UTILIZATION OF AGGLOMERATIVE HIERARCHICAL CLUSTERING IN THE ANALYSIS OF HEDONIC SCALED CONSUMER ACCEPTABILITY DATA

摘要: Agglomerative hierarchical clustering was utilized to group consumers together based on product preference and liking in four consumer-based sensory studies. This statistical technique effective at determining variations consumer as a result of both processing techniques ingredient incorporation. Results revealed that agglomerative can often improve the interpretation data when compared currently analyses, has significant applications research projects with component. Three recommendations for conducting comprehensive analysis hedonic scaled are: (1) perform randomized complete block design test treatment effects using total set; (2) utilize panelists food products; (3) designs within each cluster. If differences occur among treatments cluster, use mean separation determine cluster. PRACTICAL APPLICATIONS Agglomerative coupled traditionally used analyses evaluation pertaining chicken nuggets, retorted ham, fluid milk cooked shrimp. Coupling cluster traditional grouping liking. Randomized were also further differentiation treatments. A full description how analyze clustering, Fisher's least difference included this paper is an analytical method data.