Search

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance ( 1 H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1 H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1 H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1 H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1 H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton ( 1 H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1 H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1 H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1 H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows ( n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

The biomedical application of discrete supramolecular metal-based structures, specifically self-assembled metallacages, is still an emergent field of study. Capitalizing on the knowledge gained in recent years on the development of 3-dimensional (3D) metallacages as novel drug delivery systems and theranostic agents, we explore here the possibility to target [Pd 2 L 4 ] 4+ cages (L = 3,5-bis(3-ethynylpyridine)phenyl ligand) to the brain. In detail, a new water-soluble homoleptic cage ( C PepH3 ) tethered to a blood brain barrier (BBB)-translocating peptide was synthesized by a combination of solid-phase peptide synthesis (SPPS) and self-assembly procedures. The cage translocation efficacy was assessed by inductively coupled mass spectrometry (ICP-MS) in a BBB cellular model in vitro . Biodistribution studies of the radiolabeled cage [[ 99m TcO 4 ] - ⊂ C PepH3 ] in the CD1 mice model demonstrate its brain penetration properties in vivo . Further DFT studies were conducted to model the structure of the [[ 99m TcO 4 ] - ⊂ cage] complex. Moreover, the encapsulation capabilities and stability of the cage were investigated using the [ReO 4 ] - anion, the "cold" analogue of [ 99m TcO 4 ] - , by 1 H NMR spectroscopy. Overall, our study constitutes another proof-of-concept of the unique potential of supramolecular coordination complexes for modifying the physiochemical and biodistribution properties of diagnostic species.

Tobacco products generally contain tobacco-derived nicotine (TDN; having ∼99+% ( S )-(-)-nicotine). Recent United States regulation has led some producers to transition to synthetic ("tobacco-free") nicotine. For example, Puff Bar is now marketed with tobacco-free nicotine (TFN; presumed to be racemic). To evaluate the claim that these new products contain TFN, we evaluated the presence of the two nicotine optical isomers by 1 H NMR spectroscopy, polarimetry, and gas chromatography-mass spectrometry. Older Puff Bars were found to contain ( S )-(-)-nicotine, and newer "TFN" Puff Bars were found to contain both ( R )-(+) and ( S )-(-) isomers-indicating TFN, albeit with slightly more of the ( S )-(-)-nicotine form.

We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls ( n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients ( n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients ( n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N -acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [ p = 2.52 × 10 -10 (GlycA) and 1.25 × 10 -9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the - + N-(CH 3 ) 3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC-B). The integrals of the summed SPC signals (SPC total ) were reduced in SARS-CoV-2 positive patients relative to both controls ( p = 1.40 × 10 -7 ) and SARS-CoV-2 negative patients ( p = 4.52 × 10 -8 ) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal components was determined using one and two dimensional diffusional, relaxation, and statistical spectroscopic experiments. The SPC total /GlycA ratios were also significantly different for control versus SARS-CoV-2 positive patients ( p = 1.23 × 10 -10 ) and for SARS-CoV-2 negatives versus positives ( p = 1.60 × 10 -9 ). Thus, plasma SPC total and SPC total /GlycA are proposed as sensitive molecular markers for SARS-CoV-2 positivity that could effectively augment current COVID-19 diagnostics and may have value in functional assessment of the disease recovery process in patients with long-term symptoms.

The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance ( 1 H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1 H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R 2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.

Thanks to magic-angle spinning (MAS) probes with frequencies of 60-100 kHz, the benefit of high-sensitivity 1 H detection can now be broadly realized in biomolecular solid-state NMR for the analysis of microcrystalline, sedimented, or lipid-embedded preparations. Nonetheless, performing the assignment of all resonances remains a rate-limiting step in protein structural studies, and even the latest optimized protocols fail to perform this step when the protein size exceeds ∼20 kDa. Here, we leverage the benefits of fast (100 kHz) MAS and high (800 MHz) magnetic fields to design an approach that lifts this limitation. Through the creation, conservation, and acquisition of independent magnetization pathways within a single triple-resonance MAS NMR experiment, a single self-consistent data set can be acquired, providing enhanced sensitivity, reduced vulnerability to machine or sample instabilities, and highly redundant linking that supports fully automated peak picking and resonance assignment. The method, dubbed RAVASSA (redundant assignment via a single simultaneous acquisition), is demonstrated with the assignment of the largest protein to date in the solid state, the 42.5 kDa maltose binding protein, using a single fully protonated microcrystalline sample and 1 week of spectrometer time.

Magic angle spinning (MAS) NMR is a powerful method for the study of pharmaceutical compounds, and probes with spinning frequencies above 100 kHz enable an atomic-resolution analysis of sub-micromole quantities of fully protonated solids. Here, we present an ultrafast NMR crystallography approach for structural characterization of organic solids at MAS frequencies of 100-111 kHz. We assess the efficiency of 1 H-detected experiments in the solid state and demonstrate the utility of 2D and 3D homo- and heteronuclear correlation spectra for resonance assignments. These experiments are demonstrated for an amino acid, U- 13 C, 15 N-histidine, and also for the significantly larger, natural product Posaconazole, an antifungal compound investigated at natural abundance. Our results illustrate the power for characterizing organic molecules, enabled by exploiting the increased 1 H resolution and sensitivity at MAS frequencies above 100 kHz.

