ArticleMicromachines2022
An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comprehensive analysis of security threats and privacy issues in indoor localization systems.Scientific reports · 2025Article
- Quantification of Volatile Compounds in Mixtures Using a Single Thermally Modulated MOS Gas Sensor with PCA-ANN Data Processing.Sensors (Basel, Switzerland) · 2025Article
- A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance.Micromachines · 2025Article
- Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry.Sensors (Basel, Switzerland) · 2023Review
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Authors and funding
4 authors.
Funding
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Abstract
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.
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