将随机森林分类器应用于 ToF-SIMS 成像数据。

IF 3.1 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS
Mariya A Shamraeva, Theodoros Visvikis, Stefanos Zoidis, Ian G M Anthony, Sebastiaan Van Nuffel
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引用次数: 0

摘要

飞行时间二次离子质谱(ToF-SIMS)成像是一种有效的分析工具,可在微观尺度上提供空间分辨的表面化学信息。然而,ToF-SIMS 数据集的高光谱特性给分析和解释带来了挑战。有监督和无监督机器学习(ML)方法越来越有助于分析 ToF-SIMS 数据。随机森林(RF)已成为处理质谱数据的强大算法。这种机器学习方法具有多种优势,包括适应非线性关系、对数据中异常值的稳健性、管理高维特征空间以及降低过拟合风险。将 RF 应用于 ToF-SIMS 成像有助于对复杂的化学成分进行分类,并识别有助于这些分类的特征。本教程旨在帮助非机器学习或 ToF-SIMS 专家将随机森林应用于复杂的 ToF-SIMS 数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
257
审稿时长
1 months
期刊介绍: The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role. Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
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