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.

中文翻译：

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塑料制品中可萃取物和浸出物碰撞截面值的预测

离子淌度分离 (IMS) 与高分辨率质谱法相结合的使用已被证明是一种可靠且有用的技术，可用于表征塑料产品中的小分子。来自 IMS 的碰撞截面 (CCS) 值可用作结构描述符以帮助化合物识别。将 IMS 应用于从塑料中识别化学品的一个限制是缺乏公布的经验 CCS 值。因此，机器学习技术可以通过生成预测的 CCS 值来提供替代方法。在此，从文献中收集了超过一千种与塑料相关的化学物质的实验 CCS 值，并用于开发塑料产品中可提取物和可浸出物的准确 CCS 预测模型。评估了不同分子描述符和机器学习算法对模型性能的影响。基于化学开发工具包 (CDK) 描述符的支持向量机 (SVM) 模型提供了最准确的预测，其中 [M + H] 的 CCS 值为 93.3%^{+加合物和 95.0% 的 [M + Na] +}加合物的 CCS 值在测试集中以 <5% 的误差预测。^{[M + H] +}和 [M + Na] ⁺加合物的 CCS 值的中值相对误差分别为 1.42 和 1.76%。随后，使用本文开发的 SVM 模型预测了与塑料包装数据库和食品接触化学品数据库相关的化学品的 CCS 值。这些值被整合到我们的结构解析工作流程中，并应用于河水中与塑料相关的化学物质的鉴定。通过在嫌疑人筛查工作流程中加入预测的 CCS 值，减少了误报，提高了识别置信度。