The application of hierarchical clusters analysis to the study of the composition of foods

The nutritional value of prepared foods is frequently questioned or requested at restaurants by its costumers. Therefore, this paper aimed to apply HCA to categorize foods according to their nutritive values. Fifty three samples of prepared foods from four different restaurants were analyzed for proximate composition using the AOAC methods [AOAC (1995). Association of Official Analytical Chemists. Official method of analysis. Arlington, Cereal foods, Supplement (pp. 7–11).]. The multivariate statistical analysis of data using the hierarchical cluster analysis (HCA) technique was obtained through the SPSS (10.0) program. French fries were the most caloric preparation; they also have considerable total fiber content. Milled white rice is rich in carbohydrates; and calories and fiber-poor. Arugula offers the highest protein and total fiber content, whereas lettuce presented the smallest amount of these two nutrients. Beans are the main source of dietary fiber and have a low caloric value. The most caloric preparations are French fries and fried zucchini Milanese Type of processing employed in preparing the foods might account for the variation in results between the establishments studied HCA was a useful guide for looking at and analyzing the different types of foods comparatively. Given how easy it is to calculate this with software, it is recommendable to be used even as an exploratory tool, capable of aiding intuition in the analysis of the set of data. HCA can be useful guide/tools to educate consumers on the nutritive values of foods selling in a restaurant and to help the consumers to select the foods which are suitable to their help, if required.