Development of quantitative screen for 1550 chemicals with GC-MS
Development of quantitative screen for 1550 chemicals with GC-MS
Alan J. Bergmann 0 1
Gary L. Points 0 1
Richard P. Scott 0 1
Glenn Wilson 0 1
Kim A. Anderson 0 1
0 Department of Environmental and Molecular Toxicology, Oregon State University , 1007 Agricultural and Life Sciences Bldg., Corvallis, OR 97331 , USA
1 Kim A. Anderson
With hundreds of thousands of chemicals in the environment, effective monitoring requires high-throughput analytical techniques. This paper presents a quantitative screening method for 1550 chemicals based on statistical modeling of responses with identification and integration performed using deconvolution reporting software. The method was evaluated with representative environmental samples. We tested biological extracts, low-density polyethylene, and silicone passive sampling devices spiked with known concentrations of 196 representative chemicals. A multiple linear regression (R2 = 0.80) was developed with molecular weight, logP, polar surface area, and fractional ion abundance to predict chemical responses within a factor of 2.5. Linearity beyond the calibration had R2 > 0.97 for three orders of magnitude. Median limits of quantitation were estimated to be 201 pg/μL (1.9× standard deviation). The number of detected chemicals and the accuracy of quantitation were similar for environmental samples and standard solutions. To our knowledge, this is the most precise method for the largest number of semi-volatile organic chemicals lacking authentic standards. Accessible instrumentation and software make this method cost effective in quantifying a large, customizable list of chemicals. When paired with silicone wristband passive samplers, this quantitative screen will be very useful for epidemiology where binning of concentrations is common.
Gas chromatography; Multiple linear regression; Chemometrics; Response prediction; Automated mass spectral deconvolution and identification system (AMDIS); Passive sampling devices
Hundreds of thousands of chemicals exist in the environment.
Many chemicals are known to pose health risks and many
more are yet to be evaluated. Semi-volatile organic chemicals
(SVOCs) are of interest because they are often bioavailable,
have potential adverse health impacts, and can be persistent in
the environment. Rapid and cost-effective methods for
sampling and analysis are necessary to improve our ability to
monitor environmental contamination and prioritize
chemicals for health research.
Current analytical techniques to identify and measure
SVOCs can precisely quantify small numbers (e.g., < 100)
of chemicals within specific classes of chemicals in targeted
quantitation, e.g., Anderson et al. [
]. Typically, targeted gas
chromatography mass spectrometry (GC-MS) methods use
in-house chemical standards to develop method-specific
libraries of retention times, mass spectra, and response factors.
Alternatively, non-target analysis thoroughly investigates
complex chromatograms for unknown components [
Nontarget methods use high-resolution mass spectrometry and
reference libraries of mass spectra such as the National Institute
of Standards and Technology (NIST) Mass Spectral Library.
A third approach is to perform targeted screening for a
hundreds of chemicals which balances the analysis time of
targeted quantitation with the thoroughness of non-target
Targeted screening methods are frequently performed on
GC or liquid chromatography with mass spectrometry. GC is
well suited for the analysis of SVOCs. Non-target and targeted
screening are performed by capturing the full scan ion profile
including potentially interfering chemicals. Deconvolution
s o f t w a r e , s u c h a s t h e A u t o m a t e d M a s s S p e c t r a l
Deconvolution and Identification System (AMDIS, NIST),
is available to extract specific signals from complex
chromatograms. A challenge with targeted screening is to develop
libraries of retention times, mass spectra, and response factors
for hundreds or thousands of chemicals. It is unfeasible for a
laboratory to determine these parameters for each chemical
individually but it can rely on libraries for mass spectra such
as the NIST MS Library, and to a more limited extent,
retention time. Agilent Technologies developed the Deconvolution
Reporting Software (DRS) package for ChemStation to
incorporate retention time indices in a standardized GC method
with AMDIS and the NIST MS library [
]. This type of
analysis is good for identifying chemicals in complex
chromatograms but quantification requires more work.
In order to accurately quantify chemicals in targeted
screening methods, response factors for a large list of
chemicals need to be determined. While calibrations can
be manually constructed for every chemical [
], this is
time and resource intensive especially for methods with
hundreds to thousands of target analytes. Another method
is to assign an internal standard to groups of chemicals in
the method. Bu et al. assigned 14 internal standards based
on chemical class to a list of 847 chemicals analyzed by
]. They were able to quantify within a factor
of 4 the true value of target analytes in spiked sediment
extracts. Differences in response factors between the
internal standard and targets contributed to the uncertainty
in quantification. A third method is to use chemometrics
to predict instrument response from chemical properties
. Chemical-specific parameters that vary within
chemical classes such as molecular weight, polarity, and
fractional ion abundance [
] affect chemical response.
