An Actionable Annotation Scoring Framework For Gas Chromatography - High Resolution Mass Spectrometry (GCHRMS)

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An Actionable Annotation Scoring Framework
for Gas Chromatography - High Resolution
Mass Spectrometry (GC-HRMS)
Jeremy P. Koelmel1, Hongyu Xie 2, Elliott J. Price3, Elizabeth Z. Lin1,
Katherine E. Manz4, Paul Stelben1, Matthew Paige1, Stefano Papazian2,
Joseph Okeme1, Dean P. Jones5, Dinesh Barupal6, John Bowden11,12,
Pawel Rostkowski7, Kurt D. Pennell4, Vladimir Nikiforov8, Thanh Wang9,
Xin Hu5, Yunjia Lai10, Gary W. Miller10, Douglas I. Walker6*,
Jonathan W. Martin2, Krystal J. Godri Pollitt1*
1 Department of Environmental Health Science, Yale School of Public Health, New
Haven, CT, 06520, United States
2 Department of Environmental Science, Science for Life Laboratory, Stockholm
University, Stockholm 10691, Sweden
3 RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
4 School of Engineering, Brown University, Providence, RI 02912, United States
5 Department of Medicine, School of Medicine, Emory University, Atlanta 30322, United
States
6 Department of Environmental Medicine and Public Health, Icahn School of Medicine at
Mount Sinai, New York, NY, 10029, United States
7 NILU- Norwegian Institute for Air Research, 2007 Kjeller, Norway
8 NILU- Norwegian Institute for Air Research, Framsenteret, 9007 Tromsø, Norway
9 MTM Research Centre, Örebro University, SE-701 82, Örebro, Sweden
10 Department of Environmental Health Sciences, Mailman School of Public Health,
Columbia University, New York, NY 10032, United States
11 Center for Environmental and Human Toxicology & Department of Physiological
Sciences, University of Florida, Gainesville, FL, 32603, United States
12 Department of Chemistry, University of Florida, Gainesville, FL, 32603 United States
*Corresponding author
Krystal J. Godri Pollitt - krystal.pollitt@yale.edu
Douglas I. Walker - douglas.walker@mssm.edu

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Abstract
Omics-based technologies have enabled comprehensive characterization of our
exposure to environmental chemicals (exposome) as well as assessment of the
corresponding biological responses at the molecular level (e.g., metabolome, lipidome,
proteome, and genome). By systematically measuring personal exposures and linking
these stimuli to biological perturbations, researchers can determine specific chemical
exposures of concern, identify mechanisms and biomarkers of toxicity, and design
interventions to reduce exposures. However, further advancement of metabolomics and
exposomics approaches is limited by a lack of standardization and approaches for
assigning confidence to chemical annotations. While a wealth of chemical data is
generated by gas chromatography high-resolution mass spectrometry (GC-HRMS),
incorporating GC-HRMS data into an annotation framework, and communicating
confidence in these assignments is challenging. It is essential to be able to compare
chemical data for exposomics studies across platforms to build upon prior knowledge and
advance the technology. Here we discuss the major pieces of evidence provided by
common GC-HRMS workflows, including retention time and retention index, electron
ionization, positive chemical ionization, electron capture negative ionization, and
atmospheric pressure chemical ionization spectral matching, molecular ion, accurate
mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide
a qualitative framework for incorporating these various lines of evidence for
communicating confidence in GC-HRMS data by adapting the Schymanski scoring
schema developed for reporting confidence levels by liquid chromatography high-
resolution mass spectrometry (LC-HRMS). Validation of our framework is presented using
standards spiked in plasma, and confident annotations in outdoor and indoor air samples,
showing a false positive rate of 12% for suspect screening for chemical identifications
assigned as Level 2 (when structurally similar isomers are not considered false positives).
This framework is easily adaptable to various workflows and provides a concise means
to communicate confidence in annotations. Further validation, refinements, and adoption
of this framework will ideally lead to harmonization across the field, helping to improve
the quality and interpretability of compound annotations obtained in GC-HRMS.

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1. INTRODUCTION
Environmental risk factors are major determinants of health, with current estimates
suggesting that environmental exposures are responsible for about 23% of the global
disease burden (Prüss-Üstün et al., 2016) and chemical exposures responsible for one
in six deaths (Landrigan et al., 2018). However, research has historically prioritized the
characterization of genetic risk factors (Prüss-Üstün et al., 2016; Rappaport, 2016;
Wheelock and Rappaport, 2020; Wild, 2012, 2005). Characterization of environmental
factors primarily relies on self-reporting or community-level exposures (Rappaport, 2016).
Identifying and preventing/reducing harmful environmental exposures could lead to
significant reductions in global disease. Assessment of the chemical exposome, which
includes the comprehensive identification of endogenous and exogenous compounds at
the individual level, is essential to better understanding the link between the environment
and human health (Vermeulen et al., 2020).
Hundreds of thousands of chemicals are used in commercial and industrial applications.
Once released to the environment, these chemicals often transform, increasing the
number of potential chemical exposures to millions of compounds. Traditional methods
for high-confidence annotation and quantitation require synthesized chemical standards
for each analyte of interest, semi-manual peak integration, calibration curves, and often
specific chromatographic methods. Therefore based on cost and feasibility, the diversity
of chemicals existing in the exposome presents a monumental challenge to traditional
analytical methods, which typically interrogate less than 100 compounds (Doherty et al.,
2021; Mazur et al., 2021). To overcome this limitation, non-targeted analytical methods
that employ high resolution mass spectrometry (HRMS) have been developed to screen
and identify thousands of chemicals that may be present environmental and biological
samples. These methods use HRMS evidence indicative of universal chemical properties
to annotate structure and estimate chemical abundances, and hence these methods are
not solely focused on known or commonly expected chemicals. Because non-targeted
results are more tentative then targeted results, a key need for such exposome research
is to establish and apply transparency in instrumentation, data-processing, and reporting
criteria when communicating research findings.
Confirming the molecular structure of identified compounds using HRMS data remains a
challenge. Due to limitations in scope and spectral quality of chemical databases and the
difficulty of capturing mass fragmentation patterns for low abundance peaks, the
confidence of chemical annotations can vary widely. Therefore, reporting may be limited
to chemical formula, or even simply a set of retention time and mass spectral
measurements, rather than an exact chemical structure. Chemical features that are not
fully characterized can provide novel insights in exposomics research and links between
environment, biological effects, and disease. For example, chemical formulas assigned
to accurate mass features can aid in the characterization of unexpected and previously
unknown chemical exposures that can be prioritized for study in other populations and
sample types to understand their distribution in the environment. The advantage of using
this approach for identifying environmental exposures is highlighted by the discovery of
polychlorinated biphenyls (PCBs), which are one of the most widely studied persistent

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organic pollutants. Their initial discovery in environmental and biological samples was
based upon detection of unknown peaks in multiple sample types, and it was their
measurement across multiple samples and recognition of their accumulation in the food
chain that led to the eventual identification of PCBs as highly persistent pollutants
(Jensen, 1972). More recent examples of the use of non-targeted mass spectrometry,
include the discovery of 6PPD-quinone as a chemical responsible for fish die offs
(Stokstad, 2020), and a brominated pesticide transformation products as responsible for
eagle die offs (US EPA, 2021).
When reporting annotations, it is essential to report measurements within a consistent
framework that allows interpretation of the uncertainty in compound annotation. Defining
a framework for reporting the confidence of chemical annotations is especially important
given the potential for high false positive rates and over-reporting in non-targeted and
suspect screening HRMS studies across various classes of compounds (Koelmel et al.,
2017; Scheubert et al., 2017; Schymanski et al., 2014; von Mering et al., 2002). One of
the first examples of a confidence scoring framework for non-targeted environmental
chemical profiling was proposed by Schymanski et al. for liquid chromatography high-
resolution tandem mass spectrometry (LC-HRMS/MS) (Schrimpe-Rutledge et al., 2016;
Schymanski et al., 2012). Gas Chromatography high-resolution mass spectrometry (GC-
HRMS) provides complementary coverage of exposome chemicals that are of lower
solubility, higher volatility, or are not ionized by LC-HRMS approaches (Zhang et al.,
2021) (Table S1). The applicability of Schymanski schema was reported to GC-HRMS to
be limited due to differences in ionization, data acquisition, and data-processing
(Rostkowski et al., 2019).
Recently, a scoring framework for GC-HRMS using electron ionization (EI) was proposed
(Travis et al., 2021) categorizing confidence of feature identifications into four levels.
Levels ranged from the most confident annotations in Level 1 with confirmed
identifications to the weakest in Level 4 with features described only by exact mass. While
this framework provides an important first step in defining scoring metrics for GC-HRMS,
the parameters proposed for assigning confidence levels are potentially subject to high
false positive and false negative rates. For example, Level 2 assignment requires isotopic
distribution while Level 3 is assigned for exact mass matches to fragment ions. The
necessity of an isotopic pattern match requires the molecular ion, which is often not
observed. Assignment of Level 3 is further challenged by the requirement of an isotopic
pattern and exact mass matches which require exact mass libraries with known formulas,
which are not currently available for many compounds. The schema does not incorporate
retention index, which we demonstrate to be an essential piece of information for reducing
false positives. Thus, this approach may be suitable for databases with establish, high
mass accuracy fragmentation spectra, but may be limited when using larger databases
and in silico approaches.
Here, we present a new annotation confidence scoring framework specific for non-
targeted GC-HRMS that can be used to determine and communicate confidence of
chemical assignments made using several strategies. The framework draws on input from
a community of GC-HRMS users and considers the breadth of chemical evidence that

