Sensory Modeling of Coffee with a Fuzzy Neural Network

• Published in: Journal of food science Vol. 67; no. 1; pp. 363 - 368
• Main Authors: Tominaga, O., Ito, F., Hanai, T., Honda, H., Kobayashi, T.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01-01-2002; Institute of Food Technologists; Wiley Subscription Services, Inc
• Subjects: Biological and medical sciences; Coffee; Coffee, tea and other stimulative beverage industries; Food industries; Fundamental and applied biological sciences. Psychology; Fuzzy logic; fuzzy neural network; mixture design; modeling; Neural networks; sensory evaluation; Sensory perception; Taste; Organoleptic properties; Stimulative beverage; Sensory analysis; Neural network; Coffee; Flavor; Modeling; Mixture
• ISSN: 0022-1147; 1750-3841
• DOI: 10.1111/j.1365-2621.2002.tb11411.x

Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty‐two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup‐tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development.