This document describes a study to improve the productivity of EPA 8270 semivolatile compound analysis in complex environmental samples using spectral deconvolution and the Agilent Deconvolution Reporting Software. Semivolatile samples contain many interfering compounds that make identification and quantitation challenging. The study found that using a semivolatile-specific library and deconvolution database with AMDIS produced "cleaned" spectra that improved identification accuracy and allowed for more reliable quantitation on deconvoluted target ions. Analysis of real samples demonstrated that the combined use of the mass spectrometry software and deconvolution software resulted in fewer false positives and negatives compared to using either method alone.

1. Fast and Accurate EPA 8270
Quantitation Using Deconvolution
Abstract
Environmental GC/MSD semivolatiles samples can be complex due to matrix interfer-
ences. A semivolatiles-specific library and deconvolution database are used in conjuc-
tion with AMDIS to clean spectra and target ions. Full spectra and quantitative results
from the deconvoluted target ion can be compared in the familiar QEdit view.
Identification and quantitation of samples are compared using the Agilent MSD
ChemStation software and Deconvolution Reporting Software.
Authors
Mike Szelewski and Chinkai Meng
Agilent Technologies, Inc.
2850 Centerville Road
Wilmington, DE 19808
USA
Jin Yang, Dawei Zhang, and James Xue
SGS Shanghai
Environmental Services
3rd Building, 889 Yishan Rd.
Shanghai 200233
China
Application Note
Environmental

2. 2
Introduction
EPA 8270 semivolatiles analysis is one of the most widely
used GC/MS methods in the world. The method is developed
to analyze acids, bases, and neutrals in soils and water. The
list of target compounds typically ranges from 160 to 250,
including internal standards and surrogates. SGS Shanghai
analyzes more than 5,000 semivolatile samples a year. Due to
the complexity of sample matrices (background ion interfer-
ences), it is always time-consuming and challenging to con-
firm found target compounds and get accurate quantitation
results.
This study is designed to improve productivity by simplifying
the data review process and improving the ability to ensure
positive peak identification using the Deconvolution Reporting
Software (DRS) [1, 2]. Agilent G1716AA DRS is an application
for target compound analysis that processes results from the
Agilent GC/MSD ChemStation using the NIST Automated
Mass Spectral Deconvolution and Identification System
(AMDIS) and companion Mass Spectral Search Program into
one easy-to-read report.
This "deconvolution" process reduces the manual spectral
interpretation time significantly, whenever a total ion chro-
matogram (TIC) from a complex matrix is being analyzed. In
addition, after removing interfering and background ions in
the deconvolution process, quantitation on the "cleaned" ions
can provide more reliable results.
Experimental
Sample Preparation
Soil and sediment samples (mechanical shaker)
• Decant and discard any water layer on a sediment sample.
Discard any foreign objects, such as sticks, leaves, and
rocks. Weigh wet subsample 0.5 to 20 g (as-received con-
dition) and place in a 250-mL Schott bottle.
• Nonporous or wet sample must be mixed with 20 g of
anhydrous sodium sulfate, using a spatula. If required,
more sodium sulfate may be added. After addition of
sodium sulfate, the sample should be free flowing.
• Add 250 µL of surrogate standard solution (100 ng/µL) to
all samples, spiked samples, QC samples, and blanks.
• For the sample in each batch selected for spiking, add
250 µL of the matrix spiking solution (100 ng/µL).
• Immediately add 100 mL of dichloromethane (DCM)/ace-
tone (1:1 v/v), Place on the mechanical shaker for 2 hours
at 200 rpm.
• Concentrate the extract to 5 mL using a Kuderna-Danish
(KD) concentrator. Quantitatively transfer a 200-µL sample
into a 2-mL GC autosampler vial. Add 5 µL of the internal
standard (200 µg/mL) to give 5 µg/mL in solution. Seal the
vial with a Teflon-faced cap and mix briefly for GC/MS.
Water samples (liquid/liquid extraction)
• Take 1 L water sample to 2 L funnel, check the pH, and
adjust, if necessary, to pH 6~8 using 1:1 (v/v) sulfuric acid
or 10N sodium hydroxide. Add 50 µL stock surrogate stan-
dard solution (100 µg/mL) to each sample and mix well.
• For the sample in each batch selected for uses as a matrix
spike sample, add 50 µL of the matrix spiking solution
standard.
• Add 50 g of NaCl, extract with 60 mL DCM, adjust pH < 2,
and extract with 60 mL DCM twice.
• Combine and reduce the extracts in volume to 1 mL using
KD concentrator. Quantitatively transfer 200-µL sample into
2-mL GC autosampler vial. Add 5 µL of the SVOC internal
standard (200 µg/mL) to give 5 µg/mL in solution. Seal the
vial with a Teflon-faced cap and mix briefly for GC/MS.
Instrument Parameters
The GC/MS operating parameters are shown in Table 1.
Deconvolution
In GC/MS, deconvolution is a mathematical technique that
separates overlapping mass spectra into "cleaned" spectra of
the individual components. Figure 1 is a simplified illustration
of spectral deconvolution. Here, the TIC and apex spectrum
are shown on the left. In a complex matrix, a peak may be
composed of multiple overlapping components and matrix
background ions; therefore, the apex spectrum is actually a
composite of these constituents. A mass spectral library
search would give a poor match, at best, and certainly would
not identify all the individual components that make up the
composite spectrum.

