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The Use of Linear Retention Indices in Pesticide Residue
Analysis using a Tandem GCMS with MRM Database
Hendrik J. Schulte, Rebecca Kelting, Hans-Ulrich Baier, Stéphane Moreau
Shimadzu Europa GmbH, Duisburg, Germany
1. Linear Retention Indices (LRIs)
Linear retention indices (LRIs) have been used in different analytical fields. With GCMS full
scan data they are applied as an additional filter for identification besides the similarity search
with library spectra. This is especially helpful when coelutions are observed or different target
compounds have similar electron impact spectra. The LRI of a target compound is calculated
from its retention time (RT) and the retention times and indices of the relevant n-alkanes:
𝑅𝑅𝑇 = 𝑅𝑅𝐶𝑛+1 − 𝑅𝑅𝐶𝑛
The prediction reliability for column cutting has been investigated systematically using a
pesticide mix with more than 50 compounds. The sample has been analyzed in two steps
starting from a 30 m column with a cut of 2 m leading to a 28 m column followed by the cut of
another 5 m resulting in a 23 m column. For both cuts, the retention times of the target
compounds have been predicted using an n-alkane standard measurement on the cut column
and the retention indices derived from the 30 m column. The results have been compared to
real measurements of the pesticide mix to show the differences between predicted and
measured retention times. Table 3 summarizes the values for some representative pesticides
showing the high precision of the retention time prediction.
𝑅𝑅𝑇 − 𝑅𝑅𝐶𝑛
×
𝑅𝑅𝐶𝑛+1 − 𝑅𝑅𝐶𝑛
2. Prediction of Pesticide Retention Times
28 m column
Pesticide
Linear retention indices can also be used for determining expected retention times for a
specific stationary phase after injection of an n-alkane mix. In this case, the retention times of
target compounds are calculated from the retention times of the relevant n-alkanes and known
retention indices of the targets and the n-alkanes:
𝑅𝑅𝑇 (𝑅𝑅𝐶𝑛+1 − 𝑅𝑅𝐶𝑛 )
𝑅𝑅𝑇 =
+ 𝑅𝑅𝐶𝑛
𝑅𝑅𝐶𝑛+1 − 𝑅𝑅𝐶𝑛
In pesticide analysis the most common phase of the capillary columns used is Rxi-5Sil or
similar. Therefore, indices for this phase were used for the Smart MRM Pesticide Database
which contains more than 450 compounds (Table 1). The advantage over retention time
registered databases is that unlimited target compound retention times are updated from one
injection of n-alkanes while the GC method including the column head pressure remains
unchanged. Using such a database therefore makes it possible to predict the pesticide
retention times eliminating the need for a scan of the target compounds.
23 m column
RT measured
RT predicted
ΔRT
RT measured
RT predicted
ΔRT
Dichlorvos
6.120
6.103
0.017
5.653
5.652
0.001
Butylate
7.927
7.923
0.004
7.384
7.394
-0.010
Isoprocarb
9.143
9.134
0.009
8.559
8.571
-0.012
Ethoprophos
10.174
10.162
0.012
9.560
9.577
-0.017
Bendiocarb
10.599
10.594
0.005
9.986
10.001
-0.015
alpha-HCH
10.968
10.952
0.016
10.320
10.353
-0.033
beta-HCH
11.526
11.513
0.013
10.871
10.906
-0.035
Terbufos
11.700
11.689
0.011
11.058
11.079
-0.021
Tefluthrin
12.113
12.117
-0.004
11.500
11.501
-0.001
delta-HCH
12.134
12.121
0.013
11.469
11.505
-0.036
Ethiofencarb
12.500
12.489
0.011
11.843
11.867
-0.024
Tolclofos-methyl
13.000
12.988
0.012
12.331
12.360
-0.029
Methiocarb
13.442
13.432
0.010
12.775
12.799
-0.024
Pirimiphos-methyl
13.453
13.452
0.001
12.801
12.818
-0.017
Malathion
13.628
13.627
0.001
12.975
12.992
-0.017
Cyfluthrin-1
20.363
20.356
0.007
19.177
19.200
-0.023
Cyfluthrin-2
20.446
20.443
0.003
19.632
19.653
-0.021
Cyfluthrin-3,4
20.562
20.555
0.007
19.717
19.739
-0.022
Halfenprox
20.718
20.708
0.012
19.831
19.850
-0.019
Silafluofen
21.095
21.086
0.009
19.975
19.999
-0.024
Fenvalerate-1
21.597
21.584
0.013
20.354
20.375
-0.021
Fenvalerate-2
21.802
21.788
0.014
20.839
20.867
-0.028
Difenoconazole-1
22.054
22.039
0.015
21.034
21.055
-0.021
Difenoconazole-2
22.126
22.111
0.015
21.249
21.276
-0.027
Imibenconazole
23.516
23.503
0.013
21.313
21.340
-0.027
Table 3: comparison of the predicted and measured retention times for the target pesticides in fast GC-MS/MS
3. Method Transfer
Linear retention indices mainly depend on the phase of the column and only to a minor extent
on the column dimensions. Therefore, method transfer from the standard GC-MS/MS method
to fast GC-MS/MS analysis was checked using the known linear retention indices. After
measuring the n-alkanes on the fast GC column, the retention times of all target compounds
could be adjusted simultaneously to the new setup using the new n-alkane file and the
pesticide retention indices given in the database.
