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Quantification of biomedical NMR data using artificial neural network analysis: Lipoprotein lipid profiles from 1 H NMR data of human plasma
Author(s) -
AlaKorpela M.,
Hiltunen Y.,
Bell J. D.
Publication year - 1995
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.1940080603
Subject(s) - human plasma , carbon 13 nmr , lipoprotein , chemistry , artificial neural network , nuclear magnetic resonance , chromatography , cholesterol , biochemistry , computer science , artificial intelligence , physics , organic chemistry
Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient ‘black‐box’ tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed. Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to those of sophisticated lineshape fitting (LF) analysis. The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks. The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap. The ANN analysis enables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a 1 H NMR spectrum of a plasma sample in <1 h. The ANN extension presented is believed to increase the value of the 1 H NMR based lipoprotein quantification to the point that it could be the method of choice in some advanced research settings. Furthermore, the excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in biomedical NMR.