2. 284 K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295
variety of fruits and vegetables by GC using single quadrupole [3],
ion trap [4], and triple quadrupole mass analyzers [5,6].
In general, low-energy collision induced dissociation tandem
mass spectrometry analysis (CID-MS/MS) using the multiple reac-
tion monitoring (MRM) scan mode is used for the identification
and quantification of a target list of compound residues. The appli-
cation, scope and success of such methods essentially require the
availability of certified reference standards. To obtain a compre-
hensive knowledge on the food safety status of any sample with
unknown history of contamination, a full scan analysis based on
elemental composition and accurate mass (as offered by time-of-
flight mass spectrometry) could be required. However, high costs
and the complexity of data processing related to application of high
resolution GC–MS limits its usage in routine residue analysis. Multi-
ple benefits could be accrued from a high throughput multi-residue
method targeting a large number of analytes by a single GC–EI-
MS/MS run covering all probable compounds that could appear in
fruits and vegetables from direct as well as indirect sources. Data
acquisition methods comprising a large number of MRM transi-
tions as described in this paper can be applied for the detection
and quantification of a target list of analytes for which the reference
standards are available. In addition, it can also offer the benefits of
qualitative analysis and semi-quantification of those compounds
for which reference standards are not available, on the basis of
their compound-specific quantitative and qualitative MRM tran-
sitions, their abundance ratio and application of the calibration of
compounds with similar GC–MS/MS responses.
To evaluate the practical applicability of the above discussion
over a range of compounds, a fast and sensitive method based on
ethyl acetate extraction and estimation by GC–EI-MS/MS was vali-
dated for analysis of 375 compounds including pesticides, PAHs and
PCBs in fruits viz., grapes, pomegranate and vegetables viz., onion,
okra and tomato. The method was employed to generate a database
consisting of target compound name, quantifier and qualifier MRM
transitions, and the slopes of calibration curves from which rel-
ative ratios were calculated and applied for semi-quantification
of the detected residues. Our aim was to evaluate the efficiency
of the semi-quantitative approach with reasonable accuracy and
consistency.
2. Experimental
2.1. Chemicals
The solvents, viz. ethyl acetate and acetonitrile, were of residue
analysis grade and purchased from Thomas Baker (Mumbai, India).
Reagent-grade anhydrous sodium sulfate was purchased from
Merck (Mumbai, India). The QuEChERS extraction tubes containing
4 g magnesium sulfate and 1 g sodium chloride were procured from
Agilent Technologies (Bangalore, India). The bulk sorbents, PSA
(primary secondary amine) bonded silica (C18, 100 g) and graphi-
tized carbon black (GCB) were supplied by Agilent Technologies
(Bangalore, India).
The standards of all the test compounds (Table 1) were
obtained from Dr. Ehrenstorfer GmbH (Augsburg, Germany) and
Sigma–Aldrich (Saint Louis, USA).
2.2. Apparatus
The analysis of samples was performed using an Agilent
GC (7890A) equipped with a CTC Combipal (CTC Analytics,
Switzerland) autosampler, connected to a triple quadrupole mass
spectrometer (7000B, Agilent Technologies, Santa Clara, USA).
The system was controlled using MassHunter software (ver
B.05.00.412). The analytical separation was performed using two
HP-5MS (15 m × 0.25 mm, 0.25 m) capillary columns with mid-
point backflush set up. During backflush the inlet pressure was
maintained at 2 psi whereas the backflush pressure was 35.322 psi
and backflush flow to the inlet was 3.6 mL/min for which additional
helium flow was supplied through a purged ultimate union. The
backflush was carried out for 2.5 min after the completion of the
analytical run. The column oven temperature during this period
was maintained at 300 ◦C. A gooseneck liner (78.5 mm × 6.5 mm,
4 mm) from Agilent Technologies (Santa Clara, USA) was used with
helium as carrier gas set at constant flow rate of 1.2 mL/min. The
oven temperature program was set as follows: initial temperature
of 70 ◦C (1 min hold), ramped to 150 ◦C at 25 ◦C/min (0 min hold),
then at 3 ◦C/min up to 200 ◦C (hold 0 min) and finally to 285 ◦C at
8 ◦C/min (8 min hold) resulting in a total run time of 39.49 min. The
transfer line temperature was maintained at 285 ◦C.
The multi-mode inlet (MMI) was operated in solvent vent mode
for large volume injection and 5 L of sample was injected. The
programmable temperature vaporizer (PTV) was set at the initial
temperature of 70 ◦C (0.07 min hold), raised to 87 ◦C at 50 ◦C/min
(0.1 min hold) followed by rapid heating at 700 ◦C/min up to 280 ◦C
(3 min hold). The purge flow to solvent vent was set at 50 mL/min,
2.7 min after injection and vent flow was maintained at 50 mL/min
until 0.17 min.
The mass spectrometer was operated in MRM mode with acqui-
sition starting from 4.4 min. The electron impact ionization (EI+)
was achieved at 70 eV and the ion source temperature was set at
280 ◦C. The specific MRM transitions for all the test compounds and
other parameters are given in Table 1.
2.3. Standard preparation and calibration
Stock standard solutions of each compound were prepared by
weighing 10 ± 0.1 mg and dissolution in 10 mL ethyl acetate and
stored in amber colored glass vials at −20 ◦C. A total of seven inter-
mediate mixtures (containing 50–60 compounds each) of 10 mg/L
concentration were prepared by diluting adequate quantity of
each compound in ethyl acetate. A working standard solution
(1 mg/L) was prepared by mixing adequate quantity of interme-
diate standard solution and dilution with ethyl acetate and stored
at −20 ◦C. The calibration standards at 2.5, 5, 10, 20, 40, 80 and
160 g/L were freshly prepared for construction of the calibration
curves.
