Honey Botanical Origin Authentication Using HS-SPME-GC-MS Volatile Profiling and Advanced Machine Learning Models (Random Forest, XGBoost, and Neural Network).

ABSTRACT: This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources-coriander, orange blossom, astragalus, rosemary, and chehelgiah. While HS-SPME-GC-MS combined with traditional chemometrics (e.g., PCA, LDA, OPLS-DA) is well-established for honey discrimination, the application and direct comparison of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models represent a significant advancement in multiclass prediction accuracy and model robustness. A total of 57 honey samples were analyzed to generate detailed volatile organic compound (VOC) profiles. Key che

SUBMITTER: Pourmoradian A 

PROVIDER: S-EPMC12841070 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature