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FOOD DENSITY ESTIMATION USING FUZZY INFERENCE

chengliu, li (2012) FOOD DENSITY ESTIMATION USING FUZZY INFERENCE. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

This work presents a new fuzzy logic approach to food density estimation in supporting research in diet and nutrition. It has been a historical problem to measure people’s daily food intake in real life. Recent advances in electronic devices have provided novel tools for recording volumetric information of food, while the current food databases often list nutrients and calories in terms of gram weights instead of volumes. Thus, a density value, which connects the volume to weight, is required to use the existing databases when the volumetric information is unavailable. In this work, we approach the density estimation problem using fuzzy inference which “guesses” the food density by collecting and organizing relevant human knowledge about a food. French fries are taken as an example of this new approach. A fuzzy Inference System (FIS) is constructed to estimate the bulk density of French fries under different cooking conditions. Our experimental results show that our FIS system built upon human knowledge about the frying time and temperature can accurately estimate the density of French fries under controlled conditions.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
chengliu, lichengliu.li@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSun, Minguidrsun@pitt.eduDRSUN
Committee MemberMao, zhihongzhm4@pitt.edu ZHM4
Committee MemberFernstrom, Johnfernstromjd@upmc.edu
Committee MemberSclabassi, Robertbobs@cdi.com
Committee MemberLi, C.C.ccl@pitt.edu CCL
Committee MemberJia, Wenyanjiawenyan@gmail.com
Thesis AdvisorSun, Minguidrsun@pitt.eduDRSUN
Date: 2 February 2012
Date Type: Publication
Defense Date: 30 November 2011
Approval Date: 2 February 2012
Submission Date: 1 December 2011
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 46
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Fuzzy logic, Food density
Date Deposited: 02 Feb 2012 13:52
Last Modified: 15 Nov 2016 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/10634

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