2464
Anal. Chem. 1985, 57, 2464-2469
Automated Procedures for Mass Spectrometric Determination of Polychlorinated Biphenyls as Isomer Groups Laurence E. Slivon, Judith E. Gebhart, and Timothy L. Hayes Battelle Columbus Laboratories, 505 King Avenue, Columbus, Ohio 43201
Ann L. Alford-Stevens and William L. Budde* Environmental Monitoring and Support Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 26 West St. Clair Street, Cincinnati, Ohio 45268
Software was developed to perform fully automated identlfication and measurement of polychlorlnated biphenyls (PCBs) in envlronmentai sample extracts analyzed with capillary column gas chromatography (GC)/mass spectrometry (MS). Unprocessed GC/MS data were handled without human interactlon with the software operating on a dedlcated mlnlcomputer. PCBs were ldentlfled by level of chiorinatlon, and a Concentration was measured for each isomer group. Total PCB concentratlon was obtained by summlng Isomer group concentratlons. Results obtained with the software were compared with results obtained when an experienced mass spectrometrlst used standard GC/MS data system software to process the same GC/MS data. Automated procedures produced results that were as reliable and accurate as those produced with tedlous, labor-intensive human Interpretation and computatlon. Analytical costs were slgnlflcantly lower with automated ldentlficatlon and measurement procedures than with human interpretation and computatlon.
A previous report ( I ) described and demonstrated a procedure to measure concentrations of polychlorinated biphenyls (PCBs) by using nine selected PCB congeners as concentration calibration standards. With that procedure, PCBs are not identified and measured as commercial Aroclor mixtures. Instead, a gas chromatograph (GC) equipped with a capillary column and interfaced to a mass spectrometer (MS) is used to detect and identify PCBs by level of chlorination. Concentrations are measured for each isomer group and for total PCBs present. For each level of chlorination, one PCB is used as the concentration standard for all isomers in that group, except that decachlorobiphenyl represents congeners containing both nine and ten chlorines. One internal standard, chrysene-d,,, is used to obtain gas chromatographic relative retention time data (if desired) and to calibrate MS detector response. The advantages of this procedure for PCB determinations were discussed in the previous report ( I ) . This approach to PCB determinations requires processing large amounts of data produced by GC/MS analyses of multicomponent sample extracts separated with a capillary column. Most data systems interfaced to contemporary commercial GC/MS systems are sufficiently powerful to handle this task. Specialized software is necessary, however, to implement fast, rigorous, consistent, and correct applications of the necessary algorithms. This paper describes software that enables a dedicated minicomputer to perform fully automated identification and measurement of PCBs from unprocessed capillary column GC/MS data. Results obtained with the software are compared with results obtained when an experienced mass spectrometrist used standard data system software to process the same GC/MS data. The automated procedures described here were designed for potential use not
only for PCBs but also for other groups of chlorinated compounds in a homologous series, such as chlorinated dibenzop-dioxins (CDDs) and dibenzofurans (CDFs).
