Quantification and identification of components in solution mixtures from 1D proton NMR spectra using singular value decomposition.

One-dimensional proton NMR spectra of complex solutions provide rich molecular information, but limited chemical shift dispersion creates peak overlap that often leads to difficulty in peak identification and analyte quantification. Modern high-field NMR spectrometers provide high digital resolution with improved peak dispersion. We took advantage of these spectral qualities and developed a quantification method based on linear least-squares fitting using singular value decomposition (SVD). The linear least-squares fitting of a mixture spectrum was performed on the basis of reference spectra from individual small-molecule analytes. Each spectrum contained an internal quantitative reference (e.g., DSS-d6 or other suitable small molecules) by which the intensity of the spectrum was scaled. Normalization of the spectrum facilitated quantification based on peak intensity using linear least-squares fitting analysis. This methodology provided quantification of individual analytes as well as chemical identification. The analysis of small-molecule analytes over a wide concentration range indicated the accuracy and reproducibility of the SVD-based quantification. To account for the contribution from residual protein, lipid or polysaccharide in solution, a reference spectrum showing the macromolecules or aggregates was obtained using a diffusion-edited 1D proton NMR analysis. We demonstrated this approach with a mixture of small-molecule analytes in the presence of macromolecules (e.g., protein). The results suggested that this approach should be applicable to the quantification and identification of small-molecule analytes in complex biological samples.