was standardized so that the analyte is well separated having good peak shape and the matrix interference is minimized to increase the sensitivity. Four different other methods were compared with the present methodregarding the standardization of GC–MS. The present method took 50 min run time in comparison to 70.33 min [16], 60.17 min [24], 60 min [25], 55 min [26]. The less run time ofthepresent method in comparison to others was established. Standardization of MS parameters was done by identification of peaks in total ion chromatogram of mixed standard solution in scan mode by their specific RT and characteristic mass fragmentation pattern. The most abundant ion that had the highest S/N ratio and showed no matrix interference was selected as quantifier ion. The other two ions were selected as qualifier. A SIM method is prepared by fixing the RT window of each compound from the full scan chromatogram and the RT of individual compounds. Twenty-five pesticides, analysed in LC–MS/MS for apple, banana and guava matrices resulted the recovery range of 85–111%, 84–114% and 86–110% at LOQ levelrespectively with therespective RSD values of 1.42–11.31%, 1.87–13.38% and 1.77–14.53%. LC–MS/MS conditions have been standardized to achieve good separation, satisfactory selection and increased sensitivity which enable to analyse samples having complex matrices with a high degree of confidence. Different combination of mobile phases were tested because mobile phase has the direct effect on the peak shape and the retention time of the analyte in the column as well as on MS sensitivity. The different combination of water, methanol, acetonitrile with ammonium acetate, ammonium formate buffers were surveyed. The well defined shape and reproducibility of retention time of pesticides were achieved by using mobile phase (A) water with 5 mM ammonium formate (B) methanol with 5 mM ammonium formate by using reverse phase Zorbax SB-C18 (4.6 × 150 mm, 5 µm) column.The total ion chromatograms (TIC) of 0.1 µg ml−1for GC–MS and LC–MS/MS are presented in Fig. 3.
Fig. 3

TIC of 0.1 ugml-1 of a GC–MS and b LC–MS/MS

3.3 Method validation

The LOD and LOQ of the GC amenable pesticides were found to be within the ranges of 0.001–0.04 mg kg−1 and 0.005–0.11 mg kg−1 respectively. The linearity of the calibration curve was established with R2 value in the range of 0.988–0.999. In case of LC amenable pesticides, the values for LOD, LOQ, R2 were within the range of 0.001–0.008 mg kg−1, 0.008–0.01 mg kg−1 and 0.971–0.999 respectively.The mean recoveries were found in the range of 80–120% (Table 3). Good accuracy was observed for all analytes with relative standard deviation ≤ 20% which is as per the requirements of SANTE regulating the performance of analytical method. Relative percentage deviation (%RPD) was calculated for inter and intraday assay recovery for three matrixes at LOQ level. The recovery precision was expressed by averagerecovery percentage ± SD along with RSD value. The inter- and intra-day precision of the method for apple matrix were found to be respectively < 13% and < 17% and  %RPD value was < 12%.For guava matrix respective inter and intraday valueswere < 16% and < 17% and  %RPD value was < 10%and inter and intradayvaluesfor banana were < 19% and < 16% respectively and  %RPD value was < 18%. All these values of precision satisfy SANTE and European Commissionguidelines (Fig. 4) and therefore the method is precise. To define the quality of analytical results, both traceability and degree of confidence are equally important. The uncertainty was determined at the LOQ level for all the pesticides as per the EURACHEM/CITAC (Table 3) showed MU values for individual pesticides with the majority of compounds having uncertainties < 20%. In apple,  %ME values ranged between(− 20.42 and 22.65%) and therefore are almost within ≤ 20%. The exceptions are chlorothalonil (22.65%), alachlor (21.95%), butachlor (21.75%) and fenvalerate (21.91%) which were undergonematrix enhancement effect. Whereas in guava, only one pesticide trifloxystrobinwas found to have matrix enhancement effect (23.37%).All the % ME values were presented in Table 3 for GC–MS and LC–MS/MS respectively. In case of banana, 15% pesticides showed matrix suppression effect out of 64 pesticides, whereas matrix enhancement effect was found for 12% pesticides. A comparison for banana matrix with presence and absence of GCB as cleaning up agent was designed for showing the matrix effect in Fig. 2.
Fig. 4

Comparison of Inter and Intra- assay precision recovery of pesticide residues in apple, guava and banana matrixes

3.4 Analysis of real samples

The method was successfully applied to analyse of market samplesof apple, banana, guavawhich were collected from four different districts (Kolkata, Howrah, Hooghly and Burdwan) of West Bengal, India and detected different numbers of pesticides (Table 4). Among the samples, guava collected from Pandua and Howrah were detected with chlorpyriphos (0.25 ± 6.19) and profenophos (0.62 ± 1.61) respectively. Two pesticides namely carbendazim (0.9 ± 2.32) and quinalphos (0.12 ± 1.26) were detected in banana samples collected from Howrah. Apple sample collected from Pandua was detected with dimethoate (0.18 ± 4.20). But apple sample collected from Kolkata was detected with three pesticides namely trifloxystrobin (0.05 ± 5.88), tebuconazole (0.10 ± 6.37) and carbendazim (0.05 ± 4.37). Therefore, highest numbers of pesticides (3) were detected in the apple samples collected from Kolkata.
Table 4

