In everyday life, eating follows patterns and occurs in context. We present an approach to discover daily eating routines of a population following a multidimensional representation of eating episodes, using data collected with the Bites'n'Bits smartphone app. Our approach integrates multiple contextual cues provided in-situ (food type, time, location, social context, concurrent activities, and motivations) with probabilistic topic models, which discover representative patterns across these contextual dimensions. We show that this approach, when applied on eating episode data for over 120 people and 1200 days, allows describing the main eating routines of the population in meaningful ways. This approach, resulting from a collaboration between ubiquitous computing and nutrition science, can support interdisciplinary work on contextual analytics for promotion of healthy eating.