Some work has been done to predict chemical-specific
MS responses, mostly in electrospray ionization-MS for
liquid chromatography applications , and for GC-MS
with thermal desorption for a small set of VOCs [
goal was to develop a predictive model for response factor
calibration of SVOCs lacking authentic standards and to
apply the method to samples with a high-throughput
Passive sampling devices (PSDs) are versatile and simple
tools for measuring contaminants in the environment and
]. The polymers low-density polyethylene (LDPE)
and polydimethylsiloxane (silicone) are used as PSDs to
sample for SVOCs. These PSDs accumulate lipophilic chemicals
from the environment through passive diffusion. Commonly
deployed for days to weeks at a time, PSDs can concentrate
trace contaminants and lower environmental detection limits.
Consequently, environmentally deployed LDPE can contain
many hundreds to thousands of individual chemicals.
Wristband passive sampling devices made of silicone are a
recent development in passive sampling. Silicone wristbands
are used to monitor a person’s external exposure to SVOCs
and can have more variable backgrounds than
environmentally deployed LDPE [
]. Human biomonitoring, such as
with silicone wristbands, generates immense data sets.
Wristband passive sampling devices can be used in large
numbers because they are cheap, non-invasive, hold chemicals
stably for weeks at ambient temperature, and can be prepared
for deployment in large batches using minimal solvent [
Still, the majority of laboratory time and cost associated with
wristband biomonitoring is in performing the chemical
extraction and analysis.
Our objective was to create a quantitative GC-MS method
for a list of more than 1500 SVOCs to pair with the high
sample generation of wristbands and other PSDs. We aimed
to leverage the integrated deconvolution and chemical
confirmation available with the DRS package and generate a library
of calibrations for chemicals that we do not physically have in
the laboratory. We present the analytical and statistical results
of predicting response. We called the resulting multi-class
quantitative screen the Many Analyte Screen Version 1500
(MASV1500). We validated the accuracy, precision, and
sensitivity of this method in real environmental samples with a
focus on passive sampling devices. As the first report of a
high-throughput quantitative screen using the deconvolution
freeware AMDIS, this work demonstrates an analytical
method that compliments the sample generating capacity of passive
A complete list of 224 chemicals used for calibration model
building and testing can be found in Table S1 (see Electronic
Supplementary Material (ESM)). Standards were purchased
from a variety of sources including AccuStandard,
SigmaAldrich, TCI America, SantaCruz Biotechnology, and
Chiron. Standards were prepared as singles or simple mixes
in ethyl acetate, n-hexane, or isooctane (Fisher Scientific,
optima grade) at concentrations typically between 0.5 and 10 μg/
All data was acquired using an Agilent 7890A GC coupled
with an Agilent 5975C MSD operated in in full scan mode
with electron ionization. The GC was equipped with an
Agilent DB-5MS column (30 m × 0.25 mm). The inlet
pressure was locked to the retention time of chlorpyrifos at 19.23
(± 0.20) minutes. Full details of the instrument operating
parameters are available in Table S2 (see ESM).
AMDIS version 2.66 (NIST), as part of the DRS (Agilent)
was used to deconvolute and identify all peaks. All AMDIS
software parameters are given in Table S3 (see ESM). AMDIS
integrations of deconvoluted peaks were used for
Adding chemicals to libraries
The complete chemical library consisted of 1550 chemicals
including 21 chemicals that are isotopically labeled or
otherwise not generally found in the environment. We achieved this
list by manually adding approximately 450 chemicals to
libraries purchased with the deconvolution software.
Singlecomponent standards were prepared from neat or purchased
in ethyl acetate, methylene chloride, n-hexane, or isooctane at
a concentration between 0.5 and 10 ng/μL. Mass spectra and
retention time for each new chemical were acquired using the
GC-MS method described above and added to a new
ChemStation probability-based matching library. Each entry
included chemical name, retention time, retention index
(retention time in seconds), mass spectra, molecular formula
(from which the software generates MW) and Chemical
Abstracts Service registration number (CASRN). After new
chemicals were added, the new library was appended to the
master library. AMDIS library files and an updated method
file were then generated from this master library using
In order to calibrate for 1550 chemicals, we developed a
predictive model based on the response factors of chemicals
available in our laboratory. A modeling set of 196 chemicals
was analyzed. The modeling set consisted of PAHs, several
classes of pesticides, polychlorinated biphenyls (PCBs),
polybrominated diphenylethers (PBDEs), and phosphate
flame retardants, phenols, and anilines (Table S1, see ESM).
Molecular weight (MW), topical polar surface area (PSA),
Henry’s law, octanol-water partitioning coefficient (logP),
octanol-air partitioning coefficient (Koa), acid disassociation
constants (pKa), halogen and heteroatom substitution
abundance, and fractional ion abundance were examined as
potential explanatory variables. These parameters were obtained
from Advanced Chemistry Development (ACD) Labs through
Chemspider (R version 3.3, webchem package). For
chemicals that were not available through Chemspider (n =
97), we manually retrieved the parameters from Episuite 4.1
Software or from ChemStation and AMDIS libraries.
