Innovative Framework Developed for Classifying Processed Foods Based on Health Impact

Recent advancements in nutrition research have aimed to better understand and categorize processed foods according to their health effects. Traditionally, systems like Nova, established in 2009, classify foods into four broad categories—from unprocessed to ultra-processed—highlighting their level of processing. However, this approach often groups diverse foods like candy bars and fortified cereals under the same umbrella, which can obscure their distinct health impacts.

To address this limitation, scientists from WISEcode have created a more nuanced classification system that evaluates processed foods based on specific ingredients and associated health risks. This new model integrates data on the percentage of calories from added sugars, the presence of ingredients linked to health concerns, and the scientific understanding of their risks. It assigns scores that categorize foods as minimal, light, moderate, ultra, or super-ultra processed.

The WISEcode system was tested on a database of over 650,000 foods and 5,500 ingredients, demonstrating greater differentiation than the Nova system, especially among foods classified as ultra-processed. This approach offers consumers a clearer way to assess and choose healthier processed food options, and enables manufacturers to compare their products more accurately.

Developed by Richard Black, Ph.D., chief scientific officer at WISEcode, this framework is designed to evolve with ongoing scientific discoveries. Black emphasized that the system aims to provide a more accurate reflection of the complex nature of modern food formulations and their health impacts. It also serves as a valuable research tool to identify ingredients and processing methods associated with health risks.

Black will present these findings at the upcoming Nutrition 2025 conference, organized by the American Society for Nutrition. This innovative classification aims to enhance consumer awareness, guide healthier food choices, and support nutritional research with more precise data, moving beyond the limitations of existing broad-spectrum systems.

For further details, the research is available at the American Society for Nutrition's official platform.