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Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks.
Collantes, E R; Duta, R; Welsh, W J; Zielinski, W L; Brower, J.
Afiliação
  • Collantes ER; Department of Chemistry, University of Missouri-St. Louis 63121, USA.
Anal Chem ; 69(7): 1392-7, 1997 Apr 01.
Article em En | MEDLINE | ID: mdl-9105180
ABSTRACT
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, respectively. This performance equals or exceeds that obtained previously (Welsh, W.J.; et al.Anal.Chem. 1996, 68, 3473) using 46 inputs from a so-called Window preprocessor (93%). KNN performance with 20 inputs (97%) or 30 inputs (90%) from the WP preprocessor also exceeded that obtained from the Window preprocessor (85%), while SIMCA performance with 20 inputs (86%) or 30 inputs (82%) from the WP preprocessor was slightly inferior to that obtained from the Window preprocessor (87%). These results indicate that, at least for the ANN and KNN classifiers considered here, the WP preprocessor can yield superior performance and with fewer inputs compared to the Window preprocessor.
Assuntos
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Contaminação de Medicamentos / Redes Neurais de Computação Idioma: En Revista: Anal Chem Ano de publicação: 1997 Tipo de documento: Article País de afiliação: Estados Unidos
Buscar no Google
Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Contaminação de Medicamentos / Redes Neurais de Computação Idioma: En Revista: Anal Chem Ano de publicação: 1997 Tipo de documento: Article País de afiliação: Estados Unidos