Virtual Booth

AquilScent introduces an AI-based authentication model to classify Aquilaria oil species, addressing limitations in traditional methods that are often subjective and inconsistent. Chemical profiling data from gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID) analyses, sourced from the Bioaromatic Research Centre of Excellence (BARCE), were used to develop a k-Nearest Neighbour (k-NN) model based on four significant aromatic compounds. The model achieved high classification accuracy, successfully distinguishing species-specific chemical signatures. AquilScent demonstrates that integrating chromatographic data with machine learning not only enhances quality control and traceability but also sets a new standard for sustainable practices within the essential oil industry.