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Physical model for multi-point normalization of dual-inlet isotope ratio mass spectrometry data



Abneesh Srivastava


A simple model is presented for multi-point normalization of dual-inlet isotope ratio mass spectrometry (DI-IRMS) data. The model incorporates the scale contraction coefficient and the normalized working reference gas isotope delta value as its two physical parameters. The model allows the full use of isotope measurement data and outputs the normalized sample isotope delta value along with the mentioned parameters. The model reduces to the expected linear behavior on application to a natural range CO2 isotopic composition sample, under typically observed scale contraction levels. Next, DI-IRMS measurements of the NIST CO2 gas isotopic reference materials (RMs) 8562, 8563, and 8564 are used to construct a three-point linear calibration, spanning 40‰ for the δ45CO2 and 20‰ for the δ46CO2 raw data. Accuracy of the regression at the 0.009‰ level for δ13C and 0.01‰ for δ18O is observed for the three NIST RMs. The model derived scale contraction term is found to be a more accurate measure of cross-contamination in contrast to its end of day measurements by the enriched sample method. The constructed multi-point normalization model is next used to assign δ13CVPDB-CO2 and δ18OVPDB-CO2 isotope delta values on the Vienna PeeDee Belmnite-CO2 (VPDB-CO2) scale, for pure CO2 gas samples in the natural isotopic range. A Monte Carlo analysis of the uncertainty, including estimates for the normalization step, is provided to assist future multi-point normalization with more than three reference points.
Analytical and Bioanalytical Chemistry


Linear scale normalization, Scale contraction, cross-contamination correction, one-point, two-point, three-point, multi-point, isotopic reference material, VPDB-CO2 traceability, del13C, del18O, DI-IRMS, Monte Carlo simulation, uncertainty


Srivastava, A. (2022), Physical model for multi-point normalization of dual-inlet isotope ratio mass spectrometry data, Analytical and Bioanalytical Chemistry, [online],, (Accessed April 16, 2024)
Created June 1, 2022, Updated November 29, 2022