한빛사 논문
POSTECH
Abstract
Seeun Joa,1, Woosuk Sohngb,1, Hyeseon Leea, Hoeil Chungb,*
aDepartment of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37666, Republic of Korea
bDepartment of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea
*Corresponding author
Abstract
The utility of an autoencoder (AE) as a feature extraction tool for near-infrared (NIR) spectroscopy-based discrimination analysis has been explored and the discrimination of the geographic origins of 8 different agricultural products has been performed as the case study. The sample spectral features were broad and insufficient for component distinction due to considerable overlap of individual bands, so AE enabling of extracting the sample-descriptive features in the spectra would help to improve discrimination accuracy. For comparison, four different inputs of AE-extracted features, raw NIR spectra, principal component (PC) scores, and features extracted using locally linear embedding were employed for sample discrimination using support vector machine. The use of AE-extracted feature improved the accuracy in the discrimination of samples in all 8 products. The improvement was more substantial when the sample spectral features were indistinct. It demonstrates that AE is expandable for vibrational spectroscopic analysis of other samples with complex composition.
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