3. 3
The deconvolution process first corrects for the spectral skew
that is inherent in quadrupole mass spectra and determines a
more accurate apex retention time (RT) of each chromato-
graphic peak. Then, the deconvolution process groups ions
whose individual abundances rise and fall together and have
the same RT. As illustrated in Figure 1, deconvolution can pro-
duce a "cleaned" spectrum for each overlapping component.
These "cleaned" spectra ("components" as they are called in
AMDIS) are compared against a library. This comparison uses
full spectra (not just three ion ratios) and is RT independent.
However, Agilent GC/MSD systems can be retention time
locked (RTL), and AMDIS can, after spectral identification,
accept or reject components based upon their proximity to
the locked RTs. Identified targets are saved in a table for later
use and are also sent to NIST for further confirmation.
Table 1. GC and MSD Operating Parameters
GC Agilent Technologies 6890
Inlet EPC split/splitless
Mode Splitless, 1.0 µL injected (10-µL syringe,
p/n 5181-1267)
Inlet temperature 300 °C
Pressure 48 kPa
Purge flow 100.0 mL/min
Purge time 0.75 min
Total flow 56.5 mL/min
Septum purge 3 mL/min
Gas saver On
Saver flow 20 mL/min
Saver time 2 min
Gas type Helium
Liner 4 mm, p/n 5181-3316
Liner O-ring Nonstick, p/n 5188-5365
Septum Advanced green, 11 mm, p/n 5183-4759
Column ferrule 0.4 mm id Vespel/Graphite 85%/15%,
p/n 5181-3323
Column Agilent J&W DB-5ms, p/n 122-5532
Length 30.0 m
Diameter 0.25 mm
Film thickness 0.25 µm
Mode Constant flow
Nominal initial flow 1.0 mL/min
Outlet MSD
Oven
Oven ramp °C /min Final (°C) Hold (min
Initial 40 4
Ramp 1 10 160 1
Ramp 2 10 280 4
Ramp 3 10 300 5
Runtime 40 min
Oven equilibration time 0.5 min
Thermal AUX 2 MSD transfer line, 280 °C
MSD Agilent Technologies 5975 MSD
Tune file dftpp.u
Mode SIM/Scan
Solvent delay 3.80 min
EM voltage offset 200 V
Low mass 35 amu
High mass 550 amu
Threshold 150
Sampling 2
Quad temperature 150 °C
Source temperature 230 °C
Software
GC/MSD ChemStation G1701EA rev E.02.00.493
Deconvolution Reporting Software G1716AA rev A.04
Semivolatiles DRS Database G1677AA rev A.01 (273 compounds)
NIST08 Search Engine and Library, including AMDIS 2.66, build 121.68
AMDIS Settings – There is no single group of settings that will guarantee
finding all compounds in all matrices. The following settings produced the
best results for the data set studied.
Component width 24
Adjacent peak subtraction 1
Resolution Medium
Sensitivity High
Peak shape Low
Minimum match factor 30
RI window 10
Level Infinite
Maximum penalty 100
TIC and spectrum
TIC
Component 1 extracted spectrum
Component 3 extracted spectrum
Component 2 extracted spectrum
Deconvolution
Deconvoluted peaks and spectra
Library search each component to identify
Figure 1. Illustration of spectral deconvolution in AMDIS.

