Comparator-based identification of food edibility from natural language description

Abstract

Assessing the edibility of food based on consumer perception remains an underexplored yet practically significant challenge in food safety. This paper presents a novel framework for evaluating food suitability using natural language descriptions of sensory experiences, such as odor, appearance, and texture. By extracting structured features from unstructured, subjective input, our system leverages a comparatorbased identification approach to infer missing attributes and assess overall edibility. The model aligns incomplete descriptions with prototypical instances from labeled data, enabling robust classification even under uncertainty. We demonstrate that this method can support nuanced, human-like judgments and serve as a foundation for intelligent decision-support tools in consumer and public health contexts. The proposed framework opens avenues for integrating qualitative perception with structured inference in critical application domains.