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.