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\begin{document}

\title{Laser ablation mass spectrometry blast through detection in R}



\author[1]{Alex Searle-Barnes}%
\author[1]{Andy Milton}%
\author[1]{Christopher Standish}%
\author[1]{Gavin Foster}%
\author[1]{Thomas Ezard}%
\affil[1]{University of Southampton}%


\vspace{-1em}



  
  \date{May 29, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Rational: Organisms that grow a hard carbonate shell or skeleton, such
as foraminifera, corals or molluscs, incorporate trace elements into
their shell during growth that absorbs the environmental change and
biological activity they experienced. These geochemical signals locked
within the carbonate are archives used in proxy reconstructions to study
past environments and climates, to decipher taxonomy of cryptic species
and to resolve evolutionary responses to climatic changes. Methods: Here
we use a laser ablation inductively coupled plasma mass spectrometry
(LA-ICP-MS) as a time resolved acquisition to quantify the elemental
composition of carbonate shells. We present the LABLASTER (Laser
Ablation BLASt Through Endpoint in R) package, which imports a single
time resolved LA-ICP-MS analysis, then detects when the laser has
ablated through the carbonate as a function of change in signal over
time, and outputs key summary statistics. We provide two worked examples
within the package: a planktic foraminifera and a tropical coral.
Results: We present the first R package that improves signal: noise
ratios in data reduction workflows by automating the detection of when
the laser has ablated through a sample using a smoothed time-series and
subsequent removal of off-target data points. The functions are flexible
and adjust dynamically to enhance the signal: noise ratio of the desired
geochemical target. Visualisation tools for manual validation are also
included. Conclusions: LABLASTER increases transparency and
repeatability by algorithmically identifying when the laser has either
ablated fully through a sample or across a mineral boundary and is thus
no longer documenting a geochemical signal associated with the desired
sample. LABLASTER's focus on better data targeting means more accurate
extraction of biological and geochemical signals.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{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 foraminifer\textsuperscript{1-3}, coral
skeletons\textsuperscript{4} and molluscs\textsuperscript{5}, 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
throughput\textsuperscript{6}. 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 elementR\textsuperscript{7} or the discontinued
LAICPMS\textsuperscript{8}, both of which use the R environment, and
LATools\textsuperscript{9}, 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. TERMITE\textsuperscript{10} is not a
package\emph{per 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 clustering\textsuperscript{7}, fixed time
stamps\textsuperscript{2}, analyte signal below a given
threshold\textsuperscript{11}, the mid-point between high and background
signal counts\textsuperscript{9} and even manual identification when the
complexity of the samples is too great\textsuperscript{9}. 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
variable\textsuperscript{7}.

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.

\par\null

Figure 1: LA-ICP-MS holes from each shot are visible.

\section*{Materials and Methods}

{\label{materials-and-methods}}

The LABLASTER package contains one function named endPoint that
calculates four items, and four example data sets to illustrate its use,
including the foraminifer and coral examples presented here. The package
requires R ([?] 3.5.0)\textsuperscript{12} and has a number of
dependencies as it calls functionality from the
stats\textsuperscript{12}, smooth\textsuperscript{13}, ggplot2,
magrittr\textsuperscript{14} and scales\textsuperscript{15} packages.

\subsection*{endPoint detection}

{\label{endpoint-detection}}

The main function of the LABLASTER package is to detect when either the
laser has ablated through a target (e.g., a carbonate shell or coral
skeleton) or across a boundary in a transect. In the following sections,
we illustrate this behaviour on a planktic foraminifer and a tropical
coral as case studies.

Identifying the time range in a time resolved acquisition when the laser
is ablating the target is essential for accessing and correlating the
relevant data within the recorded time series. The LABLASTER package
assumes the data frame supplied begins with the laser in focus of the
desired target and the endPoint function determines the time stamp when
the laser has ablated through the sample or across a boundary where the
isotope signal changes rapidly. Keeping only the data between the start
time and end time focusses subsequent analysis on only relevant target
data.

