000171110 001__ 171110 000171110 005__ 20260506144057.0 000171110 0247_ $$2doi$$a10.1039/d6ja00062b 000171110 0248_ $$2sideral$$a149208 000171110 037__ $$aART-2026-149208 000171110 041__ $$aeng 000171110 100__ $$ade Souza, André L. M. 000171110 245__ $$aSingle- and dual-isotopic analysis using high-resolution continuum-source graphite-furnace molecular absorption. Strategies for data selection, processing, and modeling 000171110 260__ $$c2026 000171110 5060_ $$aAccess copy available to the general public$$fUnrestricted 000171110 5203_ $$aThis 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 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 promising for isotopic abundances of at least 10% (median absolute percentage error of 1.21%), while the error escalates when one of the isotopes shows a lower abundance. 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. 000171110 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E43-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-122455NB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-136454NB-C22$$9info:eu-repo/grantAgreement/ES/MICINN/PRE2019-091118$$9info:eu-repo/grantAgreement/ES/MICIU/PID2024-156411NB-I00 000171110 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es 000171110 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000171110 700__ $$0(orcid)0000-0002-3916-9992$$aAramendía, Maite$$uUniversidad de Zaragoza 000171110 700__ $$0(orcid)0000-0003-2640-8496$$aGarcía-Ruiz, Esperanza$$uUniversidad de Zaragoza 000171110 700__ $$0(orcid)0000-0001-7087-9901$$aNakadi, Flávio V.$$uUniversidad de Zaragoza 000171110 700__ $$0(orcid)0000-0002-7532-2720$$aResano, Javier$$uUniversidad de Zaragoza 000171110 700__ $$0(orcid)0000-0002-7450-8769$$aResano, Martín$$uUniversidad de Zaragoza 000171110 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica 000171110 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput. 000171110 773__ $$g(2026), [13 pp.]$$pJ. anal. at. spectrom.$$tJournal of Analytical Atomic Spectrometry$$x0267-9477 000171110 8564_ $$s3233418$$uhttps://zaguan.unizar.es/record/171110/files/texto_completo.pdf$$yVersión publicada 000171110 8564_ $$s2715671$$uhttps://zaguan.unizar.es/record/171110/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000171110 909CO $$ooai:zaguan.unizar.es:171110$$particulos$$pdriver 000171110 951__ $$a2026-05-06-13:58:54 000171110 980__ $$aARTICLE