Food Texture Quantification of Tempura Using Magnetic Food Texture Sensor and Time-series Data

Abstract

Food texture is one of the characteristics of food and is an important factor in food development. In this paper, a method of food texture quantification based on expressions for texture instead of physical properties is proposed. The method obtains two sets of time-series data of force and vibration during food compression using a magnetic food texture sensor. Dynamic time warping (DTW) matches the time-series data and standard data and calculates the degree of texture similarity. The standard data are determined by DTW barycenter averaging (DBA). The experimental results for tempura showed that the degree of texture similarity based on the vibration data had a strong correlation with the sensory evaluation data.