New Algorithm for Ranking the Nutritional Value of Foods
New research funded by the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Planning and Evaluation, and published in the Journal of the Academy of Nutrition and Dietetics, has led to the development of a new algorithm which ranks foods based on a continuous numerical score. Higher scores identify healthier foods and lower scores represent foods to avoid.
The HHS sponsored investigation was designed, in part, to help consumers better understand a food’s nutritional value. Researchers at RTI International in North Carolina developed the algorithm that gives food an overall nutrition score that could make it easier for consumers to make healthy food choices. According to the lead author Joanne Arsenault, PhD., “Our goal was to develop a nutrient profiling algorithm that was evidence based and included the latest dietary recommendations.” She suggests, “This research will be useful for developing point-of-purchase nutrition labeling systems, educating consumers, and assessing overall dietary quality.”
Certain nutritional profiles provided significant positive scores such as fruits and vegetables (raw spinach scored 215.7) whereas undesirable foods provide very low scores, which may even be negative values (sweetened soft drinks scored -24.8). In some cases the nutrient balance upsets the total score. The author cited the dill pickle as an example, as it earned a score of -240.9, due to the food’s very high sodium content per serving.
According to the author, unlike most nutrient profiling systems that weigh nutrients equally, the new RTI algorithm predicts overall dietary quality as measured by the Healthy Eating Index. According to the article, the algorithm provides positive weighting factors for protein, unsaturated fats, fiber, calcium and vitamin C, and negative weighting factors for saturated fat, sodium, and added sugars. Compared to prior systems of classifying one’s diet, the RTI algorithm provides the advantage that it can easily be used to score the nutritional quality of overall diets as well as individual foods/meals. Further information is likely to follow as the implementation strategy for the new algorithm is developed and revealed. The next question is - what impact will food scoring have on consumer selection of ranked items?