The calibration graphs (seven points in triplicates) for all the
compounds were obtained by plotting the individual peak areas
against the concentration of the corresponding calibration stan-
dards. Matrix-matched standards at the same concentrations were
simultaneously prepared using pre-tested, residue free, organically
grown matrix of grape, pomegranate, okra, tomato and onion. To
evaluate the matrix influence in terms of suppression or enhance-
ment of analyte signals, the slopes of the matrix calibration graph
for each analyte was divided by its corresponding solvent standard
and the ratios were compared.
2.4. Sample preparation
The samples (2 kg each) of grape, onion, okra and tomato were
blended directly in a mixer-grinder while pomegranate samples
were blended after adding water (1:1, v/v) using the procedure
described in earlier publications [7]. From the crushed material,
10 ± 0.1 g of the sample (15 ± 0.1 g for crushed pomegranate) was
transferred to 50 mL centrifuge tubes and extracted with 10 mL
ethyl acetate in the presence of 10 g sodium sulfate, followed
by homogenization at 10,000 rpm for 2 min using high speed
homogenizer (Heidolph, Germany) and centrifugation (3000 rpm,
5 min). Dispersive solid phase extraction (DSPE) cleanup of the
supernatant (1 mL) was performed using 25 mg PSA and 7 mg GCB
8. 290 K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295
and centrifuged (10,000 rpm, 5 min) to obtain a clear supernatant
from which 5 L was injected into GC–EI-MS/MS. In the case of
onion, 800 L of the supernatant was evaporated to near dryness
under gentle flow of nitrogen (5 psi) and reconstituted up to 800 L
with ethyl acetate and 5 L was injected into GC–EI-MS/MS.
2.5. Validation data analysis and statistical calculations
The analytical method validation was carried out using SANCO
guidelines (SANCO/12495/2011) [8]. The sensitivity of the method
was evaluated in terms of limit of detection (LOD) and limit of
quantification (LOQ). LOD is the concentration at which the sig-
nal to noise ratio (S/N) for the quantifier ion is ≥3, whereas, LOQ is
the concentration at which S/N of the quantifier MRM is ≥10 and
qualifier MRM ≥3.
2.5.1. Precision and accuracy
The recovery experiment was carried out in replicates (n = 6)
in all the tested matrices at three different concentration levels of
0.005, 0.01 and 0.025 mg/kg. The samples were fortified with mix-
ture of all the compounds and extracted by the method described
above. The quantification was carried out using matrix matched
calibration standards. The precision in the conditions of repeatabil-
ity (three analysts prepared six samples each on a single day) and
intermediate precision (three analysts prepared six samples each
on six different days) were determined separately at the fortifica-
tion level of 0.01 mg/kg. Since Horwitz ratio (HorRat) [9,10] was
not applicable at this concentration the Thompson equation was
applied [9].
Precision RSDR (reproducibility) for 1 to 120 ng/g is expressed by
RSDR = 22.0 (for C ≤ 120 g/kg or c ≤ 120 × 10−9), and the maximum
permitted value of observed RSD for reproducibility is 2 × RSDR.
Precision RSDr (repeatability) for 1–120 ng/g is expressed as 0.66
RSDR = 0.66 × 22. The maximum permitted value of observed RSD
for repeatability is 2 × RSDr. These equations are generalized preci-
sion equations, which have been found to be independent of analyte
and matrix but solely dependent on concentration for most routine
methods of analysis.
The accuracy in terms of percent recovery was calculated by the
following equation:
Recovery (%) =
peak area of pre-extraction spike
peak area of postextraction spike
× 100
2.5.2. Assessment of uncertainty
The combined uncertainty was assessed as per the statistical
procedure described in EURACHEM/CITAC Guide CG 4 [11] in the
same way as reported earlier [12,13]. Uncertainty associated with
the calibration graph (U1), day-wise uncertainty associated with
precision (U2), analyst-wise uncertainty associated with precision
(U3), day-wise uncertainty associated with accuracy/bias (U4), and
analyst-wise uncertainty associated with accuracy/bias (U5) was
evaluated for all the test compounds. The combined uncertainty
(U) was calculated as
U = U2
1
+ U2
2
+ U2
3
+ U2
4
+ U2
5
and reported in relative measures as expanded uncertainty which
is twice the value of the combined uncertainty. Relative uncertainty
stands for the ratio of uncertainty value at a given concentration to
the concentration at which the uncertainty is calculated.
2.5.3. Data analysis
The validation carried out for 375 compounds in 5 different
matrixes resulted in a huge volume of data. An MS Excel macro
was developed and applied for analysis of data.
2.6. Semi-quantitative approach for determination of residues
The developed method was employed to generate a database
consisting of the compound name, MRM transitions, and the peak
areas of the quantifier ion of each compound. For development
of the database repetitive injections (n = 20) of solvent based and
matrix matched calibration standards were performed. The peak
areas obtained for each analyte from a specific set of transitions
were noted and the peak area ratios obtained along with the
respective standard deviations. The mean ratio from the set of
20 matrix matched standards was then applied for the quantifi-
cation of residues in recovery samples from the same and different
batches. The precision and accuracy in quantification of the residues
of any compound using the calibrations of other compounds vis-à-
vis its own calibration were evaluated. Initially, the dataset was
generated for around 95 compounds routinely monitored in Indian
grape samples. Based on the success of the conversion factors gen-
erated for 95 compounds, a database comprising of 375 analytes
was subsequently generated.
