Based on examination of food scores using the various algorithms, it is difficult to assess the differences of the various algorithms to determine which nutrients should be included in an algorithm; however, the process does provide insights into the effects of various modifications. Examination of food scores using the various algorithms demonstrates that the algorithm discriminates between more and less healthy versions of foods; for example, low-fat dairy products score higher than high-fat dairy products, and fruits score higher than sweetened beverages. Food scores are sensitive to the nutrients in the algorithm; for example, scores for nuts, which are high in unsaturated fat, decreased when unsaturated fat as a positive nutrient was removed from the algorithm. Some anomalies became evident when comparing the scores based on per 100 kcal or RACC basis; for example, green leafy vegetables score very high on a per 100 kcal basis because of their low energy content but 100 kcal is a very large portion size for these foods.
As noted previously, we did not conduct formal validity tests focused on the food scores themselves. We did conduct formal validity tests of the ability of the algorithms to predict overall diet quality by scoring foods in diets and comparing with the HEI, a measure of diet quality. Section 4.3 describes the results of regression models predicting HEI scores using the various algorithms to score foods in diets.