Journal of Chemistry

Volume 2013 (2013), Article ID 908586, 13 pages

http://dx.doi.org/10.1155/2013/908586

Research Article

Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods

Elaheh Konoz, Amir H. M. Sarrafi, Alireza Feizbakhsh, and Zahra Dashtbozorgi

Department of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran 13185-768, Iran

Received 14 January 2012; Accepted 30 April 2012

Academic Editor: Yenamandra S. Prabhakar

Copyright © 2013 Elaheh Konoz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

A quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. The six molecular descriptors that were considered to account for the effect of molecular structure on the retention time are number of nitrogen, Solvation connectivity index-chi 1, Balaban Y index, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted. A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. The standard error values of ANN model for training, test, and validation sets are 1.559, 1.517, and 1.249, respectively, which are less than those obtained reveals by multiple linear regressions model (2.402, 1.858, and 2.036, resp.). Results obtained the reliability and good predictability of nonlinear QSRR model to predict the retention time of pesticides.

1. Introduction

Pesticides are used on a large scale for agricultural purposes. The adverse effects of pesticides on both human health and the environment are a matter of public concern. Thus, both the actual state and the transition of pesticide residues in various matrices including water, soil, and agricultural products should be extensively monitored. These researches should be undertaken using an efficient analytical system with a laborsaving and cost-effective device, as pesticides as well as applicable fields of research rang over a broad spectrum. Conventional sample preparation methods used to analyze pesticide residues in various matrices require expensive instrumentation, an expert analyst [1–4]. Besides the above mentioned, the experimental determination of chromatographic retention parameters of pestocides is time consuming and expensive. Alternatively, quantitative structure-retention relationship (QSRR) provides a promising method for the estimation of retention time based on descriptors derived solely from the molecular structure to fit experimental data. The advantages of this approach lie in the fact that it requires only the knowledge of chemical structure and is not dependent on any experiment properties.

QSRR studies [5, 6] started from the calculation and selection of descriptors, to finding their relation to retention times and derivation of mathematical models that involve these multivariate data in order to be used for predictive purposes in chromatographic system. Multivariate data consist of the results of observations of many different variables (molecular descriptors) for a number of individuals (molecules). Known methods for this include the multiple regression analysis, experimental design techniques, and nonlinear regression. The drawback, sometimes, of these very popular techniques is their inability to give highly predictive models due to hidden nonlinearity inside the data variables or the prerequisite to specify the mathematical model before the fitting of the data. So there is a need to improve further such kind of models in order to extract the most accurate prediction. To this end, artificial neural networks (ANNs) could be used successfully in QSRR studies providing better results than the conventional regression models.

Today artificial neural networks [7–10] have become an important modeling technique for QSAR and QSPR, and also this technique has been applied in numerous application areas of chemistry and pharmacy [11–16]. The mathematical adoptability of ANN commends them as powerful tool for pattern classification and building predictive models. A particular advantage of ANNs is their inherent ability to incorporate nonlinear dependencies between the dependent and independent variables without using an explicit mathematical function. There are few reports about the application of QSRR in the chromatographic studies: D’Archivio et al. modeled the combined effect of solute structure and eluent composition on the retention behavior of 26 pesticides in isocratic reversed-phase high-performance liquid chromatography using multilinear regression and artificial neural networks [17]. In another study, they applied a six-parameter nonlinear QSRR model to predict the retention behavior of 26 pesticides including commonly used insecticides, herbicides, and fungicides as well as some metabolites in reversed-phase high-performance liquid chromatography [18]. Also Ghasemi and his coworkers used multiple linear regression and partial least squares regression to QSRR study of the gas chromatography retention time of 38 diverse chlorinated pesticides, herbicides, and organohalides by using molecular descriptors [19]. In the present study, the application of ANN is being described in order to predict accurately the retention time values of 300 pesticides in four groups with different molecular structures [20].

2. Methods

2.1. Dataset

Development of the multiple linear regression and artificial neural networks in the present work relies on a data set taken from reference [20]. This dataset (Table 1) consists of 300 pesticides in animal tissues such as beef, mutton, pork, chicken, and rabbit ranging in retention time from 5.62 to 35.77 min. All of these 300 pesticides divided into four groups, depending upon properties and retention time of each pesticide. Each group is consisting different kind of pesticides such as acaricides, insecticides, and Fungicides. To apply the ANN modeling, the dataset was randomly divided into three groups of training, test, and validation sets consisting of 256, 22, and 22 pesticides, respectively. The training set was used for the model generation. The test set plays a different role in the cases of the MLR and the ANN models. For the ANN model, this set was used for early stopping to optimize learning iteration and avoid overtraining. The validation set was used to assess the accuracy of the ANN predictions. On the other hand, in the case of the MLR model, the test set and the validation set were used to evaluate the model. As can be seen from Table 1, the pesticides in the test and validation sets were chosen in a way that adequately represents the training set in term of retention time.

