These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models.
    Author: Hasani M, Moloudi M, Emami F.
    Journal: Anal Biochem; 2007 Nov 01; 370(1):68-76. PubMed ID: 17662683.
    Abstract:
    A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.
    [Abstract] [Full Text] [Related] [New Search]