Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain.
Waters Corporation, Altrincham Road, SK9 4AX Wilmslow, United Kingdom.
J Agric Food Chem. 2022 Aug 3;70(30):9499-9508. doi: 10.1021/acs.jafc.2c03615. Epub 2022 Jul 20.
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.
由于复杂的基质和有限的商业标准品的可用性，从食品接触材料（FCMs）中鉴定迁移物具有挑战性。基于机器学习的预测工具的使用有助于鉴定此类化合物。本研究提出了一种基于液相色谱-离子淌度-高分辨质谱以及计算保留时间（RT）和碰撞截面（CCS）预测工具来鉴定 FCMs 中非挥发性迁移物的工作流程。通过筛选从聚酰胺（PA）刮刀中迁移的化学品来评估该工作流程的适用性。分别应用 RT 和 CCS 预测过滤器后，候选化合物的数量减少了约 75%和 29%。在 PA 刮刀中共鉴定出 95 种化合物，其中 54 种化合物使用参比标准品进行了确认。开发一个包含与 FCMs 相关化合物的预测 RT 和 CCS 值的数据库，可以帮助鉴定 FCMs 中的化学品。