Table 1: Experimental, ANN, and MLR predicted values of the retention times () of training, test, and validation sets.

Number
Pesticide
(EXP)
(MLR)
(ANN)
()EXP − ()ANN

Training set

1
Allidochlor
8.78
10.22
9.83
−1.05

2
Dichlormid
9.74
10.27
9.88
−0.14

3
Etridiazol
10.42
16.11
12.9
−2.48

4
Chlormephos
10.53
11.88
14
−3.47

5
Propham
11.36
14.17
12.13
−0.77

6
Cycloate
13.56
16.32
16.4
−2.84

7
Diphenylamine
14.55
18.13
15.58
−1.03

8
Chlordimeform
14.93
17.44
13.69
1.24

9
Ethalfluralin
15.00
16.17
14.56
0.9

10
Thiometon
16.20
18.1
17.82
−1.62

11
Atrazine-desethyl
16.76
13.96
17.25
−0.49

12
Clomazone
17.00
20.95
18.63
−1.63

13
Fonofos
17.31
20.48
16.24
1.07

14
Simazine
17.85
16.99
18.93
−1.08

15
Propetamphos
17.97
19.72
18.93
−0.96

16
Secbumeton
18.36
20.2
19.21
−0.85

17
Dichlofenthion
18.80
21.02
17.38
1.42

18
Pronamide
18.72
19.61
21.15
−2.43

19
Mexacarbate
18.83
17.05
18.64
0.2

20
Aldrin
19.67
22.92
21.33
−1.66

21
Dinitramine
19.35
18.57
16.45
2.9

22
Ronnel
19.80
22.63
21.2
−1.4

23
Cyprazine
20.18
19.82
18.43
1.75

24
Beta-HCH
20.31
16.11
16.93
3.38

25
Metalaxyl
20.67
20.19
21.92
−1.25

26
Chlorpyrifos (-ethyl)
20.96
25.26
22.59
−1.63

27
Methyl-parathion
20.82
22.85
21.44
−0.62

28
Malathion
21.54
21.2
22.9
−1.36

29
Fenitrothion
21.62
23.17
21.19
0.43

30
Paraoxon-ethyl
21.57
22.19
21.39
0.18

31
Triadimefon
22.22
25.41
25.62
−3.4

32
Parathion
22.32
24
23.28
−0.96

33
Pendimethalin
22.59
20.84
19.77
2.82

34
Linuron
22.44
21.13
19.94
2.5

35
Bromophos-ethyl
23.06
23.18
21.51
1.55

36
trans-chlordane
23.29
23.4
22.61
0.68

37
Phenthoate
23.30
24.91
25.59
−2.29

38
Fenothiocarb
23.79
23.76
24.85
−1.06

39
Prothiophos
24.04
24.99
23.69
0.35

40
Dieldrin
24.43
24.77
24.55
−0.12

41
Procymidone
24.36
22.91
26.47
−2.11

42
Methidathion
24.49
27.18
26.96
−2.47

43
Napropamide
24.84
24.35
23.12
1.72

44
Fenamiphos
25.29
23.4
26.85
−1.56

45
Aramite
25.60
26.63
25.36
0.24

46
Bupirimate
26.00
25.11
24.63
1.37

47
Carboxin
26.25
23.23
25.54
0.71

48
Flutolanil
26.23
22.66
27.39
−1.16

49
Ethion
26.69
26.7
26.6
0.09

50
Sulprofos
26.87
25.6
25.14
1.73

51
Etaconazole-2
26.89
22.06
26.1
0.79

52
Myclobutanil
27.19
26.34
26.45
0.74

53
Diclofop-methyl
28.08
28.79
27.35
0.73

54
Propiconazole
28.15
30.15
28.96
−0.81

55
Fensulfothin
27.94
25.84
26.94
1

56
Bifenthrin
28.57
27.56
29.69
−1.12

57
Mirex
28.72
28.48
28.33
0.39

58
Benodanil
29.14
22.77
27.08
2.06

59
Nuarimol
28.90
26.9
26.93
1.97

60
Oxadexyl
29.59
23.49
25.44
4.06

61
Tetramethirn
29.59
28.2
28.88
0.71

62
Phosmet
30.46
29.34
29.94
0.52

63
Oxycarboxin
31.00
25.76
28.63
2.37

64
cis-Permethrin
31.42
31.5
32.3
−0.9

65
trans-Permethrin
31.68
32.74
32.6
−0.92

66
Pyrazophos
31.60
32.87
29.97
1.63

67
Cypermethrin
33.19
33.64
33.73
−0.54

68
Fenvalerate
34.45
35.59
34.07
0.38

69
Deltamethrin
35.77
32.69
33.93
1.84

70
EPTC
8.54
9.64
11.15
−2.61

71
Butylate
9.49
10.01
10.12
−0.63

72
Dichlobenil
9.75
11.27
11.75
−2

73
Pebulate
10.18
11.32
11.54
−1.36

74
Nitrapyrin
10.89
10.95
12.12
−1.23

75
Chloroneb
11.85
15.37
13.01
−1.16

76
Tecnazene
13.54
15.8
16.61
−3.07

77
Heptenophos
13.78
18.84
15.01
−1.23

78
Hexachlorobenzene
