The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

J Am Soc Mass Spectrom

Maastricht MultiModal Molecular Imaging Institute (M4i), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.

Published: December 2024

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622239PMC
http://dx.doi.org/10.1021/jasms.4c00324DOI Listing

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