Abstract The chemicals endosulfan-α, endosulfan-β, en-dosulfan sulfate, endosulfan ether, endosulfan lactone,PCB-21, PCB-48, PCB-61, and PCB-136 have been de-termined in 100 serum samples from women living inagricultural areas of Almería (Spain). The study includesa surgically treated breast cancer patient and a matchinggroup of women with no sign of estrogenic-dependentdisease. The determination was performed by gas chro-matography with electron capture detection (GC–ECD)and tandem mass spectrometry (GC–MS–MS). Recovery,precision, linear range, detection limit (LOD), and quan-tification limit (LOQ) were calculated for each pesticideas was the expanded uncertainty (U). The method wasevaluated for our laboratory and could be applied to sub-sequent results with relevant quality-control data. Com-parison of the results was performed. The advantage ofMS–MS over ECD for determination of endocrine-dis-rupting compounds in complex matrices is presented. Themost commonly encountered pesticide was endosulfan-α.Endosulfan-β and the PCB-48 congener were detected atconcentrations lower than their LOQ. Endosulfan sulfate,endosulfan ether, endosulfan lactone, PCB-21, PCB-61and PCB-136 were not found in any of the serum samplesanalysed.

Keywords Endocrine-disrupting pesticides · Polychlorinated biphenyls · GC-ECD · GC-MS-MS ·Organochloride compounds · Human serum · Uncertainty

Introduction

Organochlorine compounds (OC), e.g. endosulfan, andpolychlorinated biphenyls (PCB), extensively used as in-secticides and as fluid insulators of electrical components,respectively, are sequestered in fatty tissues and are knownto be persistent environmental contaminants and animalcarcinogens [1, 2]. In addition, endosulfan and relatedchemicals, e.g. PCB and polybrominated biphenyls (PBB)have been shown to be tumour promoters [3], to have en-docrine-disrupting potential [4, 5], and, possibly, to com-promise immune function [6].

The semi-volatile chemically stable nature of most ofthese compounds, combined with their resistance to bio-degradation and photolysis, has resulted in “global distil-lation” and redistribution via the atmosphere [7, 8]. Mea-surements of OC in precipitation and in the atmosphere inarctic regions support this hypothesis [9, 10] and there isa close correspondence between world-wide productionand deposition in remote areas [11]. Approximately halfof the world production of PCB from older electricalequipment and landfill sites has not yet escaped into theenvironment [12].

Commercial PCB products are mixtures of a variety ofPCB congeners and isomers with levels of chlorinationbetween 20% and 80%. Humans may be exposed to PCBby ingestion, inhalation, and dermal exposure, after whichthey are transported similarly through the circulation. De-pending on the extent and position of chloro-substitutionof the biphenyl molecule PCB isomers differ in bioaccu-mulation and, therefore, the PCB profile in biologicalspecimens does not necessarily fit the profile in commer-cial PCB.

OC residues are found throughout the body and at con-centrations proportional to the lipid content of a tissue.Exposure to environmental chemicals has been investi-gated by analysis of human adipose tissue [13], mother’smilk [14], and blood [15], and by monitoring urinary ex-cretory level [16] and environmental factors, especiallynutrition [17]. Blood is one of the most accessible media

J. L. Martínez Vidal · M. Moreno Frías ·A. Garrido Frenich · F. Olea-Serrano · N. Olea

Determination of endocrine-disrupting pesticides and polychlorinated biphenyls in human serum by GC–ECD and GC–MS–MS and evaluation of contributions to the uncertainty of the results

Anal Bioanal Chem (2002) 372 :766–775DOI 10.1007/s00216-002-1272-4

Received: 3 September 2001 / Revised: 25 February 2002 / Accepted: 26 February 2002 / Published online: 4 April 2002

ORIGINAL PAPER

J.L.M. Vidal (✉ ) · M.M. Frías · A.G. FrenichDepartment of Analytical Chemistry, University of Almería, 04071 Almería, Spaine-mail: jlmartin@ual.es

F. Olea-SerranoDepartment of Nutrition and Food Science, University of Granada, 18071 Granada, Spain

N. OleaDepartment of Radiology, University of Granada, 18071 Granada, Spain

© Springer-Verlag 2002

for ascertaining residue levels of OC, given the obviousdifficulties in obtaining human fat tissue samples to assessexposure to PCB and other OC.

