# Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging

## TL;DR

This paper introduces a new method using UMAP and k-means clustering to improve the accuracy of elemental quantification in geological samples using LA-ICP-MS imaging.

## Contribution

The novel workflow enables phase-specific normalization in LA-ICP-MS imaging through unsupervised clustering and stoichiometric matching.

## Key findings

- The method reduces quantification errors by up to 60% in mineralogically heterogeneous samples.
- UMAP-based clustering allows for accurate phase-resolved geochemical comparisons across depth profiles.
- The approach is validated using dawsonite-bearing sandstones from Mt. Amiata, Italy.

## Abstract

Laser Ablation-Inductively
Coupled Plasma-Mass Spectrometry (LA-ICP-MS),
particularly in its time-of-flight (TOF) configuration, enables rapid,
high-resolution elemental imaging across complex geological materials,
offering spatial and chemical insights at the micrometer scale. However,
quantitative accuracy is often limited in fine-grained or mineralogically
heterogeneous matrices due to the failure of global normalization
strategies, such as 100 wt % oxide assumptions, to account for mixed-phase
compositions. Here, we present a workflow that leverages Uniform Manifold
Approximation and Projection (UMAP) for unsupervised dimensionality
reduction and k-means clustering to segment mineralogical
phases directly from per-pixel elemental concentration maps. Cluster
compositions are matched to known minerals based on stoichiometric
similarity, enabling pixel-wise, phase-specific normalization (e.g.,
oxides vs carbonates). Validated with dawsonite-bearing sandstones
from Mt. Amiata, Italy, this approach significantly reduces quantification
errors, correcting systematic over- or underestimations of up to 60%.
The method also enables a consistent, phase-resolved geochemical comparison
across depth profiles. This study establishes UMAP not only as an
exploratory tool but also as a practical guideline for accurate and
interpretable quantification in multielemental imaging.