4. 4
DRS Database/Library
The G1677AA Environmental Semivolatiles RTL Database/
Library (DBL) is a set of mass spectral libraries in the Agilent
and NIST/AMDIS formats. There are three separate sets of
libraries and methods. An 8270 set includes the mass spectra
and locked retention times for 243 single-component semi-
volatiles compounds and internal standards specified by
USEPA Method 8270 plus 30 additional compounds of envi-
ronmental interest – a total of 273 compounds. Additional
information can be found in reference 3.
The system used in this study was not RTL to the DRS DBL.
The DRS DBL RTs were updated with the SGS quant database
RTs via a menu function that is supplied with the DRS A.04
revision. This is a useful feature for labs that have established
methods that they do not want to change.
Results and Discussion
A typical TIC from one sample can be seen in Figure 2, and is
quite complex. DRS can be used as a tool to aid in both iden-
tification and quantitation of complex samples. As part of the
process, deconvoluted data from AMDIS are imported into the
MSD ChemStation QEdit View (see Figure 3).
1) The panel in the middle of the display shows a list of all
target compounds. Target compounds quantified by
ChemStation are labeled by x and target compounds quan-
tified by DRS-AMDIS are labeled by A.
2) The top left panel shows a multi-ion overlay where MSD
target (quant) ion, up to three qualifier ions, and the target
(quant) ion from AMDIS (if a compound with an AMDIS hit
A is selected), are displayed for quick confirmation. If the
AMDIS ion does not overlay with the MSD ions, it implies
that two different compounds were quantified.
3) The bottom left panel shows three spectra – the raw
"dirty" spectrum, the AMDIS "clean" deconvoluted spec-
trum, and the AMDIS library spectrum are displayed if a
compound with an AMDIS hit A is selected or if the
AMDIS target ion is manually integrated. By visually com-
paring the bottom two spectra (deconvoluted spectrum
and the library spectrum), a good or poor match can be
determined easily.
4) At the top right of the screen, the MSD target ion used for
quantitation and 5) the AMDIS deconvoluted target ion are
shown in two separate panels. Redrawing the baseline
(manual integration) may be performed in each panel to
improve the accuracy of the quantitation results. The panel
of the AMDIS target (quant) ion has a very flat and clean
baseline due to the deconvolution process. This helps to
get a more reliable quant value.
6) The bottom right panel shows both the integrated area and
calculated amounts for MSD and AMDIS target ions.
Qualifier ion ratios are also listed for evaluation purposes.
A # flags the out-of-range ion ratio.
All these panels make it easy to confirm the deconvoluted
compounds and compare quantitation results (refer to the
DRS A.04 Help file for more information).
6 8 10 12 14 16 18 20 22 24 26 28
Figure 2. Total ion chromatogram of a semivolatiles sample.

5. 5
A detailed analysis of one sample is in Table 2, information
combined from two separate DRS reports for presentation
here. The first three columns, blue highlighted, list the reten-
tion time at which the compound was found, the CAS#, and
the name. The four columns to the right, green highlighted,
starting with the AMDIS Match, list the match factor of the
deconvoluted component compared to the DRS Semivolatiles
DBL and its RT difference from the expected. A low minimum
match factor of 30 was used in AMDIS. This may result in a
few more false positives but will minimize the number of false
negatives. The NIST Match factor is a further check on identi-
fication, comparing the component to the NIST library. The
last column lists the hit number in the list of the top 100 hits
from NIST.
The three remaining columns in Table 2 show amounts calcu-
lated against the MSD ChemStation quant database. The
Auto column lists the amounts that were originally found by
the ChemStation with automatic integration. If a compound
showed qualifier ion ratios that were out of range, this is indi-
cated by Qualifier MisMatch (QMM). The first three peaks
with QMM were successfully integrated automatically, but
the amounts were wrong. Either the wrong peak was detect-
ed or the integration was incorrect due to matrix. False posi-
tives and false negatives are indicated by FP and FN, respec-
tively. The Man column lists amounts that were based on
manual integration. The RT used for integration was based on
the AMDIS component time. Both of the columns labeled
MSD used raw data as normally seen without deconvolution.
The two compounds labeled "Can't int" showed no distinct
peak that could even be manually integrated. The AMDIS col-
umn shows the amount based on the deconvoluted "clean"
extracted ion.
Figure 3. QEdit screen displaying MSD and AMDIS information, Acenaphthene selected.
2
1
3
4 5
6