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can be obtained from these powerful instruments. The current work focuses on
environmental contaminants. While this framework can be applied to certain biological
chemicals, the use of derivatization (Halket et al., 2005) and class-based mass or
fragmentation patterns may be required.
2. LEVERAGING EVIDENCE FROM GC-HRMS TO REDUCE FALSE POSITIVES
AND NEGATIVES
GC-HRMS provides data-rich evidence that can be used in identifying structural
annotation of detected features, including various modes of ionization and fragmentation,
accurate mass, retention information, and isotopic pattern. It is important to understand
the unique advantages and limitations for each layer of evidence to decide the necessary
pieces of information for accurate assignment of chemicals (Table S1).
2.1. Ionization
The most common ionization method used in GC-HRMS is electron ionization (EI). Any
molecule introduced to the source in this ionization mode is theoretically ionized with
equal efficiency. In practice, however, on-column degradation, inlet/liner discrimination,
and other factors (Barwick, 1999), total summed ion signal can still be a factor of chemical
structure.
EI is a ‘hard’ ionization method, meaning that each molecule generates a spectrum with
a high degree of fragmentation in which only traces of the intact molecular ion are usually
detected in most cases. In a GC-EI-MS analysis, all the reported peaks have an
associated fragmentation spectrum which is reconstructed by a deconvolution algorithm.
Having fragmentation from all ions makes it possible to generate annotation levels for
every sample, therefore improving confidence of chemical assignments. Generation of
fragmentation spectra for all ionized compounds enables MS1 data can be used to
confirm identified chemicals, eliminating the need for precursor selection and
fragmentation by tandem mass spectrometry (MS/MS). To establish reproduceable EI
spectra libraries, ionization voltages of 70 eV are commonly used and combined with
libraries containing retention index. Retention index and fragmentation are often similar
irrespective of chromatographic method and instrument (within the same instrument type,
e.g., quadrupole time-of-flight MS (Q-TOF-MS)), and therefore have been successfully
shared across labs. (Mallett, 2014).
GC-EI-MS spectra provide key evidence for compound annotation, yet several challenges
may still lead to false positives. While the diverse fragments observed in EI spectra allow
for better structural characterization, the spread of signal across fragment ions can
reduces overall sensitivity, often with no molecular ion signal present. Without observation
of the molecular ion, many compounds can give similar spectra, even when they are not
isomers. Because there is rarely a high signal from the molecular ion, ion selection
followed by fragmentation cannot be applied in GC-EI-MS. Therefore, GC spectra must
be deconvoluted across chromatographic time, often leading to the inclusion of artifact
peaks due to high background or co-eluting compounds. Deconvolution can be a
significant issue in increasing false negatives and false positives.

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Alternative ionization strategies can be used with GC-HRMS to improve detection of the
molecular ion, although these often lack the robustness, sensitivity, and reproducibility of
EI. These techniques include positive chemical ionization (PCI), electron capture negative
ionization (ECNI), and atmospheric pressure chemical ionization (APCI). The advantages
and disadvantages of these techniques are described in the supplemental information.
2.2. Accurate Mass
Continual advances in high resolution Orbitrap and Q-TOF MS instrumentation have
enabled accurate measurement of mass to charge ratios ranging from sub-ppm to 30
ppm mass accuracy windows and resolutions up to 240,000 to date. Accurate mass
(ability to measure the correct mass) and high resolution (ability to distinguish close
masses) provides additional evidence for compound annotation such as fine isotopic
patterns. Unequivocal formula prediction may be possible for low mass fragments,
especially when including isotopic patterns. Isotopic pattern and mass defects can further
be used to determine unique chemical structures containing certain functional groups
(e.g., Cl, Br, F), aiding in the detection of unknown chemical exposures.
Suspect screening using accurate mass matching can reduce false positives. The largest
GC-EI-MS libraries (e.g., NIST and Wiley) include spectra acquired using unit mass
instruments; only a handful of smaller libraries area available for spectral matching using
accurate mass. Algorithms can be used to reduce false positives when matching
experimental spectra obtained with GC-HRMS to unit resolution libraries (Stein, 2012).
One example is applying a high-resolution mass filter (HRMF) (Kwiecien et al., 2015).
HRMF calculates the percentage of fragment ions with formulae which can be predicted
when setting atom constraints for formula matching to only those contained in the
proposed molecular formula. Reverse HRMF can also be used, but this approach limits
scoring to only peaks found in the library, ignoring other peaks in the experimental spectra
for scoring purposes. Reverse HRMF reduces influences of artifacts during deconvolution
on scoring, hence reducing false negatives, but may also increase false positives when
experimental peaks are real and not found in the library.
2.3. Retention Index
Retention time is an orthogonal measurement that can improve annotation accuracy
when combined with EI MS spectral matches. As retention time is influenced by column
type, batch, and manufacturer as well as the method temperature gradient, standardized
retention indices can be calculated using a series of compounds that span the entire
chromatographic run (i.e., alkanes (Kováts, 1958), fatty acid methyl esters (Miwa et al.,
1960)). retention indices can also be useful for identifying isomers with the same or similar
EI spectra. Compared to LC, GC retention times are typically more consistent within and
across laboratories (Aalizadeh et al., 2021; Hall et al., 2012). This increases the reliability
of retention index calibrations and enables matching of experimental retention indices to
public libraries as well as prediction of retention time indices using chemical properties
(Peng, 2010a; Stein et al., 2007a; Strehmel et al., 2008).

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Retention indices provide key evidence to improve accuracy of compound annotation.
There are a few considerations when implementing retention indices into a workflow.
Methods using linear gradients may be preferable, to reduce errors in retention index
calculation (Zellner et al., 2008), although non-linear temperature gradients may still be
deployed with accurate RI calculated (Peng, 2010b). While retention time indices improve
annotation confidence, their incomplete availability in spectral libraries may limit their use.
The largest libraries (e.g., NIST and Wiley) have a high percentage of chemicals without
retention index information, limiting the search space and coverage if retention index
matching is required. The current release of NIST libraries contain predicted retention
indices for many compounds; however, these predictions may not be accurate for all
chemical classes (Stein et al., 2007b). Careful consideration of retention time indices is
also necessary depending on the chemicals being measured. For example, alkanes are
not appropriate for calculating RI for persistent mobile organic compounds including per-
and polyfluoroalkyl substances (PFAS), organophosphate esters (OPEs), and certain
polar compounds amendable to GC (Boegelsack et al., 2021; Grőbler, 1972).
2.4. Compound Metadata for Improving Candidate Ranking
While spectral and retention time information can be used to assign a structure or
substructure to a chemical, additional evidence available from GC-HRMS can increase
confidence of chemical identifications. Study context and likelihood of exposure are also
important to consider. For example, a sizable portion of 100+ million chemical entries in
PubChem have never been synthesized, their existence is limited to a patent or database
(Kim et al., 2021), or if synthesized were never generated at large scale. Hence tens of
millions of these entries are irrelevant for exposomics studies and will lead to false positive
annotations (Schymanski et al., 2021). To reduce false positives and remove top-ranked
compounds which are not likely to exist in the environment, more selective databases
such as the EPA CompTox Chemistry Dashboard (Williams et al., 2017) or PubChem Lite
(Schymanski et al., 2021) can be used. For GC-HRMS a more actionable solution, given
that the NIST and WILEY libraries are commonly used, is to include meta-data on
database, patent and/or literature occurrence (reference counting), or production levels,
for determining the likelihood a chemical exists in the environment. This can drastically
reduce false positives by removing unlikely candidates and ease interpretation by
retaining chemical species with known information (McEachran et al., 2017; “National
Report on Human Exposure to Environmental Chemicals,” 2021; Williams et al., 2017).
This method is biased to more well characterized chemicals, and any scoring methods
are highly dependent on which databases/algorithms are used for determining compound
occurrences. Hence, this method may lead to false negatives in the case of uncommon
or unexpected chemicals, although the drastic reduction in false positives may far
outweigh the potential of missed compounds.
There are several tools that can provide meta-data on a chemical’s likely occurrence to
improve non-targeted annotations in exposomics including; NHANES Predicted
Exposures (“National Report on Human Exposure to Environmental Chemicals,” 2021),
Data Sources (McEachran et al., 2017), Number of PubMed articles containing chemical
name, Number of PubChem data sources, and CPDat Product Occurrence Count
(McEachran et al., 2017).

