TITLE

Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks

AUTHOR(S)
Culzoni, María J.; Goicoechea, Héctor C.
PUB. DATE
December 2007
SOURCE
Analytical & Bioanalytical Chemistry;Dec2007, Vol. 389 Issue 7/8, p2217
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detection. Determination of both analytes was performed on the basis of differences between the acid–base and spectral features of each drug species. Non-linearities were detected by using both qualitative and quantitative tools. As a consequence of the non-linearity, a recently reported algorithm, artificial neural networks followed by residual bilinearization (ANN/RBL), was shown to furnish more satisfactory results. Recoveries of 99.7% (LOR) and 95.6% (PES) were obtained when analyzing a validation set containing unexpected components (the usual excipients found in pharmaceutical preparations). The average value obtained by implementation of the method on four replicates was compared with that obtained by a reference method based on HPLC (difference not significant; p > 0.05).
ACCESSION #
27657804

 

Related Articles

  • IS THERE A RELATIONSHIP BETWEEN PH MEASUREMENTS AND SYMPTOMS? A PROBABILITY BASED ANALYSIS USING NEURAL NETWORKS. Haylett, K.R.; Vales, P.; McCloy, R.F. // Gut;Apr2003 Supplement 1, Vol. 52, pA48 

    Background and Aims: Ambulatory pH studies are widely accepted as the "Gold Standard" for detecting acid reflux within the oesophagus. However, many difficulties with analyses still exist. Patients frequently have evidence of reflux, on the day of the test, but no correlation with symptoms can...

  • Searching of Predictors to Predict pH Optimum of Cellulases. Yan, Shaomin; Wu, Guang // Applied Biochemistry & Biotechnology;Oct2011, Vol. 165 Issue 3/4, p856 

    The optimal working conditions for enzymes are very much elegant, and their determination is often through experimental approach, which generally is costly and time-consuming. Therefore, it is important to develop methods to use as simple as possible information to predict the optimal working...

  • ARTIFICIAL NEURAL NETWORK (ANN) APPROACH FOR MODELING CU (II) ADSORPTION FROM AQUEOUS SOLUTION USING A CUSTARD APPLE PEEL POWDER. KRISHNA, D.; SREE, R. PADMA // Journal on Future Engineering & Technology;May-Jul2013, Vol. 8 Issue 4, p9 

    In this paper, an Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Cu (II) from aqueous solution using a custard apple peel powder as adsorbent. The effect of operational parameters such as pH, adsorbent dosage, and initial Cu (II) concentration are...

  • Prediction of Optimal pH and Temperature of Cellulases Using Neural Network. Shao-Min Yan; Guang Wu // Protein & Peptide Letters;Jan2012, Vol. 19 Issue 1, p29 

    Cellulase is an important enzyme widely used in various industries, and now in fermentation of biomass into biofuels. Enzymatic function of cellulase is closely related to pH, temperature, substrate concentration, etc. For newly found cellulase, it would be more cost-effective to predict its...

  • Modeling of rheological properties of mellorine mix including different oil and gum types by combined design, ANN, and ANFIS models. KARASU, Salih; DOĞAN, Mahmut; TOKER, Ömer Said; CANIYILMAZ, Erdal // Turkish Journal of Agriculture & Forestry;2014, Vol. 38 Issue 5, p745 

    In the present study, the effects of 2 different oil types (soybean and olive oil) and 3 different gums (xanthan gum, sodium alginate, locust bean gum, and their blends) on the rheological and physicochemical properties (pH, titratable acidity, moisture, and color), overrun, melting rate,...

  • APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELS FOR PREDICTING DISSOLVED OXYGEN CONCENTRATION FOR SURMA RIVER, BANGLADESH. AHMED, A. A. MASRUR; HOSSAIN, M. I.; RAHMAN, M. T.; CHOWDHURY, M. A. I. // Journal of Applied Technology in Environmental Sanitation;Oct2013, Vol. 3 Issue 3, p135 

    In this study dissolved oxygen (DO) of Surma River is predicted using fitforward neural network (FTNN) with different neurons in the hidden layer and linear neuron in the output layer. Phosphate, pH, biochemical oxygen demand (BOD), Total Solids (TS), Total Suspended Solids (TSS), Alkalinity,...

  • Soil NO emissions modelling using artificial neural network. Delon, Claire; Serça, Dominique; Boissard, Christophe; Dupont, Richard; Dutot, Alain; Laville, Patricia; De Rosnay, Patricia; Delmas, Robert // Tellus: Series B;Jul2007, Vol. 59 Issue 3, p502 

    Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the...

  • A Compact Optical Instrument with Artificial Neural Network for pH Determination. Capel-Cuevas, Sonia; López-Ruiz, Nuria; Martinez-Olmos, Antonio; Cuéllar, Manuel P.; del Carmen Pegalajar, Maria; JoséPalma, Alberto; de Orbe-Payá, Ignacio; Fermin Capitán-Vallvey, Luis // Sensors (14248220);2012, Vol. 12 Issue 5, p6746 

    The aim of this work was the determination of pH with a sensor array-based optical portable instrument. This sensor array consists of eleven membranes with selective colour changes at different pH intervals. The method for the pH calculation is based on the implementation of artificial neural...

  • Prediction of Biosorption of Total Chromium by Bacillus sp. Using Artificial Neural Network. Masood, Farhana; Ahmad, Masood; Ansari, Mujib; Malik, Abdul // Bulletin of Environmental Contamination & Toxicology;Apr2012, Vol. 88 Issue 4, p563 

    An artificial neural network (ANN) model was developed to predict the biosorption efficiency of Bacillus sp. for the removal of total chromium from aqueous solution based on 360 data sets obtained in a laboratory batch study. Experimental parameters affecting the biosorption process such as pH,...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics