ArticleEnvironmental science & technology2022
Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products.
Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Green Sorptive Sampling Coupled With UHPLC-IMS-MS and GC×GC-TOFMS for Characterization of Migrating Substances From Plastic Food Contact Materials.Journal of separation science · 2026Article
- Integrating Computational Chemistry and Ion Mobility Spectrometry into Non-Targeted Analysis Workflows for the Structural Elucidation of Emerging PFAS.Journal of the American Society for Mass Spectrometry · 2026Article
- A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.Drug testing and analysis · 2026Article
- Determination of Collisional Cross Section Using Microscale High-Field Asymmetric Waveform ion Mobility Spectroscopy-Mass Spectrometry (FAIMS-MS).Rapid communications in mass spectrometry : RCM · 2025Article
- PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data.Environmental science & technology letters · 2025Article
- A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.Eco-Environment & Health · 2024Article
- Potential of measured relative shifts in collision cross section values for biotransformation studies.Analytical and bioanalytical chemistry · 2024Article
- Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants.Environmental science & technology · 2023Review
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
- Collision Cross Section Prediction Based on Machine Learning.Molecules (Basel, Switzerland) · 2023Review
- Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.Journal of agricultural and food chemistry · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]
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