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2.5. Peak Filtering to Remove Background Contaminants and Artifacts
Analytical artifacts can be introduced during sample collection, transport, storage,
preparation/ extraction, and acquisition, as well as through instrument noise. Complete
physical removal of contaminants is not possible, in particular when background and
artifact peaks overlap. Additional information, such as clustering of fragments across
multiple samples can be used to identify highly correlated fragments based on peak
intensities. Matrix effects in GC (Fujiyoshi et al., 2016; Sakthiselvi et al., 2020) are
important for determining the extent of background noise. These matrix effects will
depend on sample type, concentration, preparation, and introduction, as well as the
chromatography and acquisition strategies employed. These factors can impact spectral
matches and annotation confidence, and are challenging to incorporate into scoring
metrics.
One approach to removing these interferences is blank feature filtering where sample
abundances are retained if above a blank threshold. Blank feature filtering is ideally
conducted using field blanks that have been treated identical to samples, undergoing the
same transport, storage, sample preparation, and acquisition protocols. Multiple methods
are available for blank filtering to remove background contaminants and artifacts
(Borgsmüller et al., 2019; Koelmel et al., 2021; Mahieu et al., 2014; Matsuo et al., 2017;
Patterson et al., 2017; Risum and Bro, 2019). Various methods for blank filtering are
described in the supplemental information.
3. COMPILING GC-HRMS EVIDENCE TO COMMUNICATE ANNOTATION
CONFIDENCE
There are three approaches for communicating confidence in annotations: non-
probabilistic scores, probabilistic scores, and qualitative assignments of confidence.
Individual layers of GC-HRMS evidence have community accepted standards for scoring.
For example, EI match scores are generally calculated using dot-product or reverse dot-
product, retention index matches are usually based on an absolute deviation, meta-data
scores are based on numbers of instances, and HRMF scores are based on a percentage
of fragment formulae which can be predicted given the proposed structures composite
atoms. Cut-offs for spectral match scores, retention index delta, or other metrics based
on experience, community accepted values, or optimized via experiments can then be
assigned to remove compounds unlikely to be true positives.
Combining different scores from various layers of GC-HRMS evidence to communicate
confidence is non-trivial. Weighted linear models, neural networks, and mixed models, for
example, have all been used to combine spectral similarity and retention index
information to optimize a single match function (Matyushin et al., 2020; Wei et al., 2014).
Scores determined using these quantitative methods can improve candidate sorting
compared to qualitative methods; however, statistical and coding experience are required
which may be beyond the expertise of most users. Establishing harmonized algorithms
and development of user-friendly software would ensure consistency across different user
groups. Consistency across different software, workflows and laboratories is much easier

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to achieve using qualitative assignment of confidence, e.g. (Schymanski et al., 2014).
Here we introduce a qualitative means to assign and communicate confidence of feature
annotations. We also show the use of more quantitative scoring metrics alongside
qualitative methods for ranking candidates within a certain qualitative confidence
assignment.
Proposed System for Assigning Confidence
Level 1: Confirmed Identification (Retention time, EI spectra and reference
masses) using In-House library
⮚ Retention time matches standard database generated in-house using the same
method, background matrix, and instrument (spectra from certified reference
standards, not from an external library). Retention times should match within the
RSD of the associated standards (no more than 1% deviation).
⮚ Observation of 2+ EI spectral reference peaks from standards at correct ratios
(within 20%) or EI spectral match with in-house library >600. Often more than two
reference masses and ratios are needed for confident annotation, 2 is minimum.
For high-throughput screening with automated data extraction, verification of
identity of each chemical in each sample by these criteria may be impractical. In
such cases, procedures and assumptions should be clearly defined.
Advantage:
⮚ Confident identification for exact structure (with a few possible exceptions,
especially enantiomers or structurally similar isomers which cannot be separated
by retention time).
Limitation:
⮚ Expensive to purchase, prepare, run, and generate database for large number of
chemicals. Standards may need to be reacquired each experiment if retention
times are not stable between batches (e.g., column clipping).
⮚ Coverage is limited to standards available for purchase. Synthesized standards
are often too costly for routine library building.
Level 2: Probable structure or close isomer using external libraries (RI match,
[molecular ion], and EI match to exact mass library or including metrics
incorporating accurate mass information)
⮚ Reverse dot-product EI spectral match (>600) and dot-product >500. As EI
spectral prediction improves, predicted spectra may also be used.
⮚ NCI/PCI/APCI when spectra contain more information than EI and at least 5
fragments are matched to a library database.
⮚ Retention index match (<50 and <1.5%) or predicted retention index (<100
difference) using validated methods.
⮚ Match to an exact mass library (reverse dot-product >600) or using metrics
incorporating exact mass (e.g., high reverse high-resolution mass filter score
(>75)).
Advantage:
⮚ Correct annotation, or the assigned structure is a structurally similar isomer (with
certain exceptions)

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Limitation:
⮚ Retention index match requirement may limit annotations if databases do not
contain many compounds with retention index. Therefore, this assignment of
confidence is ideal when libraries have high retention index coverage
(experimental or predicted).
⮚ In certain edge cases, reverse high-resolution mass filter might lead to false
positives when gas phase reactions in Orbitraps lead to fragments which cannot
be predicted using molecular formula.
Level 3: Tentative candidate (EI accurate mass spectral match or EI match with
metrics incorporating accurate mass) using external library
⮚ Reverse dot-product EI spectral match (>600) and dot-product (>500). As EI
spectral prediction improves, predicted spectra may also be used.
⮚ NCI/PCI/APCI when spectra contain more information than EI and at least 5
fragments are matched to a library database.
⮚ Match to an exact mass library (reverse dot-product >600) or using metrics
incorporating exact mass (e.g., high reverse high-resolution mass filter score
(>75)).
Advantage:
⮚ Widest coverage while assigning a possible structure.
Limitation:
⮚ High rate of false positives (both in terms of exact structure and assigning a
structurally similar isomer).
⮚ Possibility of multiple peaks with similar fragmentation patterns (i.e., structurally
similar isomers), but different retention times.
⮚ In certain edge cases, reverse high-resolution mass filter might lead to false
positives when gas phase reactions lead to fragments which cannot be predicted
using molecular formula.
Level 4: Chemical Group or Exact Formula
Level 4A: Identification of unequivocal chemical formula from databases (PCI, NCI,
or in certain cases, EI)
⮚ Only one formula is possible given the exact mass of the precursor ion and isotopic
distribution.
o OR multiple formulas are possible, but after only including atoms which can
predict all dominant fragment ions, only one formula is possible for the
molecule.
Level 4B: Possible chemical series (for example homologous series with repeating
chemical constituents)
⮚ Follows mass defect series (e.g., using Kendrick mass defect (Kendrick, 1963)).
o Homologous series have linear retention indices – so detection via GC can
be precise – even for variable temperature programs.
⮚ Has one or more fragments (with exact mass match) indicative of class.

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o OR shows defined subnetwork clustering in spectral similarity networks.
Level 4C: Possible chemical class (chemicals grouped based on structural motifs
or similarity)
⮚ Has one or more fragments (with exact mass match) indicative of class.
o OR clusters in chemical similarity networks.
OR other well accepted class specific method (e.g., Lee index for polycyclic
aromatic hydrocarbons (PAHs) (Lee et al., 1979; Škrbić and Onjia, 2006)
Note: Levels 4A-C are not necessarily in order of confidence or utility, for example in
PFAS analysis the goal may be to find series of 3 or more compounds (Level 4B). In non-
targeted analysis across all chemical classes molecular formula may be of more
importance (Level 4A). Note that for Level 4B and 4C one could also have an exact
formula match, these could be indicated as Level 4AB / 4AC.
Level 5: Unknown feature (retention time and reference mass or deconvoluted
spectra)
⮚ Reproducibly detected deconvoluted spectra with reference mass intensity or
combined intensity of all spectral ions. This feature may also have an annotation,
and other information, but does not fit any of the criteria to be deemed Level 1, 2,
3, or 4.
Important Considerations
1) To avoid annotation of background artifacts, peak filtering conditions must be met
for all confidence levels (e.g., see discussion of blank feature filtering in the
supplemental information).
2) If ion selection and fragmentation with CID/HCD is employed (PCI, NCI, and APCI,
for example), then the Schymanski schema can be used to assign confidence
levels.
3) For large studies it is important to account for spectral signals across multiple
samples - for example, was there only one good spectral match in one sample
for a study with 300 samples? This case often indicates a false positive (larger
sample size increases the number of detected features, enhancing the chance
for matching noise in library spectra). However, it is critical to note that in
exposomics studies, these features may represent unique exposures of interest.
Multiple injections of a single sample, when possible, could aid in annotating rare
chemical occurrences.
4) In reporting results, it should be clear whether multiple structures have met the
proposed criteria. If more than one structure meets the criteria for an assignment
of confidence level other information can be used to discern the likely correct
annotation: e.g., top hit has a significantly higher evidence count (by factor of 10),
the compound is the only one expected for the matrix studied, the compound has
a much more accurate retention index (by 30), the compound has a significant
higher reverse dot-product score (by at least 50) or the compound has a much
more accurate reverse high-resolution mass filter score (by at least 10). This is
also where quantitative scoring metrics for ranking candidates can be utilized

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(total scores). Note that even if certain conditions above are met, there may still
be false positives (see Results Section). If multiple top-hits still exist after
applying our schema, the feature should be flagged accordingly.
5) While we have not made necessary observation of the molecular ion for Level 2,
especially since there is a retention index match, observation of the molecular ion
provides additional confidence and should be achieved when possible.
Figure 1: Proposed schema for assigning five levels of confidence in compound
annotation using common evidence provided by GC-HRMS. Acronyms: retention
time (RT), reverse search index (RSI), retention index match (ΔRI), and reverse high-
resolution mass filter (RHRMF).

