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http://dx.doi.org/10.5515/KJKIEES.2017.28.8.628

Classification of Tablets Using a Handheld NIR/Visible-Light Spectrometer  

Kim, Tae-Dong (School of Electronic Engineering, Kookmin University)
Lee, Seung-hyun (School of Electronic Engineering, Kookmin University)
Baik, Kyung-Jin (School of Electronic Engineering, Kookmin University)
Jang, Byung-Jun (School of Electronic Engineering, Kookmin University)
Jung, Kyeong-Hoon (School of Electronic Engineering, Kookmin University)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.28, no.8, 2017 , pp. 628-635 More about this Journal
Abstract
It is important to prescribe and take medicines that are appropriate for symptoms, since medicines are closely related to human health and life. Moreover, it becomes more important to accurately classify genuine medicines with counterfeit, since the number of counterfeit increases worldwide. However, the number of high-quality experts who have enough experience to properly classify them is limited and there exists a need for the automatic technique to classify medicine tablets. In this paper, we propose a method to classify the tablets by using a handheld spectrometer which provides both Near Infra-Red (NIR) and visible light spectrums. We adopted Support Vector Machine(SVM) as a machine learning algorithm for tablet classification. As a result of the simulation, we could obtain the classification accuracy of 99.9 % on average by using both NIR and visible light spectrums. Also, we proposed a two-step SVM approach to discriminate the counterfeit tablets from the genuine ones. This method could improve both the accuracy and the processing time.
Keywords
NIR(Near Infra-Rred); Spectrometer; SVM(Support Vector Machine); Machine Learning; Confusion Matrix;
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Times Cited By KSCI : 1  (Citation Analysis)
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