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

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...

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Bibliographic Details
Main Author:	Jin Gyeong Son (1544851) (author)
Other Authors:	Hyun Kyong Shon (1294419) (author), Ji-Eun Kim (60619) (author), In−Ho Lee (10922117) (author), Tae Geol Lee (1294416) (author)
Published:	2024
Subjects:	Ecology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified principal component analysis dimensionality reduction method could successfully classify successfully classify plastics plastic type classification based machine learning sims data could machine learning plastic type based algorithms plastic samples measurement data data distribution six types random forest preprocessing step local maxima gradient boosting first examined feature importance enhance explainability decision tree chemical properties
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Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry

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