This study, for the first time, predicts oxidative stability in camellia oils by partial least squares (PLS) built with proton nuclear magnetic resonance ( 1 H NMR) and α-tocopherol content. The prediction models were established by the PLS method. Outlier detection, latent variables optimization, data pretreatment, and important variables selection were applied for models optimization. All the developed models exhibited good performance as indicated by R 2 > 0.895 and root mean square error of estimation and root mean square error of prediction less than 0.322 and 0.307. For verification of the contribution of 1 H NMR spectra and α-tocopherol for prediction performance, a PLS model with fatty acids composition instead of 1 H NMR spectra and one with only 1 H NMR spectra as input variables were developed, respectively. The results showed that the model based on 1 H NMR data was more accurate and precise than that based on fatty acid composition data. And the performance of the models was significantly degraded without α-tocopherol as input variables.

In vivo nuclear magnetic resonance (NMR) is rapidly evolving as a critical tool as it offers real-time metabolic information, which is crucial for delineating complex toxic response pathways in living systems. Organisms such as Daphnia magna (water fleas) and Hyalella azteca (freshwater shrimps) are commonly 13 C-enriched to increase the signal in NMR experiments. A key goal of in vivo NMR is to monitor how molecules (nutrients, contaminants, or drugs) are metabolized. Conventionally, these studies would normally involve using a 13 C-enriched probe molecule and feeding this to an organism at natural abundance, in turn allowing the fate of the probe molecule to be selectively analyzed. The drawback of such an approach is that there is a limited range of 13 C-enriched probe molecules, and if available, they are extremely cost prohibitive. Uniquely, when utilizing 13 C organisms, a reverse strategy of isotopic filtering becomes possible. The concept described here uses 1 H detection in combination with a 13 C filter on living organisms. The purpose is to suppress all 1 H signals from the organism (i.e., 1 H attached to 13 C), leaving only the probe molecule ( 1 H attached to 12 C). Because the probe molecule can be selectively observed using this approach, it then makes it possible to follow and discern processes such as bioconversion, bioaccumulation, and excretion in vivo. As the approach uses 1 H detection, it provides excellent detection limits in the nanogram range. In this article, the approach is introduced, optimized on standards, and then applied to follow nicotine biotransformation and lipid assimilation in vivo to demonstrate the concept.

Continuous manufacturing of biologics is one of the priorities of the biopharmaceutical industry. However, its widespread implementation is hampered by a lack of noninvasive/nondestructive process analytical technology (PAT) systems capable of real-time in-line monitoring of product flow parameters, such as concentration and/or aggregate content. We have previously demonstrated that, under nonflow conditions, the water proton transverse relaxation rate, R 2 ( 1 H 2 O), is sensitive to protein concentration and aggregate content in biopharmaceutical formulations. In the present work, we explored the potential of water proton NMR under flow conditions (flow- w NMR) to use R 2 ( 1 H 2 O) as a quantitative indicator of protein concentration variations and aggregate levels in the process flow. We show that, under flow conditions, R 2 ( 1 H 2 O) is sensitive to rather small changes in protein concentration (<1 mg/mL) and is capable to detect variations in the aggregate content of <1%. Our findings suggest that flow- w NMR could be advantageously used as a real-time in-line noninvasive PAT for continuous biomanufacturing.

The polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women in reproductive age. Obesity and low-grade chronic inflammation are frequently associated with PCOS. Recently, proton nuclear magnetic resonance ( 1 H-NMR)-derived glycoprotein profiles have emerged as potential biomarkers that reflect systemic inflammation in type 2 diabetes, obesity, and other pathological processes. The aim of this work is to study plasma glycoprotein profiles as metabolic/inflammatory biomarkers underlying PCOS and its association with inflammation and obesity. We used 1 H-NMR spectroscopy to study five glycoprotein variables, namely GlycA, GlycB, and GlycF and the height-to-width ( H / W ) ratio of GlycA and GlycB, in 17 women with PCOS (9 non-obese and 8 obese), 17 control women (9 non-obese and 8 obese), and 19 healthy men (10 non-obese and 9 obese). H / W ratios of GlycA and GlycB, but not glycoprotein areas, were specifically associated with PCOS independently of obesity. When considered as a whole, obese subjects presented higher GlycA, GlycB, and GlycF areas and higher H / W GlycA and GlycB ratios than their non-obese counterparts. All glycoprotein variables were associated with hsCRP, IL-6, and TNF-α, showing different correlations among PCOS, women, and men. Our present exploratory results suggest that 1 H-NMR-derived glycoprotein profiles might serve as novel diagnostic markers of low-grade chronic inflammation in women with PCOS.