Analysis of market samples of apple, banana and guava

Location

Substrate

Number of samples

Pesticides detected (µg g−1±RSD)

Mode of analysis

(GC–MS/LC–MS/MS)

Analysed

Detected

Pandua

Apple

12

01

Dimethoate (0.18 ± 4.20)

GC–MS

Kolkata

Apple

12

01

Trifloxystrobin (0.05 ± 5.88)

Tebuconazole (0.1 ± 6.37)

Carbendazim (0.05 ± 4.37)

LC–MS/MS

Pandua

Guava

12

01

Chlorpyriphos (0.25 ± 6.19)

GC–MS

Hawrah

Guava

12

01

Profenophos (0.62 ± 1.61)

LC–MS/MS

Hawrah

Banana

12

02 (01 + 01)

Carbendazim (0.09 ± 2.32)

LC–MS/MS

Quinalphos (0.12 ± 1.26)

4 Conclusion

In this era of good health and diet consciousness, food stuffs are being monitored regularly to check the presence of pesticide residues. A quick, accurate, precise and efficient method is therefore necessary to detect and determine pesticide residues in real samples. The present method is validated as per Eurachem [14] and SANTE guidelines [15]. The efficiencies of methanol, ethyl acetate and acetonitrile were checked as extracting solvents and finally acetonitrile was chosen as extracting solvent in the method. The use of GCB + PSA mixture as cleaning up agentin case of banana reduced the interference of heteropolysaccharide and carotenoids and thus nullified matrix interferences. The present method can be used for both the instruments at a time. This modified QuEChERS method is useful for quick determination of multiclass multipesticide residues in apple, guava and banana meant for export.

Notes

Funding

The authors are grateful to Indian Council of Agricultural Research (ICAR), New Delhi, India for the financial support.

Compliance with ethical standards

Conflict of interest

We all authors declare that we have no conflict of interest.

Human and animal rights

This article does not contain any study with human participants or animals performed by any of the authors.

