한국근적외분광분석학회:학술대회논문집 (Proceedings of the Korean Society of Near Infrared Spectroscopy Conference)
- 한국근적외분광분석학회 2001년도 NIR-2001
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- Pages.1131-1131
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- 2001
Near infrared spectroscopy for classification of apples using K-mean neural network algorism
- Muramatsu, Masahiro (Graduate School of Media and Governance, Keio University) ;
- Takefuji, Yoshiyasu (Graduate School of Media and Governance, Keio University) ;
- Kawano, Sumio (National Food Research Institute, Ministry of Agriculture, Forestry and Fisheries)
- 발행 : 2001.06.01
초록
To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR
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