EXPERIMENTAL SECTION Materials. Eleven of the PCB calibration congeners were obtained from Ultra Scientific, Hope, RI; the heptachlorobiphenyl was obtained from Wellington Environmental Consultants, Inc., Guelph, Ontario. All PCB congeners were used without further purification. The internal standard, chrysene-d,,, was obtained from Aldrich Chemical Co., Milwaukee, WI. Aliquots of particular formulations of Aroclors 1248 and 1260 were generously supplied by Phillip W. Albro, National Institute of Environmental Health Sciences, Research Triangle Park, NC. Two environmentally contaminated sediments from the harbor at New Bedford, MA had been collected for an interlaboratory study previously described (2). Instrumentation. Mass spectral data were obtained with a Finnigan Model 3200 MS operated in the electron ionization mode and interfaced with a Carlo Erba Model 2130 GC equipped with an on-column injector. A 30 m X 0.31 mm id. fused silica capillary column coated with a 0.25-pm film of polydiphenylvinyldimethylsiloxane (SE-54,Alltech Associates, Deerfield, IL) was used. A Finnigan Incos Model 4160 data system was used to acquire and process mass spectral data. This system consisted of a Data General Nova 3 computer with 32K words of 16-bit memory, two 10 Mbyte disk drives, a printer/plotter, and a graphic display terminal. Automated interpretation software described in this report are implemented in Data General Fortran IV and the Finnigan Incos procedural language. Details of the software and a users' guide are available from the authors. Preparation of Samples and Calibration Solutions. A single-componentsolution of each of the nine PCB concentration calibration congeners, each of the three PCB retention time congeners, and the internal standard (chrysene-d,,) was prepared by weighing neat material and dissolving in hexane. Three multicomponent calibration solutions were then prepared by combining appropriate aliquots of the single-component solutions to provide the concentrations needed for GC/MS data acquisition. Solution concentrations for full-mass-range data acquisition have been previously described ( 1 ) . Less concentrated calibration solutions (Table I) were used for monitoring selected groups of ions (mass ranges) to detect and measure PCBs in sediments. Each Aroclor sample was dissolved in hexane to provide two solutions, one at approximately 620 ng/pL for Aroclor 1248 and one at approximately 710 ng/pL for Aroclor 1260. Another report has described in detail the preparation of the sediment samples from New Bedford Harbor, MA (2). Briefly, triplicate 30-g aliquots of two air-dried sediments were extracted with Soxhlet procedures using a 1:l (vo1ume:volume) mixture of hexane/acetone. Extracts were concentrated in a Kuderna-Danish apparatus, subjected to Florid column chromatography, eluted with 6% ethyl ether in hexane, and concentrated to a final volume of 400 pL with a Kuderna-Danish apparatus. GC/MS Data Acquisition. One minute after injection of an aliquot of calibration solution, Aroclor solution, or sediment extract, the GC column temperature was increased at a rate of 10 "C/min from 70 "C t o 310 "C, where it was held for 15 min. Details of full-range MS data acquisition for analysis of Aroclors
0003-2700/65/0357-2464$0 1.50/0 0 1985 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
2485
Table I. Composition of Calibration Solutions for Determination of PCB Isomer Groaps in Environmentally Contaminated Sediments calibration congener/internal standard
concentration, ntr/uL sol 2
chlorine substitution
sol 1
sol 3
2 2,3 2,4,5 2,2’,4,6 2,2’,3,4,5’ 2,2’,4,4’,5,6’ 2,2’,3,4’,5,6,6’ 2,2’3,3’,4,5’,6,6’ 2,2’,3,3‘4,4‘,5,5‘,6,6‘
0.05 0.05 0.05 0.1 0.1 0.1 0.15 0.15 0.25
0.5 0.5 0.5 1 1 1 1.5 1.5 2.5
5 5 5 10 10 10 15 15 25
3,3’,4,4‘ 2,2’,4,6,6’ 2,2’,3,3’,4,5,5’,6,6’
0.1
0.1 0.2
1 1 2
10 10 20
0.25
0.25
PCB concentration calibration congeners c11 C12 c13 c14 c15
c17 Cl8 CllO PCB retention time congeners c14 c15 C19
internal standard chrysene-d,,
0.25
Table 11. Sets of Ions for Mass Spectral Data Acquisition for Determination of PCB Isomer Groups in Environmentally Contaminated Sediments isomer group/int std C11
c1, Cl3 c4 c15 c16
c17 C18
c19
ClIO chrysene-d,,
ion sets
nomina1 mol wt
quant ion
mass range monitored
no. of ions
I
188 222 256 290 324 358 392 426 460 494 240
188 222 256 292 326 360 394 430 464 498 240
152-153, 186-190 220-224 254-260 288-294 322-328 356-362 390-396 424-430 460-466 496-500 240-241
7 5
I
I
I I I
total
“ m/z 254 monitored to confirm presence of (M - 70)’ for C1,-PCBs.