2.6.1. Approach for calculation of conversion factor for
semi-quantification
Assuming that the multiresidue mixture consists of the chem-
icals (1, 2, 3, . . ., n) having peak areas of P1, P2, P3, . . ., Pn, at
a particular concentration level, the ratios were calculated as:
P2−1 = P1/P2, P3−1 = P1/P3, P3−2 = P2/P3, for the (n(n − 1))/2 number
of combinations, where “n” is the total number of analytes. From
the replicate ratios (20 replicates) generated for each combination,
the average and the RSDs were calculated. For compound ‘1’ and
‘2’, at a concentration of ‘C’ with peak areas of P1 and P2,
P1 = m1C + A1 (1)
and
P2 = m2C + A2 (2)
where m1 and m2 are the slopes of each calibration curve with
intercepts A1 and A2. The ratio thus would be
P2−1 =
P1
P2
=
m1C + A1
m2C + A2
(3)
Assuming a real situation where the compound ‘2’ has peak area
of P 2 and the calibration for compound ‘2’ is unavailable, the actual
peak area from ‘2’ is converted to the equivalent peak area obtained
from the compound ‘1’ (say P 1) with the help of Eq. (3). Thus, P1
=
P2−1 × P2
. Applying this to Eq. (1), the equivalelnt concentration =
((P2−1 × P2
) − A1)/m1. For most practical situations, the intercept
(A1) the slope of the calibration curve (m1). Therefore, ((P2−1 ×
P2
) − A1)/m1
∼= (P2−1 × P2
)/m1. Also, Eq. (3) could be expressed as
P2−1 = P1/P2
∼= m1/m2. Thus, the equivalent concentration is approx-
imately equal to (P2−1 × P2
)/m1 = P2
/m2 = C2
, which is the actual
concentration. Thus the ratio of peak areas was used as the con-
version factor for semi-quantification (examples demonstrated in
Supplementary material S1).
2.7. Application of method for analysis of incurred samples
The reproducibility of the method was confirmed by analyzing
the incurred samples at two laboratories (National Research Cen-
tre for Grapes, Pune and Agilent Technologies, Bangalore). Around
10 incurred samples of each commodity were analyzed using the
validated method described above and quantified by both the
quantitative and semi-quantitative approach. The samples were
collected from the local markets and supermarkets in the city of
Pune and Bangalore.
9. K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295 291
3. Results and discussion
3.1. Optimization of instrumental conditions
Since a large numbers of analytes (375 numbers) were consid-
ered in this study, the chromatographic separation and the mass
spectrometric conditions played a vital role in determining the
selectivity and sensitivity of the analysis. Now-a-days most instru-
ment vendors supply a database of MRM transitions that could be
applied to analyze a large list of compounds. The new generation
quadrupole instruments are supported with fast data acquisition
rates or scan speeds. Moreover, because of the fast detector elec-
tronics it is possible to run the instrument at shorter dwell times
which helps in acquiring hundreds of compounds in a single chro-
matographic run, provided the instrument parameters are properly
optimized. With the current generation triple quadrupole mass
spectrometers, acquisition of a large number of MRM transitions
(≈10,000 for the instrument used) is possible. But, for a large mix-
ture of molecules, as data is acquired at dwell times typically of
<10 ms, the sensitivity of the analysis is adversely affected [14],
especially for compounds known to have lower response such
as synthetic pyrethroids (e.g. cyfluthrin, cypermethrin). Therefore,
chromatographic separation and the dwell time have to be simul-
taneously adjusted so that sufficient sensitivity is attained. In the
current endeavor, multiresidue analysis of 375 compounds by a sin-
gle method involved screening of at least 750 MRM transitions (one
for quantifier and one for qualifier). Accommodating such a large
number of MRM transitions requires segmenting of the chromato-
graphic run time into appropriate sections in such a manner that
the dwell times and number of data points (to attain proper peak
shapes, sensitivity and quantification) together facilitate achiev-
ing required selectivity, specificity and sensitivity. Besides, there
are other factors such as chromatographic separation and injection
conditions that need to be optimized to attain required selectivity
and sensitivity. Therefore, a thorough instrumental optimization
was necessary, as presented in Supplementary material S2.
3.2. Sample preparation
The ethyl acetate based sample preparation method reported
earlier [12] resulted in satisfactory recovery of the test compounds
from grapes, okra and tomato with minor modifications in cleanup
strategy. Since okra contains chlorophyll pigments in considerably
higher concentrations, cleanup with only PSA could not remove
color from the extracts. Upon injection of this dark green extract
(5 L), deposition of matrix on the GC liner was observed after few
(≈20) injections. This resulted in variable responses (RSDs > 20%)
as observed while doing repeatable injections of the same extract.
In addition, degradation of some compounds such as iprodione
and carbaryl was also observed when the GC liner got contami-
nated with the matrix components. The cleanup strategy was thus
optimized by recovery experiments and the matrix effects eval-
uated. Introduction of 7 mg of GCB along with PSA (25 mg) was
sufficient in attaining the required cleanup resulting in repeatable
responses. Comparison of RSDs from repeatable injections (n = 20)
of the extracts showed that RSDs in case of the extract treated
with GCB and PSA were lower than the extracts treated with PSA
only. An increase in the quantity of GCB above 7 mg/mL resulted
in lower recoveries for chlorothalonil which is also reported in
earlier studies [15,16]. Addition of 7 mg GCB also did not require
any additional step of recovering adsorbed pesticides by addition
of toluene as reported in literature [16]. Recoveries of most com-
pounds did not change significantly with increase in the amount of
GCB up to 15 mg. The overall recoveries of PCBs and PAHs were not
affected till 10 mg GCB. However, further addition of GCB reduced
the recoveries significantly to <67%.
