Single-and dual-isotopic analysis using high-resolution continuum-source graphite-furnace molecular absorption. Strategies for data selection, processing, and modeling

Abstract

This work evaluates different strategies for data processing, aiming at achieving isotopic information via high-resolution continuum-source graphite-furnace molecular absorption. For this purpose, two different molecules are investigated: CaF and CaCl. In the first case, only the measurement of 44Ca and 40Ca is pursued, whereas in the second case, isotopic variations affect both elements present in the molecule (44Ca and 40Ca, but also 37Cl and 35Cl). Thus, two different approaches are proposed. For Ca isotopic analysis through the monitoring of CaF, the effects of selecting the number of detection pixels and the number of molecular spectra, as well as of using a novel regression approach for temporal data, are discussed. Overall, using three detector pixels and using this regression approach tend to produce the best results (0.5-1.0% RSD) for isotopic analysis via HR CS GFMAS in those situations in which the signal can be derived from two separate peaks. On the other hand, to perform simultaneous Ca and Cl isotopic analysis by monitoring CaCl, a machine-learning strategy is proposed. The performance of such a model is satisfactory for most of the ratios investigated (median absolute percentage error 1.21%), except for situations characterized by the low abundance of one isotope (around 10% or lower). To detect such underperforming situations in real-world settings, it is recommended to monitor the prediction uncertainty to set thresholds and flag results with poor reliability.

Supplementary files

Article information

Article type
Paper
Submitted
18 Feb 2026
Accepted
21 Apr 2026
First published
23 Apr 2026
This article is Open Access
Creative Commons BY-NC license

J. Anal. At. Spectrom., 2026, Accepted Manuscript

Single-and dual-isotopic analysis using high-resolution continuum-source graphite-furnace molecular absorption. Strategies for data selection, processing, and modeling

A. L. L. M. Souza, M. Aramendía, E. Garcia-Ruiz, F. V. Nakadi, J. Resano and M. Resano, J. Anal. At. Spectrom., 2026, Accepted Manuscript , DOI: 10.1039/D6JA00062B

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