  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.
