Fig. 7 : The slope of all multipoint normalizations by isotope
range of the standards. Dashed horizontal lines indicate the slopes for
each facility derived from an 8-point normalization composed of all
certified reference standards.
Normalizations are significantly impacted by matrix and
extrapolation
effects
Even when results are constrained to three-point normalizations, the
matrix of the standard relative to the quality controls has a
significant effect on the accuracy of the normalization (Fig. 3).
Bounded normalizations where the matrixes of the standards were mixed
relative to the quality controls exhibited median errors 63%-195%
greater than those where the matrixes were matched. The mechanism behind
this matrix effect is unclear, particularly because the isotope
composition of the plant-based standards were quantified using high
organic certified reference materials20. In accordance
with typical EAIRMS usage, the plant-based standards were weighed to a
higher mass and analyzed with a higher oxygen dosing and a greater
sample dilution for C, and thus it is possible that these
instrumentation factors are contributing to the matrix effect observed
in this study. Although past work has suggested that matrix matching
between organic and inorganic samples would reduce normalization
errors28, this study posits that matrix effects should
be considered even within organic samples. In studies where
matrix-matched certified reference materials do not exist (e.g.,
sediment; glass fiber filters), the effects of matrix-mixing should be
considered as a source of imprecision that will not be reflected in the
variance of the standards alone.
Similarly, this study shows that extrapolating beyond the isotope range
of the standards– not an uncommon occurrence when analyzing a wide
variety of biological samples – increases median normalization errors
by 81%-135%, even when the analysis is constrained to matrix-matched
normalizations (Fig. 4). Normalizations with a smaller isotope range are
more likely to require extrapolation, but the lack of significant
relationship between isotope range and normalization error for bounded,
matrix-matched, three-point normalizations (Fig. 5) indicates that
extrapolation is the primary factor driving inaccuracy. Overall, our
results provide experimental evidence to support the emerging
consensus37 that calibration standards for EAIRMS
should have isotope values that span the full natural range of the
measured elements, regardless of the isotope values of the samples being
analyzed.
Instrument linearity cannot be predicted by reference gas
diagnostics alone.
Although reference gas linearity diagnostics are anecdotally used as a
means of assessing instrument performance, we quantified instrument
linearity using replicate analyses of working standards across a range
of sample weights. At both facilities, a large linearity effect was
observed in the reported isotope composition of the working standards
that was not reflected by reference gas linearity diagnostics, which
inject pulses of reference gas into the IRMS to assess the linearity
effect. The linearity effect does not correspond to incomplete
combustion in the elemental analyzer (Fig. S1), suggesting that other
factors, such as a N blank introduced during sample preparation, are
driving our observations. These results show that, while reference gas
linearity diagnostics may be useful for confirming normal instrument
operation, they are not representative of the linearity effect that will
be observed when analyzing solid samples. We suggest running replicate
measurements of a solid standard at varying sample weights to determine
the peak amplitudes at which linearity has an effect and applying a
linearity correction curve when necessary. In our study, N linearity was
observed at peak amplitudes corresponding to ~0.06 mg of
N, meaning that instrument linearity is of particular importance for
samples with a low N content such as sediment and plant tissue. The high
magnitude of the linearity effect observed in both facilities across a
range of sample matrixes reinforces the importance of this instrument
effect for any facility measuring stable isotopes in biological samples.
Conclusion – best practices for biological applications of
EAIRMS
Through an experimental assessment of isotope normalizations across a
variety of sample matrixes and isotope ranges, we assessed how the
number, matrix, and isotope range of the calibration standards effected
normalization error on two EAIRMS systems. In the first known assessment
of normalization methods for both N and C, we found that three-point and
four-point normalizations have the lowest normalization errors and are
most resilient to the effects of sample matrix effects and
extrapolation.
Past work has identified two-point normalizations as a common and
acceptable means of normalizing isotope results2,17.
In contrast, some of the observed two-point normalizations had
deviations more than 1‰ and were significantly less accurate than
three-point and four-point normalizations. We posit that two-point
normalizations are vulnerable to the effects of matrix-mixing and
extrapolation and should not be considered sufficient for EAIRMS
normalization in biological applications. Although normalizations using
at least three standards were more resilient to these factors, we found
that mixing the matrix between the samples and the standards and
extrapolating outside of the curve reduced accuracy regardless of how
many standards were analyzed. No significant impact was observed with
the isotope range of three-point normalization but maximizing the
isotope range will reduce the likelihood of extrapolation. Thus, we
recommend users of EAIRMS systems normalize their results using at least
three calibration standards that span a large isotope range and are
matrix matched with the samples being analyzed, and to include at least
one additional independent quality control standard.
In our interlaboratory comparison of instrument linearity, we found that
linearity error was substantial, especially for N, regardless of
instrument or sample matrix. Diagnostic reference gas linearity testing
was unable to reproduce the observed linearity effect, suggesting that
the reference gas is not a useful predictor of real-world instrument
linearity. Although reference gas diagnostic testing may be a beneficial
tool for assessing nominal instrument operation, the linearity response
of the instrument should be assessed experimentally by analyzing solid
standards across a range of masses.
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