Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-07

AUTHORS

María S. Cámara, Félix M. Ferroni, Mercedes De Zan, Héctor C. Goicoechea

ABSTRACT

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods. More... »

PAGES

838-843

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-003-1977-z

DOI

http://dx.doi.org/10.1007/s00216-003-1977-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027430463

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12802564


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