7
I I
5
I1 5 7
I I 7
7 7
I11
1” 2* 7 7 7 7
I
IV
7 7
I 7 5
5 2
2 33
33
33
33
m / t 288 and 290 monitored to confirm presence of (M - 70)’ for
Cla-PCBs.
1248 and 1260 have been reported previously (I). For sediment extract analysis, four groups of mass ranges (ion sets I-IV, Table 11) were monitored to provide MS data necessary for both automated and nonautomated data interpretation. Ion sets being monitored were changed during elution of sample components, with the time for changes determined by knowledge of elution of PCB congeners with the GC column being used (3). The presence of the three retention time congeners in calibration solutions (Table I) allowed the analyst to determine the time for ion set changes with the analytical conditions being used. Ion set I was designed for detection and measurement of mono-, di-, tri-, and tetrachlorobiphenyls; ion set 11, for tetra-, penta-, and hexachlorobiphenyls; ion set 111, for penta-, hexa-, hepta-, and octachlorobiphenyls and the internal standard, chrysene-dI2; ion set IV for octa-, nona- and decachlorobiphenyls. Data acquisition with ion set I began before elution of the monochlorobiphenyl concentration calibration congener, the first eluting PCB congener. Acquisition with ion set I1 was initiated before elution of the first eluting pentachlorobiphenyl (retention time congener with chlorine substituents in 2,2’,4,6,6’-positions). After the last tetrachlorobiphenyl (retention time congener with chlorine substituents in 3,3‘,4,4’-positions)eluted, data acquisition with ion set I1 was halted, and ion set I11 acquisition was initiated. Data acquisition with ion set IV was initiated before elution of the nonachlorobiphenyl retention time congener (which is also the first eluting nonachlorobiphenyl) and continued until after decachlorobiphenyl eluted. Each of the three calibration solutions was analyzed in triplicate to calibrate the MS response to each of the nine PCB calibration
congeners. Nine RFs for each calibration congener were computed from the quantitation ion peak areas and injected quantities of calibration congener and internal standard. The mean RF for each calibration congener was used, along with measured quantitation peak areas of the internal standard and extract components, and known quantities of the internal standard added to sample extracts, to calculate concentrations of the appropriate PCB isomer group in sample extracts.
RESULTS AND DISCUSSION Data Interpretation and Computation. With both automated and nonautomated procedures, the current major data interpretation problem is caused by the lack of a GC column that can separate all members of a large class of chlorinated compounds, such as PCBs, CDDs, and CDFs. For determination of isomer group concentrations for these compound classes, coeluting congeners do not present a problem if they all contain the same number of chlorine atoms. Even with capillary columns, however, an apparent single-component GC peak may contain coeluting or partially coeluting congeners with different levels of chlorination. Although retention times of the 209 PCB congeners tend to increase with increasing chlorine content, individual members of isomer groups display retention times that are highly dependent on structure (3). For example, some PCB isomers containing five chlorines have retention times equivalent to those for isomers containing four or six chlorines. The same situation may exist
2466
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
with CDDs and CDFs, but the paucity of standards has limited measurement of relative retention times of CDD and CDF isomers. Therefore, interpretation procedures cannot rely on GC retention times to determine levels of chlorination for individual members of compound classes such as PCBs, CDDs, and CDFs. To determine the level of chlorination, both automated and nonautomated data interpretation require knowledge of the characteristic features of analyte mass spectra. For classes of chlorinated compounds such as PCBs, CDDs, and CDFs, the most important electron ionization mass spectral feature is the molecular ion (M+) cluster produced by the natural abundances of 35Cland 37Clisotopes. In this study of PCBs, the masses and known relative abundances of M+ cluster ions were used as the primary identification criteria, and one (or more) of the M+ cluster ions was selected to be the quantitation ion(s) for each isomer group. Coelution of PCB congeners containing fewer chlorines than a given isomer group does not interfere with detection and measurement of M+ ions produced by a member of that isomer group. Fragment ions produced by coeluting PCB congeners containing more chlorines, however, can interfere. Therefore, tentative assignment of a level of chlorination based on detection of an M+ ion cluster must be verified by the absence of pertinent higher mass ions. For example, PCBs undergo a characteristic loss of two chlorine atoms to produce a (M - 70)’ ion cluster that partially overlaps the M+ ion cluster produced by a congener containing two fewer chlorines. These clusters contain ions that have the same masses but different relative abundances. Therefore, to verify a tentative assignment of level