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  • 09월 03일 23시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제122회 대한화학회 학술발표회, 총회 및 기기전시회 안내 Evaluation of Fuzzy Rule Building Expert System Tree and Restricted Boltzmann Machine for NIR Spectroscopic Identification of Geographical Origins of Agricultural Products

등록일
2018년 8월 23일 15시 43분 35초
접수번호
2615
발표코드
ANAL2.O-5 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
금 09시 : 40분
발표형식
구두발표
발표분야
Analytical Chemistry - Oral Presentation of Young Analytical Chemists II
저자 및
공동저자
Woosuk Sohng, Yonjin Shin1, Hoeil Chung2,*
Chemistry, Hanyang University, Korea
1Pohang University of Science and Technology, Korea
2Department of Chemistry, Hanyang University, Korea
Fuzzy rule building expert system tree (FuRES) and Restricted Boltzmann machine (RBM) have been evaluated as a potential method for NIR spectroscopic identification of geographical origins of agricultural samples. FuRES is a tree algorithm with fuzzy expression of classification entropy. FuRES has benefits from simulated annealing and gradient optimization. Restricted Boltzmann machine (RBM) is a restricted form of Boltzmann machines with gradient descent and back-propagation. It can be trained in either supervised or unsupervised ways, depending on the task. In this study, RBM is applied for supervised classification and unsupervised way to extract features. For the evaluation, NIR spectra of imported and domestic agricultural samples (8 different samples: adzuki, angelica root, bellflower root, bracken, carrot, green kernel black bean, kidney bean and perilla seed) were used. For each sample, the discrimination accuracies were acquired using both methods and compared with those using conventional methods such as linear discriminant analysis (LDA) and support vector machine (SVM). The advantages and disadvantages of FuRES and RBM, and their potential in vibrational spectroscopic discriminant analysis will be discussed.

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