Abstract
Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.
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