Food Powder Classification Using a Portable Visible-Near-Infrared Spectrometer

• Main Authors: Hanjong You, Youngsik Kim, Jae-Hyung Lee, Byung-Jun Jang, Sunwoong Choi
• Format: Article
• Language: English
• Published: The Korean Institute of Electromagnetic Engineering and Science 2017-10-01
• Series: Journal of the Korean Institute of Electromagnetic Engineering and Science
• Subjects: Classification; Food Powder; Machine Learning; Near Infrared Spectroscopy; Portable VIS-NIR Spectrometer
• Online Access: http://jees.kr/upload/pdf/jees-2017-17-4-186.pdf

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.