Furan fatty acids (FuFAs) are a class of naturally occurring minor fatty acids with fish as the richest food source. Typically, FuFA analysis is cumbersome and involves several steps. We developed a quantitative 1 H NMR method (qNMR) in which fish oil samples were directly measured after dilution with CDCl 3 stabilized with silver (which was essential to prevent formation of radicals) and addition of an internal standard. The singlet at δ = 1.89 ppm was suitable for quantitation of monomethyl FuFAs, whereas the signal at δ = 1.83 ppm was suitable to quantitate dimethyl FuFAs. Using standard NMR tubes with 650 μL solvent, the limit of quantitation was 0.5 μg (dimethyl FuFAs) and 1.0 μg (monomethyl FuFAs). Applied to three fish oil and two enriched fish oil samples (sample weight, 10 mg), the final qNMR method resulted in similar total FuFA contents as determined in parallel by gas chromatography coupled to mass spectrometry.

The localization of metabolic profiles within a tissue sample is of particular interest when the sampling size is considerably small, i.e., in the order of a microgram (μg) scale. Small sampling size is inevitable when the sample availability is limited, or when different metabolic profiles are suspected in small disparate sample regions. Capitalizing a recently introduced high-resolution micro-MAS probe (HR-μMAS) for its capability of high-quality NMR data acquisition of μg samples, this study explores the localized metabolic NMR profiling of a single garlic clove and compares the methodology and results with the standard HR-MAS. One advantage of HR-μMAS is the feasibility of analyzing homogeneous μg samples within a large heterogeneous tissue. As a result, the sampling mass (<0.5 mg) allows to selectively profile four homogeneous anatomical garlic regions by HR-μMAS (skin, flesh, inner epidermis, and sprout), in contrast to three regions (skin, flesh, and core ≡ inner epidermis and sprout) by HR-MAS, with a sampling mass of ca. 8 mg. Discriminant analysis was carried out to identify the significant variables in the different regions. It found a significant presence of fructose in both skin and flesh, while sucrose and glucose are predominant in the core. Among the garlic characteristic sulfur compounds, allicin is dominant in the core and allyl mercaptan in both skin and flesh. Quantification analysis was conducted and demonstrated its potential by quantifying metabolites at the μg-level. This study offers an important basis for designing HR-μMAS NMR-based metabolomics studies that can benefit from profiling μg-scale samples.

Surface chemistry is a critical factor for determining the behavior of a nanomaterial after incorporation in composites, devices, and biomedical products, and is also important for nanotoxicology studies. We have developed an optimized protocol for dissolution of aminated silicas and determination of functional-group contents by quantitative 1 H NMR (qNMR) analysis of the released amines. A number of variables were optimized for the dissolution protocol, including the base concentration, mass of silica, time, temperature, and method of sample agitation, in order to achieve adequate NMR signals for quantification. The protocol was tested using nanoparticles from a single commercial supplier with sizes ranging from 20 to 120 nm that were functionalized with 3-aminopropyl groups. Interestingly the batch-to-batch variability for some sizes of these aminated silicas was as high as 50%. Amine contents measured by a ninhydrin colorimetric assay were typically ∼20% lower than those measured by qNMR, consistent with measurement of only ninhydrin-reagent accessible amines. The dissolution-qNMR protocol was compatible with aminated silicas from other commercial suppliers, and in these cases, an even larger variability in surface coverage was observed. Silica nanoparticles with longer-chain amines and variable amine loadings were synthesized to demonstrate the ability to quantify amines with more complex structures and to assess the limit of quantification for the dissolution-qNMR method. Finally, the stability of the aminated nanoparticles was examined. Loss of 3-aminopropyl groups occurred in water at room temperature and was significantly more rapid at higher temperatures. Amine loss increased with increasing surface coverage and was slower for long-chain amines, consistent with studies of amine stability on planar silica. Overall, this work highlights the importance of developing methods for quantifying surface functionalization, particularly given the variability in surface coverage for commercial samples, and for ensuring that the amine group is stable under its usage conditions.

A total of 262 authentic samples was analyzed by 1 H NMR spectroscopy for the geographical discrimination of hazelnuts ( Corylus avellana L.) covering samples from five countries (Germany, France, Georgia, Italy, and Turkey) and the harvest years 2013-2016. This article describes method development starting with an extraction protocol suitable for separation of polar and nonpolar metabolites in addition to reduction of macromolecular components. Using the polar fraction for data analysis, principle component analysis was applied and used to monitor sample preparation and measurement. Several machine learning algorithms were tested to build a classification model. The best results were obtained by a linear discrimination analysis applying a random subspace algorithm. The division of the samples in a trainings set and a test set yielded a cross validation accuracy of 91% for the training set and an accuracy of 96% for the test set. The identification of key features was carried out by Kruskal-Wallis test and t test. A feature assigned to betaine exhibits a significant level for the classification of all five countries and is considered a possible candidate for the development of targeted approaches. Further, the results were compared to a previously published study based on LC-MS analysis of nonpolar metabolites. In summary, this study shows the robustness and high accuracy of a discrimination model based on NMR analysis of polar metabolites.