14.69
16.1
18.31
−3.62

79
Ethoprophos
14.40
13.64
12.5
1.9

80
cis-Diallate
14.75
14.67
15.7
−0.95

81
Propachlor
14.73
16.12
13.97
0.76

82
Trifluralin
15.23
19.7
14.95
0.28

83
Chlorpropham
15.49
16.41
16.17
−0.68

84
Sulfallate
15.75
12.79
12.78
2.97

85
Alpha-HCH
16.06
15.99
18.04
−1.98

86
Terbufos
16.83
16.38
17.13
−0.3

87
4,4-DDE
23.92
24.19
24.68
−0.76

88
Chlorbufam
17.85
18.15
18.66
−0.81

89
Fluotrimazole
28.39
26.67
28.64
−0.25

90
Terbuthylazine
18.07
18.42
19.7
−1.63

91
Monolinuron
18.15
19.36
20.27
−2.12

92
Cyanophos
18.73
20.34
19.53
−0.8

93
Chlorpyrifos-methyl
19.38
23.86
21.45
−2.07

94
Desmetryn
19.64
17.4
18.43
1.21

95
Alachlor
20.03
19.24
19.27
0.76

96
Terbutryn
20.61
20.03
19.37
1.24

97
Thiobencarb
20.63
20.77
19.73
0.9

98
Dicofol
21.33
23.76
23.45
−2.12

99
Metolachlor
21.34
21.05
21.68
−0.34

100
Methoprene
21.71
23.08
22.07
−0.36

101
Bromofos
21.75
23.72
22.31
−0.56

102
Ethofumesate
21.84
24.27
23.8
−1.96

103
Isopropalin
22.10
26.6
24.73
−2.63

104
Propanil
22.68
16.94
19.79
2.89

105
Crufomate
22.93
21.31
19.59
3.34

106
Chlorfenvinphos
23.19
23.53
23.32
−0.13

107
cis-Chlordane
23.55
23.4
21.71
1.84

108
Tolylfluanide
23.45
22.76
23.52
−0.07

109
Butachlor
23.82
22.55
24.27
−0.45

110
Chlozolinate
23.83
25.54
23.06
0.77

111
Crotoxyphos
23.94
24.26
24.8
−0.86

112
Iodofenphos
24.33
23.85
21.87
2.46

113
Chlorbromuron
24.37
21.89
20.71
3.66

114
Profenofos
24.65
22.73
23.34
1.31

115
Buprofezin
24.87
24.53
23.88
0.99

116
2,4′-DDD
24.94
23.16
23.56
1.38

117
Endrin
25.15
24.92
25.1
0.05

118
Hexaconazole
24.92
26.84
26.97
−2.05

119
2,4-DDT
25.56
22.31
23.68
1.88

120
Methoprotryne
25.63
26.87
25.6
0.03

121
Erbon
25.68
25.93
26.83
−1.15

122
Chloropropylate
25.85
25.43
25.32
0.53

123
Nitrofen
26.12
23.08
28.85
−2.73

124
Oxyfluorfen
26.13
25.59
29.37
−3.24

125
Chlorthiophos
26.52
26.26
25.96
0.56

126
Endosulfan I
26.72
23.96
26.39
0.33

127
4,4-DDT
27.22
22.9
25.15
2.07

128
Carbofenothion
27.19
27
26.87
0.32

129
Benalyxyl
27.54
27.7
27.98
−0.44

130
Edifenphos
27.94
25.87
25.96
1.98

131
Triazophos
28.23
27.84
26.84
1.39

132
Chlorbenside sulfone
28.88
26.15
27.85
1.03

133
Endosulfan-sulfate
29.05
28.31
28.93
0.12

134
Bromopropylate
29.30
25.93
27.74
1.56

135
Benzoylprop-ethyl
29.40
28.81
28.84
0.56

136
Fenpropathrin
29.56
28.79
29.79
−0.23

137
Phosalone
31.22
31.11
31.56
−0.34

138
Azinphos-methyl
31.41
31.36
30.37
1.04

139
Fenarimol
31.65
29.56
28.81
2.84

140
Azinphos-ethyl
32.01
32.91
31.79
0.22

141
Prochloraz
33.07
33.03
31.52
1.55

142
Coumaphos
33.22
28.77
30.86
2.36

143
Cyfluthrin
32.94
34.18
33.61
−0.67

144
Dichlorvos
7.80
9.72
7.98
−0.18

145
Biphenyl
9.00
16.62
11.96
−2.96

146
Vernolate
9.82
11.57
10.9
−1.08

147
3,5-Dichloroaniline
11.20
10.42
10.55
0.65

148
Molinate
11.92
14.07
13.92
−2

149
Methacrifos
11.86
15.7
13.49
−1.63

150
2-Phenylphenol
12.47
16.32
12.14
0.33

151
cis-1,2,3,6-tetrahydrophthalimide
13.39
12.35
11.96
1.43

152
Fenobucarb
14.60
14.42
13.7
0.9

153
Prometon
16.66
18.42
16.82
−0.16

154
Triallate
17.12
15.55
13.9
3.22

155
Pyrimethanil
17.28
18.45
18.46
−1.18

156
Gamma-HCH
17.48
16.12
18.09
−0.61

