Food Texture Prediction Method Using Multiple Measurement and Template Data

Abstract

Food texture is an essential factor in the perception of chewing. Compared to taste and aroma, food texture is dominant in the palatability of solid and semi-solid foods. Hence, food development processes require a method for evaluating broad food textures. This study proposes a prediction method for food texture using multiple measurements and template data. First, a measurement system recorded the force, vibration, and sound pressure data during food compression. The moisture rate of food was also measured by a moisture meter. Second, many template data are automatically determined from the outline waveforms of measurement data. Third, the dynamic time warping calculates distance vectors between measurement and template data. Finally, the Gaussian process regression algorithm determines the relationship between the distance vectors and sensory evaluation data. The advantage of using template data is that there is no need to extract specific features from measurement data. The effectiveness of the proposed method was validated through sensory evaluation and measurement experiments. The proposed method was able to predict food texture value with low errors through the experiment with nine textures and 21 samples.

Keywords

• Food texture
• machine learning
• measurement
• sensory evaluation