ArticleJournal of agricultural and food chemistry2022
Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials.
Article in Journal of agricultural and food chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Identification of Glycosylated and Prenylated (Iso)flavonoids Using cIMS-MS.Analytical chemistry · 2026Article
- Identifying Food Packaging Migrants: Current Analytical Capabilities, Challenges, and Future Prospects.Comprehensive reviews in food science and food safety · 2026Review
- Assigning Peptide Structure from Ion-Mobility Mass Spectrometry Collision Cross Section Data.Journal of the American Society for Mass Spectrometry · 2025Article
- 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
- 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
- MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies.Journal of chemical information and modeling · 2023Article
- Collision Cross Section Prediction Based on Machine Learning.Molecules (Basel, Switzerland) · 2023Review
- Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products.Environmental science & technology · 2022Article
- A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials.Journal of agricultural and food chemistry · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.