Lee Chung-Hong, Chen I-Te, Yang Hsin-Chang, Chen Yenming J
Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan.
Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Micromachines (Basel). 2022 Aug 13;13(8):1313. doi: 10.3390/mi13081313.
Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.
长期以来，香气和味道一直被视为优质咖啡的重要指标。特别是来自咖啡种植区单个庄园、农场或村庄的特色咖啡，具有反映咖啡产区的独特香气。为了实现咖啡产地的可追溯性，在本研究中，我们开发了一种电子鼻系统，以区分不同产区新鲜烘焙咖啡的香气。在案例研究中，我们使用电子鼻系统对各种机器学习模型进行实验，以识别几种收集到的咖啡豆，如来自耶加雪菲和科纳的咖啡。此外，我们的贡献还包括开发一种方法，为特定咖啡豆创建芳香数字指纹以识别其产地。实验结果表明，所开发的电子鼻系统在咖啡香气识别方面取得了良好的识别性能。提取的数字指纹极有可能存储在一个可扩展的咖啡香气数据库中，该数据库类似于一个特定咖啡豆香气特征的综合库，用于追溯和再次确认其产地。
