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

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information of surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets constitutes 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 the accommodating non-linear 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 non-experts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.