The determination of low concentrations of these pes-ticides in serum samples requires the application of effec-tive extraction and sample-purification techniques, fol-lowed by chromatographic determination. An essential as-pect of any quantitative analysis nowadays is giving ameasure of the confidence that can be placed in the result,including the extent to which a result would be expectedto agree with other results. An extremely useful measureis measurement uncertainty [18, 19], defined as a valueassociated with the result of a measurement that charac-terizes the dispersion of the values that could reasonablybe attributed to the measurand.

GC in combination with the selective and sensitiveECD has become a routine technique for analysis of OCin biological fluids. The high sensitivity of this detectorcontrasts with its lack of identification power; retention-time-based identifications and determinations of pollu-tants are no longer sufficient. The occurrence of an un-known analytical response might be indicative of a poten-tial health hazard, particularly if the compound is found tocontain halogen. In such circumstances mass spectrome-try (MS) has been used to identify the compounds en-countered. MS–MS mode can enable detection and con-firmation of presence, often in a single analysis, by meansthe relationship between product ion spectra and precur-sor ions of the target analytes. Usually, electronic impact(EI) spectra contain sufficient structurally related frag-ment ions to enable absolute identification and, with theestimated uncertainty, ensure reliable results, to confirmthe presence of absence of specific compounds in the an-alysis of biological samples.

In this work, endosulfan-α and -β, three conversionproducts, endosulfan sulfate, -ether, and -lactone, and PCB-21,PCB-48, PCB-61, and PCB-136 were simultaneouslydetermined in 100 serum samples, by use of a previouslydeveloped extraction step [20, 21] combined with GC–ECD and GC–MS–MS. The limitations and advantages ofGC–ECD and GC–MS–MS for quantitative and qualita-tive determinations of endosulfan, their metabolites andPCB in human serum samples, with an estimate of the un-certainty of the analytical methods, are presented.

Theoretical basis of the calculation of uncertainty

Chemists are coming under increasing pressure to demon-strate the quality of their results, i.e. to demonstrate theirfitness for purpose by giving a measure of the confidencethat can be placed in them. Evaluation of the uncertaintyassociated with a result is an essential part of quantitativeanalysis. Figure 1 summarizes the process used to estimateuncertainty.

Estimating sources of uncertainty for GC–ECD and GC–MS–MS analysis

Estimation of uncertainty requires a knowledge of thepossible sources of uncertainty.

Gravimetric steps

This results from the combination of two sources.

1. The relative reference standard uncertainty, ur(s):

ur (s) =

(T olre f

2

)m

(1)

where Tolref represents the tolerance of the referencestandard given by the supplier and m represents theweight of the reference standard.

2. The relative balance calibration standard uncertainty,ur(b):

ur (b) =

(T olre f

2

)m

(2)

where Tolbal represents the reported tolerance of thebalance for the range used. This source of uncertaintywill be counted twice because the weighing process in-volves a difference.

The sources are combined into the equation:

ur (g) =√

(u, (s))2 + (ur (b))2 x2 (3)

Volumetric step

This corresponds to the volumetric sample relative uncer-tainty, ur(vs):

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Fig.1 Procedure used to estimate uncertainty

ur (vs) =

(T olvol√

3

)VP

(4)

where Tolvol represents the tolerance obtained from the lit-erature supplied with the catalogues of the pipette used (5 mL) and Vp represents the volume of sample pipetted(4 mL).

Chromatographic quantification step

This results from the combination of two sources:

1. Statistical evaluation of relative uncertainty associatedwith recovery from ten equal quantities of the samplematrix spiked at a single concentration level (100 µg L–1)and analysed intraday, ur(crecovery). This results from thecombination of two components:

The relative uncertainty associated to the calibra-tion curve ur(cal):

ur (cal) =

[( sres

b )2

n = d2c + ( sb

b

)2]1/2

cc(5)

where sres is the residual standard deviation of the cali-bration line, sb is the standard deviation of the calibra-tion curve, b is the calibration curve slope, n is thenumber of the calibration standards, and dc is the dif-ference between the average concentration of the cali-bration standards and the representative concentration(100 µg L–1) of the sample (cc).