Fractional ion abundance was obtained from the AMDIS
spectral library and is calculated as the ratio of the most
abundant ion to the sum of all ion abundances. Python (version 3.6)
code used to calculate fractional ion abundance from the
AMDIS library files and to pull values from Chemspider can
be found in the ESM. We assessed the representativeness of
the modeling set by comparing the distributions of the final
selected chemical parameters to the entire 1550 list
(Kolmogorov-Smirnov test, alpha = 0.05).
The responses of triplicate injections at 500 pg/μL were
used to construct a multiple linear regression (JMP Pro 13)
to predict responses at that concentration based on chemical
parameters. Chemicals in the modeling set were randomly
assigned to either a training set (75%, 147 chemicals) or a test
set (25%, 49 chemicals). The distributions of the GC-MS
response and explanatory variables were first evaluated for the
need for transformation. Many parameters that were not
already in logarithmic scale (e.g., logP and pKa) were
left-centered. To normally distribute these data, log10 (log)
transformations were applied. Model optimization proceeded through
forward and backward stepwise regression to maximize the
adjusted R2 while minimizing root mean square error (RMSE)
and minimizing the Akaike information criterion. We
interpreted RMSE as an estimate of the standard deviation of
the model residuals. Assuming that two standard deviations
plus and minus the mean encompass 95% of the observations,
the untransformed precision of the calibration model is given
The equation of the optimized multiple linear regression
used to predict the response of a given chemical at 500 pg/
logð500 pg=μL Predicted ResponseÞ ¼ 9:372 þ ½0:05678*logP
þ ½0:7394*logðfractional ion abundanceÞ −½1:169*logðMWÞ
−½0:173*logðPSA þ 1Þ þ ½ðlogP−5:045Þ*ððlogðMWÞ−2:432Þ*ð−0:2466ÞÞ
Raw instrument responses were directly divided by
response factors to estimate concentration in picogram per
microliter as given by:
Response 500 pg=μL
Concentration ¼ Predicted Response500 pg=uL
We evaluated the calibrated method with an overspike
solution of 112 chemicals in isooctane (Table S1, see ESM) that
represent a range of physico-chemical properties from
chemical classes including pesticides, PAHs, phenols, and anilines.
With clean standard solutions at several concentrations, and
matrix-matched overspikes, we determined method linearity,
precision, accuracy, and estimated limits of quantitation.
Because our prediction model was designed using only a
single concentration level, we performed experiments to evaluate
the range of concentration over which it could be applied. We
measured response of 112 chemicals in the overspike solution
at 100, 500, 2500, and 10,000 pg/μL in isooctane. The
accuracy of the response prediction was evaluated at each
concentration level and the adjusted R2 was used to evaluate the linear
response range of each modeled chemical.
Method transferability was tested by running the same method
on a second GC-MS with identical parameters. The method
calibration verification (CV) standards described in the quality
control section were evaluated on two GC-MS instruments on
the same day.
Typical samples that we anticipate analyzing with the method
described in this paper include biological tissue extracts [
LDPE passive sampling device extracts deployed in river
], and silicone wristbands that were worn by people [
]. We evaluated the detection rate and quantitative accuracy
and precision of 112 chemicals added to samples
representative of typical background matrices. Silicone wristbands tend
to have variable and high backgrounds of silicone, fatty acids,
and steroidal chemicals (cholesterol, squalene). Five people
wore a silicone wristband for 5 days to generate samples with
background matrices typical of deployed wristbands. The
samples were randomized and no personal information was
collected which might be used to identify the participants.
Aliquots of the five deployed wristbands were cleaned with
C18 solid-phase extraction as described elsewhere . Both
pre- and post-SPE-cleaned wristband samples were evaluated.
We also tested the overspike solution in one crayfish extract
], one pre-deployment LDPE, one pre-deployment
wristband, and four deployed LDPE [
Instrumental limit of quantitation
Instrumental limits of quantitation (LOQs) were calculated in
accordance with other methods in our laboratory [
] and as
described by the U.S. EPA . Several adjustments were
made to this method to account for modeling chemical
responses rather than measuring them directly. The previously
described overspike mixture (112 chemicals) was prepared at
500 pg/μL and was injected seven times to assess instrument
variability. For each chemical with at least three detections by
AMDIS, the standard deviation of the responses was
calculated and multiplied by a single-tailed Student t value with the
appropriate degrees of freedom (d.f. = n − 1; where n was the
number of times that the chemical was identified). The
average adjusted standard deviation for all evaluated chemicals
was used in Eq. 2 to estimate LOQ for 1550 chemicals. We
refer to this method of calculating LOQ as average response
We also estimated LOQs using a second method, termed
linear extrapolation, and compared the two. For all chemicals
used to evaluate linearity that were detected in at least three of
the four concentrations measured (n = 64), we extrapolated to
the x-intercept for each chemical and used that value as a
prediction of LOQ.
An analyst evaluated all chromatograms processed with
AMDIS for identification and integration quality.
