Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Abstract

Numerous coffee devotees believe that the coffee smell plays a vital role in the coffee-drinking insight, complementing the taste and enhancing delight. In the traditional strategy, aroma patterns and profiles are observed by extensive investigation of human olfaction. However, the outcome tends to be imprecise. Tackling the difficulties encountered in distinct scent profiles linked to various coffee bean varieties, including Arabica, Robusta, Monsoon Malabar, Chikmagalur, and Coorg coffee, as well as diverse roasting techniques, through the utilization of Electronic Nose Applications for the investigation of coffee aromas. The suggested methodology employs e-nose technology utilizing conducting polymer sensors to detect aroma volatile chemicals found in coffee, including furaneol, 2-methylisoborneol, and 3-methylindole. The e-nose olfactory characteristics of coffee beans at various stages of roasting are systematically examined and discernible patterns are duly identified. The average intensity of the coffee aroma perceived at a distance of 10 centimeters was rated as 3.9 on a scale of 5. The observed standard deviation of coffee aroma intensity at a distance of 10 centimeters was determined to be 3.8 on a scale of 5. The p-value associated with the disparity in average fragrance scores was determined to be 0.05.