Application of linear prediction singular value decomposition for processing in vivo NMR data with low signal-to-noise ratio

Nuclear Magnetic Resonance (NMR) spectroscopy has become a powerful method to study cell metabolism in vivo. Low sensitivity of nuclear magnetic resonance (NMR) measurements of living cell composition by conventional methods requires samples with high cell density compared to that in growing cultures. Reasonably accurate intracellular concentration estimates from lower cell density samples can be obtained by treating the time-domain NMR data by linear prediction singular value decomposition (LPSVD) prior to Fourier transformation. Alternatively, application of LPSVD enables intracellular concentrations estimates in less NMR acquisition time, improving time resolution in NMR measurements of intracellular transients.