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E u r o s c i c o n C o n f e r e n c e o n
Physical Chemistry and
Analytical Separation Techniques
October 08-09 , 2018
Amsterdam, Nether l ands
Journal of Organic & Inorganic Chemistry
ISSN: 2472-1123
Physical Chemistry and Analytical Separation Techniques 2018
L
evodopa, carbidopa and entacapone in pharmaceutical formulation were
simultaneously quantified by UV-Vis spectrophotometric studies and
artificial neural networks (ANN). Absorption spectra of three components
were recorded in 200-400 nm spectral region with an interval of 1 nm. The
calibration models were thoroughly evaluated at several concentration levels
using the spectra of synthetic ternary mixture. Two layer feed-forward neural
networks using the back-propagation algorithm (BP) has been employed for
building and testing models. The number of neurons in the hidden layer was
optimized. The relative standard deviation (RSD) for each component in the
real sample was calculated as 0.045, 0.486 and 0.214 for levodopa, carbidopa
and entacapone, respectively. The results showed a very good agreement
between true values and predicted concentration values and were compared
with the standard chromatographic method results. The proposed procedure is
a simple, precise and convenient method for the simultaneous determination
of levodopa, carbidopa and entacapone in commercial tablets
Biography
Mahsa khalili has completed her BSC in Applied Chemistry, in
2007 from IAU (Karaj Branch). She followed her education at
Chemistry and Chemical Engineering Research Center of IRAN
and received her MSc degree in Analytical Chemistry. Due to her
motivation, she has experienced a vast range of quality control
and research activities in the field of Food and Drug. She has
started her PhD education in 2014. She also has job experience
as a QC Supervisor in a pharmaceutical company. She is Chair
of the Board of Directors in NAPLAB, her own R&D Laboratory
as well. She has published two ISI papers until now.
chem.khalili@gmail.comSpectral resolution and simultaneous quantification of
levodpa, carbidopa and entacapone by artificial neural
networks
Mahsa Khalili, M R Sohrabi and V Mirzabeyg
Islamis Azad University (IAU), North Tehran Branch, Iran
Mahsa Khalili et al., J Org Inorg Chem 2018 Volume: 4
DOI: 10.21767/2472-1123-C6-017