Cluster analysis to improve food classification within commodity groups.

Abstract

Mathematical clustering algorithms were used to classify foods within dairy, grain, and fat commodity groups on the basis of nutrients with limited availability in the food supply as well as those posing a possible health risk due to excess consumption. The procedure overcomes the problem that has made objective and accurate grouping, i.e., dealing simultaneously with 10 or more nutrients, difficult. The clustering routine classifies foods on the basis of similar nutrient content for any number of food attributes and assigns a degree of association to each food to indicate its compositional similarity to a prototype food for the cluster group. Foods within dairy, grain, and fat commodity groups were clustered on the basis of similar content of vitamin B-6, calcium, iron, magnesium, folacin, zinc, and added sugar, fat, cholesterol, and sodium. Whole milk and natural cheese clustered together on the basis of their moderate nutrient and relatively high fat and sodium content. Whole wheat breads, pumpernickel bread, and pancakes from mix constituted a grain subgroup with highest nutrient content, lowest cholesterol and sugar, lower fat, and higher sodium. Other subgroups based upon similarities in attributes were identified within food commodity categories. The result is an expansion of some food groups to incorporate concepts of both nutritional adequacy and moderation of food components of current nutritional concern.