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http://dx.doi.org/10.26866/jees.2017.17.4.186

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

You, Hanjong (Department of Secured Smart Electric Vehicle, Kookmin University)
Kim, Youngsik (Stratio Inc.)
Lee, Jae-Hyung (Stratio Inc.)
Jang, Byung-Jun (Department of Electrical Engineering, Kookmin University)
Choi, Sunwoong (Department of Secured Smart Electric Vehicle, Kookmin University)
Publication Information
Journal of electromagnetic engineering and science / v.17, no.4, 2017 , pp. 186-190 More about this Journal
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
Classification; Food Powder; Machine Learning; Near Infrared Spectroscopy; Portable VIS-NIR Spectrometer;
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1 F. Keinosuke and P. M. Narendra, "A branch and bound algorithm for computing k-nearest neighbors," IEEE Transactions on Computers, vol. 100, no. 7, pp. 750-753, 1975.
2 L. Andy and M. Wiener, "Classification and regression brandomForest," R News, vol. 2, no. 3, pp. 18-22, 2002.
3 P. D. Martin, "Evaluation: from precision, recall and Fmeasure to ROC, informedness, markedness and correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.
4 K. H. Norris, "Design and development of a new moisture meter," Agricultural Engineering, vol. 45, no. 7, pp. 370-372, 1964.
5 B. G. Osborne and T. Fearn, "Near-infrared spectroscopy in food analysis," BRI Australia Ltd, North Ryde, Australia, 2004.
6 D. J. Kang, J. Y. Moon, D. G. Lee, and S. H. Lee., "Identification of the geographical origin of cheonggukjang by using fourier transform near-infrared spectroscopy and energy dispersive X-ray fluorescence spectrometry," Korean Journal of Food Science and Technology, vol. 48, no. 5, pp. 418-423, 2016.   DOI
7 T. D. Kim, S. H. Lee, K. J. Baik, B. J. Jang, and K. H. Jung, "Classification of tablets using a handheld NIR/visible-light spectrometer," The Journal of Korean Institute of Electromagnetic Engineering and Science, vol. 28, no. 8, pp. 628-635, 2017.   DOI
8 L. Xie, Y. Ying, and T. Ying, "Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique," Journal of Food Engineering, vol. 82, no. 3, pp. 395-401, 2007.   DOI
9 A. J. Das, A. Wahi, I. Kothari, and R. Raskar, "Ultraportable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness," Scientific Reports, vol. 6, article no. 32504, 2016.
10 LinkSquare, http://www.linksquare.io.
11 scikit-learn machine learning in Python, http://scikitlearn.org.