In case of pomegranate and onion, the same method had limita-
tions as evidenced by the interfering matrix peaks that affected the
quantification of the target compounds. Modification in the cleanup
strategy was therefore essential. The ethyl acetate extract of onion
treated with PSA alone resulted in tR shifts up to 1–2 min for most
of the early eluting compounds and the chromatographic resolu-
tion between the closely eluting compounds was severely affected
(Fig. 1). The shift in tR could be explained by the overloading effects
that are strongly related to the sample capacity of stationary phases.
During PTV injection, time given for removal of the solvent or low
boiling matrix components through evaporation is short and it fails
to remove many of the co-extracted interfering matrix components
when an onion extract is injected. The screening application in such
cases also appears difficult due to change in retention times, sensi-
tivity, etc. Such shifts in tR could be avoided when the same ethyl
acetate extract obtained after cleanup of the onion extract with PSA
was evaporated under gentle stream of nitrogen (to vaporize off the
volatile matrix compounds), reconstituted in ethyl acetate and sub-
sequently injected into the GC–EI-MS/MS. In case of pomegranate,
the matrix induced signal suppressions were noted for most of the
compounds. Satisfactory results could be obtained by cleanup using
25 mg PSA and 25 mg C18 per mL of the extract as described earlier
[7,17].
3.3. Method validation
Linearity of the calibration curves of all the test compounds
in each of the five matrices could be established with r2 > 0.98.
Detection of false positives in the control sample extracts for
each matrix was <1% indicating the specificity and sensitivity of
the method. The method had sufficient sensitivity as indicated
by the MDLs in all the five tested matrices which were within
1–2 g/kg and below the prescribed EU-MRLs. However, due to
the fact that the method linearity is not adequate at these low
concentrations the practical LOQ was considered as the standard
concentration corresponding to the first calibration point. The LOQs
for most of the compounds were <5 g/kg whereas for few com-
pounds the LOQs ranged between 5 and 10 g/kg (Fig. 2A). In
most cases, the LOQ of individual compounds followed the order
grape < okra ≈ tomato < onion < pomegranate. Although LOQs were
somewhat higher in certain compound-matrix combinations such
as onion and pomegranate, in every case these were below the MRLs
for all the tested matrices. Examples of compounds having higher,
but still adequate LOQs are carbaryl, dicofol, fenvalerate, esfen-
valerate, and prallethrin. The evaporation step used in case of onion
reduced the matrix co-extractives. However, the same was not
applicable in case of pomegranate and evaporation of the sample
extracts had negligible effect on removal of matrix coextractives.
In case of pomegranate, the matrix induced signal suppressions
resulted in higher LOQs as compared to onion.
Negligible matrix effect was noted for most of the test analytes in
grape samples. The application of CID-MS/MS is also of high signif-
icance in this respect, since the sensitivity and selectivity achieved
are due to the possibility of monitoring compound specific set of
precursor and product ions, which could discriminate the target
compounds from matrix co-extractives. When using calibration
standards prepared in solvent, significant matrix enhancement was
noted for samples of pomegranate and onion, particularly for the
early eluting compounds, such as dichlorvos, fenobucarb, propoxur,
monocrotophos, etc. This results from the relatively higher con-
centration of co-extractives in these matrices which compete for
active sites in the flow path [18]. Moderate enhancement in signals
was observed for tomato and okra. However, in order to obtain
accurate quantifications, the matrix matched calibration standards
were preferred.
10. 292 K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295
A-Matrix matched
standard after drying
B-Matrix matched
standard before
drying
C-Solvent standard
Fig. 1. Partial separation of -HCH and lindane could be obtained after drying and reconstituting onion extracts (A) while chromatographic resolution between the closely
eluting compounds, -HCH and lindane was severely affected in onion extracts without drying (B) as compared to solvent standards (C).
The recovery for the test compounds at 5, 10 and 25 g/kg
was within 70–120% with the associated relative standard devi-
ations <20% in all the test matrices. Recoveries in grapes at
10 g/kg were >90% (Fig. 2B) for most of the compounds whereas
in okra, tomato and pomegranate the recovery values were com-
paratively lower than the observed values in grapes for most of
the compounds which could be attributed to the matrix induced
signal suppressions. Similar trend of relatively lower recoveries
(<90%) were observed for okra and tomato at 25 g/kg. In onion,
chlorothalonil disappeared rapidly and was not detectable in ethyl
acetate extracts. Chlorothalonil added to ethyl acetate extracts of
onion also disappeared due to reaction with matrix co-extractives
and conversion to more polar compounds [19]. For other test matri-
ces the recovery of chlorothalonil was >70% with RSDs below 20%.
Similarly, due to the interaction of carbosulfan with the matrix
components [20] carbosulfan disappeared in all the tested matri-
ces with recovery of <10%. Recoveries of polar organophosphorous
compounds viz. acephate, methamidophos, monocrotophos, etc.
were >75% at all the tested concentrations. The ratio of the RSD
for reproducibility to RSDR and RSD for repeatability to RSDr of
all the analytes calculated at 10 ng/mL level of fortification were
below 2, indicating satisfactory level of intra-laboratory precision
and accuracy.
The measurement uncertainty of the analytes was estimated at
their respective LOQs. Based on the expanded uncertainty values
the analytes could be broadly classified into three groups.
