Beyond NOVA: Reimagining Food Processing Classification with the CHIPS (Combining Health, Intuition, Processing and Science) Framework

Background/Objectives: Classifying foods by processing has gained traction, with NOVA the most widely used framework. NOVA helped shift focus away from nutrients toward food processing, but has been critiqued for rigidity, inconsistent classifications, and weak links to health outcomes. This paper introduces the CHIPS (Combining Health, Intuition, Processing and Science) framework (previously known as the Human Interference Scoring System (HISS)), which retains NOVA’s food-based perspective while addressing these limitations by integrating processing level with health evidence. Methods: CHIPS was developed through critique synthesis, epidemiological evidence, and expert input from nutrition professionals. Foods were classified using a three-layered approach: (1) baseline placement by processing level, (2) adjustment based on evidence of health benefit or harm, and (3) an intuition check to ensure pragmatic classification. Key divergences from NOVA were recorded. Results: CHIPS places foods with demonstrated benefits in lower categories and those with consistent evidence of harm in higher ones, while also resolving common inconsistencies and better aligning classifications with real-world understanding. Conclusions: A CHIPS builds on NOVA’s strengths while addressing its limitations by combining processing level, health evidence, and a pragmatic or intuitive lens. This approach resolves inconsistencies in existing systems and better reflects how foods contribute to health in real-world contexts. The framework has been successfully integrated into an AI enabled tool, demonstrating feasibility for reliable food classification and potential for further validation in diverse populations.