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Comparison to the Schymanski Schema for LC-HRMS/MS
While our schema is based on the Schymanski schema for LC-HRMS/MS, it was modified
to incorporate criteria specific to GC-HRMS. Included criteria were selected based on
feasibility for replication across labs with the overall objective of implementing a
harmonized scoring rubric. In addition, we include blank feature filtering, or similar, as a
necessary criterion for all annotations, as well as chemical series and class-based
annotation which is important in various fields.
Level 1 – Criteria for GC-HRMS is similar to Schymanski Level 1 and provides compound
identification. For GC-HRMS confidence scoring, the molecular ion and MS2 is not
required due to fragmentation spectra available from MS1 when using EI.
Level 2 – Criteria for GC-HRMS is similar to Schymanski Level 2, however the use of
retention indices has the potential to improve annotation confidence and assign
unambiguous structures to isomers. The Schymanski schema states “spectrum-structure
match is unambiguous” (Level 2A) or “represents the case where no other structure fits
the experimental information” (Level 2B), which may be too stringent of an interpretation
given the evidence provided. We would not confidently state this is true based on the
suggested criteria for either LC or GC.
Level 3 – Criteria for GC-HRMS fit within the Schymanski schema for Level 3, except
predicted retention index can be included as evidence for Level 2 given the accurate
predictions.
Levels 4A-C – These levels represent unequivocal formulae and/or chemical series and
chemical class, which are important in applications where the exact structure is not known
but the series or formula provides valuable information. For example, these could be
useful for polymers, PFAS, petrol chemicals, chlorinated by-products, and other
applications where repeating series and discernable chemical classes of importance may
be found. Unequivocal molecular formula is the same as Schymanski Level 4. It is useful
to note that there was no place for series/determination of chemical class in the original
Schymanski schema.
Level 5 – This is different than in Schymanski Level 5 in that the exact mass of the feature
cannot be discerned necessarily from EI spectra. Level 5 for GC-HRMS scoring to any
reproducibly detected deconvoluted spectra which does not meet the criteria to be ranked
in any other level and remains after filtering for artifacts and background contamination
(e.g., above blank signal thresholds).
4. METHODS: ESTIMATING FALSE POSITIVES AND FALSE NEGATIVES FOR
DIFFERENT CONFIDENT LEVELS USING AN OPEN-SOURCE SOFTWARE
We developed a software tool, SIF-GC (Scoring, Integrating total spectral ion abundance,
and Filtering for Gas Chromatography), to automatically assign confidence levels to
features identified in GC-HRMS data. The software covers all steps covered in the
described scoring framework provided the user has an output with retention index,

14
reverse similarity index, molecular ion, reverse high-resolution mass filter (or similar), and
integrated peak areas, heights, or total spectral ion abundance, for all samples and
blanks. Our software initially assigns Level 2, 3, or 5, and reports the top molecule in
terms of a weighted score or other metric for each level, and whether multiple hits exist
for each level (Level 1 is reserved for manual targeted techniques, and Level 4 may be
project specific and is not implemented at this time). The software then can be used to
apply blank feature filtering (user modifiable parameters), and further filter features to only
retain compounds with unique CAS-RN or other identifiers and compound names
(removes duplicates). Furthermore, the SIF-GC software can be used to compute total
spectral ion abundance for all compounds given an average ratio of total spectral ion
abundance to peak area or height. All steps are automated for Compound Discoverer
(Thermo) outputs but could work, with some minor adjustments, with other vendor
platforms. The SIF-GC software is open source and freely available at
innovativeomics.com/software/.
To evaluate the performance of our SIF-GC software and our schema for reporting
confidence, we applied the tool to datasets acquired from biological and environmental
samples and investigated the false positive and false negative rate assigned for each
level. The first dataset was of human serum samples spiked with 112 standards (90 native
and 22 isotopic labeled chemicals; Table S1A), with a final concentration range of 6.7 to
33.6 ppb. The spiked serum validation dataset consisted mainly of brominated flame
retardants, chlorinated flame retardants, organochlorine pesticides, polycyclic aromatic
hydrocarbons, oxygenated polycyclic aromatic hydrocarbons, nitrated polycyclic aromatic
hydrocarbons, polychlorinated biphenyls, polychlorinated dibenzodioxins, and
polychlorinated dibenzofurans. The second dataset describes indoor and outdoor air
samples collected using passive samplers from homes near natural gas compressor
stations. A targeted panel of 81 standards (70 native and 11 isotopic labeled chemicals;
Table S1B) with a final concentration range of 32 to 1000 ppb were assessed for these
environmental samples. This validation set consisted of an alkaloid, benzodioxoles,
benzopyrans, brominated flame retardants, chlorinated hydrocarbons, haloethers,
nitroaromatics, nitrosamines, phthalates, pyrethroids, organochlorine pesticides,
organophosphates, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and
volatile organic compounds. We compared Level 1 assigned compounds (targeted
methods used to confidently assigned compounds) with compounds assigned Level 2, 3,
or 5 using the GC-SIF software. A list of experimental protocols is provided in the
supplemental information.
5. RESULTS: HOW CONFIDENT ARE THE CONFIDENCE LEVELS?
We selected qualitative criteria that can be readily implemented given common metrics
provided by most GC open source and vendor library search software. These common
metrics included retention index (delta), spectral match (in this case reverse similarity
index was used), and a metric incorporating exact mass (in this case reverse high-
resolution mass filter criteria was used). Using the GC-SIF software, we determined the
false negative, false positive, and true positive rate when utilizing our criteria for assigning
confidence at Levels 2 and 3. For the human serum samples, of the 90 spiked unlabeled

15
standards which were detected by GC-HRMS, 80 were deconvoluted by Thermo
Compound Discoverer. Of these 80 chemicals, 61 were assigned a Level 2 annotation
(76%), and of these Level 2 annotations, the correct exact structure was only identified
for 28 compounds (46%). Hence in absolute terms, using our scoring criteria, the highest
level of confidence for suspect screening, there was a 54% false positive for Level 2
assignments, 60% false positive rate for Level 3 assignments, and 84% false positive rate
for top-ranked assignments without Level 2 or 3 criteria (Figure 2A). Of the deconvoluted
spiked standards, 19 (24%) had no Level 2 assignment and hence were considered false
negatives. In many cases these false negatives are due to unavailable libraries or
retention index values for the standard.
Figure 2: False positive rates and number of assignments across each
confidence level for 81 observed unlabeled standards spiked into human serum
(A), and 19 Level 1 assignments from outdoor and indoor air samples (B).

16
For air samples, there was a 22% false positive rate for Level 2 assignments, 89% false
positive rate for Level 3 assignments, and 74% false positive rate for top-ranked
assignments without Level 2 or 3 criteria (Figure 2B). It should be noted there was a
lower false positive rate in air samples compared to the human serum samples. This
difference is attributable the standards used with each sample type: serum samples were
spiked with a mixture that was mainly comprised of halogenated isomers which could not
be distinguished by retention index and EI spectra, whereas the air samples included a
wider range of chemicals. Therefore, for exact structure the air samples may be more
representative of the false positive rate for Level 2 when the range of chemicals being
annotated is a cross a diverse array of structures, many of which do not have close
isomers.
Even when using retention index, reverse high-resolution mass filter, reverse search
index, molecular ion, and search index there are subtle isomeric differences (e.g., Figure
3), the exact structure often cannot be discerned using GC-EI-HRMS (hence the 54%
false positive rate in serum samples and 22% false positive rate in air samples). When
subtle isomeric differences in the position of methyl, chlorine, heteroatoms, and aromatic
rings were not considered false positives, 13% of the 61 top-ranked Level 2 assignments
in the spiked serum were false positives, 25% of the top-ranked Level 3 assignments
were false positives, and 61% of the top-ranked assignments without Level 2 or 3 criteria
(Level 5) were false positives (Figure 2A). Similarly, when not considering highly similar
isomers false positives, for air samples, 11% of the 18 top-ranked Level 2 assignments
were false positives, 79% of the top-ranked Level 3 assignments were false positives,
and 68% of the top-ranked assignments without Level 2 or 3 criteria (Level 5) were false
positives (Figure 2B).
Figure 3: Examples of 10 isomers (dimethyl naphthalene and ethyl naphthalene)
which were all assigned Level 2 for a single feature. The dimethyl naphthalene
species represented one of the highest abundance features in air samples.
Application of the proposed GC-HRMS scoring framework in datasets from biological and
environmental samples highlighted that: 1) retention index is essential for confident
assignments and 2) structurally similar isomers often cannot be resolved even with a
retention index match. Exact mass filters (e.g., reverse high-resolution mass filter used
here) were also shown to be valuable, especially when heteroatoms exist. There were
26% false positives using reverse high-resolution mass filter (Level 3) compared to the
63% false positive rate identified without any filters for the validation set containing mostly
chlorinated species (Figure 2A). Reverse high-resolution mass filter was also found to
be valuable in the validation set containing mostly phthalates and PAHs (Table 1).

