Estimations of accurate and reliable NMR chemical shift values, coupling patterns and constants within a reasonable timeframe remains significantly challenging, and the unavailability of reliable software strategies for the prediction of low-field (e.g., 60 MHz) spectra from those acquired at higher operating frequencies hampers their direct comparison. Hence, this study explored the applications of accessible software options for predicting these parameters in the ¹H NMR profiles of analytes as a function of magnetic field strength; this was performed for individual analytes, and also for complex biofluid matrices featured in metabolomics investigations. For this purpose, results from the very first successful experimental acquisition and simulation of the ¹H NMR profiles of intact human salivary supernatant samples acquired on a 60 MHz benchtop spectrometer were evaluated. Using salivary metabolite concentrations determined at 400 MHz, it was demonstrated that simulation of the low-field spectra of 5 biomolecules with the most prominent ¹H resonances detectable allowed multiple component fits to be applied to experimental spectra. Hence, these salivary ¹H NMR profiles could be successfully predicted throughout the 45-600 MHz operating frequency range. With the exception of propionate resonance multiplets, which revealed more complex coupling patterns at low-field and required more astute computational and fitting options, valuable quantitative metabolomics data on salivary acetate, formate, methanol and glycine could be attained from low-field spectrometers. These studies are both timely and pertinent in view of the recent advancement of low-field benchtop NMR facilities for diagnostically-significant biomarker tracking in biofluids. Experiments performed with added ammonium chloride to facilitate the release of salivary metabolites from biopolymer binding-sites provided evidence that a small but nevertheless significant proportion of propionate, but not lactate, was bound to such sites, an observation of much relevance to biomolecule quantification in salivary metabolomics investigations.