PROGRAMME

According to EUROSTAT (2022), approximately 131 kg of food waste (FW) per inhabitant were generated in EU in 2020. Restaurants and food services were responsible for 9% (12 kg) of this total. According to the best estimates, in Greece, where the hospitality sector holds significant economic importance, its share accounts for 10.7% of the total FW. Recent studies in Egypt, Italy, Portugal, Finland, Sweden, and USA, estimate that 20% – 60% of food served in the hospitality sector gets wasted (Malefors et al., 2019). The wide variance in reported FW levels can be attributed to the diverse nature of the sector. The “Restaurants and food services” sector encompasses a broad spectrum of establishments, including small family-owned restaurants and global restaurants chains. Moreover, food services can be further classified into various segments such as HORECA (Hotels, Restaurants, Café), “on the go” outlets, and catering. Furthermore, FW generation patterns within the sector exhibit considerable variation, influenced by factors like the target customer groups, operational conditions, eating habits, and the socio-economic and cultural context. Given these complexities, the recommended approach for FW measurement in the “Restaurants and food services” sector, typically involves a combination of direct and indirect methodologies, including weighing, questionnaires, and interviews, which contribute to the development of the appropriate conversion coefficients. A recent trend in professional kitchens) involves the adoption of automated, specially designed FW monitoring and quantification systems, such as WINNOW (https://www.winnowsolutions.com), KITRO (https://www.kitro.ch) and others. These AI systems not only replace traditional scales, but also offer a comprehensive solution by utilizing image processing, deep-learning technologies, and hardware components to capture and analyse food waste data. This study presents the implementation of such an AI system, specifically the KITRO system, in two hotels located on the islands of Kos and Santorini. The primary objective of this study was to investigate the potential of AI-driven food waste prevention in the “restaurants and food services” sector. The hotels have reported that the system facilitated informed management decisions and streamlined optimised work practices, which resulted in reduced food waste, lower food provisioning costs, and subsequently, a lower environmental footprint. Acknowledgements: This work is part of the H2020 project LOWINFOOD—Multi-actor design of low-waste food value chains through the demonstration of innovative solutions to reduce food loss and waste. LOWINFOOD is funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement no. 101000439. The views reflected in this article represent the professional views of the authors and do not necessarily reflect the views of the European Commission or other LOWINFOOD project partners.