Emotions are thought to play a crucial role in food behavior. Non-rational emotional decision making may be credited as the reason why consumers select what, how, and when they choose to interact with a food product. In this research, three experiments were completed for the overall goal of understanding the usefulness and validity of selected emotional measurement tools, specifically emotion questionnaire ballots and facial expression analysis, as compared to conventional sensory methods in developing a holistic view of product interest and engagement. Emotional response to 1% low-fat unflavored and chocolate-flavored milk was evaluated by using an emotion-based questionnaire as well as facial expression analysis software, to measure post-experience cognitive and in-the-moment intrinsic (implicit) emotional response, respectively. The software correlated facial movements of participants to associated basic emotions to estimate with what degree consumers were expressing these measured emotions upon presentation of each sample. Finally, the adapted facial expression method was compared to expected measurements from previous studies by measuring emotional facial response to four (sweet, salt, sour, and bitter) basic tastes. The cognitive emotion ballot and implicit facial analysis were able to differentiate between milk samples and offer a greater understanding of the consumer experience. Validity of the facial expression method was lacking for reasons including high individual taste variability, social context, intensities of stimuli, quality of video data capture, calibration settings, sample size number, analysis duration, and software sensitivity limitations. To better validate automatic facial expression methodology, further study is needed to investigate and minimize method limitations. / Master of Science in Life Sciences
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/54538 |
| Date | 15 January 2014 |
| Creators | Arnade, Elizabeth Amalia |
| Contributors | Food Science and Technology, Duncan, Susan E., Dunsmore, Julie C., O'Keefe, Sean F. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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