Group I: Expanded uncertainty up to 10%
Group II: Expanded uncertainty 10–20%
Group III: Expanded uncertainty 20–50%
Most analytes could therefore be estimated with ≤20% uncer-
tainties in all the commodities. Analytes belonging to Group
III were carbosulfan, cyfluthrin isomers, cypermethrin isomers,
dimethomorph, azoxystrobin, difenoconazole, and propanil while
those belonging to Group II were 4-bromo-2-chlorophenol
(metabolite of profenophos), alachlor, carbaril, carbofuran-3-OH,
chlorothalonil, demeton-S-methyl, dichlorvos, dicofol, difluben-
zuron, dimethoate, fenchlorphos-oxon, fluchloralin, malathion,
metribuzin, oxadiazon, oxycarboxin, phenothrin, phorate, pro-
cymidone, profenophos, pyremethanil. Examination of the indi-
vidual uncertainty components indicated that in Group II the
component U1 had maximum contribution towards the combined
uncertainty (>30% as opposed to <20% in Group I) which was the
result of poor peak shapes with considerable tailing. This resulted in
quantification losses during automated peak processing. However,
it could be resolved by manual integration of the peaks of these ana-
lytes. For analytes belonging to Group III, the contribution of U1 was
considerably higher (>50%) as compared to the other two groups.
The other components of uncertainty corresponding to precision
and accuracy were within 10–15% of the combined uncertainty.
When the individual matrices were compared, it was observed that
analytes in general had higher uncertainties in pomegranate matrix
followed by onion, okra, tomato and grape. This was in confor-
mity with the decreasing trend of matrix effects observed in these
samples.
The validation set for each of the 5 matrices consisted of
32 sample runs (7 solvent based calibration standards, 7 matrix
based calibration standards, 6 recovery samples for each of the
3 levels) with 375 compounds each resulting in a total of 60,000
data values. Analysis of validation data (for LOQ, matrix effects,
recovery and RSD/RSDr calculations) was therefore a time con-
suming and tedious job. An in house developed MS Excel based
macro was thus developed to process the data and found effec-
tive in processing such large amount of data. The excel table
exported from the quantitative file of the MassHunter software
contains compound-wise recovery data, S/N ratios, etc. Data anal-
ysis conventionally takes huge amount of time since that required
11. K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295 293
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
0 50 100 150 200 250 300 350
LOQ(µg/Kg)
Analyte Number
A) LOQ
Grape Okra Onion Pomegranate Tomato
70
75
80
85
90
95
100
105
110
115
120
0 50 100 150 200 250 300 350
AverageRecovery(%)
Analyte Number
B) Average Recovery (%)
Grape Okra Onion Pomegranate Tomato
Fig. 2. (A) LOQ of the test compounds in five tested matrices. Most of the compounds had LOQs < 5 ppb. In general, lower LOQs were observed for grape, okra and tomato.
Relatively higher LOQs were observed for onion and pomegranate. (B) The recoveries for the test compounds were within 70–120% for all the test matrices.
rearranging the data for all 375 compounds. A macro was devel-
oped specifically to rearrange the data for calculation of recoveries
at different fortification levels. The macro was initially devel-
oped for one compound only and repeated for the set of 375
compounds. Similarly macros were developed for calculation of
LOQs, and summarization of data for identification of the analytes
meeting the recovery criteria of 70–120%. The same macros were
then applied on the other four commodities. The compilation and
summarization of data for 375 compounds in five different com-
modities could be completed quickly using macros. It was observed
that the time required for processing of validation data of each
commodity could be accomplished within 2 h as opposed to 2
days.
3.4. Application of semi-quantification method
The data files obtained during the validation study and real sam-
ple analysis was divided into three sets:
(a) Set I: consisting of runs from the matrix matched standards
(validation set)
(b) Set II: consisting of runs from the recovery samples (test set)
(c) Set III: consisting of runs from the incurred samples (application
set)
The slope ratios from each of the matrix matched stan-
dards from the validation set were calculated against each other
12. 294 K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295
+ MRM (136.0 -> 94.0) PG2.D
AcquisiƟon Time (min)
6.5 7 7.5 8
3x10
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
7.664 min.
AcquisiƟon Time (min)
6.5 7 7.5 8
2x10
0
0.2
0.4
0.6
0.8
1
136.0 -> 94.0 , 142.0 -> 96.0
RaƟo = 22.6 (113.4 %)
+ MRM (185.0 -> 93.0) PP3.D
AcquisiƟon Time (min)
5 5.5
Counts
Counts
CountsCountsCounts
Counts
2x10
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5.557 min.
+ MRM (141.0 -> 95.0) PG2.D
AcquisiƟon Time (min)
5 5.5 6
3x10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
5.591 min.
+ MRM (124.9 -> 79.0) PG2.D
AcquisiƟon Time (min)
13 13.1 13.2 13.3
3x10
0
0.2
0.4
0.6
0.8
1 13.076 min.
AcquisiƟon Time (min)
13 13.1 13.2 13.3
2x10
0
0.2
0.4
0.6
0.8
1
124.9 -> 79.0 , 142.9 -> 110.7
RaƟo = 23.4 (99.3 %)
+ MRM (185.0 -> 93.0) PO4.D
AcquisiƟon Time (min)
5 5.5
3x10
0
0.5
1
1.5
2
2.5
5.546 min.
AcquisiƟon Time (min)
5 5.5
2x10
0
0.2
0.4
0.6
0.8
1
185.0 -> 93.0 , 185.0 -> 109.0
RaƟo = 30.4 (91.3 %)
+ MRM (164.0 -> 148.8) EABP4.D
AcquisiƟon Time (min)
13.3 13.4 13.5 13.6
2x10
0
1
2
3
4
5
6
7
13.367 min.
AcquisiƟon Time (min)
13.3 13.4 13.5 13.6
RelaƟveAbundance(%)
RelaƟveAbundance(%)RelaƟveAbundance(%)RelaƟveAbundance(%)
2x10
0
0.2
0.4
0.6
0.8
1
164.0 -> 148.8 , 164.0 -> 103.0
RaƟo = 80.9 (110.1 %)
A B
C D
E
Fig. 3. Incurred residues of methamidophos (A), acephate (B) and dimethoate (C) were found in grape, while residues of carbofuran (D) and dichlorvos (E) were detected in
pomegranate and onion.
