Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

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]+ adducts and 95.0%
of CCS values for [M + Na]+ adducts in testing sets predicted
with <5% error. Median relative errors for the CCS values of the
[M + H]+ and [M + Na]+ adducts were 1.42 and
1.76%, respectively. Subsequently, CCS values for the compounds in
the Chemicals associated with Plastic Packaging Database and the Food
Contact Chemicals Database were predicted using the SVM model developed
herein. These values were integrated in our structural elucidation
workflow and applied to the identification of plastic-related chemicals
in river water. False positives were reduced, and the identification
confidence level was improved by the incorporation of predicted CCS
values in the suspect screening workflow.