Experimental study of non–linear second–order analytical data with focus on the second–order advantage.

MARÍA J. CULZONI; PATRICIA C. DAMIANI; ALEJANDRO GARCÍA-REIRIZ; HÉCTOR C. GOICOECHEA; ALEJANDRO C. OLIVIERI

ANALYST

Año: 2007 vol. 132 p. 654 - 663

0003-2654

AbstractThree different experimental systems have been studied regarding the determination of analytes incomplex samples, using non-linear second-order instrumental data, which are intrinsically able toprovide the second-order advantage. This permits the quantitation of calibrated analytes in thepresence of unexpected sample components, although a suitable algorithm is required. Therecently described combination of artificial neural networks with post-training residualbilinearization has been applied to the three data sets, with successful results concerningprediction accuracy and precision, as well as profile recovery for the potential interferents in testsamples. The studies involve: (1) the determination of two pharmaceuticals in the presence of anunexpected excipient by absorbance–pH matrix measurements, (2) the quantitation of iron(II) byits catalytic effect on the kinetics of the bromate oxidation of a colorant in the presence of asecond interfering organic dye, and (3) the analysis of the antibiotic amoxicillin by fluorescenceexcitation–emission matrices in the presence of a fluorescent anti-inflammatory. The predictionresults were compared and shown to be significantly better than those yielded by the unfoldedpartial least-squares/residual bilinearization model, due to the non-linear nature of the studieddata.