Food communities in Twitter are growing every year, and food-related content permeates everyday conversations. Users meet on Twitter to share recipes, give cooking advices or simply inform others about what they are eating. While some of these food-related conversations are not associated with any special occurrence, many conversations take place instead during specific events. The detection of food-related events gives interesting insights: people do not talk only about Halloween and Easter, but they also create their own food-related events, such as the promotion of products (e.g., an online petition to propose the production of bacon-flavored chips) or themed home-made recipes (e.g., a day of recipes dedicated to chocolate). In this paper, we propose an approach that accurately captures food-related content from the tweet live stream, and analyze the detected conversations to identify food-related events. The proposed technique is general as it can be applied to the identification of other thematic events in digital streams.