This preliminary study explores the use of a smart tray prototype equipped with a weight sensor to analyze consumer eating behavior in real-life conditions. Conducted in 2022, at the restaurant “La Confluence” in Namur, with 60 participants, the experiment involved tracking the progression of a standardized meal, “Scallops in sauce with vegetables”. The collected data allowed for the calculation of variables such as bite count, applied force, meal duration, and quantity consumed. Through mathematical processing, insights into individual and group eating patterns were developed, with 39 usable datasets analyzed. Our first results show that the smart tray is capable of estimating the weight consumed and the number of bites with over 97% accuracy. Statistical analysis enabled the identification of four distinct groups of individuals based on five behavioral variables related to eating behavior. The smart tray could be used in hospitality establishments including cafeterias, restaurants, or brasseries, where it could serve as a valuable tool for monitoring meal nutrition. Further improvements will aim to enhance utensil and action recognition through artificial intelligence, which will also support a more detailed characterization of eating behavior.