Abstract
Time-domain nuclear magnetic resonance (TD-NMR) was explored as a rapid method for simultaneous assessment of the quality parameters in commercial diesel samples (B5 diesel-biodiesel blend). A principal component analysis (PCA) obtained with the relaxation decay curves revealed tight and well-separated clusters, allowing discrimination of the diesel samples according to the sulfur content: 10 (S10), 500 (S500), and 1800 (S1800) mg kg–1. Classification models based on the soft independent modeling of class analogy (SIMCA) showed a good discrimination power with a percentage of correct classification ranging from 90% (for S500 diesel samples) to 100% (for S10 and S1800 diesel samples). Partial least-squares regression (PLSR) was used to estimate the cetane index, density, flash point, and temperature achieved during distillation to obtain 50% of the distilled (T50) physicochemical parameters in the commercial diesel samples. The best PLSR models were obtained with two latent variables, providing a standard ...
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