157
Disulfoton
17.61
16.97
16.66
0.95

158
Heptachlor
18.49
21.61
19.83
−1.34

159
Isazofos
18.54
22.62
20.07
−1.53

160
Fenpropimorph
19.22
25
21.3
−2.08

161
Transfluthrin
19.04
23.96
22.15
−3.11

162
Tolclofos-methyl
19.69
21.11
18.51
1.18

163
Metobromuron
20.07
19.92
20.38
−0.31

164
HCH, epsilon-
20.78
16.05
17.54
3.24

165
Dipropetryn
20.82
20.32
21.8
−0.98

166
Formothion
21.42
17.67
18.96
2.46

167
Diethofencarb
21.43
20.66
19.42
2.01

168
Dimepiperate
22.28
21.74
21.92
0.36

169
Bioallethrin-1
22.29
22.9
21.57
0.72

170
2,4-DDE
22.64
22.46
24.45
−1.81

171
Fenson
22.54
23.65
21.87
0.67

172
Chlorthion
22.86
24.7
22.65
0.21

173
Prallethrin
23.11
23.96
22.59
0.52

174
Mecarbam
23.46
22
25.24
−1.78

175
Flumetralin
24.10
27.85
24.44
−0.34

176
Triadimenol
24.22
25.2
24.81
−0.59

177
Pretilachlor
24.67
22.65
24.59
0.08

178
Uniconazole
26.15
25.53
26
0.15

179
Flusilazole
26.19
29.21
27.8
−1.61

180
Fluorodifen
26.59
25.19
25.07
1.52

181
Diniconazole
27.03
26.95
27.24
−0.21

182
Piperonyl butoxide
27.46
28.93
26.8
0.66

183
Mepronil
27.91
25.31
27.43
0.48

184
Fenazaquin
28.97
29.38
30.6
−1.63

185
Fenoxycarb
29.57
29.01
30.52
−0.95

186
Sethoxydim
29.63
24.8
27.28
2.35

187
Anilofos
30.68
29.82
28.59
2.09

188
Permethrin
31.57
30.27
32.38
−0.81

189
Pyridaben
31.86
31.57
30.56
1.3

190
Fluoroglycofen-ethyl
32.01
34.85
33.64
−1.63

191
Bitertanol
32.25
32.05
30.82
1.43

192
Etofenprox
32.75
32.57
32.87
−0.12

193
Cycloxydim
33.05
28.55
30.36
2.69

194
Alpha-cypermethrin
33.35
28.9
31.06
2.29

195
Esfenvalerate
34.65
36.73
34
0.65

196
Difenconazole
35.40
36.14
33.03
2.37

197
Flumioxazin
35.50
32.67
32.45
3.05

198
Dimefox
5.62
−0.46
7.13
−1.51

199
Tri-iso-butyl phosphate
11.65
10.47
12.32
−0.67

200
Crimidine
13.13
12.37
12.09
1.04

201
Chlorfenprop-methyl
13.57
17.75
13.6
−0.03

202
2,3,5,6-Tetrachloroaniline
14.22
13.5
12.71
1.51

203
Tri-n-butyl phosphate
14.33
16.15
14.42
−0.09

204
2,3,4,5-Tetrachloroanisole
14.66
15.91
14.51
0.15

205
Tebutam
15.30
16.51
17.61
−2.31

206
Dioxabenzofos
16.14
17.74
15.19
0.95

207
Simetone
16.69
17.79
16.84
−0.15

208
Atratone
16.70
18.87
18.68
−1.98

209
Bromocylen
17.43
18.53
17.24
0.19

210
Cycluron
17.95
17.22
19.68
−1.73

211
Musk ambrette
18.62
18.86
17.77
−1.15

212
Musk xylene
18.66
20.74
18.81
−0.15

213
Pentachloroaniline
18.91
15.38
16.2
2.71

214
Aziprotryne
19.11
21.05
19.07
0.04

215
Sebutylazine
19.26
19.07
19.03
0.23

216
Isocarbamid
19.24
16.72
17.15
2.09

217
Musk moskene
19.46
21.79
21.16
−1.7

218
Dimethenamid
19.55
20.54
20.39
−0.84

219
Fenchlorphos oxon
19.72
21.04
18.44
1.28

220
BDMC-2
19.74
15.49
19.3
0.44

221
Paraoxon-methyl
19.83
20.87
20.76
−0.93

222
Monalide
20.02
19.22
21.33
−1.31

223
Isobenzan
20.55
22.18
21.66
−1.11

224
Pyrimitate
20.59
22.79
18.97
1.62

225
Isodrin
21.01
21.88
22.55
−1.54

226
Isomethiozin
21.06
22.14
19.89
1.17

227
Dacthal
21.25
22.38
21.38
−0.13

228
4,4-Dichlorobenzophenone
21.29
21.45
19.45
1.84

229
Nitrothal-isopropyl
21.69
21.85
21.6
0.09

230
Rabenzazole
21.73
21.17
24.36
−2.63

231
Fuberidazole
22.10
20.67
21.26
0.84

232
Isofenphos oxon
22.04
23.32
21.78
0.26

233
Dicapthon
22.44
