Artificial neural networks for qualitative and quantitative analysis of target proteins with polymerized liposome vesicles.
We investigate the feasibility of using the luminescence response of polymerized liposomes incorporating ethylenediaminetetraacetate europium(III) (EDTA-Eu(3+)) for monitoring protein concentrations in aqueous media. Quantitative analysis is based on the linear relationship between the luminescence enhancement of the lanthanide ion and protein concentration. Analytical figures of merit are presented for carbonic anhydrase, human serum albumin, gamma-globulins, and thermolysin. Qualitative analysis is based on the luminescence lifetime of the liposome sensor. This parameter, which follows well-behaved single exponential decays and provides characteristic values for each of the four studied proteins, demonstrates the selective potential for protein identification. Then partial least squares-1 and artificial neural networks are compared toward the quantitative and qualitative analysis of human serum albumin and carbonic anhydrase in binary mixtures without previous separation at the concentration levels found in aqueous humor.
Santos
Marina
M
Department of Chemistry, University of Central Florida, Orlando, FL 32816, USA.
Nadi
Suad
S
Goicoechea
Hector C
HC
Haldar
Manas K
MK
Campiglia
Andres D
AD
Mallik
Sanku
S
D017666:Deuterium Oxide; D004492:Edetic Acid; D005063:Europium; D007202:Indicators and Reagents; D008081:Liposomes; D049449:Luminescence; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D013050:Spectrometry, Fluorescence / Q000379:methods
D007202:Indicators and Reagents; D008081:Liposomes; D011506:Proteins; D005063:Europium; D004492:Edetic Acid; D017666:Deuterium Oxide