\textbf{Algorithm}

The endPoint function requires a single data frame containing a minimum
of two columns: a column containing a time index and a column containing
isotope signal counts. This data frame is supplied to the function as
the argument \textbf{detectDf} with the time index column specified
as\textbf{timeCol} and the isotope signal column specified
as\textbf{signalCol} . Any element that is abundant within the target
but is scarce in the surrounding medium could be used to detect the
ablate through endpoint time. In a case study below, we use ``Ca44''
containing the \textsuperscript{44}Ca isotope counts per second as the
signalCol as this is abundant within the foraminifera test but is not
present in high quantities in the glass slide used for mounting.

The algorithm of the endPoint function first calculates a simple moving
average over the isotope counts, with the number of points in the moving
average controlled by the \textbf{smoothing} argument. This smoothed
isotope count signal is then scaled between 0 -- 1 to convert to
relative changes that works across all time resolved acquisition data
sources. A larger amount of smoothing reduces the variance between
adjacent data points, averaging out any signal spikes and therefore
flattening the signal against time curve. A flatter time resolved
profile curve gives greater distinction between the elevated signal of
desired target ablation and the lower signal of the undesired under- or
adjacent- target surface.

Using smoothing to de-spike and flatten the signal during the time of
elevated signal reduces the magnitude of variance over time and
therefore reduces the likelihood of a false positive detection in a
change of ablated material composition, which is used as part of the
detection in later steps of the algorithm. Over-smoothing the signal
reduces the distinction from a sudden signal drop to a shallower gradual
drop between the higher on-target and the lower off-target signal
intensities that the algorithm uses to detect that the laser is no
longer ablating the desired target causing a delayed detection.
Over-smoothing can be identified manually using the visualisation tools
by comparing the black and blue lines.

Next the algorithm calculates the number of data points per time step in
the data frame supplied. The temporal resolution of the data can affect
how sensitive the algorithm is to the rate of scaled signal change. The
algorithm uses a moving window to calculate the rate of change in signal
against time. Higher temporal resolution data can result in a smoother
decline in isotope signal across the default time window causing a
delayed endpoint detection, therefore using a wider time window captures
a larger magnitude signal drop. The algorithm uses the largest magnitude
of negative rate of isotope signal change to identify that the laser is
no longer on the desired target. Without a rapid signal drop, for
example if the laser did not fully ablate through target or no mineral
boundary was crossed then the algorithm will return the final
observation in the provided data frame and a warning message is
displayed to encourage use of the manual validation tools to check the
results. The number of data points used as the width of the moving
window is controlled by the \textbf{dt} argument.

Once the algorithm has identified the largest magnitude of negative rate
of signal change, the corresponding time stamp is identified. As the
largest signal drop occurs shortly after the laser has ablated through
the sample or crossed a boundary, it is necessary to also remove the
data that occurs between the final data point when the laser was on
target and the largest signal drop. This elapsed time is calculated by
dividing the moving window width by the number of data points per time
step and is subtracted from the signal change time stamp. This earliest
time stamp is when the laser was last ablating on the desired target and
is returned as a numeric value in the returned data frame.

library(lablaster)

endPoint(detectDf, timeCol = ''Time'', signalCol = ''Ca44'', smoothing =
5, dt = 10, profile = ''FALSE'', timeUnits = ''seconds'')

\subsection*{Visualisation of the blast through
algorithm}

{\label{visualisation-of-the-blast-through-algorithm}}

The endPoint function additionally provides visualisation tools into the
mechanism of the algorithm. The smoothed scaled signal, rate of change
and inferred bounds on the geochemical target are useful for diagnosing
the real-life efficacy. A ggplot2 object is generated when
the\textbf{profile} argument is set to TRUE. If the profile argument is
TRUE, then the time units of the analysis are also required and
specified with the \textbf{timeUnits} argument.

\subsection*{Returned values}

{\label{returned-values}}

The endPoint function returns a single data frame containing the values
calculated.

\$startTime contains the earliest timestep in the supplied time resolved
acquisition as a numerical value.

\$endTime contains the last timestep before the laser ablated through
the carbonate shell as described above.

\$df contains the supplied data frame with the same structure but
containing only the rows that occur between the startTime and endTime.

\$profile contains a visualisation of the endpoint mechanism as
described above. This is only available if a profile was generated using
profile = ''TRUE''.