Chromatographic peaks that did not meet data quality
objections were rejected, and positive identifications that were
poorly integrated by AMDIS were flagged as poor AMDIS
peaks (PAP) to indicate that quantification would not be
reliable. Our minimum data quality objectives for peak
identification required retention times shifted by no more than 45 s
and at least one qualifier ion should be within 20% of its
predicted abundance relative to the quantitation ion. The
AMDIS match factor threshold was set to 60 (out of 100)
and the extracted spectra of identified chemicals were
reviewed manually for missing or extra m/z peaks.
CVs were prepared to monitor instrument conditions. To
establish target responses and acceptable deviation for the CV,
15 diverse chemicals were monitored in 12 injections over the
course of several days. An average response was taken for
each chemical across all injections in which a chemical was
identified and not flagged as a PAP. These responses were
used in Eq. 2 to give target concentrations for our CVs.
Instrument blanks (clean ethyl acetate or hexane) and CVs
were evaluated before and after every sample set analyzed
with this method. To meet our suggested data quality
objectives, no target chemicals should be identified in the
instrument blanks and greater than 70% of CV target chemicals
must be within 30% of the responses given in Table S4 (see
ESM). The CV included diagnostic chemicals to inform about
instrument condition [
]. For example p,p′-DDT degradation
to p,p′-DDE or p,p′-DDD indicates contamination at the GC
inlet . Method QC samples can contain some target
chemicals. Specifically, phthalates are regularly identified as
background in undeployed PSD matrices of both LDPE and
silicone. However, the amounts of these pervasive chemicals
in deployed samples are typically 100–10,000 times greater
than QC samples [
One of the challenges encountered at the outset of this
project was data curation. Purchased libraries often contained
errors in chemical names and/or CAS numbers. We
crosschecked the library entries for accuracy using R, Python,
and JMP. Several chemicals in purchased libraries also lacked
any retention time data. These errors were corrected when
possible with individual standards but there may be additional
errors in purchased retention time data that could not be
identified without obtaining all standards.
Results and discussion
We calibrated for chemicals lacking authentic standards by
predicting response factors based on physico-chemical
properties of model chemicals. As a predictive model, our goal was
not to interpret the explanatory variables but to optimize the
precision and accuracy of the model predictions. However, we
chose to test physico-chemical properties based on (1)
potential to affect GC-MS response and (2) availability to the
average user. After model optimization, we determined that MW,
logP, fractional ion abundance, and PSA were good predictors
of response factor.
Chemicals in the domain of the calibration model are
organic chemicals that are measurable by GC-MS. Specifically,
the ranges of each parameter for all chemicals in the method
are MW 93 to 793 g/mol; fractional ion abundance 0.0223 to
0.7849; logP − 3.77 to 10.14; and PSA 0 to 202 Å2. These
predictors were generally representative of the entire list (Fig.
S1, see ESM). The distributions of log(MW) and
log(fractional ion abundance) in the model set were not
significantly different from the non-modeled chemicals
(Kolmogorov-Smirnov test, p > 0.05). LogP and log(PSA +
1) showed evidence that the distributions were not equal
(Kolmogorov-Smirnov test, p < 0.001).
MW and fractional ion abundance were the most
significant model parameters. MW was negatively associated with
MS response because the detector measures an analyte’s
molarity, rather than mass concentration which are the units of the
calibration. At the same concentration, fewer molecules of a
large MW chemical reach the detector, compared to a lighter
chemical. The degree and pattern of fragmentation influences
the area of the quantitation ion [
] and we observed that
fractional ion abundance was a predictor of response. Quantitation
is based on the AMDIS adjusted response of the ChemStation
quantitation ion, which is the most abundant ion fragment in
the mass spectrum. Chemicals that have a greater degree of
fragmentation (e.g., endosulfan) may have lower response
than a chemical that remains relatively intact (e.g.,
We observed that greater polarity as described by logP and
PSA resulted in decreased response. Polar chemicals
commonly have functional groups that may interact with
instrument components, reducing the mass of the chemical that
reaches the detector . Polar chemicals are often less
volatile than non-polar chemicals holding everything else
constant. However, we did not observe a significant effect of
Henry’s law constant or logKoa on instrument response so
volatility does not seem to be directly related to response.
For the same reasons, we also expected acidity of analytes to
influence response but pKa was also not a significant model
parameter. This is possibly because acidic chemicals (e.g.
phenols, anilines) are a minority among the chemicals in the
library and are very weak acids. Polarity can also influence the
ionization efficiency in electrospray ionization-MS [
may also be important at the electron ionization source used in
this study. We also observed an interaction between MW and
logP. For non-polar chemicals, logP increases with MW but
instrument response is negatively associated with MW and
positively associated with logP. Interestingly, no interaction
was observed between PSA and other parameters.
The frequency of heteroatoms and halogens (N, O, Cl, Br,
I) in molecules was also evaluated as a predictor of response.
It was suspected that the number and type of these atoms
could be used to describe contributions to polarity and
possibly fragmentation patterns. However, their contribution to the
model when also including MW and PSA as explanatory
variables was not significant and were worse predictors of
response than MW and PSA.
The final optimized model had an adjusted R2 of 0.80 and
RMSE of 0.18 (Fig. 1). The RMSE translates to 95% of
measured responses were within a factor of 2.28 of the true value.
The model test and training sets had similar distributions of
residuals with standard deviations of 0.20 and 0.18,
respectively. Training and test set evaluation is commonly used for
evaluation of predictive models. Steyerberg et al. also
recommend bootstrapping of the models, but found that training/test
set validation and bootstrapping gave similar results when
events per variable were approximately 40 or more . The
events per variable in the current study were 39 (196
observations/5 variables). We also evaluated prediction error
with a leave-one-out approach using the prediction error sum
of squares (Press) statistic. The Press RMSE was 0.19. We
used the most conservative estimate of prediction error, 0.20
from the test set, to determine the precision of prediction as a
factor of 2.5.
The final model predicted the response factor for 95% of
chemicals within a factor of 2.5 of their true value. This is, to
our knowledge, better than for any previously reported
method of this type. Bu et al. were able to quantify organic
chemicals within a factor of 4 using a set of internal standards
]. Naturally, quantitative screens are as precise as
conventionally calibrated target methods which develop response
factors with standards for every chemical. The compromise
of being able to quantify over a thousand chemicals within a
factor of 2.5 or 4 is within variability assumed in some
disciplines. Epidemiology studies may bin chemistry results into
just a few groups , and human health risk assessment may
assume uncertainty factors of 100 when calculating reference
doses . The silicone wristband PSDs used as examples in
the present study commonly accumulate concentrations that
Fig. 1 Measured vs. predicted values of 196 chemicals used in modeling
GC-MS response at 500 pg/μL. Axes are log10 transformed. Model
explanatory variables were log(MW), log(fractional ion abundance), logP,
log(PSA + 1), and log(MW) crossed with logP. Training set chemicals
(closed circles) and test set chemicals (open circles). Solid line is the
model fit of the training set, dark shading is the fit 95% confidence
interval, and light gray shading is the 95% prediction interval. R2 and
root mean square error (RMSE) are given for the model fit of the training
range several orders of magnitude, much greater than the
precision of the MASV1500 method [
While the prediction of chemical concentration in the
present study is less precise than traditional targeted quantification
methods, the instrumental error is comparable. The method in
the present study includes peak integrations by AMDIS which
did not seem to add a significant variability to the
measurements. For 12 replicate injections of a standard at 100 pg/μL
evaluating 62 chemicals, the relative standard deviation of the
average and median chemical response were 17 and 16%
respectively. The entire method was also successfully
transferred to an identical GC-MS. In that inter-instrument
evaluation, linalool had the greatest difference between instruments.
The quantitation ion for linalool was 73 m/z, smaller than most
chemicals in the method. A MS tune adjustment to weight the
lower end of the mass axis could reduce this increased
The performance of identifying and quantifying chemicals in
standard solutions and matrix overspikes is shown in Figs. 2
and 3. AMDIS detected 85 of 112 chemicals (75%) among
four concentrations of the overspike mixture. For chemicals
that were detected in at least three levels, 64 had adjusted R2 >
0.97 (Fig. S2, see ESM). These estimates of fit indicate good
linearity from 100 to 10,000 pg/μL. AMDIS detection rate
and integration quality was best for clean standard solutions
at 500 pg/μL and above (Fig. 2a). This is not surprising
because background matrix can interfere with chemical
identification, even when using deconvolution. Standards at 100 pg/
μL were near the limits of quantitation and limits of detection
so had lower detection rates despite no background matrix.
The number of detections in matrix overspike solutions at
500 pg/μL was mostly similar to a standard solution at
500 pg/μL. An exception was deployed wristband samples
before SPE clean-up which had lower and more variable
number of detections than the standard solution at 500 pg/μL. The
rate of PAPs was lower for high concentrations of standard
solutions but varied around 10% for matrix overspike
solutions (Fig. 2b). The number of positive detections that were
within the expected quantitation range, excluding PAPs, was
above 80% for all samples (Fig. 2c). While background matrix
does not seem to affect quantitation, it does affect the number
of detections in a sample (Fig. 2a). Performance of AMDIS
identifications was best for high concentrations of standards in
clean matrices as indicated by a high number of detections and
few PAPs, followed by SPE-cleaned wristbands. AMDIS
performed worst for wristbands that were not cleaned.
Figure 3 shows calculated concentrations of chemicals in
spiked solutions and matrix-matched samples, and compares
them to the expected precision of within a factor of 2.5 from
the nominal value, between 200 and 1250 pg/uL. All
standards and matrix overspikes were centered around the
expected value indicating the accuracy of the method.
Matrixmatched samples had more detections beyond the expected
range, especially on the high end. This could be due to matrix
enhancement for some chemicals, despite the method using
A solution of pesticides and laboratory surrogates selected
as a diverse list of chemicals was run three times at 500 pg/μL.
AMDIS detected 73 out of 78 different chemicals at least once
among the runs. Of these, 65 chemicals were identified by
AMDIS in every replicate and 54 had RSDs less than 25%.
Of the chemicals with RSDs greater than 25%, 5 out of 11 had
higher variability because they were poorly integrated by
AMDIS. The physico-chemical properties of the compounds
evaluated for intrainstrumental variation ranged as follows:
fractional ion abundance 0.0253–0.6227; MW 201–540;
logP 1.19–8.1; PSA 0–171. In another test, method conditions
as described above were applied to a second, identical,
GCMS. The average percent difference in response between the
instruments was 13% across all chemicals identified in the CV.
The percent difference ranged from 0.5% for benzothiazole to
63% for linalool which was 35% higher than any other
chemical measured. Therefore, we think transferring the method
would be successful.
Kadokami et al. found that the accuracy of identification by
their screening method was superior to the performance of
]. However, they acknowledge that they did not
use retention time or optimize AMDIS deconvolution settings.
SPE WBs (solid-phase extraction cleaned wristband extracts) are
replicate extracts of each matrix type with the overspike mixture at 500 pg/μL.
(a) The number of positive detections by AMDIS regardless of integration
quality. (b) Proportion of positive detection that were PAPs (poor AMDIS
peaks). (c) The percentage of non-PAP positive detections that were
quantified within the expected bounds of a factor of 2.5
Fig. 3 Performance of
MASV1500 quantitation of
matrix overspikes at 500 pg/μL.
Dash-dot line is expected
concentration (500 pg/μL), pink
shade is ± a factor of 2.5, which is
the estimated prediction error of
the calibration model. Standard is
500 pg/μL in ethyl acetate. QC
(quality control) samples from left
to right: non-deployed LDPE,
non-deployed wristband, and a
crayfish extract. LDPE
lowdensity polyethylene, WB
wristband, SPE WB solid-phase
extraction cleaned wristband extract
We found that AMDIS performed well but did not identify all
chemicals. We observed that AMDIS can misidentify
chemicals as their isomers within a retention window of
approximately 45 s. Often, pairs or groups of isomers are present
in a sample and the software will identify one peak as multiple
isomers. If multiple peaks are identified as isomers but there is
uncertainty about which peak is an isomer then the analyst
user may choose to report all detected isomers as the sum of
Limits of quantitation
A major improvement in our analysis was estimating
chemical-specific LOQs. LOQs were estimated in two
different ways which corroborated each other and converged on
LOQs of approximately 40–500 pg/μL depending on the
chemical. The median predicted LOQs were nominally the
same (Fig. S3, see ESM) for both the average response
variability method (201 pg/μL) and the linear extrapolation
method (237 pg/μL). Additionally, the range and distribution of
LOQs predicted by both models were similar. The ranges of
response variability model and the linear extrapolation model
were 2580 and 2393 pg/μL, respectively. Predicted LOQs for
all chemicals by the response variability method can be found
in Table S5 (see ESM).
The method we used to report LOQ, the average response
variation method, is typically used for limit of detection
(LOD) calculations. We have determined that these
concentration limits are better described as LOQs than LODs because
AMDIS is often able to detect chemicals at concentrations
estimated below these limits but with poor performance
during integration. The frequency of PAPs in a 100 pg/μL
solution was much higher than any other standard (Fig. 2b), and
those peaks could not be reliably integrated for quantitation.
A number of assumptions were made to determine LOQs.
It is recommended to use a standard near the expected LOD
when evaluating instrument variability for LOD calculations
. For the present method, 500 pg/μL is likely near the
LOD of some chemicals in the method, but also likely to be
an order of magnitude above the true LOD for many others. At
100 pg/μL, fewer chemicals were quantifiable than at 500 pg/
μL because more detections were poorly integrated by
AMDIS. Therefore, we chose to use 500 pg/μL to capture a
greater range of chemicals, but may be overestimating the
LOQ concentration for some chemicals. Additionally, we
assume that the average measured response is representative of
all chemicals in the model. Chemical specific response factors
from the calibration model should normalize any bias from
this average. Finally, all LOQ calculations have the modeling
error implicitly built into them. We should therefore expect
that the true LOQs may be within a factor of 2.5 from the
values given here.
Analysis of 1550 chemicals in one GC run saves time and
cost compared to analyzing for the same chemicals in
separate methods. Most traditional GC-MS methods target
fewer than 50 chemicals and may take anywhere from
30 min to over an hour to acquire a chromatogram.
Using conservative estimates, this would require a
combined instrumentation time approaching 30 h by as many
as 30 individual methods compared to 1 h for the
MASV1500 method. Analyst time would increase
correspondingly as it might require 10 or more minutes per
method per sample, or about 5 total hours per sample.
The MASV1500 method requires approximately 15 min
per sample to process the chromatogram. These estimates
do not include the time spent curating standard libraries,
and establishing and maintaining each method.
