HSQC‐NMR‐based profiling approaches for raffinose family oligosaccharides in pulses

Background and Objectives Due to insufficient resolution, $^{1}$H nuclear magnetic resonance (NMR) spectroscopy-based methods are limited to quantify carbohydrates. In the past, heteronuclear single quantum coherence (HSQC)-based methods were demonstrated to be superior as the second dimension greatly improves resolution. However, whether these experiments are also suitable to determine structurally similar oligosaccharides such as raffinose family oligosaccharides (RFO) still needs to be demonstrated. Findings By optimizing NMR parameters, well resolved signals for the analysis of glucose, fructose, galactose, sucrose and the RFO raffinose, stachyose, and verbascose were identified. Application of fast HSQC methods in combination with nonuniform sampling enables analyses of sucrose and RFO in pulses (blue lupin seeds, red lentils, kidney beans) within 24 min. If the analytes are present at levels greater than 0.5 g/100 g, HSQC-based methods provide data equivalent to an anion-exchange chromatography-based reference method. Conclusions High resolution fast HSQC-based approaches are suitable tools to analyze complex carbohydrate mixtures as demonstrated for RFO in different pulses. Significance and Novelty Fast HSQC experiments were applied for the first time to analyze structurally similar oligosaccharides. In the future, this approach will be a most valuable tool to analyze complex mixtures of carbohydrates in food products

2D-HSQC-NMR-Based Screening of Feruloylated Side-Chains of Cereal Grain Arabinoxylans

Arabinoxylans of commelinid monocots are characterized by high contents of ferulic acid that is incorporated into arabinose-bearing side-chains of varying complexity. Species-related differences in the feruloylated side-chain profiles of grain arabinoxylans are observed and lead to differences in arabinoxylan functionality. Here, a semi-quantitative assay based on $^1$H-$^{13}$C-correlation NMR spectroscopy (HSQC experiment) was developed to profile feruloylated side-chains of cereal grain arabinoxylans. Following acidic liberation of the feruloylated side-chains from the xylan backbone and a clean-up step using C18 solid phase extraction, the feruloylated oligosaccharides FA (5-O-trans-feruloyl-L-arabinofuranose), FAX (β-D-xylopyranosyl-(1 → 2)-5-O-trans-feruloyl-L-arabinofuranose) and FAXG (α-L-galactopyranosyl-(1 → 2)-β-D-xylopyranosyl-(1 → 2)-5-O-trans-feruloyl-L-arabinofuranose) were analyzed by HSQC-NMR. Marker signals were identified for each compound, and experimental conditions such as solvent and internal standard as well as measurement and processing conditions were optimized for a semi-quantitative determination. The approach was validated with respect to accuracy, precision, limit of detection, and limit of quantification. The newly developed approach was applied to several cereal samples including oats, popcorn maize, wheat, and wild rice. Data were compared to an HPLC-DAD/MS approach published earlier by our group, demonstrating that the results of the HSQC approach were comparable to the more time-consuming and technically more challenging HPLC-DAD/MS method

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by $^1$H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. $^1$H NMR data was analyzed by using a non—targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published $^1$H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy

Evaluation of NMR-based strategies to differentiate fresh from frozen-thawed fish supported by multivariate data analysis

The differentiation of fresh and frozen-thawed fish is a relevant authenticity aspect as in the European Union fish holds a high statistical risk of being adulterated. Here, nuclear magnetic resonance spectroscopy (NMR) in combination with principal components analysis followed by linear discriminant analysis (PCA-LDA) was used for a non-targeted based differentiation of fresh from frozen-thawed fish. To identify the most promising NMR approach(es), six different approaches were applied to 96 fish samples (mackerel, trout, cod). These approaches included different sample preparation procedures and different NMR methods to investigate both the lipid fraction and the polar fraction of the fish samples. After cross-validation embedded in a Monte Carlo resampling design, six independent classification models were obtained. Evaluation of the multivariate data analysis revealed that the most promising approaches were the 1H NMR analysis of the lipid fraction (correct prediction of about 90.0%) and the $^1$H NMR based screening of minor components of the lipid fraction with a correct prediction of about 91.9%. $^1$H NMR analysis of the water extract of the fish samples showed a correct prediction of about 82.6%. Hence, a general differentiation of fresh from frozen-thawed fish via non-targeted NMR is feasible, even though the underlying sample batch contained different fish species. Additional fish samples need to be analyzed with the three most promising NMR approaches to further improve the developed classification models

Defined shear and heat treatment of apple pomace: impact on dietary fiber structures and functional properties

Food by-products can be modified by extrusion processing. However, the impact of thermal and mechanical stress, respectively, on the structure and thus functional properties of dietary fiber-rich food by-products is still unknown. In the extrusion process, thermal and mechanical stress are coupled, not constant, and difficult to measure or calculate. Thus, their influence on structural changes and functional properties cannot be evaluated separately. In this work, a specific shear cell, denoted by closed cavity rheometer, was used to treat apple pomace with defined thermal and/or mechanical stress. Dietary fiber composition and fiber polysaccharide structures appeared to be more susceptible to high temperatures than mechanical stress. With increasing temperature (and mechanical stress) soluble and low-molecular-weight soluble dietary fiber contents increased, whereas insoluble fiber contents decreased. Arabinans as rhamnogalacturonan type I polysaccharides and galacturonic acid containing pectic polysaccharides were identified as being most susceptible to degradation under these conditions. Furthermore, the defined treatment affected the functional properties. Although changes in the water solubility index (WSI) and/or the water absorption index (WAI) were not detected up to 90 °C, WSI and WAI decreased significantly at a treatment temperature of 120 °C. However, at very high temperatures (160 °C), WSI and WAI increased. The application of shear and longer treatment times resulted in higher WSI values and complex viscosities as compared to low shear stress