of chlorination, the observed relative abundances of isotope cluster components must be sufficiently similar to expected relative abundances. Another potential interference is produced by a (M - C1)+ fragment ion cluster containing a single naturally occurring 13Cisotope; that cluster contains ions with the same masses and relative abundances as the lZC isotope M+ ion cluster of isomers containing one less chlorine. To eliminate the possibility of this interference, the absence of a cluster of ions representing isomers containing one additional chlorine must be verified by examining mass spectral data for the appropriate M 35 ions. Nonautomated Interpretation and Computation. All of the mass spectral features described above had to be considered by the analyst who examined mass spectral data to identify and measure PCBs. Nonautomated interpretation required application of training, experience, intelligence, and judgement. With a large collection of spectra, such as those produced by capillary column GC/MS analysis of an environmental sample extract, human interpretation and computation is error-prone, tedious, mentally fatiguing, and very time-consuming. Days can be spent interpretating data obtained from analysis of one complex environmental sample. Few analytical laboratories, however, have the luxury of unlimited resources, and analysts must use available resources to obtain reliable results in a reasonable time. The nonautomated interpretation procedures described here were considered to be a compromise between all that was desirable and what was realistic with the normal constraints of an analytical laboratory. Standard GC/MS software was used to retrieve, manipulate, and display data required for the interpreter to make identification decisions. Although the data interpretation sequence may vary according to the particular situation, the required interpretation and computation procedures can be summarized as sequential steps: 1. Total ion current profiles were plotted and examined. 2. Ion current profiles (ICPs) were plotted for at least two ions from the M+ ion cluster for each level of chlorination.
+
3. Ratios of observed peak areas were calculated and compared to expected ratios from natural abundances of 35Cl and 37Cl. 4. The absence of ion clusters representing isomers containing one or two additional chlorines was confirmed by examining ICPs for a t least one characteristic ion in each group. 5. The presence of an (M - 70)+ion cluster was confirmed by examining ICPs for at least one characteristic ion. 6. When congeners containing different numbers of chlorines coeluted, ion abundances were assigned to appropriate isomer groups. 7 . When ambiguous data were obtained, complete background-subtracted mass spectra of sample components were examined. 8. The chromatographic peak area for the most intense ion in each M+ ion cluster was produced by standard software but had to be tabulated manually. 9. With a hand calculator, chromatographic peak areas from step 8 were summed for each level of chlorination, and a concentration was calculated for each isomer group and for all isomer groups. Automated Interpretation and Computation. To automate PCB determinations, software was developed to emulate the interpretation and computation logic of the human, but with much greater speed and consistency. To make the automated procedures widely applicable and useful, the software was designed to operate on the same dedicated GC/MS minicomputer that performed data acquisition. Fortran programs were devised to identify PCBs and to provide concentration data, without human interaction, if desired. More rigorous data assessment was performed with the computer than was realistic for nonautomated interpretation, because a computer performs repetitive operations rapidly, consistently, and accurately. For example, with automation, additional integrated ion abundance ratios could be checked easily, and extraneous ions could be rapidly and consistently eliminated by subtraction of appropriate background spectra from analyte spectra. Maximum use was made of existing vendor-supplied software, such as library driven reverse search, automated background correction, and integration of ICPs. These conventional software tools were linked together with a user procedure and supplemented with specially designed software to perform qualitative identification of candidate PCB spectra. With the software developed, an analyst’s examination of ICPs and spectra was no longer necessary, and the need for GC retention time data was essentially eliminated. The automated interpretation procedures can operate without human intervention using default threshold values, or the programs can operate interactively to allow the user to modify algorithm parameters, repeatedly if needed. As shown in the flow chart (Figure l),the automated procedure initially performs a reverse search (Le., spectra of known compounds are sought among spectra of sample extract components). Either the entire collection of sample component spectra or a user-selected subset based on retention time can be searched. Each identified extract component must be associated with the internal standard that was used to measure a response factor for calculation of that component’s concentration. Therefore, an internal standard is sought first. If an internal standard is not found, the reverse search is halted immediately, because no valid sample component data would result from continuation of data processing. Frequently with environmental samples, a carefully selected compound or compounds (called surrogate compounds) are added to the sample before extraction (or any other sample preparation procedures) is performed. A surrogate is treated the same as
