A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Abstract

Essential <span>oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.</span>

Datasets of chemical compounds in three different species of aquilaria using GC-MS coupled with GC-FID analysis

“…The oven program initiated at 80°C, with a 3°C/min increase until reaching 250°C, held for 3 minutes. Additionally, the auxiliary heater was set at 260°C, the MS source at 230°C, and the MS quad at 150°C, utilizing Electron Impact (EI) mode with an energy of 70 electron volts (eV) [4] .…”

Development of Agarwood Oil Research and Benefit: Bibliometric Analysis

“…Moreover, agarwood oil has been explored for its potential as an antifungal agent and for its application in edible films and hydrosol-based drinks [9], [10]. The high demand for agarwood oil has led to the development of artificial intelligent techniques for classifying its quality, emphasizing the significance of quality control in agarwood oil production [11], [12].…”