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The inflammatory response is a central aspect of the human immune system that acts as a defense mechanism to protect the body against infections and injuries. A dysregulated inflammatory response is a major health concern, as it can disrupt homeostasis and lead to a plethora of chronic inflammatory conditions. These chronic inflammatory diseases are one of the major causes of morbidity and mortality worldwide and the need for them to be managed in the long term has become a crucial task to alleviate symptoms and improve patients’ overall quality of life. Although various synthetic anti-inflammatory agents have been developed to date, these medications are associated with several adverse effects that have led to poor therapeutic outcomes. The hunt for novel alternatives to modulate underlying chronic inflammatory processes has unveiled nature to be a plentiful source. One such example is agarwood, which is a valuable resinous wood from the trees of Aquilaria spp. Agarwood has been widely utilized for medicinal purposes since ancient times due to its ability to relieve pain, asthmatic symptoms, and arrest vomiting. In terms of inflammation, the major constituent of agarwood, agarwood oil, has been shown to possess multiple bioactive compounds that can regulate molecular mechanisms of chronic inflammation, thereby producing a multitude of pharmacological functions for treating various inflammatory disorders. As such, agarwood oil presents great potential to be developed as a novel anti-inflammatory therapeutic to overcome the drawbacks of existing therapies and improve treatment outcomes. In this review, we have summarized the current literature on agarwood and its bioactive components and have highlighted the potential roles of agarwood oil in treating various chronic inflammatory diseases.

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.