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Sensors and Materials, Volume 31, Number 7(3) (2019)
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pp. 2357-2365
S&M1938 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2342
Published: July 30, 2019

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

Ninomae Souda, Hiroyuki Nakamoto, Futoshi Kobayashi, Yuya Nagahata, and Yoriyasu Hirosue

(Received February 22, 2019; Accepted May 22, 2019)

Keywords: sensor, food texture, time-series data, dynamic time warping, tempura

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.

Corresponding author: Hiroyuki Nakamoto

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Ninomae Souda, Hiroyuki Nakamoto, Futoshi Kobayashi, Yuya Nagahata, and Yoriyasu Hirosue, Food Texture Quantification of Tempura Using Magnetic Food Texture Sensor and Time-series Data, Sens. Mater., Vol. 31, No. 7, 2019, p. 2357-2365.