Makoto Nakauma

About

Makoto Nakauma is a leading researcher in food texture analysis and robotic sensing systems, with a particular focus on the objective evaluation of gel-like foods. His most cited work, "Convolutional Neural Network based Estimation of Gel-like Food Texture by a Robotic Sensing System" (2017, 13 citations), introduces a novel approach that combines mechanical and geometrical characteristics to quantitatively assess texture—mimicking human chewing perception. This contribution bridges robotics and food science, offering a more precise, repeatable method for texture evaluation that has implications for food quality control and product development. Nakauma’s research emphasizes the integration of deep learning with sensor technology, enabling systems to capture subtle textural changes that humans perceive during consumption. His work stands out for its interdisciplinary impact, influencing both food engineering and robotic perception. With a growing citation record, Nakauma continues to advance how we understand and measure food texture, laying groundwork for smarter, more responsive food processing technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Convolutional Neural Network based Estimation of Gel-like Food Texture by a Robotic Sensing System

   13 citations · 2017

Key Collaborators

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