  NMR spectral datasets, especially in systems with limited samples, can be
difficult to interpret if they contain multiple chemical components (phases,
polymorphs, molecules, crystals, glasses, etc...) and the possibility of
overlapping resonances. In this paper, we benchmark several blind source
separation techniques for analysis of NMR spectral datasets containing negative
intensity. For benchmarking purposes, we generated a large synthetic datasbase
of quadrupolar solid-state NMR-like spectra that model spin-lattice T1
relaxation or nutation tip/flip angle experiments. Our benchmarking approach
focused exclusively on the ability of blind source separation techniques to
reproduce the spectra of the underlying pure components. In general, we find
that FastICA (Fast Independent Component Analysis), SIMPLISMA
(SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF
(Non-Negative Matrix Factorization) are top-performing techniques. We
demonstrate that dataset normalization approaches prior to blind source
separation do not considerably improve outcomes. Within the range of noise
levels studied, we did not find drastic changes to the ranking of techniques.
The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure
components are predicted. Our results indicate poor performance of SVD
(Singular Value Decomposition) methods, and we propose alternative techniques
for matrix initialization. The benchmarked techniques are also applied to real
solid state NMR datasets. In general, the recommendations from the synthetic
datasets agree with the recommendations and results from the real data
analysis. The discussion provides some additional recommendations for
spectroscopists applying blind source separation to NMR datasets, and for
future benchmark studies.
