Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry.

IF 3.1 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS
Jin Gyeong Son, Hyun Kyong Shon, Ji-Eun Kim, In Ho Lee, Tae Geol Lee
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引用次数: 0

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

基于峰值的机器学习技术用于飞行时间二次离子质谱法中的塑料类型分类
本研究利用飞行时间二次离子质谱(ToF-SIMS)测量数据和机器学习对六种不同类型的塑料进行分类。为了考虑到测量数据的特性,在预处理步骤中首先对测量数据的局部最大值进行了检查。然后,我们采用了几种机器学习方法,创建了一个能够成功对塑料进行分类的模型。为了使数据分布可视化,我们采用了一种降维方法,即主成分分析法。最后,为了区分六种类型的塑料,我们使用四种基于树的算法进行了集合分析:决策树、随机森林、梯度提升和 LIGHTGBM。这种方法可以识别塑料样本的重要特征,并推断出每种塑料的化学特性。这样,ToF-SIMS 数据就可以成功地用于塑料分类并提高可解释性。
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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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