Introduction

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a powerful analytical tool to quantify the elemental composition of a wide variety of natural and anthropogenic materials. A laser beam is focussed to the surface of a target and then pulsed to ablate the sample. Particles from the ablated sample are subsequently transported into a mass spectrometer for detection based on the mass-to-charge ratio, which can be converted into a resolved isotopic or elemental profile. LA–ICP-MS has become increasingly popular with biogenic carbonates including foraminifer1-3, coral skeletons4 and molluscs5, all of which act as archives of geochemical signals that can be used as proxy measurements to both reconstruct past environments and study the evolutionary response to long-term climate change.
Recent instrumentation advances enable LA-ICP-MS setups to collect comparable trace element to calcium ratio (TE/Ca) results to traditional solution based ICP-MS but with simpler sample preparation and higher throughput6. This solid sample and laser setup also allows for higher spatial resolution of the sample and avoids the heterogeneity averaging that occurs in solution based ICP-MS. Nevertheless, there is no package for R that combines high data throughput with the additional nuance that laser ablation data processing requires to keep the maximum amount of relevant data. To fully leverage the gains of LA-ICP-MS, any software must be flexible enough to handle non-homogeneous samples.
Some ready-to-use free computer packages exist to process LA-ICP-MS data such as elementR7 or the discontinued LAICPMS8, both of which use the R environment, and LATools9, which uses the Python environment. ElementR provides a point-and-click graphical user interface that slows data reduction throughput, while giving the user fine control over the data integration period. TERMITE10 is not a packageper se but is optimised for repeatable data reduction of homogenous samples, where the data integration period must be adjusted individually for each measurement and therefore requiring manual validation.
These three pieces of software provide a general end-to-end workflow to process experimental data into results rather than specialising on a particular data reduction step. In comparison, the LABLASTER package presented here contains a function that specialises in identifying when the laser is no longer recording the geochemical target of interest and is therefore designed for high-throughput processing that doesn’t require user interaction once configured. A variety of integration time-range endpoint detection mechanisms are used in the literature, including k-means clustering7, fixed time stamps2, analyte signal below a given threshold11, the mid-point between high and background signal counts9 and even manual identification when the complexity of the samples is too great9. Here we fit a function over a first derivative to calculate the change in rate of signal change. As LA-ICP-MS increases in popularity and experiments become more complex, there is a need for repeatable algorithmic protocols that can deal with heterogeneous samples or where repeat measurements may have different integration times.
Each discipline using LA-ICP-MS measure samples that have different matrixes and properties e.g., polished rock sections, powered pellets or carbonate shells. The worked examples presented here have been tailored to the field of ecology and evolution with a planktic foraminifer and the field of paleoclimate geochemistry with a tropical coral. The LABLASTER package will however work with any sample that the laser may ablate through and hit an undesired target. The foraminifera example here demonstrates how LABLASTER can be used with the specific needs of ecologists, whose data is often skewed and highly variable7.
Here, we (1) improve current processing capabilities by dynamically identifying the end of the sample of carbonate subject and (2) implement this improved processing in the first freely available software to automate data extraction of a time resolved elemental depth profile. As demonstrated in the examples below, the end of the sample may be the maximum depth at a single spot location for a shell or a boundary between two minerals along a linear profile for a coral, but any non-homogeneous target sample is generally applicable.
An automated laser ablation setup often requires a constant firing time to be programmed into the controlling computer, with no regard for the heterogeneity or variation in thickness of the target. When samples are porous, have changes in mineralogy or variation in thicknesses within a single analytical session while using a consistent laser pulsing time, there is inevitably a chance that the laser will move across a mineral boundary or ablate through the entire depth, and thus the recorded data will not be restricted to only the area of interest. Any elemental measurement recorded after the laser has ablated through the sample is not of the target, it should be removed before subsequent statistical analysis. Because the time taken to ablate through a sample is not consistent, such corrections can be made manually on an ad hoc basic, but additional manual handling would be time-consuming, laborious and prone to subjective differences amongst operators. There are clear methodological benefits from the development of a repeatable workflow.
The LABLASTER package works alongside elementR or TERMITE application or can be run as a standalone process within private scripts, providing a flexible and versatile methodological improvement for heterogeneous samples that treats each sample individually to optimise signal: noise ratios. LABLASTER can batch process within a workflow, is customisable to the sensitivity for endpoint detection and does not require a point-and-click user interface. These features offer a higher throughput for data reduction compared to manual or alternative software methods and in retaining the maximum amount of on-target data for subsequent analysis.
Figure 1: LA-ICP-MS holes from each shot are visible.