U.S. Department of Energy

Pacific Northwest National Laboratory

Identification of Known and Unknown Chemicals in Nuclear Magnetic Resonance Data

Publish Date: 
Tuesday, February 25, 2014
Nuclear Magnetic Resonance (NMR) spectroscopy is a valuable tool for analyzing the composition of various small non-protein molecules by comparing the experimental spectrum against a library of expected peak locations. Currently, small molecule identification can be time-consuming and labor intensive as spectral results can vary over sample preparation and run conditions, and typically hundreds of molecules are identified simultaneously within a single spectrum with varying overlapping peaks with unknown shifts and concentrations. We develop a Bayesian statistical learning algorithm to automate the identification of known small molecule entities in a sample.
Bramer LM, BJM Webb-Robertson, SM Robinson, G Chin, Jr, J Yin, M Thomas, and KT Mueller. 2014. "Identification of Known and Unknown Chemicals in Nuclear Magnetic Resonance Data." Presented by Lisa Bramer at American Statistical Association -- Women in Statistics, Raleigh, North Carolina on May 16, 2014. PNNL-SA-102793.
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