An Incremental Learning Method for Preserving World Coffee Aromas by Using an Electronic Nose and Accumulated Specialty Coffee Datasets

Abstract

Specialty coffee beans have a unique aroma and flavor. The aromas of coffee in the world are affected by several issues, including growing area, climate, postharvest processing (such as dry and wet methods), roasting treatment, etc. These issues significantly contribute to the development of coffee-bean aromas. Since humans have a limited ability to recognize the aroma of coffee, we need a reliable system to resolve the method of characterizing the world's coffee aroma. Therefore, in this article, we proposed an incremental learning method for digitizing the complexity of coffee aromas using an electronic nose (E-nose) system. We also developed a method to create coffee-aroma fingerprints to represent their aromatic features among different coffees. In our experiments, the incremental learning model achieved high accuracy, proving the authenticity of recognizing various world specialty coffee aromas. The approach leverages an E-nose system and coffee-aroma datasets to preserve specialty coffee aromas around the world. In addition, the ultimate goal of this method is to build a scalable database of various coffee aromas while improving the accuracy of system recognition.