22.74
22.2
0.24

234
Isocarbophos
22.87
24.01
22.75
0.12

235
Phorate sulfone
23.15
19.74
25.53
−2.38

236
Chlorfenethol
23.29
21.98
22.52
0.77

237
trans-Nonachlor
23.62
25.1
24.51
−0.89

238
Dinobuton
23.88
22.71
22.01
1.87

239
DEF
24.08
21.24
23.61
0.47

240
Flurochloridone
24.31
18.15
21.83
2.48

241
Bromfenvinfos
24.62
25.03
24.94
−0.32

242
Ditalimfos
24.82
25.28
25.31
−0.49

243
4,4-Dibromobenzophenone
25.30
23.06
24.62
0.68

244
Disulfoton sulfone
26.16
20.66
24.24
1.92

245
Cyproconazole
27.23
26.87
26.41
0.82

246
Phthalic acid, benzyl butyl ester
27.56
27.11
26.22
1.34

247
Clodinafop-propargyl
27.74
31.2
29.61
−1.87

248
Fenthion sulfone
28.55
27.46
29.4
−0.85

249
Metamitron
28.63
21.78
27.39
1.24

250
Tebufenpyrad
29.06
30.01
29.27
−0.21

251
Cloquintocet-mexyl
29.32
29.62
29.85
−0.53

252
Lenacil
29.70
21.63
28.28
1.42

253
Bromuconazole
29.90
28.32
29.66
0.24

254
Fenamiphos sulfone
31.34
29.08
30.17
1.17

255
Fluquinconazole
32.62
32.22
30.22
2.4

256
Fenbuconazole
34.02
33.6
31.53
2.49

Test set

257
Tetradifon
30.70
29.14
30.26
0.44

258
Fluorochloridone
25.14
19.72
24.4
0.74

259
Cyanofenphos
28.43
27.78
29.74
−1.31

260
EPN
30.06
30.42
31.71
−1.65

261
Benfluralin
15.23
16.21
14.16
1.07

262
Atrizine
17.64
16.45
19.4
−1.76

263
Simetryn
20.18
18.85
19.71
0.47

264
Metribuzin
20.33
17.61
17.78
2.55

265
Bioallethrin-2
22.34
23.58
21.89
0.45

266
Kresoxim-methyl
25.04
27.47
27.78
−2.57

267
Propargite
27.87
26.85
30.06
−2.19

268
Amitraz
30.29
28.83
29.76
0.53

269
Trietazine
17.53
18.78
19.76
−2.23

270
Prosulfocarb
19.51
21.08
21.51
−2

271
Octachlorostyrene
20.60
20.81
23.38
−2.78

272
Methfuroxam
22.45
21.15
24.43
−1.98

273
Flutriafol
25.31
26.82
27.15
−1.84

274
Diclobutrazole
25.95
27.22
27.61
−1.66

275
Triphenyl phosphate
28.65
26.88
30.41
−1.76

276
Desbrom-leptophos
30.15
27.97
28.32
1.83

277
Propisochlor
19.89
16.96
19.98
−0.09

278
Ametryn
20.11
18.98
19.4
0.71

Valid set

279
Quintozene
16.75
18.1
18.4
−1.65

280
Prometryne
20.13
19.82
19.71
0.42

281
Chlorbenside
22.96
23.73
25.4
−2.44

282
Oxadiazone
25.06
26.67
26.72
−1.66

283
Tetrasul
25.85
25.48
26.27
−0.42

284
Etaconazole-1
26.81
28.41
27.98
−1.17

285
Pyridaphenthion
30.17
29.14
29.64
0.53

286
trans-Diallate
15.29
14.82
15.52
−0.23

287
Propazine
17.67
18.15
19.5
−1.83

288
Pirimiphos-methyl
20.30
23.66
22.81
−2.51

289
Dichlofluanid
21.68
22.03
22.53
−0.85

290
Profluralin
17.36
22.64
17.16
0.2

291
Tetrachlorvinphos
24.36
22.75
24.14
0.22

292
Chlorfenson
25.05
26.07
23.34
1.71

293
2,4-DDD
24.94
26.13
27.65
−1.75

294
Leptophos
30.19
30.5
29.65
0.54

295
Nitralin
30.92
29.65
29.37
1.55

296
Fenamiphos sulfoxide
31.03
26.93
30.38
0.65

297
Dicloran
17.89
26.94
17.14
0.75

298
Perthane
24.81
15.1
26.01
−1.2

299
Cyprodinil
21.94
22.19
22.63
−0.69

300
Mefenacet
31.29
29.75
30.27
1.02

2.2. Molecular Descriptors

