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To respond to the increasing demand for hyaluronic acid (HA) in dietary supplements (DSs) and nutricosmetics marketed for the treatment of osteoarthritis or moistening, it is essential to have an accurate and reliable method for its analysis in the final products. The study aimed to develop and validate alternative method for the quality control of HA in DSs using low-field (LF) and high-field (HF) nuclear magnetic resonance (NMR) spectroscopy at 80 MHz and 600 MHz, respectively. Moreover, chondroitin sulphate (CH), another active ingredient in DSs, can be simultaneously quantified. The 1H-NMR methods have been successfully validated in terms of limit of detection (LOD) and limit of quantitation (LOQ), which were found to be 0.1 mg/mL and 0.2 mg/mL (80 MHz) as well as 0.2 mg/mL and 0.6 mg/mL (600 MHz). Recovery rates were estimated to be between 92 and 120% on both spectrometers; precision including sample preparation was found to be 4.2% and 8.0% for 600 MHz and 80 MHz, respectively. Quantitative results obtained by HF and LF NMR were comparable for 16 DSs with varying matrix. HF NMR experiments at 70 ℃ serve as a simple and efficient quality control tool for HA and CH in multicomponent DSs. Benchtop NMR measurements, upon preceding acid hydrolysis, offer a cost-effective and cryogen-free alternative for analyzing DSs in the absence of CH and paramagnetic matrix components.
As a low-input crop, Miscanthus offers numerous advantages that, in addition to agricultural applications, permits its exploitation for energy, fuel, and material production. Depending on the Miscanthus genotype, season, and harvest time as well as plant component (leaf versus stem), correlations between structure and properties of the corresponding isolated lignins differ. Here, a comparative study is presented between lignins isolated from M. x giganteus, M. sinensis, M. robustus and M. nagara using a catalyst-free organosolv pulping process. The lignins from different plant constituents are also compared regarding their similarities and differences regarding monolignol ratio and important linkages. Results showed that the plant genotype has the weakest influence on monolignol content and interunit linkages. In contrast, structural differences are more significant among lignins of different harvest time and/or season. Analyses were performed using fast and simple methods such as nuclear magnetic resonance (NMR) spectroscopy. Data was assigned to four different linkages (A: β-O-4 linkage, B: phenylcoumaran, C: resinol, D: β-unsaturated ester). In conclusion, A content is particularly high in leaf-derived lignins at just under 70% and significantly lower in stem and mixture lignins at around 60% and almost 65%. The second most common linkage pattern is D in all isolated lignins, the proportion of which is also strongly dependent on the crop portion. Both stem and mixture lignins, have a relatively high share of approximately 20% or more (maximum is M. sinensis Sin2 with over 30%). In the leaf-derived lignins, the proportions are significantly lower on average. Stem samples should be chosen if the highest possible lignin content is desired, specifically from the M. x giganteus genotype, which revealed lignin contents up to 27%. Due to the better frost resistance and higher stem stability, M. nagara offers some advantages compared to M. x giganteus. Miscanthus crops are shown to be very attractive lignocellulose feedstock (LCF) for second generation biorefineries and lignin generation in Europe.
The molecular weight properties of lignins are one of the key elements that need to be analyzed for a successful industrial application of these promising biopolymers. In this study, the use of 1H NMR as well as diffusion-ordered spectroscopy (DOSY NMR), combined with multivariate regression methods, was investigated for the determination of the molecular weight (Mw and Mn) and the polydispersity of organosolv lignins (n = 53, Miscanthus x giganteus, Paulownia tomentosa, and Silphium perfoliatum). The suitability of the models was demonstrated by cross validation (CV) as well as by an independent validation set of samples from different biomass origins (beech wood and wheat straw). CV errors of ca. 7–9 and 14–16% were achieved for all parameters with the models from the 1H NMR spectra and the DOSY NMR data, respectively. The prediction errors for the validation samples were in a similar range for the partial least squares model from the 1H NMR data and for a multiple linear regression using the DOSY NMR data. The results indicate the usefulness of NMR measurements combined with multivariate regression methods as a potential alternative to more time-consuming methods such as gel permeation chromatography.
