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The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using
the chemometric approach. To create the model, spectral data from 73 samples
(with wavelengths between 200 and 400 nm) were employed. A two-layer
artificial neural network model was created, with two neurons in the output
layer and fourteen neurons in the hidden layer. The model was trained to
simulate the concentrations and spectra of piroxicam and mefenamic acid.
For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt
algorithm with feed-forward back-propagation learning produced root mean
square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients
of determination of
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