6. An example illustrating the value of quantitation of a decon-
voluted target ion can be found in Figure 4. The black trace is
the raw extracted target ion 93, and the two qualifiers are
shown in blue 95, and purple 63. Without deconvolution, the
software or an analyst may choose any of the peaks in the
region for quantitation. All of these would result in incorrect
amounts and misidentifications. The deconvoluted target ion
93 is shown in red and is easy to integrate.
Eight soil and water samples with high interference were
processed with the MSD ChemStation including DRS. The
results are summarized in Table 3. Careful manual review
shows that 183 compounds are actually present. Using only
the MSD ChemStation automated quant results, there were a
large number of false positives (FPs) and false negatives
(FNs). This indicates that all compounds in the quant data-
base would have to be individually reviewed. It should be
noted that the results presented in Table 3 are based on auto-
matic integration and data processing before manual review
by an experienced analyst. AMDIS showed fewer FPs and
FNs, but these are also not predictable.
Using the ChemStation and AMDIS together, through DRS,
showed the fewest number of FPs and FNs. There were no
6
Table 2. Details of MSD ChemStation and DRS Results for a Single File
Auto Man
RT CAS# Name MSD MSD AMDIS AMDIS AMDIS NIST NIST
ng ng ng Match RT diff Match Hit #
9.708 62533 Aniline 330 330 320 96 1.6 96 1
9.774 108952 Phenol QMM-18 2.26 0.29 31 1.4 NF
9.851 111444 Bis(2-chloroethyl) ether QMM-320 Can't int 0.69 39 –1.5 NF
11.423 95534 o-Toluidine QMM-410 Can't int 0.38 84 –0.8 73 10
11.467 621647 N-Nitroso-di-n-propylamine FP
13.204 120821 1,2,4-Trichlorobenzene 0.08 0.08 0.07 85 –0.8 72 2
13.356 91203 Naphthalene 0.04 0.04 0.05 85 –1.5 92 5
17.804 83329 Acenaphthene 0.26 0.26 0.26 98 –1.3 88 1
18.004 51285 2,4-Dinitrophenol QMM 5.3 5.5 38 –3.3 84 22
18.316 132649 Dibenzofuran 0.56 0.56 0.56 98 –0.9 92 1
19.366 7005723 4-Chlorophenyl phenyl ether 0.16 0.16 0.10 81 –0.7 74 1
19.716 122394 Diphenylamine 0.04 0.04 0.04 88 –1.4 96 2
19.792 103333 Azobenzene 0.50 0.50 0.23 84 –1.1 81 2
21.832 85018 Phenanthrene 0.17 0.17 0.13 95 –2.0 87 4
21.836 120127 Anthracene FP
23.544 84742 Di-n-butylphthalate FN 0.05 0.05 77 –1.9 NF
28.363 56553 Benzo(a)anthracene 0.19 0.19 0.16 52 –1.2 53 14
28.697 117817 Bis(2-ethylhexyl)phthalate 0.46 0.46 0.48 94 –1.4 86 3
30.226 117840 Di-n-octylphthalate FP
9.29.0 9.4 9.6 9.8 10.0 10.2 10.4 10.6
Ion 63
Ion 95
MSD Target ion 93
Deconvoluted ion 93
Expected RT
Figure 4. Bis(2-chloroethyl) ether target and qualifier ions.
FNs that were the same in both programs, which saves signif-
icant time for the analyst, as only the positives need to be
reviewed.

7. 7
Conclusions
Environmental GC/MSD semivolatiles samples can be com-
plex due to matrix interferences. Data review can be simpli-
fied, data quality improved, and productivity increased
through the use of integrated deconvolution software. Using
the familiar QEdit view, operating training is minimized. The
fewest number of false positives and false negatives will be
found in the shortest amount of time using the MSD
ChemStation in combination with DRS. Among the eight com-
plex samples analyzed, no false negatives were found.
Table 3. Summary Results for Eight Samples
References
1. Philip L. Wylie, Michael J. Szelewski, Chin-Kai Meng, and
Christopher P. Sandy, "Comprehensive Pesticide Screening
by GC/MSD Using Deconvolution Reporting Software,"
Agilent Technologies, publication 5989-1157, May 2004
2. Bruce Quimby and Mike Szelewski, "Screening for
Hazardous Chemicals in Homeland Security and
Environmental Samples Using a GC/MS/ECD/FPD with a
731 Compound DRS Database," Agilent Technologies, pub-
lication 5989-4834, February 2006
3. Michael J. Szelewski, "Semivolatiles Retention Time
Locked (RTL) Deconvolution Databases for Agilent
GC/MSD Systems," Agilent technologies, publication
5989-7875, February 2008
For More Information
For more information on our products and services, visit our
Web site at www.agilent.com/chem.
Total for
8 samples
Total real positives based on manual review 183
ChemStation false positives at 20% absolute 35
qualifier ion agreement
ChemStation false negatives at 20% absolute 56
qualifier ion agreement
AMDIS false positives 11
AMDIS false negatives 11
Both AMDIS and ChemStation same false negatives 0
Both AMDIS and ChemStation same false positives 2

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© Agilent Technologies, Inc., 2008
Published in the USA
December 9, 2008
5990-3253EN