17
A summary of each annotation filter, the number of candidates retained, and the
percentage of candidates removed after applying the filters for air samples is shown in
Table 1. The individual filters which removed the most candidates on average were
requiring molecular ion (83 ± 14% removed), followed by retention index match < 100 (76
± 10% removed), reverse high-resolution mass filter > 75% (40 ± 27% removed), and
reverse search index (40 ± 12% removed) (Table 1). These findings highlight the
importance of the retention index criteria for removing false positives. When retention
index is combined with reverse high-resolution mass filter and reverse search index for a
Level 2 assignment, 84 ± 8% candidates were removed on average. Hence combining
these three filters further lowers the false positive rate. While use of molecular ion was
the most stringent filtering criterion, reducing false positives, decreasing the false positive
rate must be balanced against the increase in false negatives (number of correct
candidates removed using the respective filter). Use of the molecular ion drastically
increased false negatives.
Table 1: Summary of confidence in annotated chemicals detected in outdoor and
indoor air samples. Individual layers of GC-HRMS evidence were not sufficient at
differentiating between similar chemicals. Detected chemicals were filtered based on
multiple criteria including retention index, reverse search index, reverse high-resolution
mass filter, and observation of the molecular ion. The number of retained candidates after
applying all criteria for Levels 2 and 3 is shown for individual chemicals as well as each
Level 2 criteria. The overall percent of candidates removed is also presented.
False negatives were designated when the correct assignment (exact structural match)
was removed by the respective filter. Requiring the molecular ion led to five false
negatives out of the 19 validated assignments (26%; Table 1), use of retention index led
to 11% false negatives (2/19), reverse search index to 5% false negatives (1/19), and
reverse high-resolution mass filter to 0% false negatives (data not shown). When
retention index, reverse search index, and reverse high-resolution mass filter were
combined (Level 2 assignment) there were 16% false negatives (3/19; Table 1). The false
negative rate for requiring molecular ion may be even higher, as this validation set
consisted of PAHs rbons and derivatives (over 50% of validation set); PAHs have a high
molecular ion signal. Therefore, the generalized criteria we present do not necessitate
Total
Detected
Level 3,
All Criteria
Level 2,
All Criteria
Level 2,
Rentention Index
Criteria
Level 2,
Reverse Search
Index Criteria
Level 2,
Reverse High-Resolution
Mass Filter Criteria
Correct Molecular
Ion Observed
Benz[a]anthracene 25 14 3 5 18 16 11 Yes Yes
Chrysene 39 19 3 4 33 24 11 Yes Yes
Fluoranthene 54 28 9 12 34 43 6 Yes Yes
Fluorene 33 29 7 9 32 29 7 Yes Yes
Acenaphthylene 33 23 7 12 33 23 5 Yes Yes
Naphthalene 40 32 8 11 40 32 9 Yes Yes
Phenanthrene 38 38 7 7 38 38 13 Yes Yes
Pyrene 53 23 7 7 36 34 6 Yes Yes
2-Chloronaphthalene 27 13 2 6 27 13 3 Yes Yes
Dibenzofuran 49 29 5 16 48 29 12 Yes Yes
Hexachlorobenzene 32 9 6 12 31 9 5 Yes Yes
Hexachlorobutadiene 32 2 1 2 26 2 1 Yes Yes
Tris(1-chloro-2-propyl) phosphate 20 5 3 7 17 7 0 No No
4,4'-Dibromooctafluorobiphenyl 27 1 1 2 27 1 1 Yes Yes
Isophorone 49 46 19 19 49 46 8 Yes Yes
N-Nitrosodiphenylamine 46 35 4 4 46 35 23 No No
Butylbenzyl phthalate 21 7 4 7 18 7 3 Yes No
Diethyl phthalate 28 18 4 6 27 18 0 Yes No
Di-n-octyl phthalate 53 45 9 10 52 45 1 No No
Percent Filtered 44 ± 28 84 ± 8 76 ± 10 9 ± 12 40 ± 27 83 ± 14
Number of Candidates
Molecular Ion
Observed for
Correct Candidate
Correct Level 2
Assignment
Unlabeled Standard

18
the molecular ion (retention index removes many of the same candidates and the false
negative rate of requiring molecular ion is too high), but depending on chemical class, for
example when looking at PAHs, necessitating molecular ion may be a beneficial
additional filter. When adding molecular ion as a requirement for Level 2 assignments a
slight decrease in false positives was observed, but a significant increase in false
negatives was also observed (Figure S1).
6. CONCLUDING REMARKS: UTILITY, POTENTIAL CHALLENGES, AND FUTURE
WORK
It is important to note that the assignment of confidence is based on filters, and often
multiple candidates remain even after stringent filtering criteria. For example, for the
passive air sampler dataset, only 2 out of the 19 features with a Level 1 assignment had
a single candidate retained after Level 2 filtering, the remaining 89% of assigned features
had more than one Level 2 candidate (Table 1). Current filter cutoffs are voluntaristic,
based on expert consensus. Optimization of filters may be able to further reduce false
positives, while limiting false negatives, but this will be compound class and workflow
specific. Furthermore, multiple candidates will always exist for multiple features
regardless of thresholds to keep false negatives low. Therefore, ranking algorithms are
essential. When adding the Level 2 criteria to the ranking algorithm from Thermo
Compound Discoverer a significant number of false positives were removed without a
significant increase in false negatives for both validation datasets (Figure 2). Using
search index scores for ranking (e.g., dot product) performed similarly, and hence simple
scoring metrics can be chosen which are not vendor specific. Further ranking based on
meta-data can also be beneficial using information such as database occurrence. For
example, in the air samples evaluated, DEET was observed, but a close isomer which is
not commonly found the environment was ranked first and DEET was ranked second
(both Level 2 assignments). Database matching could be used to rank DEET higher; this
compound was expected as individuals residing in residences where air samples were
collected reported use of this insect repellant.
We outline a qualitative scoring framework for assigning confidence to chemical
assignments in GC-HRMS data. The proposed schema was developed to be actionable
with easy implementation using most deconvolution and annotation algorithm outputs.
Furthermore, we introduce an open access software tool (SIF-GC) to automate feature
scoring. The assignment of confidence follows a five-level scoring framework that has
been well-established by Schymanski et al. for LC-HRMS/MS. The criteria used to
determine confidence for GC-HRMS are common metrics in the GC field, including blank
filtering, (reverse) spectral matches, (reverse) high-resolution mass filters or exact mass
spectral matches, retention index, and molecular ion. Application of the proposed scoring
system to biological and environmental datasets resulted in a false positive rate of 12%
and 11%, respectively, for Level 2 assignments, the highest level which can be assigned
when using suspect screening approaches based on our schema. The false negative rate
(removal of the correct candidate) for Level 2 assignments was found to be 16% for the
air samples and 24% for the spiked human serum. When close isomers (e.g., position of
chlorine(s) on PCBs or methyl groups on PAHs) are considered false positives this false
positive rate is much higher (22% for air samples and 54% for human serum). Hence

19
when using suspect screening for GC, in most cases retention index alone could not
distinguish close isomers. In this case, elution order rather than retention index can be
valuable, although this is difficult to automate and implement on a large scale.
While Level 2 assignments can provide relatively confident annotations, at least at the
level of isomers with subtle difference from the exact structure, Level 3 assignments were
more tentative, and confidence varied widely depending on structure. Nonetheless Level
3 criteria improved assignment confidence over not using any criteria when chemicals
with heteroatoms are investigated. Furthermore, Level 4 assignments can be used to
assign chemical classes, when for example, all PAHs are of interest even when exact
structure cannot be determined. It is important to note that depending on matrix,
acquisition methods, and analyte classes, different false positive and negative rates may
be determined. Future work applying the confidence levels presented here on standard
reference material can be used to have quantitative definition of levels of confidence a
priori, e.g., Level 1 – 95%, Level 2 – 80%, and Level 3 – 50%.
The use of filtering criteria decreased the false positive rate as compared to common
ranking algorithms alone (e.g., Thermo Compound Discover’s ranking algorithm). For the
>100 molecules validated in this study, use of retention index, reverse search index, and
reverse high-resolution mass filtering did not substantially increase false negatives.
Therefore, Level 2 annotations can be exclusively considered for many suspect screening
applications. The requirement of molecular ion for EI did increase false negatives
significantly and is only recommended for certain chemical classes which are highly
stable, for example most PAHs. It is important to mention that the use of accurate mass,
either through molecular ion accurate mass matches, high-resolution mass spectral
libraries, or through reverse high-resolution mass filters or other techniques is beneficial
for reducing false positives. We did not discern between instruments with different
resolving power (our validation datasets were acquired at 40,000 and 60,000 for the air
and serum samples, respectively), but this may influence the false positive and negative
rate when using Level 2 and Level 3 filters. Furthermore, Orbitraps and other trap
instruments may have ions generated which cannot be predicted from molecular formula,
and hence Q-TOFs may have a lower false negative rate in this regard.
While the criteria included in the proposed GC-HRMS scoring framework substantially
decreased false positives and can be used to assign more confident annotations, other
metrics may also be explored including chemical database occurrence (higher occurrence
means it may be more likely to exist in samples), peak shape, and use of ECNI, PCI,
APCI, and other ionization techniques for validation. Database matching may be
especially helpful when close isomers are observed, but only a common isomer exists.
Furthermore, even after Level 2 filtering, in nearly all cases multiple candidates were
retained. Therefore, more quantitative scoring metrics for ranking need still be applied to
determine the correct candidate. We find that total score provided by Compound
Discoverer or simply using reverse search index to rank candidates works well in tandem
with Level 2 filters. The Level 2 filters were further found to reduce false positives
substantially using either of these ranking algorithms, then when using these ranking
algorithms alone.