(Supplementary information). As discussed in Supplementary
information, semi-quantification of an analyte by calibration stan-
dards with conversion factors (slope ratio) ≈ 1 lead to minimum
error (%) in quantification. A preliminary study indicated that
for analytes with similar response such as dichlorvos, ␦-HCH,
acephate, pyremethanil, triphenylphosphate and pentoxazone that
had conversion factors in the range of factors 0.8–1.2 resulted in
semi-quantification with <10% error in quantification. The errors
in quantification increased to ≈20% when the analytes with con-
version factors in the range of 1.2–1.8 or 0.6–0.8 were used, as
observed for etridazole and dichlorvos (example demonstrated in
Supplementary material).
The values of the slope ratios obtained from the validation
set were examined on the “test set”. As for example, considering
the absence of calibration curve of an analyte, e.g. trifloxys-
trobin, the calibrations from the other compounds with conversion
factor ≈1 was employed to quantify the residue content of tri-
floxystrobin. For a recovery sample fortified with trifloxystrobin
residues at0.025 mg/kg concentration, the average concentration
(n = 6) calculated from the calibration curves of dichlorvos, tri-
fluralin, carbofuran, ethion, propiconazole, and etofenprox were
0.025 (±4%), 0.022 (±3%), 0.024 (±4%), 0.024 (±3%), 0.025 (±3%)
and 0.031 (±3%) mg/kg, respectively. Quantification of the same
sample through the calibration curve of trifloxystrobin itself
resulted in concentration of 0.024 (±3%) mg/kg. Thus, the calibra-
tion equation of dichlorvos, carbofuran, ethion and propiconazole
could be well applicable for the quantification of trifloxystrobin
residues, each providing more than 96% accuracy in quantifica-
tion.
After examining the applicability of semi-quantification on the
“test set”, the real world samples comprising the “application set”
were quantified in a similar way and the results obtained were
within ±5% of the concentration derived from the respective cali-
bration curves with RSDs < 10%.
3.5. Application for analysis of incurred samples
The optimized method was applied for the analysis of incurred
samples (10 samples of each matrix) obtained from local mar-
kets of Pune and Bangalore. Incurred residues of methamidophos,
acephate and dimethoate (Fig. 3) were found in grape, while
residues of dichlorvos and carbofuran were detected in onion and
pomegranate, respectively. The other samples were free from any
residues of the test chemicals. However, in all cases the residue
concentrations were below the respective EU-MRLs. The incurred
residues of these identified chemicals were also quantified by the
semi-quantification approach and the concentrations estimated
were within ±15% of the values calculated through the calibra-
tion graph of methamidophos, acephate, dimethoate, dichlorvos
and carbofuran.
Grape samples in three different sets spiked at different con-
centrations with chlorpyriphos methyl, -cyhalothrin and -HCH
13. K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295 295
Table 2
Application of the semi-quantification approach on inter-laboratory test samples.
Name of compound Laboratory 1 Laboratory 2 Laboratory 3
Own standard Semi-quantification
approach
Own standard Semi-quantification
approach
Own standard Semi-quantification
approach
Chlorpyrifos-methyl 0.083 0.085 0.069 0.073 0.090 0.089
-HCH 0.084 0.087 0.037 0.035 0.096 0.102
-Cyhalothrin 0.102 0.112 0.088 0.090 0.039 0.037
were distributed among three commercial testing laboratories in
India and analyzed using the validated method. The quantifica-
tion of the positive findings was carried out with the calibration of
their own standards and also by the semi-quantification approach.
The results obtained with the two approaches are summarized
in Table 2. From the results it could be concluded that the
semi-quantification approach could be used for large scale tar-
get screening of pesticide residues in routine residue monitoring
programs.
4. Conclusions
The multiresidue method was successful for the analysis of 375
compounds in five different commodities with satisfactory preci-
sion and accuracy, demonstrating the suitability of the method for
analysis of contaminants from various fruits and vegetables both
for regulatory as well as routine residue monitoring purposes. In
addition to the relative simplicity of the extraction method, the
wide scope of the analytes as well as the matrices tested offer the
potential of its application as a readymade method. In addition, the
method has the potential of being employed for screening residues
beyond the target list and attaining a semi-quantified result. As a
result of the wide scope of the method, the acquired data could
further be used to mine the data for non-targeted compounds
within the scope of the MRM data base and thereby aide surveil-
lance studies. In the future, an inter-laboratory collaborative study
is proposed to examine reproducibility of the semi-quantification
approach and its application under different sets of GC–EI-MS/MS
conditions.
Acknowledgments
The authors acknowledge funding support from the ICAR
National Fellow project and the National Referral Laboratory
project of APEDA. Thanks are also due to Paul Zavitsanos, WW Busi-
ness Development Manager, Agilent Technologies, for support and
funding to carry out this project.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at http://dx.doi.org/10.1016/
j.chroma.2012.10.066.
