Machine learning-assisted HS-GC-IMS for discrimination and traceability of baby bottles based on volatile fingerprints.

Ensuring the safety of food contact materials, particularly baby bottles, is crucial for infant health. In this study, a comprehensive analytical strategy integrating headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) with machine learning-based chemometrics was developed to profile volatile organic compounds (VOCs) and achieve discrimination and traceability of materials and brands among nine baby bottles. A total of 87 VOCs were identified, with aldehydes, esters, alcohols, ketones being the most diverse groups, and aldehydes exhibiting the highest concentrations. Visual representations were established, clearly illustrating the fingerprint profiles of the various baby bottle samples. Unsupervised models, principal component analysis (PCA) and hierarchical cluster analysis (HCA), revealed distinct clustering patterns. In contrast, supervised models, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), and deep learning models such as Convolutional Neural Network (CNN) and Transformers, were constructed to predict brand classification. Among them, RF was selected as the optimal model due to its superior performance-utility balance. Key discriminant markers were further determined by integrating the Variable Importance in Projection (VIP) scores from OPLS-DA with the feature importance from the RF model, significantly improving classification efficiency. This research proposes an effective and reliable analytical framework for rapid identification and traceability of baby bottles, offering valuable insights for anti-counterfeiting and safety assurance of food-contact materials.