LASER ABLATION MASS SPECTROMETRY BLAST THROUGH DETECTION IN R

Organisms that grow a hard carbonate shell or skeleton such as foraminifera, corals and molluscs incorporate geochemical trace elements into their shell during growth that reflects experienced environmental and biological activity. These geochemical signals are archives used in proxy measurement reconstructions for past environments and climates as well as the context for studying palaeobiological dynamics.

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is an increasingly available analytical tool to quantify the elemental composition of carbonate shells. Methodological processing suffers from rigid data processes with no regard for the variation in calcite thickness of the carbonate system. There is inevitably a chance that the laser will ablate through the entire calcite depth, and thus the depth profile will not only be restricted to the analytical subject.

Here we introduce freely available lablaster (Laser Ablation BLASt Through Endpoint in R) software for use in the R environment to automate the analysis of LA-ICP-MS outputs.

lablaster imports a single time resolved LA-ICP-MS analysis, then the algorithm determines the time stamp when the laser has burnt through the calcite as a function of change in signal over time. The function returns the time stamp of the final on-target shot and a data frame containing only the relevant data that represents the target. All presented code, including visualisation tools for manual validation, and example data are available from CRAN.

The package showcases improved endpoint detection methodology that increases analytical precision. This function calculates the rate of change of a smoothed signal over time to identify when the maximum rate of change occurs to isolate the point when the laser has blasted through the calcite wall and thus stops documenting geochemical signal. Subsequently the function removes the rows of data that occur between the final laser pulse of high signal counts and the maximum rate of change resulting with a data frame containing only the desired geochemical target. We demonstrate the improvements in performance in the context of signal:noise ratios using a planktonic foraminifera case study as a worked example within an automated end-to-end workflow.