20
The proposed confidence scores provide a strategy for communicating levels of evidence
that were used to predict annotation of chemicals detected using non-targeted analysis.
While false positives were still present across all annotation levels, this approach assists
in identifying the likelihood a match is correct and can be used to facilitate selection of
potential compounds for further evaluation and validation using additional analytical
techniques. This scoring framework can be easily extended to GC-HRMS data for
exposome analysis and provides a foundation for linking annotations performed across
multiple laboratories and studies and building cumulative databases.

21
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26
SUPPLEMENTAL INFORMATION
An Actionable Annotation Scoring Framework For GC-HRMS
Jeremy P. Koelmel1, Hongyu Xie 2, Elliott J. Price3, Elizabeth Z. Lin1, Katherine E.
Manz4, Paul Stelben1, Mathew Paige1, Stefano Papazian2, Joseph Okeme1, Dean P.
Jones5, Dinesh Barupal5, John Bowden11,12, Pawel Rostkowski6, Kurt D. Pennell4,
Vladimir Nikiforov7, Thanh Wang8, Xin Hu10, Yunjia Lai9, Gary W. Miller9, Douglas I.
Walker5*, Jonathan W. Martin2, and Krystal J. Godri Pollitt1*
1 Department of Environmental Health Science,
Yale School of Public Health, New Haven, CT, 06520, United States
2 Department of Environmental Science, Science for Life Laboratory,
Stockholm University, Stockholm 10691, Sweden
3 RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
4 School of Engineering, Brown University, Providence, RI 02912, United States
5 Department of Medicine, School of Medicine,
Emory University, Atlanta 30322, United States
6 Department of Environmental Medicine and Public Health,
Icahn School of Medicine at Mount Sinai, New York, NY, 10029, United States
7 NILU- Norwegian Institute for Air Research, 2007 Kjeller, Norway
8 NILU- Norwegian Institute for Air Research, Framsenteret, 9007 Tromsø, Norway
9 MTM Research Centre, Örebro University, SE-701 82, Örebro, Sweden
10 Department of Environmental Health Sciences, Mailman School of Public Health,
Columbia University, New York, NY 10032, United States
11 Center for Environmental and Human Toxicology & Department of Physiological
Sciences, University of Florida, Gainesville, FL, 32603, United States
12 Department of Chemistry, University of Florida, Gainesville, FL, 32603 United States
*Corresponding author
Krystal J. Godri Pollitt - krystal.pollitt@yale.edu
Douglas I. Walker - douglas.walker@mssm.edu

27
S1. LEVERAGING EVIDENCE FROM GC-HRMS TO REDUCE FALSE POSITIVES
AND NEGATIVES
S1.1. Feature filtering to remove artifacts and signal from background
After acquiring data from blanks which are both field and extraction blanks, algorithms
can be implemented to establish a compound specific blank threshold. If a certain
percentile of samples does not fall above this blank threshold, the feature can be
removed. For example, one method, blank feature filtering has been found to outperform
most other filtering methods (Koelmel et al., 2021; Patterson et al., 2017). Using this
method, the percentile or average of samples must be greater than the blank feature filter
threshold (BFFthreshold) calculated as:
BFFthreshold = c x (Baverage+3×Bstandard deviation)
Where c is a constant (usually ranging between 2 and 10) and B is the blank signal.
A complimentary method to remove blank signatures is to look for signals which do not
behave like well retained compounds (Borgsmüller et al., 2019; Risum and Bro, 2019).
Many background signals (e.g., column bleed) will not form sharp peaks and/or will be
highly variable in quality control samples injected across the instrument run. Consistency
of deconvoluted spectra with respect to differing intensity in samples across batch in
quality control samples or pools can therefore be used to remove background features
(when many QCs are acquired). Furthermore, quality control calibration curves can be
used to remove non-linear peaks, but this requires that the measured compounds are
generally within the linear range in the quality controls (Matsuo et al., 2017). In this case
non-linear peaks at higher concentrations of the quality controls likely indicate that the
source of the chemical is not from the sample, and if it is, that relative comparisons
between samples is challenging. All these methods which do not implement a blank
sample will not remove a well-behaved chromatographic peak which is a contaminant,
and hence blank feature filtering is recommended as well if these techniques are used.
Other methods can be used to generate a unique signature for compounds, therefore,
rather than determine and remove blank signals, the analytes can be determined uniquely
and retained. One such method is credentialing using isotopic labeling (Mahieu et al.,
2014). This method incorporates isotopic labeling into cell cultures, and screens for the
characteristic isotopic signals to only retain those metabolites which are generated in the
experiment, effectively removing nearly all background. This method is specific to in vitro
studies, and hence to only a subset of internal exposome studies.
S1.2. Alternative EI spectral libraries and ionization methods to improve coverage
For the discovery of unknown unknowns where no libraries or standards exist, the in-silico
prediction of EI spectra (Allen et al., 2016; Spackman et al., 2018; Wang et al., 2020; Wei
et al., 2019) is more straight forward than collision-induced dissociation (CID) (Koopman
and Grimme, 2021) and higher energy C-trap dissociation (HCD) spectra used for LC-
HRMS analysis. Although, it is important to recognize that false positive rates vary across
all in silico approaches (Schreckenbach et al., 2021). In addition to predicted spectral
matching, EI spectra can be used for molecular networks and substructure
characterization (e.g., (Elie et al., 2019; Hummel et al., 2010; Lai et al., 2018; Stein,

28
1995)). The high degree of information contained in EI fragmentation reduces the number
of similar spectral matches and allows for detailed substructural analysis (e.g., chemical
similarity networks).
Alternative ionization strategies can be used with GC-HRMS to improve detection of the
molecular ion, although these often lack the robustness, sensitivity, and reproducibility of
EI. These techniques include positive chemical ionization (PCI), electron capture negative
ionization (ECNI), and atmospheric pressure chemical ionization (APCI). Unlike EI, the
ionization efficiency for PCI, ECNI, and APCI is dependent on chemical structure. While
ECNI is the least universal ionization technique (in terms of ionization efficiencies across
chemical classes), this approach can be highly selective and provide high sensitivity for
ions which are formed favorably by electron capture (e.g., halide containing compounds
such as chlorinated and brominated byproducts). PCI can be adapted for various
chemical analytes because reagent gases are ionized and used to transfer positive
charge to the analytes. Whereas methane gas is usually used, other reagent gases can
be selected to increase sensitivity for specific classes of molecules (Little and Howard,
2013). APCI can provide higher sensitivity for certain molecules, for example, negative
mode APCI, like ECNI, can reveal halogenated compounds (such as halogenated
paraffins) that standard GC-EI-MS misses. While these methods can be challenging to
implement in standardized nontargeted workflows, they provide an important alternative
ionization method for detection molecular ions and predicting potential formulas in the
identification of unknowns.
S2. METHODS: ACQUISITION OF GC-HRMS DATASETS
S2.1. Acquisition of the human serum validation dataset
The GC-HRMS dataset of human serum samples was obtained by spiking 112 standards
(90 native and 22 labeled chemicals) to commercial pooled human serum (Type AB,
Sigma-Aldrich, Germany), and 500 µL aliquots were extracted after removal of proteins
and lipids at Stockholm University. After spiking with the standard mixture (list in Table
S1A), the extracts were analyzed by GC-HRMS (Q Exactive GC Orbitrap, Thermo
Scientific) using electron ionization (EI) in full scan mode (44–750 m/z, 60,000 resolution
FWHM at 200 m/z). The injection volume was 1 µL for each run. A capillary DB5 column
(30 m × 0.25 mm × 0.25 µm film thickness) was used with the temperature program: 50
°C held for 1 min, ramped 10 °C min−1 to 315 °C and held for 8 min.
S2.2. Acquisition of the outdoor and indoor air validation dataset
Passive air samplers (Fresh Air Clips) were deployed inside and outside homes proximal
to a natural gas compressor station in Jefferson County, Ohio. Methods have been
previously described (Martin et al., 2021). Briefly, airborne containments were collected
using polydimethylsiloxane (PDMS) sorbent bars that were housed in a
polytetrafluoroethylene (PTFE) chamber. This assembly was mounted in a magnetic clip
under a weather cover during the sampling period. PDMS sorbent bars were cleaned at
the Yale School of Public Health and shipped together with other materials to the study
site. Four cleaned PDMS sorbent bars were placed into the PTFE chamber and deployed
at indoor and outdoor locations at the home of study participants. At the end of the
sampling period, passive air samples were collected, shipped back to the Yale School of

29
Public Health using cold chain transport, and stored at -80 °C until analysis. Samples
were spiked with a mixture of labelled standards and extracted by thermal desorption
(Gerstel TDU) and analyzed by GC-HRMS (Q Exactive GC Orbitrap, Thermo Scientific)
using electron ionization (EI) in full scan mode (53–800 m/z, 40,000 resolution FWHM at
200 m/z). A capillary TG-5SILMS column (30 m × 0.25 mm × 0.25 µm film thickness) was
used with the temperature program: 70 °C held for 1 min, ramped 7 °C min−1 to 300 °C
and held for 4 min. A total of 81 chemicals were assessed (Table S1B).