All structures were generated with the HyperChem (Version 7) [21] and optimized with the classic potential MM⁺ included. Molecular geometry was optimized with the Austin Model 1 (AM1) method [22], and then the molecular descriptors were calculated by the software Dragon 3.0 [23]. Overall more than 1400 theoretical descriptors were calculated for each molecule by this software. These descriptors can be classified into several groups: 0D: constitutional descriptors; 1D: functional groups, atom-centered fragments, empirical descriptors and molecular properties; 2D: topological descriptors, molecular walk counts, BCUTs descriptors, Galvez topological charge indices, and 2D autocorrelations; 3D: aromaticity indices, Randic molecular profiles from the geometry matrix, geometrical, RDF, 3D-MORSE, WHIMs, and GETAWAYs descriptors. Molecular descriptor meanings and their calculation procedure are explained in Handbook of molecular descriptors by Todeschini. These molecular descriptors of different kinds were used to describe compound chemical diversity.

2.3. Regression Analysis

The main goal of the generation of the MLR model was to choose a set of suitable descriptors that can be used as inputs for construction of the ANN model. Linear models were formed by a stepwise selection of important descriptors and MLR model construction [24]. The best MLR model is one that has high correlation coefficient and -value, low standard error, and high prediction power. The statistics of the constructed MLR model is presented in Table 2.

Table 2: Specifications of the selected multiple linear regression model.

Descriptor
Notation
Coefficient
Mean effect

Number of nitrogen atoms

0.980 (±0.123)
0.091

Solvation connectivity index-Chi1
χ1sol
2.191 (±0.125)
1.467

Balaban index
index
−4.639 (±0.401)
−0.421

Moran autocorrelation-lag 2/weighted by atomic sanderson electronegativity
MATS2e
−7.386 (±1.022)
−0.104

Total absolute charge (electronic charge index—ECI)
Qtot
0.499 (±0.084)
0.211

Radial Distribution-6.0/unweighted
RDF060u
−0.156 (±0.025)
−0.140

Constant

7.045 (±1.346)

, , , .

2.4. Artificial Neural Network

A detailed description of theory behind artificial neural networks has been adequately described in several publications [25–31]. An ANN program was written in MATLAB 7 in our laboratory. This network was feed-forward fully connected that has three layers with sigmoidal transfer function. Descriptors appearing in the MLR models were used as inputs of network and signal of the output node represent the retention time of interested compound. Thus, this network has six nodes in input layer and one node in output layer. The value of each input was divided into its mean value to bring them into dynamic range of the sigmoid transfer function of the network. The back-propagation algorithm was used for the training of the network [32, 33]. Before training the network, the parameters of the number of nodes in the hidden layer, weights and biases learning rates, and momentum values were optimized [34–36]. Optimized values of these parameters were numbers of nodes in the input layer (=6), numbers of nodes in the hidden layer (=7), numbers of nodes in the output layer (=1), weights learning rates (=0.1), bias learning rate (=0.4), momentum (=0.5), and transfer function was sigmoid. The ANN-calculated values of permeability coefficient for training, test, and prediction sets, are shown in Table 1.

3. Results and Discussion

3.1. Analysis by Multiple Linear Regressions

The best MLR model (Table 2) for the training set includes six descriptors. These descriptors are number of nitrogen, Solvation connectivity index-chi 1, Balaban index, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted.

The correlation between these descriptors is shown in Table 3. As shown in this table, there are no significant correlations between these descriptors.

Table 3: Correlation matrix for descriptors applying in this work.

χ1sol
index
MATS2e
Qtot
RDF060u

1

χ1sol
−0.162
1

index
−0.120
0.540
1

MATS2e
0.315
−0.240
0.065
1

Qtot
−0.082
0.427
0.034
−0.535
1

RDF060u
0.084
0.634
−0.363
0.162
0.433
1

The first descriptor with the larger mean effect is solvation connectivity index—chi 1 (χ1sol) that defined in order to model solvation entropy and describe dispersion interactions in solution. Taking into account the characteristic dimension of the molecules by atomic parameters, they are defined as

where is the principal quantum number (2 for C, N, O atoms, 3 for Si, S, Cl, …) of the th atom in the th subgraph and the corresponding vertex degree; is the total number of th order subgraphs; is the number of vertices in the subgraph. The normalization factor is defined in such a way that the indices and for compounds containing only second-row atoms coincide. The first-order solvation connectivity index is

where runs over all the bonds; and are the principal quantum numbers of the two vertices incident to the considered bond. This index coincides with the Randic connectivity index for the hydrocarbons; for all the atoms. These molecular descriptors are defined for an H-depleted molecular graph [37].

The positive sign for the mean effect of this descriptor reveals that molecules have higher numerical value of χ1sol, therefore, they have longer retention time. For example, HCH-epsilon (compound 164) with 6 chloride atoms in its structure has bigger χ1sol (7.75) than Allidochlor (compound 1) with one chloride atom in its structure (5.61) and also has longer retention time than Allidochlor (20.78 and 8.78 min, resp., Figure 1). Compounds that have more atoms in their structure have larger numerical value of χ1sol and so they have longer retention time, for example, compound 194 (Alpha-cypermethrin) has χ1sol value of 13.32 and retention time of 33.35 min but compound 4 (Chlormephos), which has less atoms and shorter structure than Alpha-cypermethrin, has χ1sol and retention time of 7.21 and 10.53 min, respectively (Figure 2). The second important descriptor is Balaban index which was calculated by the same formula as the Balaban distance connectivity index , but by using atomic information indices instead of vertex distance degrees [38]. The index is defined based on atomic information indices calculated for vertices of a H-depleted molecular graph as follows:

where runs over all of the different topological distances from the th vertex, is the number of distances from the th vertex equal to , and is the th atom eccentricity (i.e., the maximum topological distance from the considered atom) [38]. So, the Balaban index is calculated as

where is the number of bonds and is the cyclomatic number. As the number of cycles, and branching in molecular structures increased (so in (2) increased), the value of index decreased. For example, compounds 70 (EPTC), 80 (Cis-Diallate), and 97 (Thiobenarb) which have amine group in their structures with retention time of 8.54, 14.75, 20.63 min have index values of 2.27, 2.15, 1.02, respectively. This descriptor has negative sign for its mean effect. Therefore, as the numerical value of this descriptor increased the retention time of compounds decreased. For example in compounds 149 (Methacrifos), 27 (Methyl-parathion), 131 (Triazophos) which are organophosphorous compounds index values are 2.08, 1.18, and 0.81, and retention times are 11.86, 20.82, and 28.23 min, respectively.

Figure 1: Comparison of χ1sol values for HCH-epsilon and allidochlor.

Figure 2: Comparison of χ1sol values for α-cypermethrin and allidochlor.

The total absolute charge is the other descriptor which, also known as the electronic charge index (ECI), is the sum of absolute charge over all atoms in a molecule and is a measure of molecule polarity [37]. Therefore, compounds with polar bonds have more numerical value of Qtot and have longer retention on a polar stationary phase than others. For example, compound 288 (Pirimiphos-methyl) has more retention and polarity (20.30 min and 10.14, resp.) than Dichlobenil (compound 72) that has Qtot of 1.02 and retention time of 9.75 min (Figure 3), also Compounds 9 (Ethalfuralin), 21 (Dinitramine), and 175 (Flumetralin) that contain CF3 and two nitro groups in their structure have retention time of 15, 19.35, 24.10, and Qtot value of 6.66, 6.86, and 7.33, respectively. The other molecular descriptors with lower mean effects are number of nitrogen atoms, Moran autocorrelation-lag 2/weighted by atomic sanderson electronegativity and radial distribution-6.0/unweighted. As number of nitrogen atoms increase, in molecule’s structure due to increasing the interaction of molecules with polar stationary phase, the retention time of compound increases. For example, in etridiazol, atrazine-desethyl, and fluquinconazole (compounds 3, 11, and 255, resp.) which have 2, 5, and 6 nitrogen atoms in their structure, their retention time is 10.42, 16.76, and 32.62, respectively. A Moran coefficient is a general index of spatial autocorrelation that, if applied to a molecular graph, can be defined as

where is any atomic property (here Sanderson electronegativity), is its average value on the molecule, is the atom number, is a Kronoker delta ( if , zero, otherwise). is the sum of the Kronoker deltas, that is, the number of vertex pairs at distance equal to [39].

