Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks

Analytical Chemistry
E R CollantesJ Brower


The immediate objective of this research program is to evaluate several computer-based classifiers as potential tools for pharmaceutical fingerprinting based on analysis of HPLC trace organic impurity patterns. In the present study, wavelet packets (WPs) are investigated for use as a preprocessor of the chromatographic data taken from commercial samples of L-tryptophan (LT) to extract input data appropriate for classifying the samples according to manufacturer using artificial neural networks (ANNs) and the standard classifiers KNN and SIMCA. Using the Haar function, WP decompositions for levels L = 0-10 were generated for the trace impurity patterns of 253 chromatograms corresponding to LT samples that had been produced by six commercial manufacturers. Input sets of N = 20, 30, 40, and 50 inputs were constructed, each one consisting of the first N/2 WP coefficents and corresponding positions from the overall best level (L = 2). The number of hidden nodes in the ANNs was also varied to optimize performance. Optimal ANN performance based on percent correct classifications of test set data was achieved by ANN-30-30-6 (97%) and ANN-20-10-6 (94%), where the integers refer to the numbers of input, hidden, and output nodes, respectiv...Continue Reading


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Related Concepts

High Pressure Liquid Chromatography Procedure
Drug Impurity
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