The relative uncertainty associated with the repeata-bility of the method ur(rep)

ur(rep) =

(S Drep√

nrep

)Rrep

(6)

where SDrep represents the standard deviation from therecoveries obtained from replicate analyses, nrep is thenumber of replicates analysed, and Rrep is the mean re-covery obtained.

Both sources are combined into the equation:

ur(crecovery

) =√

(u, (cal))2 + (ur

(rep

))2(7)

2. Statistical evaluation of the relative uncertainty asso-ciated with the intermediate precision of the method,and calculated from ten equal quantities of the samplematrix spiked at the same single concentration level(100 µg L–1) and analysed interday, ur(ip):

ur(

ip) =

(S Di p√

ni p

)Ri p

(8)

where SDip represents the standard deviation of recov-eries obtained from replicate analyses, nip is the num-ber of replicates analysed, and Rip is the mean of therecovery obtained.

Calculation of combined and expanded uncertainty

Once listed, the terms and their associated uncertaintiesthat contribute to the uncertainty for the method as a whole,the individual uncertainties, are combined in the uncer-tainty budget to give uc(y):

uc (y) =√ (

ur(

g))2 + (ur (vs))

2

+ (ur

(crecovery

))2 + (ur

(ip))2 (9)

The expanded uncertainty, U, is obtained by multiplyinguc(y) by a coverage factor k, assuming normal distributionof the measurand. Usually k is 2, which provides an ap-proximate level of confidence of 95%:

U = uc (y) xk (10)

Experimental

Chemicals and reagents

Standards of the pesticides were obtained from Riedel–de-Haën(Seelze-Hannover, Germany); the purity was always higher than99%. The internal standard (ISTD), heptachlor (99% pure), wassupplied by Aldrich (St Louis, MO, USA). Stock standard solu-tions, 200 µg mL–1, were prepared by exact weighing and dissolu-tion in n-hexane. Working standard solutions were prepared by ap-propriate dilution and stored under refrigeration (≤4°C). Pesticide-quality solvents: n-hexane, methanol, diethyl ether, and 2-propanolwere supplied by Panreac (Barcelona, Spain), as was reagent-gradeconcentrated sulfuric acid. Organic-free water was prepared bydistillation and then Milli-Q SP treatment (Millipore Corporation,USA).

Apparatus

HPLC was performed with a Waters (Milford, MA, US) model 990system equipped with a model 600 E constant-flow pump, a Rheo-dyne six-port injection valve with 1 mL sample loop, a 250 mm×4 mm, 5-µm particle size, LiChrospher Si column from E. Merck(Darmstadt, Germany), a model 990 UV–visible photodiode-arraydetector (DAD), a printer/plotter, and a microcomputer using theWaters 991 software package.

Gas chromatography was performed with a Hewlett–Packard(Palo Alto, CA, USA) model 5890 instrument equipped with an63Ni ECD, a split/splitless injector operated in the splitless mode, a60 m×0.25 mm i.d.×0.25 µm film thickness HP-1 fused-silica cap-illary column (Hewlett–Packard), and an HP 7673 autosampler.HP 3365 Chemstation software was used for instrument controland data treatment. Nitrogen (purity 99.999%) was used as carrierand make-up gas.

A Saturn 2000 ion-trap mass spectrometer from Varian Instru-ments (Sunnyvale, CA, USA) was used for mass spectrometricanalysis. The integrated gas chromatograph was fitted with an au-tosampler (model 8200), an SPI/1078 split/splitless programmed-temperature injector operated in the splitless mode, and a 30 m×0.25 mm i.d.×0.25 µm film thickness DB5-MS capillary column(J&W Scientific, Folsom, CA, USA). The ion-trap mass spectrom-eter was operated in electron impact (EI) mode and the MS–MSoption was used. The computer, which controlled the system, hadan EIMS–MS library specially created for the target analytes underour experimental conditions. Other EIMS libraries were also avail-able. Helium (purity 99.999%) was used as carrier gas.