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![Journal of Chromatography A, 1270 (2012) 283–295
Contents lists available at SciVerse ScienceDirect
Journal of Chromatography A
journal homepage: www.elsevier.com/locate/chroma
Multiresidue determination of 375 organic contaminants including pesticides,
polychlorinated biphenyls and polyaromatic hydrocarbons in fruits and
vegetables by gas chromatography–triple quadrupole mass spectrometry with
introduction of semi-quantification approach
Kaushik Banerjeea,∗,1
, Sagar Utturea,1
, Soma Dasguptaa,1
, Chandrasekar Kandaswamyb,1
,
Saswati Pradhana
, Sunil Kulkarnib
, Pandurang Adsulea
a
National Referral Laboratory, National Research Centre for Grapes, P.O. Manjri Farm, Pune 412307, India
b
Agilent Technologies, Bangalore 560048, India
a r t i c l e i n f o
Article history:
Received 19 May 2012
Received in revised form
26 September 2012
Accepted 31 October 2012
Available online 6 November 2012
Keywords:
Gas chromatography–triple quadrupole
mass spectrometry
Multiresidue analysis, semi-quantification
Method validation
Dioxin-like polychlorinated biphenyls
Polyaromatic hydrocarbons, pesticide
residues
a b s t r a c t
A residue analysis method for the simultaneous estimation of 349 pesticides, 11 PCBs and 15 PAHs
extracted from grape, pomegranate, okra, tomato and onion matrices, was established by using a gas
chromatograph coupled to an electron impact ionization triple quadrupole mass spectrometer (GC–EI-
MS/MS). The samples were extracted by ethyl acetate and cleaned by dispersive solid phase extraction
with PSA and/or GCB/C18 by the methods reported earlier. The GC–EI-MS/MS parameters were optimized
for analysis of all the 375 compounds within a 40 min run time with limit of quantification for most of the
compounds at <10 g/L, which is well below their respective European Union-Maximum Residue Levels.
The coefficient of determination (r2
) was >0.99 within the calibration linearity range of <5–250 ng/mL
for compounds with LOQs < 5 ng/mL. While for the compounds with LOQs within 5–10 g/kg, the low-
est calibration level was 5 and 10 g/kg as applicable. The recoveries at 10, 25 and 50 ng/mL were
within 70–110% (n = 6) with associated RSDs < 20% indicating satisfactory precision. The information
generated from the single laboratory validation was further utilized for building a semi-quantitative
approach. The accuracies in quantification obtained via individual calibration standards vis-à-vis semi-
quantification approach were comparable. For incurred samples, the concentrations estimated by the
semi-quantification approach were within ±10% of the values obtained by direct quantification. This
approach complements the existing GC–EI-MS/MS methods by offering targeted screening and quantifi-
cation capabilities.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
India is a habitat of plant genetic diversity. With its current
production of around 32 million MT, India accounts for about 8%
of the world’s total fruit production. India also has the credit of
being the second largest producer of vegetables in the world and
accounts for about 15% of the world’s total production. Considering
the high pest and disease pressure, the multitude of agrochemicals
used for plant protection in India is diverse. Currently, 230 plant
protection products (PPP) are registered for agricultural use [1] in
India with more than 820 compounds being in schedule for intro-
duction into Indian market in due course of time. Moreover, every
year the agrochemical industries keep introducing newer PPPs in
∗ Corresponding author. Tel.: +91 20 26956091; fax: +91 20 26956099.
E-mail address: kbgrape@yahoo.com (K. Banerjee).
1
The authors equally contributed in accomplishing this work.
the Indian market targeting management of various crop and pest
combinations. Although a limited number of pesticides might be
recommended for use in any specific crop, there are possibilities of
transmission of non-recommended pesticide residues from adjoin-
ing farms where other crops are cultivated with a different set
of recommended pesticides being sprayed on them. Additionally,
the residues of persistent organic pollutants like polychlorinated
biphenyls (PCB) and polyaromatic hydrocarbons (PAH) could find
their ways into the food chain through various sources, e.g. sur-
face deposition, etc. necessitating simultaneous monitoring of the
residues of pesticides, PCBs and PAHs in crops for holistic risk
assessment.
A preliminary assessment reveals that among the approxi-
mately 450 pesticides for which maximum residue limits (MRLs)
are currently set [2] in the European Union (EU) on various agri-
cultural commodities, more than 300 compounds are amenable
for analysis by GC–EI-MS/MS. Several studies have been reported
for targeted analysis of multiclass, multiresidue compounds in a
0021-9673/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.chroma.2012.10.066](https://image.slidesharecdn.com/f973be5c-9d54-49c4-b460-c5104c5fea6c-170103140537/85/375-pesticide-method-journal-of-chromatography-publication-1-320.jpg)
![284 K. Banerjee et al. / J. Chromatogr. A 1270 (2012) 283–295
variety of fruits and vegetables by GC using single quadrupole [3],
ion trap [4], and triple quadrupole mass analyzers [5,6].
In general, low-energy collision induced dissociation tandem
mass spectrometry analysis (CID-MS/MS) using the multiple reac-
tion monitoring (MRM) scan mode is used for the identification
and quantification of a target list of compound residues. The appli-
cation, scope and success of such methods essentially require the
availability of certified reference standards. To obtain a compre-
hensive knowledge on the food safety status of any sample with
unknown history of contamination, a full scan analysis based on
elemental composition and accurate mass (as offered by time-of-
flight mass spectrometry) could be required. However, high costs
and the complexity of data processing related to application of high
resolution GC–MS limits its usage in routine residue analysis. Multi-
ple benefits could be accrued from a high throughput multi-residue
method targeting a large number of analytes by a single GC–EI-
MS/MS run covering all probable compounds that could appear in
fruits and vegetables from direct as well as indirect sources. Data
acquisition methods comprising a large number of MRM transi-
tions as described in this paper can be applied for the detection
and quantification of a target list of analytes for which the reference
standards are available. In addition, it can also offer the benefits of
qualitative analysis and semi-quantification of those compounds
for which reference standards are not available, on the basis of
their compound-specific quantitative and qualitative MRM tran-
sitions, their abundance ratio and application of the calibration of
compounds with similar GC–MS/MS responses.
