Sulfate detection in glycoprotein-derived oligosaccharides by artificial neural network analysis of Fourier-transform infrared spectra.
We report the use of an artificial neural network to analyze the fingerprint region of Fourier-transform infrared (ir) spectra of oligosaccharides for the presence of sulfate groups. This assay can rapidly and nondestructively detect the presence of sulfate in as little as 1 nmol (approximately 2 micrograms) of a glycoprotein-derived monosulfated decasaccharide. The neural network was trained to recognize the presence of sulfate groups by presenting it with 45 ir spectra of sulfated and nonsulfated mono- and oligosaccharides. No prior knowledge of the characteristic ir spectral features of a sulfate group was needed as input. The training process required between 3 and 10 h, while analysis of a spectrum with the trained neural network requires only 0.1 s.
Powell
D A
DA
Complex Carbohydrate Research Center, University of Georgia, Athens 30602-4712.
Turula
V
V
de Haseth
J A
JA
van Halbeek
H
H
Meyer
B
B
D002240:Carbohydrate Sequence; D006023:Glycoproteins / Q000737:chemistry*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D009844:Oligosaccharides / Q000737:chemistry*; D017550:Spectroscopy, Fourier Transform Infrared; D013463:Sulfuric Acid Esters / Q000032:analysis*
D006023:Glycoproteins; D009844:Oligosaccharides; D013463:Sulfuric Acid Esters