30
Table S1A. List of native and isotopic labeled standards spiked into human serum
samples. Acronyms: brominated flame retardants (BFR); chlorinated flame retardants
(CFR); organochlorine pesticides (OCP); polycyclic aromatic hydrocarbons (PAH);
oxygenated polycyclic aromatic hydrocarbons (oxy-PAH); nitrated polycyclic aromatic
hydrocarbons (nitro-PAH); polychlorinated biphenyls (PCB); polychlorinated
dibenzodioxins (PcDD); polychlorinated dibenzofurans (PcDF).
Chemical
Class
Detected Metabolite CAS-RN
Molecular
Formula
Concentration
(ppb)
BFR 2,4,4´-Tribromodiphenyl ether (BDE-28) 41318-75-6 C12H7Br3O 16
BFR 2,2´,4,4´-Tetrabromodiphenyl ether (BDE-47) 5436-43-1 C12H6Br4O 16
BFR 2,3´,4´,4-Tetrabromodiphenyl ether (BDE-66) 189084-61-5 C12H6Br4O 16
BFR 2,2´,3,4,4´-Pentabromodiphenyl ether (BDE-85) 182346-21-0 C12H5Br5O 16
BFR 2,2´,4,4´,5-Pentabromodiphenyl ether (BDE-99) 60348-60-9 C12H5Br5O 16
BFR 2,2´,4,4´,6-Pentabromodiphenyl ether (BDE-100) 189084-64-8 C12H5Br5O 16
BFR 2,2´,4,4´,5,5´-Hexabromodiphenyl ether (BDE-153) 68631-49-2 C12H4Br6O 16
BFR 2,2´,4,4´,5,6´-Hexabromodiphenyl ether (BDE-154) 207122-15-4 C12H4Br6O 16
CFR Dechlorane 603 13560-92-4 C17H8Cl12 16
OCP Pentachlorobenzene (PeCB) 608-93-5 C6HCl5 16
OCP alpha-Hexachlorocyclohexane (α-HCH) 319-84-6 C6H6Cl6 16
OCP Hexachlorobenzene (HCB) 118-74-1 C6Cl6 16
OCP beta-Hexachlorocyclohexane (β-HCH) 319-85-7 C6H6Cl6 16
OCP gamma-Hexachlorocyclohexane (γ-HCH) 58-89-9 C6H6Cl6 16
OCP delta-Hexachlorocyclohexane (δ-HCH) 608-73-1 C6H6Cl6 16
OCP Heptachlor 76-44-8 C10H5Cl7 16
OCP Aldrin 309-00-2 C12H8Cl6 16
OCP Heptachlor epoxide B 1024-57-3 C10H5Cl7O 16
OCP Heptachlor Epoxide A 28044-83-9 C10H5Cl7O 16
OCP β-chlordane(trans-Chlordane) 5103-74-2 C10H6Cl8 16
OCP O, P’-DDE 3424-82-6 C14H8Cl4 16
OCP α-chlordane(cis-Chlordane) 5103-71-9 C10H6Cl8 16
OCP trans-Nonachlor 39765-80-5 C10H5Cl9 16
OCP p,p’-DDE 72-55-9 C14H8Cl4 16
OCP Dieldrin 60-57-1 C12H8OCl6 16
OCP O, P’-DDD 53-19-0 C14H10Cl4 16
OCP Endrin 72-20-8 C12H8Ocl6 16
OCP p,p’-DDD 72-54-8 C14H10Cl4 16
OCP cis-Nonachlor 5103-73-1 C10H5Cl9 16
OCP o,p’-DDT 789-02-6 C14H9Cl5 16
OCP p,p’-DDT 50-29-3 C14H9Cl5 16
Oxy-PAH Phenalen-1-one 548-39-0 C13H8O 16
Oxy-PAH Anthracene-9,10-dione 483-35-2 C14H8O2 16
Oxy-PAH 4H-Cyclopenta[def]phenanthrene-4-one 5737-13-3 C15H8O 16
Oxy-PAH 2-Methylanthracene-9,10-dione 84-54-8 C15H10O2 16
Oxy-PAH 9H-Fluoren-9-one 486-25-9 C13H8O 16
Nitro-PAH 1-Nitro naphthalene 86-57-7 C10H7NO2 18
PCB 2-Chlorobiphenyl (PCB 1) 2051-60-7 C12H9Cl 16
PCB 4-Chlorobiphenyl (PCB 3) 2051-62-9 C12H9Cl 16
PCB 2,2’-Dichlorobiphenyl (PCB 4) 13029-08-8 C12H8Cl2 16
PCB 2,2’,6-Trichlorobiphenyl (PCB19) 38444-73-4 C12H7Cl3 16
PCB 4,4’-Dichlorobiphenyl (PCB 15) 2050-68-2 C12H8Cl2 16
PCB 2,2’,6,6’-Tetrachlorobiphenyl (PCB54 15968-05-5 C12H6Cl4 16
PCB 2,2’,4,6,6’-Pentachlorobiphenyl (PCB 104) 56558-16-8 C12H5Cl5 16
PCB 3,4,4’-Trichlorobiphenyl (PCB37) 38444-90-5 C12H7Cl3 16
PCB 2,2’,4,4’,6,6’-Hexachlorobiphenyl (PCB 155 33979-03-2 C12H4Cl6 16
PCB 3,3’,4,4’-Tetrachlorobiphenyl (PCB 77 32598-13-3 C12H8Cl4 16
PCB 3,4,4’,5-Tetrachlorobiphenyl (PCB 81) 70362-50-4 C12H6Cl4 16
PCB 2’,3,4,4’,5-Pentachlorobiphenyl (PCB 123) 65510-44-3 C12H5Cl5 16
PCB 2,3’,4,4’,5-Pentachlorobiphenyl (PCB 118) 31508-00-6 C12H5Cl5 16
PCB 2,3,4,4’,5-Pentachlorobiphenyl (PCB 114) 74472-37-0 C12H5Cl5 16

31
PCB 2,2’,3,4’,5,6,6’-Heptachlorobiphenyl (PCB 188 74487-85-7 C12H3Cl7 16
PCB 2,2’,3,4,4’,5’ -Hexachlorobiphenyl (PCB 138) 52712-04-6 C12H4Cl6 16
PCB 2,3,3’,4,4’-Pentachlorobiphenyl (PCB 105) 32598-14-4 C12H5Cl5 16
PCB 3,3’,4,4’,5-Pentachlorobiphenyl (PCB 126) 57465-28-8 C12H5Cl5 16
PCB 2,3’,4,4’,5,5’-Hexachlorobiphenyl (PCB 167) 52663-72-6 C12H4Cl6 16
PCB 2,2’,3,3’,5,5’,6,6’-Octachlorobiphenyl (PCB 202) 2136-99-4 C12H2Cl8 16
PCB 2,3,3’,4,4’,5-Hexachlorobiphenyl (PCB 156) 38380-08-4 C12H4Cl6 16
PCB 2,3,3’,4,4’,5’-Hexachlorobiphenyl (PCB 157) 69782-90-7 C12H4Cl6 16
PCB 3,3’,4,4’,5,5’-Hexachlorobiphenyl (PCB 169) 32774-16-6 C12H4Cl6 16
PCB 2,3,3’,4,4’,5,5’-Heptachlorobiphenyl (PCB 189) 39635-31-9 C12H3Cl7 16
PCB 2,2’,3,3’,4,5,5’,6,6’-Nonachlorobiphenyl (PCB 208 52663-77-1 C12HCl9 16
PCB 2,3,3’,4,4’,5,5’,6-Octachlorobiphenyl (PCB 205 74472-53-0 C12H2Cl8 16
PCB 2,2’,3,3’,4,4’,5,5’,6-Nonachlorobiphenyl (PCB 206) 40186-72-9 C12HCl9 16
PCB
2,2’,3,3’,4,4’,5,5’,6,6’-Decachlorobiphenyl (PCB
209)
2051-24-3 C12Cl10 16
PcDD 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) 1746-01-6 C12H4Cl4O2 6.7
PcDD
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
(PnCDD/PeCDD)
40321-76-4 C12H3Cl5O2 16.8
PcDD 1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin (HxCDD) 57653-85-7 C12H2Cl6O2 16.8
PcDD
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
(HpCDD)
35822-46-9 C12HCl7O2 16.8
PcDD 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin (OCDD) 3268-87-9 C12Cl8O2 33.6
PcDF 2,3,7,8-Tetrachlorodibenzofuran (TCDF) 51207-31-9 C12H4Cl4O 6.7
PcDF
1,2,3,7,8-Pentachlorodibenzofuran
(PnCDF/PeCDF)
57117-41-6 C12H3Cl5O 16.8
PcDF 1,2,3,6,7,8-Hexachlorodibenzofuran (HxCDF) 57117-44-9 C12H2Cl6O 16.8
PcDF 1,2,3,4,6,7,8-Heptachlorodibenzofuran (HpCDF) 67562-39-4 C12HCl7O 16.8
PcDF 1,2,3,4,6,7,8,9-Octachlorodibenzofuran (OCDF) 39001-02-0 C12Cl8O 33.6
Phthalate Dimethyl phthalate 131-11-3 C10H10O4 28
Phthalate Diethyl phthalate 84-66-2 C12H14O4 28
Phthalate Diisobutyl phthalate 84-69-5 C16H22O4 28
Phthalate Di-n-butyl phthalate 84-74-2 C16H22O4 28
Phthalate Diisohexyl phthalate 84-63-9 C20H30O4 28
Phthalate Diethoxyethyl phthalate 605-54-9 C16H22O6 28
Phthalate Dipentyl phthalate 131-18-0 C18H26O4 28
Phthalate Dihexyl phthalate 84-75-3 C20H30O4 28
Phthalate Benzyl butyl phthalate 85-68-7 C19H20O4 28
Phthalate Di(2-butoxyethyl) phthalate 117-83-9 C20H30O6 28
Phthalate Dicyclohexyl phthalate 84-61-7 C20H26O4 28
Phthalate Di-2-ethylhexyl phthalate 117-81-7 C24H38O4 28
Phthalate Diphenyl phthalate 84-62-8 C20H14O4 28
Phthalate Di-n-octyl phthalate 117-84-0 C24H38O4 28
Phthalate Dinonyl phthalate 84-76-4 C26H42O4 28
Labeled PAH Acenaphthene-d10 -- C12D10 28
Labeled PAH Fluorene-d10 -- C13D10 28
Labeled PAH Phenanthrene-d10 -- C14D10 28
Labeled PAH Anthracene-d10 -- C14D10 28
Labeled PAH Fluoranthene-d10 -- C16D10 28
Labeled PAH Pyrene-d10 -- C16D10 28
Labeled PAH Benz[a]anthracene-d12 -- C18D12 28
Labeled PAH Chrysene-d12 -- C18D12 28
Labeled PAH Benzo[b]fluoranthene-d12 -- C20D12 28
Labeled PAH Benzo[k]fluoranthene-d12 -- C20D12 28
Labeled PAH Benzo[a]pyrene-d12 -- C20D12 28
Labeled PAH Indeno[1,2,3-cd]pyrene-d12 -- C22D12 28
Labeled PAH Dibenzo[a,h]anthracene-d14 -- C22D14 28
Labeled PAH Benzo[g,h,i]perylene-d12 -- C22D12 28
Labeled PAH Naphthalene-d8 -- C10D8 28
Labeled PAH Acenaphthylene-d8 -- C12D8 28
Labeled PCB 2,4,4’-Trichlorobiphenyl-13C12 (PCB 28) -- C12H7Cl3 10
Labeled PCB 2,2’,5,5’-Tetrachlorobiphenyl-13C12 (PCB 52) -- 13C12H6Cl4 10
Labeled PCB 2,2’,4,5,5’-Pentachlorobiphenyl-13C12 (PCB 101) -- 13C12H5Cl5 10

