Correlation of experimental and ab initio 13C-NMR chemical shifts for monomeric lignin model compounds

Metabolomics-oriented isolation and structure elucidation of 37 compounds including two anthocyanins from Arabidopsis thaliana

In order to conduct metabolomic studies in a model plant for genome research, such as Arabidopsis thaliana (Arabidopsis), it is a prerequisite to obtain structural information for the isolated metabolites from the plant of interest. In this study, we isolated metabolites of Arabidopsis in a relatively non-targeted way, aiming at the construction of metabolite standards and chemotaxonomic comparison. Anthocyanins (5 and 7) called A8 and A10 were isolated and their structures were elucidated as cyanidin 3-O-[2-O-(beta-D-xylopyranosyl)-6-O-(4-O-(beta-D-glucopyranosyl)-E-p-coumaroyl)-beta-D-glucopyranoside]-5-O-[6-O-(malonyl)-beta-D-glucopyranoside] and cyanidin 3-O-[2-O-(2-O-(E-sinapoyl)-beta-D-xylopyranosyl)-6-O-(4-O-(beta-D-glucopyranosyl)-E-p-coumaroyl)-beta-D-glucopyranoside]-5-O-[beta-D-glucopyranoside] from analyses of 1D NMR, 2D NMR ((1)H NMR, NOE, (13)C NMR, HMBC and HMQC), HRFABMS, FT-ESI-MS and GC-TOF-MS data. In addition, 35 known compounds, including six anthocyanins, eight flavonols, one nucleoside, one indole glucosinolate, four phenylpropanoids and a derivative, together with three indoles, one carotenoid, one apocarotenoid, three galactolipids, two chlorophyll derivatives, one steroid, one hydrocarbon, and two dicarboxylic acids, were also isolated and identified from their spectroscopic data.

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.