768

Chromatographic conditions

HPLC–DAD

The mobile phase, under gradient conditions, was: initially 2 minisocratic with 100% mobile phase A (n-hexane), 15 min linear gra-dient to 60% mobile phase A, 40% mobile phase B (n-hexane–methanol–2-propanol, 40:45:15, v/v), 5 min linear gradient to100% B, and 10 min linear gradient to 100% A; total run time 32 min. An additional 5 min with 100% A was enough to returnthe system to the initial conditions for subsequent analysis. Sam-ples (1 mL) were injected with the solvent flow-rate maintained at1 mL min–1. Photometric detection was performed in the range200–280 nm, with a spectral resolution of 1.4 nm.

GC–ECD

A 1-µL aliquot of the extract was injected by means of the au-tosampler with the injector splitter closed for 2 min. The injectorand detector temperatures were 250°C and 300°C, respectively.The column temperature was programmed from 130°C (held for 1 min) at 10° min–1 to 200°C (held for 10 min) and then at 1 ° min–1

to 232°C. The carrier gas flow was set at 0.85 mL min–1 and themake up at a flow rate of 60 mL min–1 (both set at an oven tem-perature of 150°C).

GC–MS–MS

A 2-µL volume was injected by means of the autosampler at a flowrate of 1 µL s–1. The GC was fitted with a septum-equipped tem-perature-programmable injector that was initially held at 90°C for0.1 min before being ramped to 280°C at 200° min–1. The GCoven was initially held at 80°C for 2.5 min then ramped at 50°min–1 to 140°C, and finally at 5 ° min–1 to 260°C (held for 3 min).

GC–MS

For GC–MS analysis the solvent delay was 11 min, the electron-impact energy 70 eV, the scan-rate 0.6 scans s–1, and the rangescanned m/z 85–450. The transfer line was kept at 260°C and theion-trap manifold at 200°C. The automatic gain control (AGC)was switched on with a target fixed at 5000 counts. Helium(99.999%) at a flow rate of 1 mL min–1 was used as carrier and col-lision gas. The mass spectrometer was calibrated weekly. The con-ditions used for MS–MS are shown in Table 1.

Sampling and sample storage

Venous blood (approximately 10 mL) was collected into vacu-tainer tubes (Venoyect, Terumo Europe N.V. 3001, Leuven, Bel-

gium) without preservatives, and the samples were left to coagu-late naturally for 3–7 h. The samples were then centrifuged to sep-arate the serum, which was transferred into glass vials capped withTeflon-faced silicone rubber septa. These vials were stored at –20°Cuntil analysis.

The serum samples used were obtained from patients from Tor-recardenas Hospital, Almeria (Spain). All the patients gave theirwritten informed consent and the study was approved by the hos-pital ethical committee.

Procedure for extraction of pesticides from serum

The extraction procedure followed was similar to that previouslydeveloped in our laboratory [21]. Aliquots (4 mL) of spiked blankserum were equilibrated for 3 h at medium temperature (18°C) ina test tube. Methanol (2 mL), with mixing on a rotatory mixer for1 min, and n-hexane–diethyl ether (1:1, v/v; 5 mL), agitating on arotary mixer for 2 min, were then added to the samples and themixtures were centrifuged at 2500 g for 5 min. The organic phasewas collected and the aqueous phase was extracted twice morewith n-hexane–diethyl ether (1:1, v/v; 5 mL). The organic extractswere combined and concentrated to 1 mL under a gentle stream ofnitrogen.

Chemical clean-up

H2SO4 (0.5 mL) was added to the concentrated 1-mL extract, fol-lowed by mixing for 1 min and centrifugation for 5 min at 2500 g.The organic phase was collected and the aqueous phase was ex-tracted with n-hexane (2×1 mL). The organic extracts were com-bined and evaporated to complete dryness under a stream of nitro-gen. The samples were dissolved in 1 mL n-hexane and then in-jected into the HPLC system.

