Modern coffee-making technologies: from alternative methods to artificial intelligence
| dc.contributor.author | Davydova O. |
| dc.contributor.author | Udovikova V. |
| dc.contributor.author | Denusenko T. |
| dc.description.abstract | The modern hospitality industry faces quality instability due to the human factor, requiring new management tools. This article analyzes the integration of Industry 4.0 into the coffee industry. Using a systematic review and statistical analysis of deep learning (YOLOv8) and near-infrared (NIR) spectroscopy, the study objectifies raw material quality. Results show Custom-YOLOv8n achieves 99.5% accuracy in green bean defect detection. Predictive control of roasting thermodynamics via LSTM neural networks effectively automates the "first crack" point. Robotic baristas achieve high productivity with extraction stability. The scientific novelty is a "bionic" synergy concept between craft production and technological automation. The practical value lies in flavor profile digitization and cost optimization. |
| dc.identifier.citation | Davydova O. Modern coffee-making technologies: from alternative methods to artificial intelligence / O. Davydova, V. Udovikova, T. Denusenko // Економіка та суспільство. - 2026. – Вип. 86. |
| dc.identifier.uri | https://repository.hneu.edu.ua/handle/123456789/40729 |
| dc.language.iso | en |
| dc.subject | coffee |
| dc.subject | hospitality industry, |
| dc.subject | barista |
| dc.subject | artificial intelligence |
| dc.subject | craft technologies |
| dc.subject | YOLOv8 |
| dc.subject | automation |
| dc.subject | FoodTech |
| dc.title | Modern coffee-making technologies: from alternative methods to artificial intelligence |
| dc.type | Article |
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