To evaluate the practical applicability of the above discussion
over a range of compounds, a fast and sensitive method based on
ethyl acetate extraction and estimation by GC–EI-MS/MS was vali-
dated for analysis of 375 compounds including pesticides, PAHs and
PCBs in fruits viz., grapes, pomegranate and vegetables viz., onion,
okra and tomato. The method was employed to generate a database
consisting of target compound name, quantifier and qualifier MRM
transitions, and the slopes of calibration curves from which rel-
ative ratios were calculated and applied for semi-quantification
of the detected residues. Our aim was to evaluate the efficiency
of the semi-quantitative approach with reasonable accuracy and
consistency.
2. Experimental
2.1. Chemicals
The solvents, viz. ethyl acetate and acetonitrile, were of residue
analysis grade and purchased from Thomas Baker (Mumbai, India).
Reagent-grade anhydrous sodium sulfate was purchased from
Merck (Mumbai, India). The QuEChERS extraction tubes containing
4 g magnesium sulfate and 1 g sodium chloride were procured from
Agilent Technologies (Bangalore, India). The bulk sorbents, PSA
(primary secondary amine) bonded silica (C18, 100 g) and graphi-
tized carbon black (GCB) were supplied by Agilent Technologies
(Bangalore, India).
The standards of all the test compounds (Table 1) were
obtained from Dr. Ehrenstorfer GmbH (Augsburg, Germany) and
Sigma–Aldrich (Saint Louis, USA).
2.2. Apparatus
The analysis of samples was performed using an Agilent
GC (7890A) equipped with a CTC Combipal (CTC Analytics,
Switzerland) autosampler, connected to a triple quadrupole mass
spectrometer (7000B, Agilent Technologies, Santa Clara, USA).
The system was controlled using MassHunter software (ver
B.05.00.412). The analytical separation was performed using two
HP-5MS (15 m × 0.25 mm, 0.25 m) capillary columns with mid-
point backflush set up. During backflush the inlet pressure was
maintained at 2 psi whereas the backflush pressure was 35.322 psi
and backflush flow to the inlet was 3.6 mL/min for which additional
helium flow was supplied through a purged ultimate union. The
backflush was carried out for 2.5 min after the completion of the
analytical run. The column oven temperature during this period
was maintained at 300 ◦C. A gooseneck liner (78.5 mm × 6.5 mm,
4 mm) from Agilent Technologies (Santa Clara, USA) was used with
helium as carrier gas set at constant flow rate of 1.2 mL/min. The
oven temperature program was set as follows: initial temperature
of 70 ◦C (1 min hold), ramped to 150 ◦C at 25 ◦C/min (0 min hold),
then at 3 ◦C/min up to 200 ◦C (hold 0 min) and finally to 285 ◦C at
8 ◦C/min (8 min hold) resulting in a total run time of 39.49 min. The
transfer line temperature was maintained at 285 ◦C.
The multi-mode inlet (MMI) was operated in solvent vent mode
for large volume injection and 5 L of sample was injected. The
programmable temperature vaporizer (PTV) was set at the initial
temperature of 70 ◦C (0.07 min hold), raised to 87 ◦C at 50 ◦C/min
(0.1 min hold) followed by rapid heating at 700 ◦C/min up to 280 ◦C
(3 min hold). The purge flow to solvent vent was set at 50 mL/min,
2.7 min after injection and vent flow was maintained at 50 mL/min
until 0.17 min.
The mass spectrometer was operated in MRM mode with acqui-
sition starting from 4.4 min. The electron impact ionization (EI+)
was achieved at 70 eV and the ion source temperature was set at
280 ◦C. The specific MRM transitions for all the test compounds and
other parameters are given in Table 1.
2.3. Standard preparation and calibration
Stock standard solutions of each compound were prepared by
weighing 10 ± 0.1 mg and dissolution in 10 mL ethyl acetate and
stored in amber colored glass vials at −20 ◦C. A total of seven inter-
mediate mixtures (containing 50–60 compounds each) of 10 mg/L
concentration were prepared by diluting adequate quantity of
each compound in ethyl acetate. A working standard solution
(1 mg/L) was prepared by mixing adequate quantity of interme-
diate standard solution and dilution with ethyl acetate and stored
at −20 ◦C. The calibration standards at 2.5, 5, 10, 20, 40, 80 and
160 g/L were freshly prepared for construction of the calibration
curves.
The calibration graphs (seven points in triplicates) for all the
compounds were obtained by plotting the individual peak areas
against the concentration of the corresponding calibration stan-
dards. Matrix-matched standards at the same concentrations were
simultaneously prepared using pre-tested, residue free, organically
grown matrix of grape, pomegranate, okra, tomato and onion. To
evaluate the matrix influence in terms of suppression or enhance-
ment of analyte signals, the slopes of the matrix calibration graph
for each analyte was divided by its corresponding solvent standard
and the ratios were compared.
2.4. Sample preparation
The samples (2 kg each) of grape, onion, okra and tomato were
blended directly in a mixer-grinder while pomegranate samples
were blended after adding water (1:1, v/v) using the procedure
described in earlier publications [7]. From the crushed material,
10 ± 0.1 g of the sample (15 ± 0.1 g for crushed pomegranate) was
transferred to 50 mL centrifuge tubes and extracted with 10 mL
ethyl acetate in the presence of 10 g sodium sulfate, followed
by homogenization at 10,000 rpm for 2 min using high speed
homogenizer (Heidolph, Germany) and centrifugation (3000 rpm,
5 min). Dispersive solid phase extraction (DSPE) cleanup of the
supernatant (1 mL) was performed using 25 mg PSA and 7 mg GCB](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)