\section*{Case study}

{\label{case-study}}

\subsection*{A foraminifera setup}

{\label{a-foraminifera-setup}}

\textbf{Experimental setup}

A single planktonic foraminifer case study is included in the package as
a worked example. Planktonic foraminifera are unicellular zooplankton
distributed throughout the world's oceans as a key resource in
understanding Earth's climate system\textsuperscript{1, 2} and the
evolution of biodiversity\textsuperscript{16}. The thickness of a
fossilised foraminifera test can vary substantially even within an
individual. Some have thick solid chamber walls while others have a
highly porous structure as a result of species specificities, biological
controls and environmental influences. This natural variability is the
motivation for our development of automated processing methods and thus
better control of geochemical ``vital effects'' (Kearns et al, submitted
to~\emph{Paleoceanography and Paleoclimatology} ). Foraminifera grow
connected chambers throughout their life with those in the final whorl
accessible for LA-ICP-MS analysis\textsuperscript{17} (Figure 1). The
case study is from the antepenultimate chamber of \emph{Menardella
exilis} foraminifera 72, identified hereon as ``Foram-72-shot-3''.

The experimental setup is described fully in Kearns et al. (in review).
Briefly major and trace elements in the foraminifera test were analysed
using a New Wave UP193 laser ablation system (ArF source, 30 \selectlanguage{greek}µ\selectlanguage{english}m spot
diameter, a fluence of 0.73 J/cm\textsuperscript{3} and 5 Hz pulse rate)
coupled to an Agilent 8900 triple quadrupole inductively coupled plasma
(ICP-QQQ) mass spectrometer in single quadrupole mode using a He and Ar
gas mixture (900 ml/min) at the University of Southampton. Each laser
spot pattern was sequenced with a 30 s warmup, 50 s laser pulse and 30 s
washout. The default values within this endPoint function are based on
outputs from this setup.

Initial processing of the time resolved acquisition data was performed
within the R environment (version 4.2.2). The first 30 seconds were used
to calculate the background signal and then removed. The next five
seconds were removed as the isotopic signal begins to rise for the laser
has started pulsing but the ablated material is still travelling through
the system piping. As our washout time was enough to purge the system
after each analysis, the duration of this signal rise was regular and
reproducible.

\textbf{endPoint detection function}

The data frame containing the background corrected remainder of the
acquisition (Figure 2b) was supplied to the endPoint function. With the
first 35 seconds removed, we ensured that the laser was pulsing on the
foraminifera's outer test wall and the ablated material was reaching the
mass detector for the first row in the data frame supplied. In this
example, we use the \textsuperscript{44}Ca isotope measurements due to
the high signal: noise ratio and the abundance of \textsuperscript{44}Ca
within the foraminifera test, which provides greater distinction between
higher signal counts for the duration when the laser was on target and
lower signal counts when the laser was ablating the glass slide mount.
The endPoint function was used to dynamically identify the time step
when the laser had fully ablated through the foraminifera test and to
keep only the observations while the laser was ablating the target
(Figure 2c).

endPoint(detectDf = foram72shot3, dt = 10, smoothing = 5, timeCol =
''Time'', signalCol = ''Ca44'', profile = ''TRUE'', timeUnits =
''seconds'')\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: The processes implemented in the endPoint function in a time
resolved acquisition of Foram-72-shot-3 as a visualisation of the
returned data frame \$df. Panel (a) visualises the entire raw
\textsuperscript{44}Ca data collected; panel (b) visualises the
background corrected\textsuperscript{44}Ca with the first 35 seconds
removed as this was before the laser was turned on and the ablated
material is still travelling through the system and this is the data
frame passed into the endPoint function; plot (c) visualises the target
data retained after running the endPoint function, with both the first
35 seconds and post endPoint detection \textsuperscript{44}Ca data
removed.

