Realistic atomistic modeling of mineral and soil systems requires chemically meaningful representations of organic matter (OM). Bulk 13C nuclear magnetic resonance (NMR) data have been proposed as compositional inputs for stochastic generation of OM structures, and prior studies using nonnegative multivariate curve resolution (MCR) suggested that bulk 13C NMR spectra of OM may be represented as mixtures of only a few components. However, these studies typically relied on single-block decompositions and did not explicitly assess decomposition uniqueness. The objective of this work was to examine whether a quantitative and chemically interpretable nonnegative MCR decomposition of OM can be obtained while explicitly evaluating (1) residual rotational ambiguity controlling the uniqueness of components, and (2) the variance captured by the decomposition. Using a dataset of International Humic Substances Society (IHSS) humic acids, fulvic acids, and aquatic OM, we applied single- and multi-block nonnegative MCR–alternating least squares (ALS) analyses integrating 13C NMR spectra, elemental composition (C, H, O, N, S), and titratable carboxylic and phenolic group contents. The multi-block approach effectively narrowed the feasible solution space and enriched the chemical characterization of the resulting MCR components. Across all analytical blocks, two chemically distinct components, an aromatic-rich and an aliphatic-rich motifs, consistently emerged, together explaining ~97–98% of the total variance and exhibiting near-zero residual rotational ambiguity. These findings support that diverse OM types can be represented quantitatively as mixtures of a small set of unique recurring compositional motifs. These motifs serve as ensemble-level averages whose underlying molecular diversity may vary substantially across materials. They provide quantitative, chemically justified inputs for molecular modeling of mineral–OM systems, which could contribute to chemical interpretability of modeling and provide better mechanistic insights into OM variation across diverse sample series.
Borisover et al. (Wed,) studied this question.
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