Physical clean-up

The fraction corresponding to the first 15 min eluting from theHPLC system was collected, evaporated to dryness under a nitro-gen stream, mixed with 1 mL n-hexane, and spiked with the inter-nal standard. The extract was then analysed by GC–ECD andGC–MS–MS; the volumes injected were 1 and 2 µL, respectively.

Quality control and quality assurance

Analyses of laboratory reagent and serum blanks were performedto check contamination and interferences. Calibration and recov-ery rates for spiked serum blank were performed with every set oftwenty serum samples. Quantification was performed if there wasno exogenous peak in the blank and the recovery of the spiked ma-

769

Table 1 MS–MS conditionsPesticide Activation m/z Range Parent Excitation Excitation

time (min) ion amplitude storage (V) level (m/z)

Endosulfan ether 14.18–14.24 85–280 241 77 80Endosulfan lactone 17.87–17.95 85–330 321 92 141Endosulfan-α 19.33–19.37 85–250 241 81 80Endosulfan-β 21.77–22.05 85–290 241 79 80Endosulfan sulfate 23.02–23.12 85–290 272 62 80PCB-21 15.03–15.10 85–290 256 92 90PCB-48 16.29–16.34 85–310 292 72 85PCB-61 18.08–18.15 85–330 292 73 80PCB-136 20.45–20.56 85–380 360 86 100Internal standard 15.24–15.28 85–290 272 80 100

trix sample was between 60 and 130%. Expanded uncertainty wasalso calculated for each pesticide in both methods.

Results and discussion

Table 2 shows the retention time windows (RTW), de-fined as the retention time averages±3 standard deviationsof the retention times, of the pesticides with the GC–MS–MS method (n=10). Table 3 shows the retentiontimes windows (RTW) of the pesticides with the GC–ECD method (n=10). Figure 2a shows the chromatogramobtained by GC–MS–MS for a serum blank. Because ofthe high selectivity of the tandem MS–MS mode, no in-terferences were noticed in the retention time of the targetpesticides.

Figure 2b shows the chromatogram obtained by GC–ECD for the same sample. Matrix interferences are pre-sent in all the chromatograms at the retention times 20.76,25.43, and 36.72 min, which coincided with the elutiontimes of endosulfan ether, PCB-48, and PCB-136, respec-tively. The interference eluting in the RTW of PCB-136was identified by GC–MS–MS as 4,4′-DDE at trace lev-els. No interferences were found at the retention time ofheptachlor (internal standard).

Table 2 summarizes calibration data for the GC–MS–MS method; correlation coefficients were 0.99. Detectionlimits (LOD) were determined as the lowest concentrationgiving a response three-times the average baseline noiseand quantification limits (LOQ) as the lowest concentra-tion giving a response ten-times the average baseline noise.The values obtained for LOD varied between 0.05 µg L–1

(endosulfan ether, PCB-21, and PCB-136) and 0.62 µgL–1 (endosulfan β). This chromatographic response for thelast compound was poor. Intraday and interday recoverystudies (n=10) were performed by spiking serum blanks at12.5 µg L–1. Recoveries usually ranged from 76% (endo-sulfan lactone) to 100% (PCB-48); RSD values for intra-day repeatability were <26%.

Calibration data for GC–ECD are shown in Table 3; cor-relation coefficients were 0.99. The LOD and LOQ of tar-get analytes were calculated as three- and ten-times the re-spective standard deviation (five injections) of the baselinesignal, corresponding to a blank matrix extract chromato-gram at the analyte retention times divided by the respec-tive slopes of the calibration plots of the analytes. The val-ues obtained for LOD varied between 0.01 µg L–1 (PCB-61)and 0.28 µg L–1 (endosulfan sulfate). Recoveries rangedbetween 72% (endosulfan lactone) and 106% (endosulfanether); RSD values for intraday repeatability were <11%.

