LAtools: A data analysis package for the reproducible reduction of LA-ICPMS data

Oscar Branson, Jennifer S. Fehrenbacher, Lael Vetter, Aleksey Y. Sadekov, Stephen M. Eggins, Howard J. Spero

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) is an increasingly popular analytical technique, that is able to provide spatially resolved, minimally destructive analyses of heterogeneous materials. The data produced by this technique are inherently complex, and require extensive processing and subjective expert interpretation to produce useful compositional data. At present, laboratories employ diverse protocols for data processing, and the reporting of these protocols is usually insufficient to allow data processing to be independently replicated, rendering the resulting data untraceable. Importantly, different expert users can obtain significantly different results from the same raw data using nominally identical processing workflows, depending on how ‘contaminants’ are identified and excluded, and which regions of signal are selected as representative of the composition of the sample. The irreproducibility of LA-ICPMS is a significant problem for the technique, but the complexity of the raw data has been a major hindrance to developing traceable data processing workflows. Here, we present LAtools – a free, open-source Python package for LA-ICPMS data processing designed with reproducibility at its core. The software performs basic data processing with similar efficacy to existing software, and brings a number of new data selection algorithms to facilitate reproducible reduction of LA-ICPMS data. We discuss the key advances of LAtools, and compare its output to trace metal analysis of marine CaCO3 (foraminifera) processed both manually and with Iolite, and to manually processed trace element data from zircon grains.

Original languageEnglish
Pages (from-to)83-95
Number of pages13
JournalChemical Geology
Early online date9 Nov 2018
Publication statusPublished - 20 Jan 2019


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