The species Streptomyces lincolnensis is known as a producer of lincomycin A, a clinically important lincosamide antibiotic with activity against Gram-positive bacteria. Here, we report that S. lincolnensis produces a new cysteate-containing lactone product, cysteoamide (1), which arises from nonribosomal peptide synthetase-programmed sequential assembly of the monomers phenylacetic acid, valine, cysteate, threonine, β-hydroxyleucine, and β-alanine and subsequent intramolecular cyclization to form a lactone ring. The structure of 1 was determined by combined analysis of NMR and MS spectra, while the amino acid absolute configurations in 1 were assigned by Marfey's analysis following acid hydrolysis. The biosynthetic gene cluster of 1 was defined in the genome of S. lincolnensis by bioinformatics analysis and in vivo genetic study. In addition, in vitro assay revealed that OrfA, a pyridoxal 5'-phosphate-dependent protein, is responsible for the formation of the unusual cysteate unit. Cysteate-containing nonribosomal peptides appear to be widely present in various Streptomyces strains, and this study generates interest in their intrinsic functions that remain poorly understood.

1 H nuclear magnetic resonance (NMR)-based metabolomics can rapidly detect metabolic shift under various stimulus; thus, it facilitated the dissection of the therapeutic mechanisms of compounds. (-)-5-Hydroxy-equol is an isoflavone metabolite that be obtained by microbial biotransformation. In the current work, the effect of (-)-5-hydroxy-equol on hepatocellular carcinoma (HCC) cells and its mechanism have been explored based on 1 H NMR-based metabolomics approach. Our results revealed that (-)-5-hydroxy-equol can significantly inhibit the proliferation, migration, and invasion of SMMC-7721 cells and inhibit the proliferation of HepG2 cells. Metabolomics revealed that 17 differential metabolites involving in amino acid metabolism and energy metabolism were significantly changed inside and outside of the cells after treatment of (-)-5-hydroxy-equol. Specifically, (-)-5-hydroxy-equol at a concentration of 30 μM significantly decreased the concentrations of pyruvate, glutamate, and glucose. Because glycometabolism is a crucial feature of cancer-specific metabolism, we further verified enzymes and proteins that are closely relevant to glycometabolism. Our results indicated that (-)-5-hydroxy-equol-modulated glycolysis in HCC through the inhibition of activities of hexokinase, phosphofructokinase, and pyruvate kinase, and the expression of pyruvate kinase M2. This study revealed that metabolomic analysis integrating with further verifications at the biochemical level can facilitate understanding the anti-HCC mechanisms of (-)-5-hydroxy-equol.

Metabolic profiling by 1 H NMR spectroscopy is an underutilized technology in salivary research, although preliminary studies have identified promising results in multiple fields (diagnostics, nutrition, sports physiology). Translation of preliminary findings into validated, clinically approved knowledge is hindered by variability in protocol for the collection, storage, preparation, and analysis of saliva. This study aims to evaluate the effects of differing sample pretreatments on the 1 H NMR metabolic profile of saliva. Protocol considerations are highly varied in the current literature base, including centrifugation, freeze-thaw cycles, and different NMR quantification methods. Our findings suggest that the 1 H NMR metabolite profile of saliva is resilient to any change resulting from freezing, including freezing of saliva prior to centrifuging. However, centrifugation was necessary to remove an unidentified broad peak between 1.24 and 1.3 ppm, the intensity of which correlated strongly with saliva cellular content. This peak obscured the methyl peak from lactate and significantly affected quantification. Metabolite quantification was similar for saliva centrifuged between 750 g to 15 000 g. Quantification of salivary metabolites was similar whether quantified using internal phosphate-buffered sodium trimethylsilyl-[2,2,3,3- 2 H 4 ]-propionate (TSP) or external TSP in a coaxial NMR tube placed inside the NMR tube containing the saliva sample. Our results suggest that the existing literature on salivary 1 H NMR will not have been adversely affected by variations of the common protocol; however, use of TSP as an internal standard without a buffered medium appears to affect metabolite quantification, notably for acetate and methanol. We include protocol recommendations to facilitate future NMR-based studies of saliva.

The large intestine (cecum and colon) is a complex biochemical factory of vital importance to human health. It plays a major role in digestion and absorption by salvaging nutrients from polysaccharides via fermentation initiated by the bacteria that comprise the gut microbiome. We hypothesize that the intestinal epithelium absorbs a limited number of luminal metabolites with bioactive potential while actively excluding those with toxic effects. To explore this concept, we combined 1 H NMR detection with Ussing chamber measurements of absorptive transport by rat cecum. Numerous metabolites transported across the epithelium can be measured simultaneously by 1 H NMR, a universal detector of organic compounds, alleviating the need for fluorescent or radiolabeled compounds. Our results demonstrate the utility of this approach to delineate the repertoire of fecal solutes that are selectively absorbed by the cecum and to determine their transport rates.

