Studying aromatic compounds in infrared spectra based on support vector machine

In this work, a support vector machine (SVM)-based model was successfully developed to study the aromatic compounds in the form of infrared spectra. At first, the support vector machine and artificial neural networks (ANN) methods were applied to construct classifier system for aromatic compounds based on entire spectra. The results showed that both approaches performed well in identifying the adjacent functional group of aromatic compounds and SVM behaved appreciably better than ANN in distinguishing the substituted types of benzene. Hence, SVM was selected to further study the spectra–structure correlation based on segmental spectra. The experiment suggested that some segmental spectra may represent significant information concealed in entire spectra and C–H and C–C wagging out-of-plane vibration was the most important among the characteristic absorptions of benzene. A cross-validation procedure was used in all experiments.