Clustering of aryl carbon-13 nuclear magnetic resonance substituent chemical shifts. A multivariate data analysis using principal components

Abstract

A principal component analysis of the ¹³C substituent-induced chemical shifts of 82 monosubstituted benzenes shows that ca. 90% of the substituents belong to one of four clusters, acceptors, alkyls, donors, or halogens. This grouping is confirmed statistically. The extensions of the subclasses are not parallel. It is also shown that the predictive capability of the single-parameter models for each subclass is better than any multiparameter model applied on the whole data set. The observed grouping of substituents provides an explanation to the apparent correlation frequently found between ¹³C n.m.r. chemical shifts and dual substituent parameters. The ability of the statistical method to discover incorrect shift data is also illustrated.