The structural similarity among lipid species and the low sensitivity and spectral resolution of nuclear magnetic resonance (NMR) have traditionally hampered the routine use of 1 H NMR lipid profiling of complex biological samples in metabolomics, which remains mostly manual and lacks freely available bioinformatics tools. However, 1 H NMR lipid profiling provides fast quantitative screening of major lipid classes (fatty acids, glycerolipids, phospholipids, and sterols) and some individual species and has been used in several clinical and nutritional studies, leading to improved risk prediction models. In this Article, we present LipSpin, a free and open-source bioinformatics tool for quantitative 1 H NMR lipid profiling. LipSpin implements a constrained line shape fitting algorithm based on voigt profiles and spectral templates from spectra of lipid standards, which automates the analysis of severely overlapped spectral regions and lipid signals with complex coupling patterns. LipSpin provides the most detailed quantification of fatty acid families and choline phospholipids in serum lipid samples by 1 H NMR to date. Moreover, analytical and clinical results using LipSpin quantifications conform with other techniques commonly used for lipid analysis.

The mechanism of the reaction of dichlorvos through hydrolysis reactions and through the reaction with polysulfide (S n 2- ) and thiophenolate (PhS - ) was investigated by proton nuclear magnetic resonance ( 1 H NMR). The study confirmed product identities of an organophosphorus insecticide reacting with reduced sulfur species using 1 H NMR in oxygen sensitive solutions. The experiments of dichlorvos with polysulfide led to the detection of a previously undetected product. The thiophenolate experiments were further advanced to investigate second-order rate kinetics using an internal standard. The experiments provide new evidence for a nucleophilic attack by the reduced sulfur species at the methoxy carbon of dichlorvos. In addition, the observation of in situ reaction dynamics illustrates the applicability of 1 H NMR spectroscopy toward kinetic investigations in environmental science.

NMR metabolomics are primarily conducted with 1D nuclear Overhauser enhancement spectroscopy (NOESY) presat for water suppression and Carr-Purcell-Meiboom-Gill (CPMG) presat as a T 2 filter to remove macromolecule signals. Others pulse sequences exist for these two objectives but are not often used in metabolomics studies, because they are less robust or unknown to the NMR metabolomics community. However, recent improvements on alternative pulse sequences provide attractive alternatives to 1D NOESY presat and CPMG presat. We focus this perspective on PURGE, a water suppression technique, and PROJECT presat, a T 2 filter. These two pulse sequences, when optimized, performed at least on par with 1D NOESY presat and CPMG presat, if not better. These pulse sequences were tested on common samples for metabolomics, human plasma, and urine.

Dense tumors are resistant to conventional chemotherapies due to the unique tumor microenvironment characterized by hypoxic regions that promote cellular dormancy. Bioreductive drugs that are activated in response to this hypoxic environment are an attractive strategy for therapy with anticipated lower harmful side effects in normoxic healthy tissue. Cobalt bioreductive pro-drugs that selectively release toxic payloads upon reduction in hypoxic cells have shown great promise as anticancer agents. However, the bioreductive response in the tumor microenvironment must be better understood, as current techniques for monitoring bioreduction to Co(II) such as X-ray absorption near-edge structure and extended X-ray absorption fine structure provide limited information on speciation and require synchrotron radiation sources. Here, we present magnetic resonance imaging (MRI) as an accessible and powerful technique to monitor bioreduction by treating the cobalt complex as an MRI contrast agent and monitoring the change in water signal induced by reduction from diamagnetic Co(III) to paramagnetic Co(II). Cobalt pro-drugs built upon the tris(2-pyridylmethyl)amine ligand scaffold with varying charge were investigated for distribution and activity in a 3D tumor spheroid model by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and MRI. In addition, paramagnetic 1 H NMR spectroscopy of spheroids enabled determination of the speciation of activated Co(II)TPAx complexes. This study demonstrates the utility of MRI and associated spectroscopy techniques for understanding bioreductive cobalt pro-drugs in the tumor microenvironment and has broader implications for monitoring paramagnetic metal-based therapies.

Doxorubicin (Doxo), cisplatin (Cis), and tamoxifen (Tamo) are part of many chemotherapeutic regimens. However, there have been limited studies of the way metabolism in breast cancer is affected by chemotherapy. We studied, through 1 H high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy, the metabolic profile of human breast cancer MCF-7 control (Con) cells as well as MCF-7 cells treated with Tamo, Cis, and Doxo. 1 H HR-MAS NMR single-pulse spectra evidenced signals from the cell compounds, including fatty acids (membranes), water-soluble proteins, and metabolites. The spectra showed that phosphocholine (i.e., biomarker of breast cancer malignant transformation) signals were stronger in Con than in treated cells. Betaine (i.e., the major osmolyte in cells) was observed at similar concentrations in MCF-7 control and treated cells but was absent in nontumor MCF-10A cells. The NMR spectra acquired with the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence were used only in qualitative analyses because the signal areas were attenuated according to their transverse relaxation time (T 2 ). The CPMG method was used to identify soluble metabolites such as organic acids, amino acids, choline and its derivatives, taurine, and guanidino acetate. 1 H HR-MAS NMR spectroscopy efficiently demonstrated the effects of Tamo, Cis, and Doxo on the metabolic profile of MCF-7 cells. The fatty acid, phosphocholine, and choline variations observed by single-pulse HR-MAS NMR have the potential to characterize both responder and nonresponder tumors at a molecular level.

In this letter, we propose an alternative, effective protocol for metabolomic characterization of biofluids based on their gelification and subsequent application of high-resolution magic angle spinning (HRMAS) 1 H nuclear magnetic resonance (NMR). The sample handling is very rapid and reproducible, and much less than 40 μL of neat urine are needed to obtain a sample. Our results indicate that the HRMAS spectra of gelified urine encompass all metabolites in the NMR fingerprint, as observed by solution NMR. The proposed approach can be efficiently integrated into the NMR based metabolomics analyses routines: multivariate statistical analysis of both solution and HRMAS data produced very similar statistical models, with high classification accuracy. One of the key advantages offered by the gelification approach is the improved short-term (up to 24 h) preservation of nonfrozen HRMAS NMR gel urine samples compared to the solution samples, which could lead to an alternative way for transportation or domestic collection of biofluids, without the need of cold-storage and reducing the risks of leakage.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1 H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T 2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T 2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

Using a set of machine learning based predictors that are capable of predicting ligand-induced shielding effects on (1)H and (13)C nonexchangeable nuclei, it was discovered that holo NMR chemical shifts can be used to resolve RNA-ligand poses. This was accomplished by quantitatively comparing measured and predicted holo chemical shifts in conformationally diverse "decoy" pools for three test cases and then, for each, comparing the native pose to the pose in the decoy pool that exhibited the lowest error. For three test cases, the poses in the decoy pools that exhibited the best agreement between measured and predicted holo chemical shifts were within 0.28, 1.12, and 2.38 Å of the native poses. Interestingly, the predictors used in this study were trained on a database containing, only, apo RNA data. The agreement between the chemical shift-selected poses and the native NMR poses suggests that the predictors used in this study were able to "learn" general chemical shift-structure relationships from apo RNA data that could be used to account for ligand-induced shielding effects on RNA nuclei for the test cases studied.

The growing importance of biologics and biosimilars as therapeutic and diagnostic agents is giving rise to new demands for analytical methodology that can quickly and accurately assess the chemical and physical state of protein-based products. A particular challenge exists in physical characterization where the proper fold and extent of disorder of a protein is a major concern. The ability of NMR to reflect structural and dynamic properties of proteins is well recognized, but sensitivity limitations and high levels of interference from excipients in typical biologic formulations have prevented widespread applications to quality assessment. Here we demonstrate applicability of a simple one-dimensional proton NMR method that exploits enhanced spin diffusion among protons in well-structured areas of a protein. We show that it is possible to reduce excipient signals and allow focus on structural characteristics of the protein. Additional decomposition of the resulting spectra based on rotating frame spin relaxation allows separate examination of components from aggregates and disordered regions. Application to a comparison of two different monoclonal antibodies and to detection of partial pH denaturation of a monoclonal antibody illustrates the procedure.

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Evaluating consumer products for potentially harmful side effects of chemical ingredients is important for the protection of both the consumer and those involved in the manufacturing process. In order to assess the risk potential of chemicals, regulatory agencies have encouraged the development of several in silico, in vitro, and in chemico methods as alternatives to eliminate or minimize the use of animals. To add structural information to the existing in chemico methods, an NMR-based method is proposed for probing the reactivity and classification of the potential electrophiles (E) using a model thiol, DCYA, as a nucleophile. The major advantage of the NMR method is the quantitation of the actual adduct, DCYA-E. The degree of reaction is here provided as a direct measurement of adduct formation and/or electrophile depletion, in contrast to other in chemico assays, e.g., ADRA and DPRA, where the reactivity is inferred from the quantification of the test nucleophile depletion. Moreover, the developed NMR method should serve as a qualitative and quantitative tool in understanding the site of reaction and other structural information associated with test sensitizer. This is particularly valuable and advantageous over methods encouraged by regulatory agencies, which merely provide quantification of the reaction but lack any structural information. Several compounds with multiple reaction sites were successfully tested with the proposed NMR method. Otherwise, these compounds have proven to be a challenge to identify and classify using existing alternative methods.

