Scientists at the Fralin Biomedical Research Institute at VTC have developed a new tool to assess the rewarding and reinforcing properties of ultra-processed foods, which make up 58% of calories consumed in the United States and are linked to numerous negative health outcomes.
Published in the April issue of *Appetite*, the research provides a collection of carefully curated images of minimally processed and ultra-processed foods matched on 26 characteristics, such as macronutrients, sodium, dietary fiber, calories, price, and visual aspects like color and portion size.
The study is based on the NOVA classification system, which categorizes foods into four groups based on their level of processing. This system was developed by nutrition researchers at the University of São Paulo in Brazil while studying the country’s rising obesity rates. However, the NOVA scale has faced criticism for being challenging to use and inconsistent across different classifiers.
“A major criticism of the NOVA scale is that it’s difficult to use or that foods are classified differently by different people,” said Alexandra DiFeliceantonio, corresponding author and assistant professor at the Fralin Biomedical Research Institute. “We found that people with education in nutrition generally agreed on the food classifications, providing some data that it might not be a valid criticism.”
### What They Did
The NOVA system classifies foods into four categories:
1. **Unprocessed or minimally processed**: fresh fruit, legumes, plain yogurt.
2. **Processed culinary ingredients**: cooking oils, butter, salt.
3. **Processed foods**: cheese, canned vegetables, freshly baked bread.
4. **Ultra-processed foods**: soft drinks, flavored yogurt, processed meat, most packaged breads.
To create the picture set, psychologists, neuroscientists, and registered dietitians selected foods representing either minimally processed or ultra-processed categories. These foods were prepared in a lab and professionally photographed to ensure consistency. Researchers gathered data on price, food weights, and nutritional information for each image.
Study participants rated these images on various qualities, resulting in a final set of 28 pictures matched across 26 characteristics. To objectively measure NOVA classification, 67 nutrition professionals were asked to categorize the foods as minimally or ultra-processed.
“With this food picture set, we can start to infer that any differences between food pictures are due to the degree of food processing, and not all these other factors that we know are potentially impactful,” said Zach Hutelin, the study’s lead author and a graduate student at the Fralin Biomedical Research Institute.
### Why This Matters
Ultra-processed foods are linked to increased risks of obesity, Type 2 diabetes, heart disease, and cancer. They represent more than half of the calories consumed in the United States, Canada, and the United Kingdom, posing a global public health threat.
“There is very little experimental research on ultra-processed foods, and part of what’s been holding us back is better tools for measuring and assessing their effects,” said DiFeliceantonio, who is also the associate director of the Fralin Biomedical Research Institute’s Center for Health Behaviors Research. “The more tools we can provide, the more we can learn.”
The Virginia Tech team is making these pictures and associated data accessible through the Virginia Tech Data Repository, allowing scientists to test hypotheses in behavioral economic and neuroimaging studies. In DiFeliceantonio's lab, the photos are being used with functional MRI to reveal associated brain activity, isolating the effects of food processing from other characteristics.
The study was funded by a National Science Foundation graduate research fellowship, the National Institute of Diabetes and Digestive and Kidney Diseases, and a grant from the Seale Innovation Fund, which supports innovative pilot research projects at the Fralin Biomedical Research Institute. DiFeliceantonio received a grant from the fund to investigate metabolic, neural, and behavioral data to better understand how our brains process nutrient availability and food preference.
