Abstract
OBJECTIVES: This study aims to determine groups of unprocessed plant-based foods that have similar micronutrient profiles. METHODS: Raw and minimally processed plant foods (fruits, fruit juices, vegetables, nuts and seeds, legumes, cereal grains and pasta) were identified from the USDA National Nutrient Database for Standard Reference Legacy (2018). A dataset of concentrations of selected micronutrients per 100 g of the food was prepared. These micronutrients included calcium (Ca), iron (Fe), magnesium (Mg), phosphorus (P), potassium (K), sodium (Na), zinc (Zn), copper (Cu), manganese (Mn), selenium (Se), vitamin A, vitamin D, vitamin C, thiamin, riboflavin, niacin, pantothenic acid, vitamin B6, vitamin B12 and folate. The micronutrient concentrations were standardized ranging from 0 to 1, and analyzed using hierarchical clustering analysis (Ward's method). Squared euclidean distance was used for dissimilarity measure, and agglomeration schedule was used for determine the optimal clusters. Dendograms were plotted to visualize the clusters. RESULTS: The selected foods can be grouped into 4 clusters according to the result of agglomeration schedule. Dendrogram showed that cluster 1 (44 foods) contained 26 grains like cornmeal, rice and sorghum, 11 nut and seeds like walnuts and almond, 4 legumes and 3 vegetables. Cluster 2 (293 foods) was mainly fresh fruits and vegetables (277 foods), 9 grains like degermed cornmeal, 5 nuts and 2 legumes. Cluster 3 were 28 legumes. Cluster 4 (36 foods) contained 16 dried vegetables like dehydrated carrot and dried onions, 9 nuts, 8 legumes and 3 grains. Each cluster had distinct micronutrient profiles. On a 100 g basis, cluster 4 had almost the highest levels of all nutrients except vitamin D and B-12. Cluster 1 and 3 were rich in P, K, Zn, thiamin and cluster, but cluster 1 also had high amount of Fe and folate. Cluster 2 had the highest amount of vitamin D. CONCLUSIONS: The cluster analysis on micronutrients of raw and minimally processed foods provides an alternate means to group foods based on nutrients. These results help identify foods of similar nutrients and can provide information to support dietetic practice and patient education for improving dietary quality and variety. FUNDING SOURCES: USDA National Nutrient Databank for Food Composition (8040–52,000-064–00D).