The Liquor Quality Recognition using Magnetic Resonance Spectrum based on Kernel Principal Component Analysis and Convolutional Neural Network - Cheng, Lin-Lin; Chen, Lei-Lei; Wang, Qiao-Mei; Chen, Ming-Ju; Xiong, Xing-Zhong (Journal of Vibration Testing and System Dynamics, 2026)
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.