Artificial neural networks in classification of NIR spectral data: Design of the training set

Artificial neural networks (NN) with back-error propagation were used for the classification with NIR spectra and applied to the classification of different strengths of drugs. Four training set selection methods were compared by applying each of them to three different data sets. The NN architecture was selected through a pruning method, and batching operation, adaptive learning rate and momentum were used to train the NN. The presented results demonstrate that selection methods based on Kennard-Stone and D-optimal designs are better than those based on the Kohonen self-organized mapping and on random selection methods and allow 100% correct classification for both recognition and prediction. The Kennard-Stone design is more practical than the D-optimal design. The Kohonen self-organized mapping method is better than the random selection method.