Figure 3 shows the manual validation tools to check that the endPoint
function had successfully identified when the laser had burnt through
the entire depth of the test wall. The black line is a scaled
transformation of the smoothed signal specified in signalCol; the blue
line is the scaled rate of change in signal over the moving time window
and the shaded red areas identify the data points removed from the
returned data frame, \$df, as these exceed the end point blast through
detection time stamp.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: A visualisation of the endPoint function for the
Foram-72-shot-3 case study, showing the scaled smoothed
\textsuperscript{44}Ca signal in black, the scaled rate of signal change
in blue and the shaded red areas identify the data point rows that are
removed in the returned data frame.

\textbf{Comparison without using endPoint detection}

Foraminifera are frequently used as archives of geochemical signals that
are used in proxy measurement reconstructions. Ecological evolutionary
responses can be tracked and sea surface temperatures can be
reconstructed with calibrated equations using a ratio of magnesium to
calcium as an input, making this Mg/Ca (mmol/mol) a popular geochemical
measurement\textsuperscript{1, 6}.

When processed without the endPoint function and the integration time
was between 35 -- 80 seconds elapsed, the
median\textsuperscript{24}Mg/\textsuperscript{44}Ca ratio of
Foram-72-shot-3 was 4.37\selectlanguage{ngerman}±2.10 mmol/mol and when processed with the
endPoint function with an integration time of 6 seconds was 2.46±1.35
mmol/mol. Use of the endPoint function thus increases accuracy (by
focussing on the geochemical target) and also increases precision
through higher quality sample data for statistical analysis.

\subsection*{A coral example}

{\label{a-coral-example}}

\textbf{Experimental setup}

A transect across a sample of tropical coral \emph{Acropora polystoma} ,
cultured in artificial seawater in a closed coral mesocosm at the Coral
Reef Laboratory, National Oceanography Centre,
Southampton\textsuperscript{18} is also included in the package as an
example of an ablation line and is identified hereon as ``coral-6''. As
polished coral sections can be irregular in shape, thickness and
porosity, it is possible for the laser to ablate fully through at a
thinner location or traverse a boundary between the sample and its
mounting resin. In this example, the ablation line passes over a thinner
section of coral and consequently the laser fully ablates through before
the end of the analysis time.

\textbf{endPoint detection function}

This coral section has bands of higher and lower calcium concentration
across the laser transect and consequently fluctuating higher and lower
counts for the \textsuperscript{43}Ca signal are observed. In this
example, we select the dt and smoothing parameters to de-spike these
real signal fluctuations and using the included manual visualisation
tool (Figure 4) to validate our selection was detecting the moment when
the laser fully ablated through the coral.

endPoint(detectDf = coral6, dt = 10, smoothing = 5, timeCol = ''Time'',
signalCol = ''Ca43'', profile = ''TRUE'', timeUnits = ''seconds'')\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: A visualisation of the endPoint function for the Coral-6 case
study, showing the scaled smoothed \textsuperscript{43}Ca signal in
black, the scaled rate of signal change in blue and the shaded red areas
identify the data point rows that are removed in the returned data
frame.

\section*{Conclusion}

{\label{conclusion}}

While LA-ICP-MS has many benefits over traditional solution based ICP-MS
methods for measuring major and trace elements in various carbonate
objects, current analytical setups can be inflexible with regards to
laser firing duration. Our freely available LABLASTER package implements
methodological improvements to refine analytical workflows.

The endPoint function that implements these improvements is compatible
for use within loops, aiding high-throughput and repeatable data
cleaning of carbonate materials that are ubiquitously used in past
climate reconstructions, geochemical ecology and evolution studies and
cryptic taxonomic distinctions.

\section*{Acknowledgements}

{\label{acknowledgements}}

This work was supported by Natural Environment Research Council award
NE/P019269/1.

\section*{Conflict of interest
statement}

{\label{conflict-of-interest-statement}}

The authors do not declare a conflict of interest.

\section*{Author contributions}

{\label{author-contributions}}

ASB designed the functions and wrote the code. JAM and GLF provided
methodological support and laboratory analysis. CDS provided the coral
data for additional algorithm testing and example data published within
the LABLASTER package. THGE validated the functions and code. ASB
drafted the manuscript and JAM, GLF, CDS and THGE provided comments on
earlier drafts.

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{\label{references}}

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doi:10.1017/S0025315411001883.\selectlanguage{english}
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