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
2467
allow for variations in the relative abundance of M+ and (M
Start
- 70)+, a generous acceptance window is allowed for relative
t
Reverse Search to Locate
Internal StandardlSurrogate Compounds
c
Integrate Chromatographic Peaks. If Found
Background Correct Candidate Spectrum; Apply Test Criteria; If Passes
Yes
Sum All Areas tor
Last Level?
Generate Quantitative Report
Flgure 1. Structure of software developed for automated data inter-
pretation and computation. every other analyte of concern, including incorporation into the reverse search and association with an internal standard. Because surrogate compound data may not be essential, the user can modify the software to halt the search if a surrogate is not found or to continue the search for analytes of concern. A spectrum of each internal standard, surrogate compound, and PCB isomer group must be included in the library used for the reverse search. The library spectrum for each PCB isomer group is a reduced generic spectrum containing the characteristic features of mass spectra of all isomers at that level of chlorination. Therefore, only 11library spectra are required to represent one internal standard and all PCBs, which can contain one to ten chlorine atoms. The library can, however, be easily modified by the user to accommodate additional library entries, including additional internal standards or surrogate compounds. The search is performed in order of library entries, with internal standards and surrogate compounds preceding analytes. The chromatographic peak area of each quantitation ion for each internal standard and surrogate compound is stored in a temporary file. For PCB library spectra used for the reverse search, a reduced generic spectrum consists of principal ions of the M+ and (M - 70)' clusters along with their relative abundances. In addition, the reduced spectrum includes a single (M 70)+ ion with a relative abundance of 0.1%. This low allowable relative abundance helps prevent false identification of a fragment produced by the loss of two chlorines from a higher chlorinated PCB than the chlorination level being sought. The presence of this (M + 70)' ion is important, because its relative abundance must then be considered. If the library did not include this spectral feature, the appropriate ion would not be checked to prevent misidentifications. To detect all PCBs, including those present in relatively small amounts (therefore producing weak spectra), and to
+
abundance match criteria. This produces few false negatives but many false positives, which are eliminated later. The spectrum number of each positive match with the reverse search is stored in a temporary storage file. When the reverse search is completed for each level of chlorination, each analyte candidate spectrum is sequentially retrieved and evaluated automatically. Each spectrum is automatically background corrected and examined by the qualitative interpretation software. Using data for a minimum number of ions, this software applies rules based on characteristic fragmentation of PCBs at a given level of chlorination. These rules are used to identify PCBs by levels of chlorination. To eliminate false positives, the anticipated molecular ion region is examined and compared to a predefined set of masses and known relative abundances for the M+ ions containing only 12C isotopes. A sequence of tests is performed: 1. The candidate spectrum must contain at least two ions in common with the expected M+ cluster. 2. In the candidate spectrum the most intense ion in common with the expected M+ cluster must exceed a minimum absolute intensity. 3. The average abundance ratio of M+/(M - ')1 in the candidate spectrum must exceed a threshold value. Examination of (M - 1)' ions allows the software to distinguish between ions for a given level of chlorination and those produced by the naturally occurring 13C contribution to the (M - C1)+ ion cluster produced by a PCB containing one additional chlorine atom. The software also can handle a mixture of these ions. Such a mixture results from coelution of congeners whose composition differs by one chlorine, such as coelution of tetra- and pentachlorobiphenyls. The M+ ion cluster of a candidate spectrum that passes these tests is compared to the expected M+ ion cluster for that isomer group. The comparison is made by using a multidimensional vector algorithm similar to that described by Rosenthal and Bursey ( 4 ) and subsequently utilized in the Finnigan Incos forward library search (5). A score is computed for the candidate M+ ion cluster
(2U,T,)2 score = 1000
i=l
(1)
C U?? T?
