TITLE

휴대형 근적외선/가시광선 분광기를 이용한 의약품 분류기법

AUTHOR(S)
김태동; 이승현; 백경진; 장병준; 정경훈
PUB. DATE
August 2017
SOURCE
Journal of Korean Institute of Electromagnetic Engineering & Sci;Aug2017, Vol. 28 Issue 8, p628
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
125530880

 

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