770

Table 3 Retention time windows (RTW) and calibration data (n=10) for GC–ECD

Pesticide RTW (min) Calibration curve R LOD LOQ %Ra %RSDintraday Linear %U(µg L–1) (µg L–1) range

(µg L–1)

Endosulfan ether 20.77–20.88 Y = 0.074+1.650 X 0.98 0.02 0.06 106 9 0.25–40 13Endosulfan lactone 28.35–28.50 Y = 0.053+0.886 X 0.99 0.03 0.11 72 7 0.25–40 12Endosulfan-α 33.99–34.16 Y = 0.207+1.412 X 0.98 0.02 0.07 85 11 0.25–40 16Endosulfan-β 40.04–40.16 Y = 0.033+0.685 X 0.98 0.04 0.15 82 6 0.25–40 18Endosulfan sulfate 45.36–45.76 Y = 0.026+0.180 X 0.97 0.28 0.93 77 4 0.90–40 12PCB-21 22.11–22.70 Y = 0.095+0.901 X 0.98 0.03 0.11 84 11 0.25–40 14PCB-48 25.28–25.43 Y = 0.107+1.762 X 0.99 0.02 0.05 100 8 0.25–40 11PCB-61 30.17–30.29 Y = 0.184+2.266 X 0.99 0.01 0.05 83 4 0.25–40 11PCB-136 37.03–37.21 Y = 0.138+1.166 X 0.99 0.03 0.09 86 9 0.25–40 15Internal standard 23.32–23.44

aSpike level 12.5 µg L–1

Table 2 Retention time windows (RTW) and calibration data (n=10) for GC–MS–MS

Pesticide RTW (min) Calibration curve R LOD LOQ %Ra %RSDintraday Linear %U(µg L–1) (µg L–1) range

(µg L–1)

Endosulfan ether 14.18–14.24 Y = -0.015+0.753 X 0.99 0.05 0.25 91 26 0.25–40 26Endosulfan lactone 17.87–17.95 Y = -0.004+0.531 X 0.99 0.15 0.50 76 6 0.50–40 20Endosulfan-α 19.33–19.37 Y = 0.002+0.282 X 0.99 0.30 1.00 90 12 1.00–40 20Endosulfan-β 21.77–22.05 Y = -0.008+0.211 X 0.99 0.62 2.00 98 12 2.00–40 25Endosulfan sulfate 23.02–23.12 Y = -0.001+0.061 X 0.99 0.20 0.75 86 22 0.75–40 20PCB-21 15.03–15.10 Y = 0.021+0.242 X 0.99 0.05 0.25 89 14 0.25–40 20PCB-48 16.29–16.34 Y = 0.014+0.208 X 0.99 0.08 0.25 100 26 0.25–40 26PCB-61 18.08–18.15 Y = 0.013+0.451 X 0.99 0.13 0.50 86 15 0.50–40 25PCB-136 20.45–20.56 Y = 0.025+0.322 X 0.99 0.05 0.25 94 21 0.25–40 25Internal standard 15.24–15.28

aSpike level 12.5 µg L–1

771

Estimation of the uncertainty of results

Results from the estimation showed that the largestsources of uncertainty were the ur(b) from the gravimetricstep, and ur(recovery) and ur(ip) from the chromatographicquantification step. The contribution of the volumetricstep is not very important. In this step we have not takeninto account the contribution to the uncertainty resultingfrom dilution of the primary standard. Our experience inthis field enables us to conclude, however, that the contri-bution of this step is not significant compared with the un-certainty associated with measurement of the sample. Fig-ure 3 shows the contributions to the measurement uncer-tainty for the determination of the target analytes byGC–MS–MS in real serum samples.

The uncertainty (U) values measured for GC–MS–MS(Table 2) ranged from 20% (PCB-21, endosulfan lactone,endosulfan-α and endosulfan sulfate) to 26% (PCB-48and endosulfan ether). The U values for GC–ECD (Table 3)ranged from 11% (PCB-21 and PCB-48) to 18% (endo-sulfan-β). The differences between these values are ex-plained by the different responses of the detectors to eachpesticide. Lower values were usually obtained for GC–ECD than for GC–MS–MS.

In summary, LOD values obtained by use of ECD werelower than those obtained by use of MS–MS, but quan-tification of some pesticides (endosulfan ether, PCB-48and PCB-136) must be performed by GC–MS–MS, be-cause the higher selectivity of this technique enables elim-ination of matrix interferences, and thus false-positive re-

Fig.2 Gas chromatogram ob-tained from blank humanserum by use of (a) MS–MSand (b) ECD

sults. For all pesticides analytical uncertainty when usingECD was lower than when using MS–MS, because theprecision of ECD is higher than that of MS–MS detection.

