Agarwood Quality Classification in the Middle East: A Mixed-Methods Study of Social, Sensory, and Data-Driven Insights

Abstract

This dissertation investigates the classification of agarwood quality in the Middle East, focusing on the Gulf Cooperation Council (GCC) countries, where oud holds profound cultural, religious, and economic value. The market lacks a unified formal grading system leading to multiple discrepan- cies. Employing a mixed-methods approach, the study first conducted a sensory panel to gain relative consumer insight. Composed of both Middle Eastern and non-Middle Eastern participants, the panel revealed how quality perception varies among non-experts. Next, semantic analysis of cultural discourse was extracted from social media that was then used to design a contextualized two-layer grading system; finally, that framework was applied on an e-commerce dataset of oud products, whereby an optimized Random Forest model leveraging TF-IDF classified quality grades using textual descriptions with 90.5% accuracy. This demonstrates how efficient machine learn- ing can effectively approximate sensory and cultural judgment from text data alone. The research concludes that digital platforms are repositories of cultural knowledge, anticipating that such frame- works can provide transparent, standardized, and scalable agarwood classification—channelling tradition and innovation for a fairer, more sustainable oud market in the region.