Takayasu arteritis (TA) is a debilitating, systemic disease that involves the aorta and large arteries in a chronic inflammatory process that leads to vessel stenosis. Initially, the disease remains clinically silent (or remains undetected) until the patients present with vascular occlusion. Therefore, new methods for appropriate and timely diagnosis of TA cases are needed to start proper therapy on time and also to monitor the patient's response to the given treatment. In this context, NMR-based serum metabolomic profiling has been explored in this proof-of-principle study for the first time to determine characteristic metabolites that could be potentially helpful for diagnosis and prognosis of TA. Serum metabolic profiling of TA patients (n = 29) and healthy controls (n = 30) was performed using 1D (1)H NMR spectroscopy, and possible biomarker metabolites were identified. Using projection to least-squares discriminant analysis, we could distinguish TA patients from healthy controls. Compared to healthy controls, TA patients had (a) increased serum levels of choline metabolites, LDL cholesterol, N-acetyl glycoproteins (NAGs), and glucose and (b) decreased serum levels of lactate, lipids, HDL cholesterol, and glucogenic amino acids. The results of this study are preliminary and need to be confirmed in a prospective study.

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.

Analyzing egg liqueurs for compliance with legal requirements means several different time-consuming preparations and analytical processes. In this paper, we describe the approach to use quantitative (1)H NMR spectroscopy as an accurate alternative technique. (1)H NMR analysis comprised two different rapid sample preparations for water-soluble or nonpolar ingredients. Fifteen egg liqueurs were analyzed for alcoholic strength and content of total sugar and egg yolk (estimated by cholesterol as a marker substance) with both classical methods and quantitative (1)H NMR spectroscopy. The results of both methods showed excellent correlations for alcoholic strength (R = 0.996, p < 0.001) and content of total sugar (R = 0.989, p < 0.001) and cholesterol (R = 0.995, p < 0.001). Besides, NMR spectra revealed further information: a signal of phosphatidylcholine at about δ = 3.20 ppm served as a second marker for the egg yolk content, and characteristic signals of lactose at δ = 4.46 ppm and butyric acid at δ = 0.97 ppm indicated the use of milk products, which has to be declared for lactose-intolerant consumers.

We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. Samples from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.

The maternal separation protocol in rodents is a widely recognized model of early life stress allowing acute and chronic physiological consequences to be studied. An (1)H NMR-based metabolomic approach was applied to urines to evaluate the systemic metabolic consequences of maternal separation stress in female rats after the beginning of weaning and 4 weeks later when the rats were reaching adulthood. Furthermore, because maternal separation is considered as a model mimicking the inflammatory bowel syndrome, the lactulose/mannitol test was used to evaluate the influence of postnatal maternal separation on gut permeability and mucosal barrier function by (1)H NMR spectroscopy analysis of urine. The results showed no statistical differences in gut permeability due to maternal separation. The application of ANOVA simultaneous component analysis allowed the contributions of physiological adaptations to the animal's development to be separated from the metabolic consequences due to postnatal stress. Systemic metabolic differences in the maternally separated pups were mainly due to the tryptophan/NAD pathway intermediate levels and to the methyladenosine level. Urinary NMR-based metabolic profiling allowed us to disentangle the metabolic adaptive response of the rats to postnatal stress during the animal's growth, highlighting the metabolic changes induced by weaning, gut closure, and maturity.

This study describes the approach of (1)H NMR profiling for the authentication of organically produced tomatoes (Solanum lycopersicum). Overall, 361 tomato samples of two different cultivars and four different producers were regularly analyzed during a 7 month period. The results of principal component analysis showed a significant trend for the separation between organically and conventionally produced tomatoes (p < 0.001 using the t test). Linear discriminant analysis demonstrated good discrimination between the growing regimens, and external validation showed 100% correctly classified tomato samples. Further validation studies, however, also disclosed unexpected differences between individual producers, which interfere with the aim of predicting the cultivation method, yet the results indicate significant differences between (1)H NMR spectra of organically and conventionally grown tomatoes.

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to μHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

The synthetic protocol for preparation of 1-(13)C-phosphoenolpyruvate-d2, precursor for parahydrogen-induced polarization (PHIP) of 1-(13)C-phospholactate-d2, is reported. (13)C nuclear spin polarization of 1-(13)C-phospholactate-d2 was increased by >30,000,000-fold (5.75 mT) in water. The reported (13)C polarization level approaching unity (>15.6%), long lifetime of (13)C hyperpolarized 1-(13)C-phospholactate-d2 (58 ± 4 s versus 36 ± 2 s for nondeuterated form at 47.5 mT), and large production quantities (52 μmoles in 3 mL) in aqueous medium make this compound useful as a potential contrast agent for the molecular imaging of metabolism and other applications.

Genetic variation is the most influential factor for carrot (Daucus carota L.) composition. However, difference in metabolite content between carrot varieties has not been described by NMR, although primary metabolites are important for human health and sensory properties. The aim of the present study was to investigate the effect of genotype on carrot metabolite composition using a (1)H NMR-based metabolomics approach. After extraction using aqueous and organic solvents, 25 hydrophilic metabolites, β-carotene, sterols, triacylglycerols, and phospholipids were detected. Multiblock PCA showed that three principal components could be identified for classification of the five carrot varieties using different spectroscopic regions and the results of the two solvent extraction methods as blocks. The varieties were characterized by differences in carbohydrate, amino acid, nucleotide, fatty acid, sterol, and β-carotene contents. (1)H NMR spectroscopy coupled with multiblock data analysis was an efficient and useful tool to map the carrot metabolome and identify genetic differences between varieties.

Abnormal lipid composition and metabolism of plasma lipoproteins play a crucial role in the pathogenesis of coronary heart disease (CHD). A (1)H NMR-based lipidomic approach was used to investigate the correlation of coronary artery stenosis with the atherogenic (non-HDL) and atheroprotective (HDL) lipid profiles in 99 patients with CHD of various stages of disease and compared with 60 patients with normal coronary arteries (NCA), all documented in coronary angiography. The pattern recognition models created from lipid profiles predicted the presence of CHD with a sensitivity of 87% and a specificity of 88% in the HDL model and with 90% and 89% in the non-HDL model, respectively. Patients with mild, moderate, and severe coronary artery stenosis were progressively differentiated from those with NCA in the non-HDL model with a statistically significant separation of severe stage from both mild and moderate. In the HDL model, the progressive differentiation of the disease stages was statistically significant only between patients with mild and severe coronary artery stenosis. The lipid constituents of lipoproteins that mainly characterized the initial stages and then the progression of the disease were the high levels of saturated fatty acids in lipids in both HDL and non-HDL particles, the low levels of HDL-phosphatidylcholine, HDL-sphingomyelin, and omega-3 fatty acids and linoleic acid in lipids in non-HDL particles. The conventional lipid marker, total cholesterol, found in low levels in HDL and in high levels in non-HDL, also contributed to the onset of the disease but with a much lower coefficient of significance. (1)H NMR-based lipidomic analysis of atherogenic and atheroprotective lipoproteins could contribute to the early evaluation of the onset of coronary artery disease and possibly to the establishment of an appropriate therapeutic option.

Single-dimension hydrogen, or proton, nuclear magnetic resonance spectroscopy (1D-(1)H NMR) has become an attractive option for characterizing the full range of components in complex mixtures of small molecular weight compounds due to its relative simplicity, speed, spectral reproducibility, and noninvasive sample preparation protocols compared to alternative methods. One challenge associated with this method is the overlap of NMR resonances leading to "convoluted" spectra. While this can be mitigated through "targeted profiling", there is still the possibility of increased quantification error. This work presents the application of a Plackett-Burman experimental design for the robust estimation of precision and accuracy of 1D-(1)H NMR compound quantification in synthetic mixtures, with application to mammalian cell culture supernatant. A single, 20 sample experiment was able to provide a sufficient estimate of bias and variability at different metabolite concentrations. Two major sources of bias were identified: incorrect interpretation of singlet resonances and the quantification of resonances from protons in close proximity to labile protons. Furthermore, decreases in measurement accuracy and precision could be observed with decreasing concentration for a small fraction of the components as a result of their particular convolution patterns. Finally, the importance of a priori concentration estimates is demonstrated through the example of interpreting acetate metabolite trends from a bioreactor cultivation of Chinese hamster ovary cells expressing a recombinant antibody.

Polyurethane libraries consisting of films with composition gradients of aliphatic polyisocyanate and hydroxy-terminated polyacrylate resin were characterized using methods of (1)H NMR microimaging (i.e., magnetic resonance imaging, (MRI)) and solid-state NMR. Molecular mobilities and underlying structural information were extracted as a function of the relative content of each of the two components. Routine NMR microimaging using the spin-echo sequence only allows investigations of transverse relaxation of magnetization at echo times >2 ms. A single-exponential decay was found, which is likely due to free, noncross-linked polymer chains. The mobility of these chains decreases with increasing content of the aliphatic polyisocyanate. The concept of a 1D NMR profiler is introduced as a novel modality for library screening, which allows the convenient measurement of static solid-state NMR spectra as a function of spatial location along a library sample that is repositioned in the rf coil between experiments. With this setup the complete transverse relaxation function was measured using Bloch decays and spin echoes. For all positions within the gradient-composition film, relaxation data consisted of at least three components that were attributed to a rigid highly cross-linked resin, an intermediate cross-linked but mobile constituent, and the highly mobile free polymer chains (the latter is also detectable by MRI). Analysis of this overall relaxation function measured via Bloch decays and spin echoes revealed only minor changes in the mobilities of the individual fractions. Findings with respect to the most mobile components are consistent with the results obtained by NMR microimaging. The major effect is the significant increase in the rigid-component fraction with the addition of the hydroxy-terminated polyacrylate resin.

This study reports for the first time the metabolic profile of cocoa (Theobroma cacao L.) beans using the (1)H NMR technique applied to polar extracts of fermented cocoa beans. The simultaneous detection and quantification of amino acids, polyalcohols, organic acids, sugars, methylxanthines, catechins, and phenols were obtained by assigning the major signals of the spectra for different varieties of cocoa beans (Forastero, Criollo, and Trinitario) from different countries (Ecuador, Ghana, Grenada, and Trinidad). The data set obtained, representative of all classes of soluble compounds of cocoa, was useful to characterize the fermented cocoa beans as a function of the variety and geographic origin.