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Snack Texture Estimation System Using a Simple Equipment and Neural Network Model

Author

Listed:
  • Shigeru Kato

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

  • Naoki Wada

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

  • Ryuji Ito

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

  • Takaya Shiozaki

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

  • Yudai Nishiyama

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

  • Tomomichi Kagawa

    (Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan)

Abstract

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed.

Suggested Citation

  • Shigeru Kato & Naoki Wada & Ryuji Ito & Takaya Shiozaki & Yudai Nishiyama & Tomomichi Kagawa, 2019. "Snack Texture Estimation System Using a Simple Equipment and Neural Network Model," Future Internet, MDPI, vol. 11(3), pages 1-16, March.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:3:p:68-:d:212289
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