Experimental assessment
of EAIRMS normalization methodologies for environmental stable isotopes
Sawyer
Balint1*, Morgan Schwartz2*, Drew
Fowler3†, Stella Linnekogel4†, Sáde
Cromratie Clemons5, Laura K.
Burkemper6
1ORISE Participant, U.S. EPA Atlantic Coastal
Environmental Sciences Division, Narragansett, RI. 02882
2U.S. EPA Atlantic Coastal Environmental Sciences
Division, Narragansett, RI. 02882
3U.S. Geological Survey, Louisiana Cooperative Fish
and Wildlife Research Unit, Baton Rouge, LA. 70810
4UK Centre for Ecology & Hydrology Lancaster, Library
Avenue, Bailrigg, Lancaster, LA1 4AP, United Kingdom
5Department of Geography, University of Boulder,
Boulder, CO
6Center for Stable Isotopes, University of New Mexico,
Albuquerque, NM
* Authors share first authorship
†Authors contributed equally to this manuscript
Corresponding Author :
Sawyer Balint
sjbalint@bu.edu
(518) 258-9510
Morgan Schwartz
schwartz.morgan@epa.gov
(401) 782-9624
CRediT author statement :
Sawyer Balint : Conceptualization, Methodology, Software,
Validation, Formal analysis, Investigation, Data Curation, Writing –
Original Draft, Visualization, Supervision, Project administration.
Morgan Schwartz : Conceptualization, Methodology, Validation,
Formal analysis, Investigation, Resources, Data Curation, Writing –
Original Draft, Supervision, Project administration.
Drew Fowler : Conceptualization, Methodology, Investigation,
Writing – Review & Editing.
Stella Linnekogel : Conceptualization, Methodology,
Investigation, Writing – Review & Editing.
Sáde Cromratie Clemons: Conceptualization, Methodology,
Investigation, Writing – Review & Editing.
Laura K. Burkemper: Conceptualization, Methodology, Validation,
Investigation, Resources, Data Curation, Writing – Review & Editing,
Supervision, Project administration.
Acknowledgements:
The authors would like to thank Viorel Atudorei, Seth Newsome, and
Zachary Sharp for their assistance in the planning and execution of
IsoCamp 2022, where the experimental design for this project was
conceived. Additionally, the authors thank Renee Brooks, Michaela
Cashman, and Autumn Oczkowski for review and feedback on the manuscript.
Stella Linnekogel (UKCEH) acknowledges support from the NERC project
Diurnal Variation in Soil Nitrous oxide Emissions (DIVINE): drivers and
mechanisms (NE/V000837/1). This article has been subjected to Agency
review and has been approved for publication. This work was supported in
part by an appointment to the Research Participation Program at the
Office for Research and Development, Center for Environmental
Measurement and Modelling, Atlantic Coastal Environmental Sciences
Division, U.S. Environmental Protection Agency (U.S. EPA), administered
by the Oak Ridge Institute for Science and Education through an
interagency agreement between the U.S. Department of Energy and U.S.
EPA. The views expressed in this presentation are those of the authors
and do not necessarily represent the views or polices of the U.S. EPA.
This article has been peer reviewed and approved for publication
consistent with USGS Fundamental Science Practices
(https://pubs.usgs.gov/circ/1367/). Any use of trade, firm, or product
names is for descriptive purposes only and does not imply endorsement by
the U.S. Government.
Abstract
RATIONALE : In stable isotope mass spectrometry, isotope values
are normalized to internationally recognized reference scales using
certified reference materials and working standards. Numerous techniques
exist for performing this normalization, but these methodologies need to
be experimentally assessed to compare their impact on reproducibility of
isotope results.
METHODS : We tested normalization methods by the number of
standards used, their matrix, their isotope range, and whether
normalization required extrapolating beyond the isotope range. Using 8
certified reference materials and 5 working standards on a
ThermoFinnigan Delta-V IRMS and Elementar VisION IRMS for nitrogen and
carbon isotope composition via solid combustion with an elemental
analyzer, we computed every possible isotope normalization (n=6272).
Additionally, we assessed how sample matrix impacted linearity effects
on both instruments.