Table 1: Selected target pesticides from Pesticide Smart MRM Database including names, retention indices and
MRM transitions
To show the prediction reliability, a pesticide mix has been analyzed on a Rtx-5 MS column in
MRM mode to compare predicted and real retention times. For a realistic szenario, not just a
simple standard has been used, but the comparison has been done with a pesticide mix in
onion matrix. As can be seen from table 2, the retention time predictions are precise enough to
detect the compounds in the processing window, even though the column phase is not exactly
the same as in the database and the pesticides show matrix dependent shifts. In a next step,
the retention times can be fine-adjusted using the software wizard.
To test the prediction reliability in this case, a standard of 365 pesticides has been analyzed on
a ZB 5 MS 20m, 0.18 mm, 0.18 µm column. In parallel, the target retention times have been
predicted using the Smart MRM Database. The accordance between predicted and measured
retention times can be seen in table 4 for a number of representative pesticides. Figure 2
exemplarily shows the detected peaks for some of these pesticides.
Pesticide
RT measured
RT predicted
Dichlorvos
3.099
3.111
Chlormephos
3.830
Methacrifos
Pesticide
RT measured
RT predicted
Dimethoate
4.903
4.892
3.827
beta-HCH
5.005
4.983
4.021
4.036
Tolclofos-methyl
5.550
5.520
2-Phenylphenol
4.145
4.127
Heptachlor
5.636
5.565
Tecnazene
4.413
4.384
Malathion
5.767
5.771
Propoxur
4.439
4.443
(E)-Chlorfenvinphos
6.080
6.072
Propachlor
4.454
4.443
Fipronil
6.113
6.109
Ethoprophos
4.548
4.544
Procymidone
6.257
6.224
Trifluralin
4.630
4.648
Cyfluthrin-1
8.504
8.492
Cadusafos
4.738
4.731
Cypermethrin-1
8.642
8.625
Table 4: comparison of the predicted and measured retention times for the target pesticides in fast GC-MS/MS
Figure 1: MRM of the pesticide mix in onion matrix using a Rtx-5 MS 30 m, 0.25 mm, 0.25 µm column
Pesticide
RT measured
RT predicted
Cadusafos
10.595
10.479
Dimethoate
11.033
beta-HCH
Pesticide
Heptachlor
Dichlorvos
RT measured
RT predicted
Phosmet
17.815
17.669
10.934
Cyfluthrin-1
20.163
20.002
11.317
11.187
Cyfluthrin-2
20.246
20.103
Tolclofos-methyl
12.821
12.620
Cyfluthrin-3
20.329
20.161
Heptachlor
12.900
12.739
Cyfluthrin-4
20.364
20.209
Malathion
13.465
13.282
Cypermethrin-1
20.470
20.324
(E)-Chlorfenvinphos
14.250
14.049
Cypermethrin-2
20.566
20.426
Fipronil
14.480
14.140
Cypermethrin-3
20.642
20.473
(Z)-Chlorfenvinphos
14.475
14.241
Cypermethrin-4
20.681
20.521
Procymidone
14.649
14.424
Fenvalerate-2 (Esfenvalerate)
21.377
21.395
Folpet
14.601
14.506
Deltamethrin-1
22.129
21.759
(x100,000)
3.0
(x1,000,000)
185.00>93.00
185.00>109.00
Cyfluthrin
(x100,000)
264.90>249.90
264.90>93.00
3.0
Cypermethrin
(x100,000)
226.10>206.10
226.10>199.10
4.0
3.0
2.0
3.0
2.0
2.0
181.10>152.10
181.10>127.10
2.0
1.0
1.0
1.0
3.0
3.5
5.5
6.0
1.0
8.5
9.0
8.5
9.0
Figure 2: 4 exemplary pesticide peaks measured with the fast GC-MS/MS setup
Table 2: comparison of the predicted and measured retention times for the target pesticides in onion matrix
4. Conclusion
Besides the comfort of predicting target retention times for a given method, registered linear
retention indices are also beneficial for column maintenance purposes. If a cut of the column
is needed due to high contamination, the retention times of all target compounds can be easily
adjusted to the new length without changing method parameters or running the target
standard again by simply reanalyzing the n-alkanes on the cut column.
The use of linear retention indices enables the prediction of retention times for standard
columns, after column cutting or even during method transfer to columns with different
stationary phases or dimensions since the changes in the real retention times compared to the
predicted ones are only minor. A fast and easy simultaneous adjustment of the retention times
of large numbers of target components without changing method parameters is possible.

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