Formulation of K-nearest neighbor model by varying the distance metrics of mahalanobis, correlation and cosine in discriminating different grades of Aquilaria oil

• Published in: JOURNAL OF ESSENTIAL OIL BEARING PLANTS
• Main Authors: Yusoff, Zakiah Mohd; Ismail, Nurlaila; Sabri, Noor Aida Syakira Ahmad
• Format: Article; Early Access
• Language: English
• Published: TAYLOR & FRANCIS LTD 2025
• Subjects: Plant Sciences
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001437517500001
• ISSN: 0972-060X; 0976-5026
• DOI: 10.1080/0972060X.2025.2469677

This study formulates a K-Nearest Neighbor (K-NN) model for the classification of Aquilaria (agarwood) oil grades by varying distance metrics: Mahalanobis, Correlation and Cosine. A total of 96 agarwood oil samples were analyzed using Gas chromatography-mass spectrometry (GC-MS), identifying key chemical compounds contributing to quality differentiation. The proposed model achieved 100% classification accuracy at k = 1 across all distance metrics with Mahalanobis distance consistently outperforming others as k increased. Cross-validation results demonstrated the lowest error rates for Mahalanobis followed by Correlation and Cosine, confirming its robustness in high-dimensional chemical datasets. These findings highlight the effectiveness of machine learning in agarwood oil grading and underscore the importance of selecting optimal distance metrics for improved classification accuracy. Future work could incorporate ensemble learning and advanced feature selection to further refine performance.