RESULTS : Normalizations composed of three or four reference
materials had better performance than one-point and two-point methods,
especially when the normalization was matrix-mixed or extrapolated, and
normalizations with an isotope range greater than 15‰ were more accurate
under these conditions. Normalizations that were matrix-matched and were
not extrapolated exhibited the highest accuracy. Linearity effects were
found to exceed instrument precision by two orders of magnitude
irrespective of sample matrix and were not predicted by reference gas
diagnostics.
CONCLUSIONS : To maximize interlaboratory comparability of
isotope results, operators of EAIRMS systems should use at least 3
calibration standards to construct their normalizations, select
standards with a large isotope range to avoid extrapolation, and match
the matrix of their standards to their samples to the best extent
possible.
Stable isotope mass spectrometry is a growing tool across disciplines,
including in biology where the nitrogen (N) and carbon (C) isotope
composition of solid samples are routinely used for investigating a
variety of physiological, ecological, and biogeochemical
questions1. In recent decades, the broad applications
of stable isotopes combined with faster and more accessible
instrumentation have led to a large growth in the utilization and
publication of stable isotope measurements as they relate to the natural
sciences2. Despite the growing importance of this
tool, immense variations in analytical methodology exist within the
field, leading to poor interlaboratory comparability3and complicating the interpretation and reproducibility of scientific
studies that use stable isotopes4.
Continuous flow stable isotope mass spectrometry5 via
solid combustion6,7 is the predominant analytical
technique used to quantify the C and N isotope composition of solid
samples for biological applications2. Samples are
delivered to the isotope ratio mass spectrometer (IRMS) in a gaseous
form through a peripheral elemental analyzer (EA) and the isotope
composition is determined relative to a working gas that is injected
sequentially with the sample gas8,9. This methodology
allows for high precision measurements, but to facilitate
interlaboratory comparison the results must be normalized to
internationally referenced isotope scales10-12.
Contemporary normalization methods call for standard reference materials
to be processed identically to the unknown samples using the “identical
treatment” principle5,13, and analyzed in tandem with
unknown samples14-16, a normalization curve is then
computed with a least-squares linear regression17. As
the number of available certified standards has increased in recent
decades18-20, operators now have a multitude of
certified standards to choose from when performing their isotope
normalizations in addition to their own working standards.
Unfortunately, the reproducibility of normalization techniques has not
advanced at the same pace as the expanding use of stable isotopes.
Although modeling work has suggested that normalization error generally
decreases with the number of standards21, stable
isotope laboratories vary immensely in the number of standards used due
in part to limited experimental assessments of related normalization
error. Furthermore, users of elemental analyzer isotope ratio mass
spectrometry (EAIRMS) for biological applications often analyze sample
matrixes for which certified reference materials do not exist (e.g.,
sediment22, marine algae23, samples
imbedded on glass fiber filters24), but the impact of
mixing organic matrixes between samples and standards on normalization
accuracy is unknown. Whether standards should be selected to maximize
isotope range or closely bracket the unknown samples is another decision
that, absent of an experimental assessment across a variety of
normalization methods, is left to anecdotal procedures that may vary
between laboratories. Some analyses, such as those that incorporate N
tracer as part of the methodology25, may require
extrapolation beyond the range of the normalization curve – again with
unknown consequences. Ultimately, readers of studies that incorporate
stable isotopes are left to determine the reproducibility of the study
for themselves – assuming the particulars of the normalization are even
included in the methods. This matter becomes increasingly more difficult
in the era of big data and meta-analysis, as multiple studies using
multiple methods are integrated and consequently compared directly.
Here, we aim to better quantify best normalizations practices for EAIRMS
analysis using an experimental assessment of a variety of normalization
methods using certified reference materials analyzed on two instrument
systems in two laboratories. We specifically assess how the accuracy of
the normalization is impacted by the number of standards, their matrix
relative to the unknown samples, their isotope range, and whether the
normalization requires extrapolation beyond the isotope range. We
further investigate how instrument linearity effects are impacted by
sample matrix through the first known interlaboratory comparison of
EAIRMS linearity effects. By developing a refined understanding of
EAIRMS best practices, we hope to better facilitate the reproducibility
of biological stable isotope applications.
Number of standards used to construct the
normalization
Although a variety of methodologies have been used to normalize results
from isotope ratio mass spectroscopy8,15, this work
focuses on the most common methods used in continuous flow applications:
one-point anchoring, two-point linear normalization, or multipoint
linear normalization 2,17. Typically, the IRMS
software will perform a one-point anchoring relative to the working gas
according to the following equations: