Understanding Food Texture Perception and Preference based on Mouth Behavior
Texture consists of a complex set of sensory attributes that are important to food liking and choice. But whose ideal is considered when it comes to texture? From a product development perspective, translating texture descriptors into predicable drivers of liking in a food product is important. Oral processing research has identified segments of consumers distinct in their oral breakdown of food. Understanding these segments, or mouth behavior groups, would allow for better targeting in product development and optimization. We provide evidence, through survey data of 6120 panelists, that the distribution of these mouth behavior groups differs by country. In addition, in a set of sensory studies we examined the perception of various texture attributes, at a point in time and also over the eating experience, as well as texture preferences, and expectations for texture by mouth behavior group. In the first experiment, 119 panelists used the classical solid oral texture attribute scales for previously defined reference samples comparing both perceived texture and their ideal texture levels across mouth behavior groups. We demonstrated that for hardness and crispness, and to a much lesser extent tooth packing, that there were differences in both perceived texture, and ideal texture for the same food between mouth behavior groups, with groups defined as liking foods that could be consumed by squashing rather than chewing finding samples harder and more crisp. In the second experiment, 116 panelists investigated the perception of texture across the eating experience between varying mouth behavior groups using several differently textured samples of a common food product, salted potato chips. Mouth behavior groups chewed for varying amounts of time (with those identified as “chewers” taking the longest), rated hardness of the samples differently by group in time intensity tasks, and experienced varying attributes in temporal check all that apply tasks. Lastly, a study of 102 panelists evaluated texture descriptor information, and how a set of snack bars met expectations for texture in informed and uninformed conditions. Analysis revealed that information given on product texture influenced whether a sample was deemed worth buying dependent on mouth behavior group. Under uninformed condition, all mouth behavior groups equally would buy the products, whereas under informed condition, those defined as “crunchers” were significantly less interested in the softer bars. Taken together, our results suggest that consumers do not experience texture in an identical manner, which could potentially impact product optimization. With this, we argue that consumers style of oral processing should be considered in the evaluation of attributes, when developing a food product.
Mouth Behavior; texture; Texture Perception; Texture Preference; Agriculture economics; Food science; Sensory; Texture Expectations
Gomez, Miguel I.; Padilla-Zakour, Olga I.
Food Science and Technology
Ph.D., Food Science and Technology
Doctor of Philosophy
dissertation or thesis
Showing items related by title, author, creator and subject.
I1. Inferring Species-Richness and Species-turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery Convertino, M.; Lowry, N.C.; Linkov, I.; Mangoubi, R.; Desai, M. (Internet-First University Press, 2012-05)The quantification of species-richness and turnover is one of the most important tasks in monitoring ecosystems. This is both for guaranteeing ecosystem function, and to understand the linkages between natural and human ...
Ramanarayanan, Ganesh; Bala, Kavita; Walter, Bruce (Cornell University, 2004-05-13)This paper introduces feature-based textures, a new image representation that combines features and texture samples for high-quality texture mapping. Features identify boundaries within a texture where samples change ...
Ramanarayanan, Ganesh; Bala, Kavita (Cornell University, 2005-04-14)This paper describes constrained graphcut texture synthesis (CGS), a graphcut-based synthesis algorithm that creates output textures satisfying constraints. We show that constrained texture synthesis can be posed in a ...