Figure 3: Comparison of Qtot values for pirimiphos-methyl and dichlobenil.

So the Moran autocorrelation-lag 2/weighted by atomic sanderson electronegativity can be a factor of electronegativity of moleculs. The last descriptor is radial distribution-6.0/unweighted. The 3D coordinates of the atoms of molecules can be transformed into a structure code that has a fixed number of descriptors irrespective of the size of a molecule. This task is performed by a structure coding technique referred to as radial distribution function code (RDF code) [40]. In general, there are some prerequisites for a structure code: independence from the number of atoms, that is, the size of a molecule, unambiguity regarding the three-dimensional arrangement of the atoms, and invariance against translation and rotation of the entire molecule.

Formally, the radial distribution function of an ensemble of atoms can be interpreted as the probability distribution to find an atom in a spherical volume of radius [41]. The equation represents the radial distribution function code as it is used in this investigation:

where is a scaling factor and is the number of atoms. By including characteristic atomic properties of the atoms and , the RDF codes can be used in different tasks to fit the requirements of the information to be represented. The exponential term contains the distance between the atoms and and the smoothing parameter that defines the probability distribution of the individual distances. was calculated at a number of discrete points with defined intervals.

The atomic properties and used in this equation enable the discrimination of the atoms of a molecule for almost any property that can be attributed to an atom. Such distribution function provides, besides information about interatomic distances in a whole molecule, the opportunity to gain access to other valuable information, for example, bond distance, ring types, planar and nonplanar systems, and atoms types. This fact is a most valuable consideration for a computer-assisted code elucidation. The radial distribution function in this form meets the entire requirement mentioned above, especially invariance against linear translations. As RDF060u has negative sign for its mean effect, as molecules became larger their RDF factor in 6 Å radius reduces. For example Tebufenpyrad (compound 250) which has larger structure than Pyrimitate (compound 224) has lower RDF factor (18.79) than Pyrimitate (29.76); so the retention time of Tebufenpyrad is higer (29.06 min) than Pyrimitate (20.59).

From the above discussion, it can be seen that all descriptors involved in the QSRR model has physical meaning, and these descriptors can account for the structural features that affect the retention time of under studied pesticides.

3.2. Comparison of Neural Network and MLR Models

A graphical comparison of ANN and MLR analysis is given in Figure 4, where the retention time values calculated by means of the respective models are plotted against the experimental values. The statistical parameters obtained by ANN and MLR models for these sets are shown in Table 4. The standard errors of training, test and validation sets for the MLR model are 2.402, 1.858, and 2.036, respectively, which would be compared with the values of 1.559, 1.517, and 1.249, respectively, for the ANN model. Comparison between these values and other statistical parameters in Table 4 reveals the superiority of the ANN model over MLR ones. Figure 5 shows the plot of the residuals against the experimental values of retention time, for the ANN model. Since the residuals are propagated on both sides of the zero line, there is no systematic error in developing of ANN model.

Table 4: Statistical parameters obtained using the ANN and MLR models.

Model

SEv

ANN
1.559
1.517
1.249
0.969
0.951
0.971
3928
188
334

MLR
2.402
1.858
2.036
0.925
0.924
0.922
1508
118
113

: training, : test, : valid.

(a)

(b)

Figure 4: Plot of the calculated by MLR method versus the experimental values for training, test, and validation sets (a) and the plot of retention time values predicted by 6–7–1 feed forward ANN ( (ANN)) against the experimental values ( (EXP)) (b).

Figure 5: Plot of the residuals versus the experimental values of for training, test and validation sets.

4. Conclussions

Few structure-activity relationships involving pesticides have been published. In this study, we use MLR and ANN to predict the retention time of 300 pesticides that were different in molecular structure. The results of this study demonstrate that QSRRs method using ANN techniques can generate a suitable model for prediction of gas chromatographic retention of pesticides.

Also the results obtained in this work indicate that the regression and ANN models exhibit reasonable prediction capabilities. Descriptors which appeared in the obtained QSRR models reveal that electronic interactions as well as steric parameters can be affected on the gas chromatographic retention time of pesticides.

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