32
Labeled PCB
2,2’,3,4,4’,5’ -Hexachlorobiphenyl-13C12 (PCB
138)
-- 13C12H4Cl6 10
Labeled PCB 2,2’,4,4’,5,5’-Hexachlorobiphenyl-13C12 (PCB 153) -- 13C12H4Cl6 10
Labeled PCB
2,2’,3,4,4’,5,5’-Heptachlorobiphenyl-13C12 (PCB
180)
-- 13C12H3Cl7 10

33
Table S1B. List of native and isotopic labeled standards used for targeted analysis
(Level 1 identifications) in air samples. Acronyms: Brominated flame retardants (BFR);
hydrocarbon (HC); organochlorine pesticides (OCP); organophosphates (OPE);
polycyclic aromatic hydrocarbons (PAH); polychlorinated biphenyls (PCB); VOC: volatile
organic compounds (VOC).
Chemical Class Detected Metabolite CAS-RN Molecular Formula
Alkaloid Nicotine 54-11-5 C10H14N2
Benzodioxole Fludioxonil 131341-86-1 C12H6F2N2O2
Benzopyran Delta-9-tetrahydrocannabinol 1972-08-3 C21H30O2
BFR 2,4,4'-Tribromodiphenyl ether (BDE-28) 41318-75-6 C12H7Br3O
BFR 2,2',4,4'-Tetrabromodiphenyl ether (BDE-66) 5436-43-1 C12H6Br4O
BFR 2,2',4,4',5-Pentabromodiphenyl ether (BDE-99) 60348-60-9 C12H5Br5O
BFR 2,2',4,4',6-Pentabromodiphenyl ether (BDE-100) 189084-64-8 C12H5Br5O
BFR 2,2',4,4',5,5'-Hexabromodiphenyl ether (BDE-153) 68631-49-2 C12H4Br6O
BFR 2,2',4,4',5,6'-Hexabromodiphenyl ether (BDE-154) 207122-15-4 C12H4Br6O
BFR 2-Ethylhexyl 2,3,4,5-tetrabromobenzoate 183658-27-7 C15H18Br4O2
Chlorinated HC 1,2,4-Trichlorobenzene 120-82-1 C6H3Cl3
Chlorinated HC Hexachlorobutadiene (HCBD) 87-68-3 C4Cl6
Chlorinated HC Hexachlorocyclopentadiene 77-47-4 C5Cl6
Chlorinated HC Hexachloroethane 67-72-1 C2Cl6
Haloethers Bis(2-chloro-1-methylethyl) ether (BCEE) 108-60-1 C6H12Cl2O
Haloethers Bis(2-chloroethyl)ether 111-44-4 C4H8Cl2O
Haloethers Carfentrazone-ethyl 128639-02-1 C15H14Cl2F3N3O3
Nitroaromatic 2,4-Dinitrotoluene 121-14-2 C7H6N2O4
Nitroaromatic 2,6-Dinitrotoluene 606-20-2 C7H6N2O4
Nitroaromatic 4-Chloroaniline 106-47-8 C6H6ClN
Nitroaromatic 4-Nitroaniline 100-01-6 C6H6N2O2
Nitroaromatic Isophorone 78-59-1 C9H14O
Nitrosoamine Nitrobenzene 98-95-3 C6H5NO2
Nitrosoamine N-Nitrosodi-n-propylamine 621-64-7 C6H14N2O
Nitrosoamine N-Nitrosodiphenylamine 86-30-6 C12H10N2O
OCP alpha-Hexachlorocyclohexane (α-HCH) 319-84-6 C6H6Cl6
OCP Chlorothalonil 1897-45-6 C8Cl4N2
OCP Dieldrin 60-57-1 C12H8Cl6O
OCP Endosulfan I 959-98-8 C9H6Cl6O3S
OCP Endrin 72-20-8 C12H8Cl6O
OCP gamma-Hexachlorocyclohexane (γ-HCH) 58-89-9 C6H6Cl6
OCP Hexachlorobenzene 118-74-1 C6Cl6
OCP Methoxychlor 72-43-5 C16H15Cl3O2
OCP p,p'-DDD 72-54-8 C14H10Cl4
OCP p,p'-DDT 50-29-3 C14H9Cl5
OCP Tetrachloro-m-xylene 877-09-8 C8H6Cl4
OPE Triphenyl phosphate (TPHP) 115-86-6 C18H15O4P
OPE Tris(1-chloro-2-propyl) phosphate (TCPP) 13674-84-5 C9H18Cl3O4P
PAH 1-Bromo-4-phenoxybenzene 101-55-3 C12H9BrO
PAH 1-Chloro-4-phenoxybenzene 7005-72-3 C12H9ClO
PAH 2-Chloronaphthalene 91-58-7 C10H7Cl
PAH 2-Methylnaphthalene 91-57-6 C11H10
PAH Acenaphthene 83-32-9 C12H10
PAH Acenaphthylene 208-96-8 C12H8
PAH Anthracene 120-12-7 C14H10
PAH Benz[a]anthracene 56-55-3 C18H12
PAH Benzo[a]pyrene 50-32-8 C20H12
PAH Benzo[b]fluoranthene 205-99-2 C20H12
PAH Benzo[ghi]perylene 191-24-2 C22H12
PAH Benzo[k]fluoranthene 207-08-9 C20H12
PAH Chrysene 218-01-9 C18H12
PAH Dibenz[a,h]anthracene 53-70-3 C22H14
PAH Dibenzofuran 132-64-9 C12H8O

34
PAH Fluoranthene 206-44-0 C16H10
PAH Fluorene 86-73-7 C13H10
PAH Indeno[1,2,3-cd]pyrene 193-39-5 C22H12
PAH Naphthalene 91-20-3 C10H8
PAH Phenanthrene 85-01-8 C14H10
PAH Pyrene 129-00-0 C16H10
PCB 2,2',3,3',4,4',5,5',6,6'-Decachlorobiphenyl (PCB 209) 2051-24-3 C12Cl10
Phthalate Bis(2-ethylhexyl) phthalate 117-81-7 C24H38O4
Phthalate Butylbenzyl phthalate 85-68-7 C19H20O4
Phthalate Diethyl phthalate 84-66-2 C12H14O4
Phthalate Dimethyl phthalate 131-11-3 C10H10O4
Phthalate Di-n-butyl phthalate 84-74-2 C16H22O4
Phthalate Di-n-octyl phthalate 117-84-0 C24H38O4
Pyrethroid Piperonyl butoxide 51-03-6 C19H30O5
VOC 1,2-dichlorobenzene 95-50-1 C6H4Cl2

35
Figure S1: Molecular ions can be used to reduce the false positive rate (albeit
increasing the false negative rate) as shown using a GC-HRMS dataset of air
samples.
A. Filtering by retention index, reverse
search index, Reverse high-resolution
mass filter criteria
B. Additional requirement for molecular
ion

An Actionable Annotation Scoring Framework For Gas Chromatography - High Resolution Mass Spectrometry (GCHRMS)

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