Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo31p NMR pig liver study

✍ Scribed by Mika Ala-Korpela; K. K. Changani; Y. Hiltunen; J. D. Bell; B. J. Fuller; David J. Bryant; S. D. Taylor-Robinson; B. R. Davidson

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 568 KB
• Volume: 38
• Category: Article
• ISSN: 0740-3194
• DOI: 10.1002/mrm.1910380522

✦ Synopsis

Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865 < or = R < or = 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments.