Food texture is an important characteristic related to food preferences. Food texture instruments are used to determine the physical profiles of foods; however, they are not sufficient for detailed evaluation of food texture. In this study, a novel method for the quantitative evaluation of food texture is proposed. The proposed method records time-series data of force and vibration in fractures for different foods that have been estimated to have a similar food texture. Then, the dynamic time warping barycenter averaging algorithm is used to determine the standard data based on the recorded time-series data. Finally, the dynamic time warping algorithm is used to calculate the similarity between the measured data and the standard data, which is regarded as the quantitative food texture. The effectiveness of the method was confirmed with experimental results.

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.

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