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

Mariya A. Shamraeva, Theodoros Visvikis, Stefanos Zoidis, Ian G. M. Anthony, and Sebastiaan Van Nuffel

Journal of the American Society for Mass Spectrometry, Special Issue “Advanced Data Analysis in Secondary Ion Mass Spectrometry (SIMS)

[doi]

Abstract: 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.

@article{doi:10.1021/jasms.4c00324,
author = {Shamraeva, Mariya A. and Visvikis, Theodoros and Zoidis, Stefanos and Anthony, Ian G. M. and Van Nuffel, Sebastiaan},
title = {The Application of a Random Forest Classifier to ToF-SIMS Imaging Data},
journal = {Journal of the American Society for Mass Spectrometry},
volume = {35},
number = {12},
pages = {2801-2814},
year = {2024},
doi = {10.1021/jasms.4c00324},
    note ={PMID: 39455427},

URL = { 
    
        https://doi.org/10.1021/jasms.4c00324
}}