GC-MS analysis is only appropriate for specific types of
chemicals. The method described here does not perform well
for non-volatile chemicals or very polar chemicals. Some
polar chemicals (e.g. nitro-anilines, halogenated phenols) gave
no response at approximately 10 ng/μL. This impacts LODs
and LOQs and is a compromise for the ability to analyze for a
large number of chemicals. Our linearity evaluation may be
biased to good performers because only those with at least
three detections among a concentration series could be
included in R2 calculation.
Estimation of physico-chemical parameters can be a
limiting factor when predicting accurate response factors
because different sources may provide very different
values . Another example is that PSA method used
by ACD Labs does not assign a contribution of polarity
for halogen substituents. For example, all PCBs and
PBDEs have PSA of 0 and 9, respectively. Different
number and orientation of halogens between congeners
should produce different PSA among the classes. The
source of input parameters can affect model results. We
chose ACD Labs as the primary source for the
thoroughness of parameters available and consistency within this
study. The Chemistry Dashboard from U.S. EPA is a
platform for centralized chemical properties, including
from ACD Labs, and offers a parameter prediction with
open quantitative structure activity relationship
application (OPERA) modeling . Kim et al. used effective
carbon number as a predictor of response for volatile
organic chemicals measured with thermal desorption
]. We did not pursue more advanced chemical
descriptors because one goal of this paper was to create
an accessible method with common and easily obtainable
The MASV1500 method described here is a targeted analysis
for a large number of chemicals that can be used effectively in
conjunction with non-targeted methods or sample
]. Quantitation using AMDIS and a predictive
model for response factor was able to quantify multiple classes
of chemicals in representative samples within a factor of 2.5,
better than comparable methods that have been previously
reported. This method can screen for over a thousand
chemicals with less analyst time than typical methods. A
different deconvolution software may offer better resolution
between isomers. Other software options are available for
deconvolution including a component of Agilent’s Mass
Hunter , and many open source packages. When
combined with high-throughput analysis such as with passive
sampling wristband work-flows, the quantitative screen described
here improves efficient environmental monitoring. Overall
analysis workflow would be improved through the reduction
of solvents in sample processing. To that end, thermal
desorption of PSDs instead of solvent extraction could increase
extraction efficiency and reduce cost of analysis.
Acknowledgements Thank you to Mike Barton, Kevin Hobbie, and Josh
Wilmarth for the technical support including software management and
programming; Amber Barnard and Holly Dixon for helping in collecting
preliminary data; and Steven O’Connell for adding chemicals to our
analyte library and for general advice. Brian Smith and Katherine
McLaughlin provided statistical advice. Ron Tackett at Agilent helped
to troubleshoot software issues with DRS.
Funding information This work was funded by National Institute of
Environmental Health Sciences grants P42-ES016465, P30 ES000210,
Compliance with ethical standards
Conflict of interest statement Kim Anderson, an author of this research,
discloses a financial interest in MyExposome, Inc. which is marketing
products related to the research being reported. The terms of this
arrangement have been reviewed and approved by Oregon State University in
accordance with its policy on research conflicts of interest. Alan
Bergmann, Gary Points, Richard Scott, and Glenn Wilson declare that
they have no conflict of interest.
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1. Anderson KA , Szelewski MJ , Wilson G , Quimby BD , Hoffman PD . Modified ion source triple quadrupole mass spectrometer gas chromatograph for polycyclic aromatic hydrocarbon analyses . J Chromatogr A . 2015 ; 1419 : 89 - 98 .
2. Chibwe L , Titaley IA , Hoh E , Simonich SLM . Integrated framework for identifying toxic transformation products in complex environmental mixtures . Environ Sci Technol Lett . 2017 ; 4 ( 2 ): 32 - 43 .
3. Wylie PL , Szelewski MJ , Meng C-K , Sandy CP. 5989 -1157EN Application Note: Comprehensive pesticide screening by GC/ MSD using deconvolution reporting software . Agilent Technologies . 2004 .
4. Kadokami K , Tanada K , Taneda K , Nakagawa K. Novel gas chromatography-mass spectrometry database for automatic identification and quantification of micropollutants . J Chromatogr A . 2005 ; 1089 ( 1-2 ): 219 - 26 .
5. Bu Q , Luo Q , Wang D , Rao K , Wang Z , Yu G . Screening for over 1000 organic micropollutants in surface water and sediments in the Liaohe River watershed . Chemosphere . 2015 ; 138 : 519 - 25 .
6. Bu Q , Wang D , Liu X , Wang Z. A high throughout semiquantification method for screening organic contaminants in river sediments . J Environ Manag . 2014 ; 143 : 135 - 9 .
7. Brereton RG , Jansen J , Lopes J , Marini F , Pomerantsev A , Rodionova O , et al. Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools . Anal Bioanal Chem . 2017 ; 409 ( 25 ): 5891 - 9 .
8. Sauter AD , Belowski LD , Ballard JM . Comparison of priority pollutant response factors for triple and single quandrupole mass spectrometers . Anal Chem . 1983 ; 55 ( 1 ): 116 - 9 .