Analysis of real serum samples

After the analytical procedure had been checked it wasapplied to the analysis of a hundred serum samples sup-plied by the Project from the EU Commission QLRT-1990–01422.

Analyte search and identification was accomplished byuse of our own GC–MS–MS library compiled under ourexperimental conditions. Target analytes were sought in

772

Fig.3 Contributions to measurement uncertainty in the determina-tion of the target analytes in real serum samples by GC–ECD (lightshading) and GC–MS–MS(dark shading)

the appropriate RTW and identified by comparing EIMS–MS spectra with spectra stored in the library. Analytes iden-tified, at a predetermined minimum spectral FIT (≥500),were listed if signal:noise (s/n)>3; they were quantified,by use of the internal standard method, if s/n>10 for quan-tification ions. Results obtained by GC–MS–MS aregiven in Table 3.

The compounds mainly detected by GC–ECD werePCB-48, endosulfan ether, endosulfan-β, endosulfan α,and PCB-136. When both modes of detection were com-pared more positives were found by GC–ECD than byGC–MS–MS, because of baseline noise that, at trace lev-els, often interfered with signals at the retention times of

the pesticides and chemicals, as mentioned above. In GC–MS–MS this noise was reduced by the selection of a spe-cific ion of the pesticide which was not present in the ma-trix, thus avoiding interference and making identificationand quantification more accurate and precise.

Thus, when GC–ECD was used PCB-48 was detectedin thirty-five of the hundred samples whereas when GC–MS–MS was used no signal was obtained for PCB-48 byselection of its quantification ion (m/z 222) (Fig.4 a, b).

Endosulfan α was confirmed in five samples, at levelsfrom 2 to 14 µg L–1±20%; four samples were from a pa-tient with breast cancer and one from a patient not af-fected with this pathology. Fig.5 depicts confirmation of

773

Fig.4 Gas chromatograms ob-tained from a human serumsample by use of: (a) MS–MSmonitoring of the quantifica-tion ion (m/z 222) of PCB-48(*) and (b) ECD

the presence of this pesticide in one real serum sample.Endosulfan ether and endosulfan-β were not confirmed inany of the samples analysed by GC–MS–MS.

Conclusions

This study provides evidence of the high persistence ofchemicals such as endosulfan, used in agricultural areas.Concerns about the role of environmental and dietary en-docrine disrupters as contributors to the increasing inci-dence of hormone-related diseases have moved epidemi-ologists and clinicians to design patient-based studies on

which to test their hypotheses. The data and analysis pre-sented here are an attempt to cope with intensifying de-mands by the public and regulatory agencies to evaluaterigorously the presence of xenoestrogens in the environ-ment.

A clear example is given of the potential advantage ofGC–MS–MS over GC–ECD in the routine analysis ofchemical pollution in serum samples. Problems arisingfrom interferences with GC–ECD analysis, as a result ofcoelution of chemicals in blank serum, were avoided byusing GC–MS–MS. As consequence, sensitivity was bet-ter for most of the pesticides when this mode of detectionwas used.

774

Fig.5 Gas chromatograms ob-tained from a human serumsample by use of (a) MS–MSmonitoring of the quantifica-tion ion (m/z 170) of endosul-fan α (**) and (b) ECD

775

The chemical most often detected in the samples an-alysed was the pesticide endosulfan α, probably becauseof its low biodegradability, accumulation, and bioconcen-tration in lipid systems. The women in the study were notnecessarily exposed to pesticides, and this indicates thatresidues have been incorporated into food chains. Studiesshould prioritize those endocrine disruptors for whichhormonal activity has previously been established by in-vivo or in-vitro studies or when overexposure of humansis suspected. The incidence of certain disorders of the en-docrine organs should be greater in areas where exposureto agents with this activity is high.

Acknowledgements The authors are grateful to the Project fromthe EU Commission QLRT-1990–01422, for the provision of hu-man serum samples for this work.

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