Journal of electromagnetic engineering and science

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
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Food Powder Classification Using a Portable Visible-Near-Infrared Spectrometer

  • Received : 2017.09.01
  • Accepted : 2017.10.17
  • Published : 2017.10.31
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Abstract

Visible-near-infrared (VIS-NIR) spectroscopy is a fast and non-destructive method for analyzing materials. However, most commercial VIS-NIR spectrometers are inappropriate for use in various locations such as in homes or offices because of their size and cost. In this paper, we classified eight food powders using a portable VIS-NIR spectrometer with a wavelength range of 450-1,000 nm. We developed three machine learning models using the spectral data for the eight food powders. The proposed three machine learning models (random forest, k-nearest neighbors, and support vector machine) achieved an accuracy of 87%, 98%, and 100%, respectively. Our experimental results showed that the support vector machine model is the most suitable for classifying non-linear spectral data. We demonstrated the potential of material analysis using a portable VIS-NIR spectrometer.

Keywords

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