9. Sauter AD , Downs JJ , Buchner JD , Ringo NT , Shaw DL , Duluk JG . Model for the estimation of electron impact gas chromatography response factors for quadrupole mass spectrometers . Anal Chem . 1986 ; 58 ( 8 ): 1665 - 70 .
10. Golubovic J , Birkemeyer C , Protic A , Otasevic B , Zecevic M . Structure-response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks . J Chromatogr A . 2016 ; 1438 : 123 - 32 .
11. Kim YH , Kim KH , Szulejko JE , Bae MS , Brown RJ . Experimental validation of an effective carbon number-based approach for the gas chromatography-mass spectrometry quantification of 'compounds lacking authentic standards or surrogates' . Anal Chim Acta . 2014 ; 830 : 32 - 41 .
12. Adams RG , Lohmann R , Fernandez LA , Macfarlane JK , Gschwend PM . Polyethylene devices: passive samplers for measuring dissolved hydrophobic organic compounds in aquatic environments . Environ Sci Technol . 2007 ; 41 ( 4 ): 1317 - 23 .
13. O 'Connell SG , McCartney MA , Paulik LB , Allan SE , Tidwell LG , Wilson G , et al. Improvements in pollutant monitoring: optimizing silicone for co-deployment with polyethylene passive sampling devices . Environ Pollut . 2014 ; 193 : 71 - 8 .
14. Paulik LB , Smith BW , Bergmann AJ , Sower GJ , Forsberg ND , Teeguarden JG , et al. Passive samplers accurately predict PAH levels in resident crayfish . Sci Total Environ . 2016 ; 544 : 782 - 91 .
15. Anderson KA , Points GL , 3rd , Donald CE , Dixon HM , Scott RP , Wilson G , et al. Preparation and performance features of wristband samplers and considerations for chemical exposure assessment . J Expo Sci Environ Epidemiol . 2017 ; 27 ( 6 ): 551 - 9 .
16. O 'Connell SG , Kincl LD , Anderson KA . Silicone wristbands as personal passive samplers . Environ Sci Technol . 2014 ; 48 ( 6 ): 3327 - 35 .
17. Bergmann AJ , Tanguay RL , Anderson KA . Using passive sampling and zebrafish to identify developmental toxicants in complex mixtures . Environ Toxicol Chem . 2017 ; 36 ( 9 ): 2290 - 8 .
18. Bergmann AJ , North PE , Vasquez L , Bello H , Del Carmen Gastanaga Ruiz M , Anderson KA. Multi-class chemical exposure in rural Peru using silicone wristbands . J Expo Sci Environ Epidemiol . 2017 ; 27 ( 6 ): 560 - 8 .
19. Kile ML , Scott RP , O'Connell SG , Lipscomb S , MacDonald M , McClelland M , et al. Using silicone wristbands to evaluate 20.
Donald CE , Scott RP , Blaustein KL , Halbleib ML , Sarr M , Jepson PC , et al. Silicone wristbands detect individuals' pesticide exposures in West Africa . Royal Soc Open Sci . 2016 ; 3 ( 8 ): 160433 .
EPA US. EPA 821 -R- 16-006 Definition and procedure for the determination of the method detection limit, revision 2 . In: Water Oo, editor. Washington, D.C. 2016 .
Environ Sci Technol . 1997 ; 31 : 905 - 10 .
O'Connell SG , Haigh TA , Wilson G , Anderson KA . An analytical investigation of 24 oxygenated-PAHs (OPAHs) using liquid and gas chromatography-mass spectrometry . Anal Bioanal Chem .
Steyerberg EW , Harrell FE , Borsboom GJJM , Eijkemans MJC , Vergouwe Y , Habbema JDF . Internal validation of predictive models: efficiency of some procedures for logistic regression analysis . J Clin Epidemiol . 2001 ; 54 ( 8 ): 774 - 81 .
Rosenbaum PF , Weinstock RS , Silverstone AE , Sjodin A , Pavuk M. Metabolic syndrome is associated with exposure to organochlorine pesticides in Anniston , AL. United States Environ Int . 2017 ; 108 : 11 - 21 .
EPA US. In: Forum RA, editor . EPA/630/P-02/ 002F a review of the reference dose and reference concentration processes . Washington, D.C.: U.S. Environmental Protection Agency; 2002 .
Linkov I , Ames MR , Crouch EAC , Satterstrom FK . Uncertainty in octanol-water partition coefficient: implications for risk assessment and remedial costs . Environ Sci Technol . 2005 ; 39 ( 18 ): 6917 - 22 .
Brack W. Effect-directed analysis: a promising tool for the identification of organic toxicants in complex mixtures? Anal Bianal Chem . 2003 ; 377 ( 3 ): 397 - 407 .
Henry AS , Quimby BD . 5991 -7834EN Application Note: Screening for water pollutants with the Agilent SureTarget GC/ MSD Water Pollutants Screener, SureTarget Workflow, and Customized Reporting . Agilent Technologies 2017 .