i=l
c=1
where T, is the theoretical relative abundance of a particular mass and V Iis the observed relative abundance of that same mass in the candidate spectrum. A score of 1000 would indicate a perfect match. The expected isotope cluster includes not only the principal I2C abundances but also an (M - 2)+ ion with an expected abundance of zero. This additional zero abundance forces a poor score for a given congener if the observed isotope cluster is due to the loss of two chlorines from a more highly chlorinated congener. If all test criteria me met, the candidate spectrum is retained as a positive analyte match. When test criteria are not met, information about the reason for rejection of a candidate spectrum is provided to permit the analyst to make decisions about selecting different algorithm parameters. When all candidates for a given level of chlorination have been tested, a summation of chromatographicpeak areas from all spectra meeting test criteria is entered in the temporary storage file containing information about internal standard(s) and surrogate compound(s). If no matches are obtained for a given level of chlorination, the entry contains a zero value for area. The temporary storage file contains one entry for each internal standard, each surrogate compound, and each level of chlorination for analyte compound classes. For PCBs,
ANALYTICAL CHEMISTRY, VOL. 57, NO. 13, NOVEMBER 1985
2488
Table 111. Distribution (Mole Percent) of PCB Isomer Groups in Aroclors 1248 and 1260
isomer group
lit. values, non-MS detectors mol % no. of isomers"
experimental values, MS detector nonautomated procedures automated procedures mol % (RSD) no. of isomers mol % (RSD) no. of isomers Aroclor 1248
ClZ c13 c14 c1.5
1.1 25.0 50.5 20.1 3.1 0.2
c17
3 (1) 8 (1) 14 (4) 13 (5) 7 (3) 2 (1)
1.7 (8) 28.0 (4) 49.9 (7) 19.5 (5) 0.9 (11)
3 8 12 11 7
1.4 (6) 24.9 (8) 54.2 (14) 18.9 (12) 0.7 (7)
2 8 14 12 7
1 7 10 11 14 7 3
0.0 (0) 0.1 (4) 4.3 (4) 22.1 (2) 34.2 (1) 29.3 (1) 8.7 (2) 1.3 (12)
1 1
Aroclor 1260
8
14 14 14 8
3
"Number of isomers for which mole percent values were measured; in parentheses, the number of additional isomers reported (6) as detected but present only in trace quantities. Table IV. PCB Concentrations" Measured in Environmentally Contaminated Sediments with Automated and Nonautomated Data Interpretation Procedures
isomer group c11 ClZ
Cls c14 c15 c16
total
sediment I1 nonautomated procedures automated procedures concn, pg/kg concn, pg/kg no. of no. of (RSD) (RSD) isomers isomers 1.47 (19) 40.1 (21) 132 (16) 337 (11) 354 (17) 148 (14) 1010
2 5 5 9 12 9 42
2.13 (3) 45.6 (5) 139 (2) 375 (5) 375 (9) 162 (9) 1099
2 7 9 12 13 9 52
sediment I11 nonautomated procedures automated procedures concn, pg/kg concn, rg/kg no. of no. of (RSD) (RSD) isomers isomers 1.07 (29) 14.7 (20) 33.5 (16) 42.4 (21) 4.80 (5) 96.5
1 7 5 10 3 26
1.07 (8) 22.5 (6) 36.0 (10) 52.9 (9) 9.20 (6) 121.7
1 9 9 11 3 33
" Mean of concentrations measured in triplicate extracts of contaminated sediments. ten entries represent ten levels of chlorination. Each entry contains the summation of chromatographic peak areas measured for the quantitation ion(s) for the internal standard(s), surrogate compound(s), or analyte isomers a t a particular level of chlorination. After the identification process has been completed for all levels of chlorination, automated quantitation calculations are performed with routines similar to those currently provided by manufacturers of GC/MS data systems. A summary report provides concentration data for each level of chlorination. Analysis of Commercial Aroclors. Determination of PCBs in two commercial Aroclor mixtures with both automated and nonautomated interpretation and computation procedures showed that the software provided reliable identifications and measurements. The same lots of two Aroclor formulations used for this