Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks.

Theoretical models relating atom-based structural descriptors to 13C NMR chemical shifts were used to accurately simulate 13C NMR spectra of lignin model compounds (poly-substituted phenols). The structure-activity relationship (SAR) studies for 15 lignins using pattern recognition methods of principal component analysis (PCA) and artificial neural networks (ANNs) were performed in this work. The most important parameters affecting the 13C chemical shifts of different carbons were descriptors consisting of the charge density of the atoms at different distances from the center carbon. Among the large number of parameters, these descriptors were selected using PCA and were used as ANN input. The least square regression analyses of the results indicate correlation coefficient (R) values in excess of 0.983 for the total data set.