References

  1. 1.
    National Horticulture Board (2019). http://nhb.gov.in/statistics/Publication/Horticulture. Accessed 23 Aug 2019
  2. 2.
    Food and Agriculture Organization of the United Nation (2019). http://www.fao.org/faostat/en/data/PI. Accessed 23 Aug 2019
  3. 3.
    Agricultural & Processed Food Products Export Development Authority (2019). https://apeda.gov.in/apedawebsite Accessed 11 Aug 2019
  4. 4.
    Barriada-Pereira M, Concha-Graña E, González-Castro MJ, Muniategui-Lorenzo S, López-Mahı́a P, Prada-Rodrı́guez D, Fernández-Fernández E (2003) Microwave-assisted extraction versus Soxhlet extraction in the analysis of 21 organochlorine pesticides in plants. J Chromatogr A 1008:115–122CrossRefGoogle Scholar
  5. 5.
    Zhao L, Szakas T, Churley M, Lucas D (2019) Multi-class multi-residue analysis of pesticides in edible oils by gas chromatography-tandem mass spectrometry using liquid-liquid extraction and enhanced matrix removal lipid cartridge cleanup. J Chromatogr A 1584:1–12CrossRefGoogle Scholar
  6. 6.
    Chatzimitakos TG, Anderson JL, Stalikas CD (2018) Matrix solid-phase dispersion based on magnetic ionic liquids: an alternative sample preparation approach for the extraction of pesticides from vegetables. JChromatogr A 1581–1582:168–172CrossRefGoogle Scholar
  7. 7.
    PylypiwH Arsenault T, Thetford C, Martina MJI (1997) Suitability of microwave-assisted extraction for multiresidue pesticide analysis of produce. J Agric Food Chem 45(9):3522–3528CrossRefGoogle Scholar
  8. 8.
    Pan P, Liu WX, Shi X, Cao J, Shen WR, Qing BP, Sun R, Tao S (2004) Sample purification for analysis of organochlorine pesticides in sediment and fish muscle. J Environ Sci Health B 39:353–365CrossRefGoogle Scholar
  9. 9.
    Sapozhnikova Y, Bawardi O, Schlenk D (2004) Pesticides and PCBs in sediments and fish from the Salton Sea, California, USA. Chemosphere 55:797–809CrossRefGoogle Scholar
  10. 10.
    Fontanals N, Marce RM (2005) New hydrophilic materials for solid-phase extraction. Trends Anal Chem 24:394–406CrossRefGoogle Scholar
  11. 11.
    Jordan TB, Nichols DS, Kerr NI (2006) Selection of SPE cartridge for automated solid-phase extraction of pesticides from water followed by liquid chromatography-tandem mass spectrometry. Anal Bioanal Chem 394:2257–2266CrossRefGoogle Scholar
  12. 12.
    Anastassiades M, Lehotay SJ, Štajnbaher D, Schenck FJ (2003) Fast and easy multiresidue method employing acetonitrile extraction/partitioning and “dispersive solid-phase extraction” for the determination of pesticide residues in produce. J AOAC Int 86:412–431Google Scholar
  13. 13.
    Carneiro RP, Oliveira FAS, Madureira FD, Souza Silva G, Rde W, Lopes RP (2013) Development and method validation for determination of 128 pesticides in bananas by modified QuEChERS and UHPLC–MS/MS analysis. J Food Control 33:413–423CrossRefGoogle Scholar
  14. 14.
    EURACHEM/CITAC Ellison SLR, Williams A (2012) In: Guide Quantifying Uncertainty in Analytical Measurement. 3rd ed.; Eds:1-141Google Scholar
  15. 15.
    SANTE/11813/2017. Guidance document on analytical quality control and validation procedures for pesticide residues analysis in food and feed. European Commission Directorate-General for Health andFoodSafety (rev.0).(https://ec.europa.eu/food/sites/food/files/plant/docs/pesticides_mrl_guidelines_wrkdoc_2017-11813.pdf. Accessed 23 Aug 2019)
  16. 16.
    Tripathy V, Sharma K, Yadav R, Devi S (2019) Development, validation of QuEChERS-based method for simultaneous determination of multiclass pesticide residue in milk, and evaluation of the matrix effect. J Environ Sci Health B54:334–406Google Scholar
  17. 17.
    Savini S, Bandini M, Anna S (2019) An improved, rapid, and sensitive ultra-high-performance liquid chromatography-high-resolution orbitrap mass spectrometry analysis for the determination of highly polar pesticides and contaminants in processed fruits and vegetables. J Agric Food Chem 67(9):2716–2722CrossRefGoogle Scholar
  18. 18.
    Jardim ANO, Mello DC, Goes FC, Junior EFF, Caldas ED (2014) Pesticide residues in cashew apple, guava, kaki and peach: GC–μECD, GC–FPD and LC–MS/MS multiresidue method validation, analysis and cumulative acute risk assessment. J Food Chem 164:195–204CrossRefGoogle Scholar
  19. 19.
    Dwivedi BC, Tiwari H, Gaur V (2017) Assessment of 27 pesticide residues in fruit juices & vegetables paste by gas chromatography with mass spectrometry (GC-MS). Int J Chem Stud 5(1):259–285Google Scholar
  20. 20.
    Chandra S, Mahindrakar AN, Kumar M, Shinde LP (2014) Determination of pesticide residues in fruits local market Nanded, India. Int J Advan Res 2:1075–1082Google Scholar
  21. 21.
    Lozowicka B, Rutkowska E, Jankowska M (2017) Influence of QuEChERS modifications on recovery and matrix effect during the multi-residue pesticide analysis in soil by GC/MS/MS and GC/ECD/NPD. Environ Sci Pollu Res 24:7124–7138CrossRefGoogle Scholar
  22. 22.
    Okihashi M, Kitagawa Y, Akutsu K, Obana H, Tanaka Y (2005) Rapid method for the determination of 180 pesticide residues in foods by gas chromatography/mass spectrometry and flame photometric detection. J Pestic Sci 30(4):368–377CrossRefGoogle Scholar
  23. 23.
    Saito Y, Kodama S, Matsunaga A, Yamamoto A (2004) Multiresidue determination of pesticides in agricultural products by gas chromatography/mass spectrometry with large volume injection. J AOAC Int 87:1356–1367Google Scholar
  24. 24.
    Hawari KEI, Mokh S, Iskandarani MAI, Halloum W, Jaber F (2019) Pesticide residues in Lebanese apples and health risk assessment. Food Addit Contam B 12:81–89CrossRefGoogle Scholar
  25. 25.
    Rai S, Singh AK, Srivastava A, Yadav S, Siddiqui MH, Mudiam MKR (2016) Comparative evaluation of QuEChERS method coupled to DLLME extraction for the analysis of multiresidue pesticides in vegetables and fruits by gas chromatography-mass spectrometry. Food Anal Methods 9:2656–2669CrossRefGoogle Scholar
  26. 26.
    Schwanz TG, Carpilovsky CK, Weis GCC, Costabeber IH (2019) Validation of a multi-residue method and estimation of measurement uncertainty of pesticides in drinking water using gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry. J Chromatogr A 1585:10–18CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Swagata Mandal
    • 1
    • 2
  • Rajlakshmi Poi
    • 1
  • Inul Ansary
    • 2
  • Dipak Kumar Hazra
    • 1
  • Sudip Bhattacharyya
    • 1
  • Rajib Karmakar
    • 1
    Email author
  1. 1.All India Network Project On Pesticide Residue Laboratory, Directorate of ResearchBidhan Chandra Krishi ViswavidyalayaKalyani, NadiaIndia
  2. 2.Department of ChemistryBurdwan UniversityBardhamanIndia

Personalised recommendations