demonstration had been extensively analyzed by Albro and co-workers (6))who identified and measured individual components by GC analyses with multiple columns and with both electron capture (EC) and flame ionization detectors. Comparison of MS data (automated and nonautomated procedures, Table 111)with literature values (6) showed that more isomers had been detected in data acquired with non-MS detectors than were identified with the MS detector used in this study. Three heptachlorobiphenyls had been detected in Aroclor 1248 with non-MS detectors, but only two had been measured; the other was reported to be a trace component. No heptachlorobiphenyls were detected
in MS data for Aroclor 1248 with either automated or nonautomated procedures. This was not surprising, because an EC detector is more sensitive than an MS detector. Somewhat surprising, however, was the occasional identification of more isomers with automated interpretation than with nonautomated interpretation. For example, with non-MS GC detectors, 22 pentachlorobiphenyls had been detected in Aroclor 1260 (6);14 were detected by automated interpretation ' of GC/MS data, but only 10 were detected by the analyst using nonautomated procedures. The validity of additional identifications with automation was confirmed by analyst inspection of appropriate spectra. Coelution of congeners with different levels of chlorination may have prevented the analyst's detection of some low level PCBs that were detected with the more rigorous data examination achieved with automated procedures. In Aroclor 1248, a total of 4 1 PCB congeners were detected and measured in data interpreted with nonautomated procedures, and two additional congeners were detected and measured with automated procedures. In Aroclor 1260 data, automated procedures resulted in detection and measurement of 63 PCB congeners, but the analyst detected and measured only 53 congeners. Overall, the results produced automatically were as reliable and accurate as results produced with extremely tedious, labor-intensive nonautomated interpretation and computation. Analysis of Environmental Samples. Automated in-
Anal. Chem. 1985, 57, 2469-2473
terpretation and computation procedures also were demonstrated with GC/MS data acquired from extracts of triplicate aliquots of two environmentally contaminated sediments from the harbor at New Bedford, MA. These sediments had previously been analyzed in the same laboratory during an interlaboratory study (2). In both sediment extracts, PCB concentrationsdetermined with automated data interpretation were higher than those determined with nonautomated procedures (Table IV). This resulted from the capability of automated procedures to detect more PCB congeners than the analyst. For example, in sediment 11,52 congeners were detected and measured with automated procedures while only 42 were identified and measured by the analyst. Inspection of appropriate spectra verified the validity of the additional identifications with automated procedures. In addition, precision of triplicate measurements was better with automated procedures than with nonautomated procedures (Table IV) . Total PCB concentrations measured with the isomer group approach compared well with concentrations measured previously in the same laboratory when different extracts of the same sediment samples were analyzed to determine PCB content as Aroclor concentrations. The previously measured mean of duplicate determinations of total Aroclor PCB concentration was 1200 pg/kg in sediment I1 and 110 pg/kg in sediment 111. When PCB concentrations were measured as isomer groups, the mean of triplicate measurements in sediment I1 was 1010 pg/kg with nonautomated data interpretation and 1099 pg/kg with automated interpretation and in sediment I11 they were 96.5 pg/kg and 122 pg/kg, respectively. Cost Effectiveness of Automated Procedures. A comparison of resources required for both human and automated interpretation and computation indicates the cost effectiveness
