An Architecture for Context-Aware Food and Beverage Preparation Systems

This paper introduces a universal architecture for CONtext-aware Food and bEverage preperation System (CONFES) addressing the optimization issue in food and beverage preparation, with the aim of achieving nutritious, sustainable, and tasteful results. The concept is based on a comprehensive review of the state of the art in Machine Learning (ML) approaches for food preparation, and the latest technical developments in Cyber-Physical System (CPS). The system requirements, overarching architecture, essential components, and data model for CONFES are defined, leading to a more concrete case study. The latter describes a context-aware coffee machine as a practical implementation of the proposed architecture. The study demonstrates how CONFES can be customized to meet the specific requirements of a coffee machine, showcasing the adaptability and versatility of the overall architectural framework. The research findings contribute to the development of intelligent and context-aware systems in the domain of food and beverage preparation.