Recognition of Food-Texture Attributes Using an In-Ear Microphone

Vasileios Papapanagiotou*, Christos Diou, Janet van den Boer, Monica Mars, Anastasios Delopoulos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


Food texture is a complex property; various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.
Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges
Subtitle of host publicationVirtual Event, January 10–15, 2021, Proceedings, Part V
Publication statusPublished - 21 Feb 2021
Event25th International Conference on Pattern Recognition, ICPR 2020 - Online conference, Virtual, Milan, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 25


Conference25th International Conference on Pattern Recognition, ICPR 2020
Abbreviated titleICPR
CityVirtual, Milan
Internet address


Dive into the research topics of 'Recognition of Food-Texture Attributes Using an In-Ear Microphone'. Together they form a unique fingerprint.

Cite this