2469
of the automated procedures. For each GC/MS data file, 12-15 h (including 5 to 6 h of computer terminal use) were required for an experienced analyst to obtain PCB concentration data. Automated procedures, however, required about 0.5 h of an analyst’s time and less than 1h of computer time. CONCLUSIONS The automated interpretation and computation procedures described here not only provide a cost-effective approach to PCB determinations but also provide accurate and precise data. As the software is used in other laboratories and for other determinations, additional refinements will improve its speed and efficiency. ACKNOWLEDGMENT The authors gratefully acknowledge the assistance and cooperation of Phillip W. Albro (Laboratory of Environmental Chemistry, National Institute of Environmental Health Sciences, Research Triangle Park, NC), who provided the commercial Aroclor mixtures. LITERATURE CITED (1) Gebhart, J. E.; Hayes T. L.; Alford-Stevens, A. L.; Budde, W. L. Anal. Chem. W ~ S 57, , 2458. (2) Alford-Stevens, A. L.; Budde, W. L.; Bellar, T. A. Anal. Chem. 1985, 5 7 , 2452. (3) Mullin, M. D.; Pochini, C. M.; McCrindle, S.; Romkes, M.; Safe, S. H.; Safe, L. M. Environ. Sci. Technol. 1984, 18, 468. (4) Rosenthal, D.; Bursey, J. T. Paper No. T4 presented at the 20th Annual Conference on Mass Spectrometry and Allied Topics, Dallas, TX, June 1972. (5) Sokolow, S.; Karnofsky, J.; Gustafson, P. “The Finnigan Library Search Program”; Flnnlgan Corporation: San Jose, CA, 1978; Application Report No. 2. (6) Albro, P. W.; Corbett, J. T.; Schroeder, J. L. J . Chromatogr. 1981, 205, 103.
RECEIWDfor review March 20,1985. Accepted June 20,1985.
Surrogate Standards for the Determination of Individual Polychlorinated Biphenyls Using High-Resolution Gas Chromatography with Electron Capture Detection S. D. Cooper, M. A. Moseley, and E. D. Pellizzari* Analytical and Chemical Sciences, Research Triangle Institute, P.O. Box 12194, Research Triangle Park, North Carolina 27709
A method for calibrating an electron capture detector (ECD) for quantitatlve analysis of ail 209 individual polychlorinated biphenyl (PCB) congeners was developed by using a secondary standard containing 31 surrogate PCB congeners. By use of the relative response factors (RRFs) for 203 of the 209 PCBs determlned by high resolution gas chromatography wlth ECD, RRFs were clustered to allow a commercially available PCB surrogate to represent a group of PCB congeners. With this statistically based approach, the callbration bias was < l o % and the range of the percent relative standard devlatlon for the 31 groups of RRFs was 0-4.9%.
The analysis of samples for polychlorinated biphenyls (PCBs) by using high-resolution gas chromatography with 0003-2700/85/0357-2469$0 1.50/0
electron capture detection (HRGC/ECD) has prompted a need for a simpler way to calibrate a gas chromatographic instrument. Since 209 PCB congeners exist, it is a timeconsuming and expensive task to carry out calibrations using the full set of PCB congeners. Alternatively, the use of Aroclors, technical mixtures of PCB congeners, can be risky since some variations may occur, e.g., from lot-to-lot differences. One method we have applied to reduce these problems is to use a set of surrogate or secondary PCB congeners, each of which represents a group of PCB congeners having similar response factors as determined by using an electron capture detector. This technique allows the use of more readily available congeners, from commercial sources, to represent the congeners which may not be available. By utilizing the response factors and retention times we have previously determined (l),we were able to statistically 0 1985 American Chemical Society
