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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


The Liquor Quality Recognition using Magnetic Resonance Spectrum based on Kernel Principal Component Analysis and Convolutional Neural Network

Journal of Vibration Testing and System Dynamics 6(1) (2022) 13--20 | DOI:10.5890/JVTSD.2022.03.002

Lin-Lin Cheng, Lei-Lei Chen, Qiao-Mei Wang, Ming-Ju Chen, Xing-Zhong Xiong

Automation and information engineering institute, Sichuan University of Science & Engineering, Yibin, Sichuan, China

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In order to effectively recognize and identify liquor quality by nuclear magnetic resonance (NMR) spectrum data, we propose a hybrid data processing and recognition algorithm. In this algorithm Kernel principal component analysis (KPCA) was used to remove the correlation and reduce dimension of the NMR spectrum, and then convolutional neural network (CNN) was used for classification and identification of the processed spectrum. The compared experiment result show that the proposed KPCA+CNN method can obtain higher accuracy than CNN and PCA+CNN methods based on the NMR spectrum. The KPCA+CNN algorithm has good application prospect and reference value.


This research was supported by the Open Fund Project of the Artificial Intelligence Key Laboratory of Sichuan Province (Grant no.2020RZY02), the Fund Project of Sichuan Yibin Wuliangye Group Co. LTD (Grant no. HX2020034).


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