Chemical Information Based on Neural Network Processing of Near-IR Spectra

Abstract

Three 100-compound spectra libraries have been used to evaluate artificial neural network classifications of functional groups. A near-IR gas-phase library was used to compare neural network classifications with those obtained by two-dimensional principal component analysis (PCA) score plots and by the use of the Mahalanobis distance metric based on multidimensional (score) vectors. The neural network using a radial basis function algorithm was able to correctly classify all aromatic and nonaromatic samples in a test set of 40 samples from the 100-compound library; PCA score plots were successful in separating ∼92% of the 100-compound library into aromatic and nonaromatic classes, whereas the Mahalanobis distance metric could not separate the in-class vs out-of-class aromatics in the library. Using principal component scores as input to the neural network training with 40 randomly selected samples, validating with 20 randomly selected samples, and testing with 40 randomly selected samples were performed in less than 5 s and produced perfect classifications. The neural network algorithm incorporating the radial basis function was then used to compare the information available in a near-IR spectral library of condensed-phase molecules with spectra of identical (or very similar) compounds in a mid-IR library. Results with the radial basis function were very good for both libraries, with classifications >85% in all cases. The near-IR library produced better results for aromatics (95 vs 88%), identical or very similar results for OH's (98%), alkyls (>85%), and halogens (98%), and poorer results for carbonyls (85 vs 98%). Better mid-IR results for carbonyls were anticipated due to the sharp band for carbonyl-containing compounds in the fingerprint region; however, the improved results for aromatics in the near-IR were not anticipated.