All publications

JASMS · 2024

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

M. A. Shamraeva, T. Visvikis, S. Zoidis, I. G. M. Anthony, S. Van Nuffel

Journal of the American Society for Mass Spectrometry 35(12), 2801–2814 (2024)

Special issue: Advanced Data Analysis in Secondary Ion Mass Spectrometry (SIMS)

• Paper
• Preprint

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.

Cite

@article{shamraeva2024application,
  title   = {The Application of a Random Forest Classifier to ToF-SIMS Imaging Data},
  author  = {Shamraeva, Mariya A. and Visvikis, Theodoros and Zoidis, Stefanos and Anthony, Ian G. M. and Van Nuffel, Sebastiaan},
  journal = {Journal of the American Society for Mass Spectrometry},
  year    = {2024},
  volume  = {35},
  number  = {12},
  pages   = {2801--2814},
  doi     = {10.1021/jasms.4c00324}
}