Sum of variance for quantifying the variation of multiple sequential data for the crispness evaluation of chicken nugget.

Abstract

Crispness is one of the words most frequently used to describe the texture of fried or dried food in addition to being a key to the determination of freshness for many non-fried foods. In this study, a new feature value called the sum of variance was assessed for its contribution to the estimation of crispness. Dynamic time warping and its averaging algorithms were employed to determine the sum of variance from a set of sequential force data measured using an instrument. The sum of variance is a feature value that expresses the variance of multiple sequential data. In an experiment, seven chicken nugget samples were prepared, and five panels evaluated their texture according to six Japanese word descriptors. An instrument experiment determined the six feature values, including the sum of variance from the measurement data, whereas multiple linear regression was applied to determine the relationship between the sensory values and feature values. For three of the six textures, the sum of variance reduced the error between the sensory values and their estimated values by up to 50%, confirming that this feature contributes to the textural estimation of food crispness.