Nuclear Magnetic Resonance Data Sets Research Articles

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

Prediction of 19F NMR chemical shift by machine learning

Fluorine-19 (19F) is a nucleus of great importance in the field of Nuclear Magnetic Resonance (NMR) spectroscopy due to its high receptivity and wide chemical shift dispersion. 19F NMR plays crucial roles in both organic synthesis and biomedicine. Herein, a machine learning-based comprehensive 19F NMR chemical shift prediction model was established based on the experimental 19F NMR dataset from the book by Dolbier and the open NMR database nmrshiftdb2. Fluorine radical SMILES (Fr-SMILES) that reflected the fluorine chemical equivalence, was designed as the representation of fluorine in the molecule. Model trained with the graph convolution network (GCN) algorithm gave a low mean absolute error (MAE) of 3.636 ppm on the testing set. This model exhibits broad applicability and can effectively predict 19F NMR shifts for a wide range of organic fluorine molecules. We believe that the current work will provide a powerful tool for not only predicting 19F NMR shifts but also aiding in the analysis and identification of these shifts in diverse organic fluorine compounds. An online prediction platform was constructed based on the current model, which can be found at https://fluobase.cstspace.cn/fnmr.

Application of singular value decomposition analysis: Insights into the complex mechanisms of amyloidogenesis

Amyloidogenesis, with its multifaceted nature spanning from peptide self-assembly to membrane-mediated structural transitions, presents a significant challenge for the interdisciplinary scientific community. Here, we emphasize on how Singular Value Decomposition (SVD) can be employed to reveal hidden patterns and dominant modes of interaction that govern the complex process of amyloidogenesis. We first utilize SVD analysis on Circular Dichroism (CD) spectral datasets to identify the intermediate structural species emerging during peptide-membrane interactions and to determine binding constants more precisely than conventional methods. We investigate the monomer loss kinetics associated with peptide self-assembly using Nuclear Magnetic Resonance (NMR) dataset and determine the global kinetic parameters through SVD. Furthermore, we explore the seeded growth of amyloid fibrils by analyzing a time-dependent NMR dataset, shedding light on the kinetic intricacies of this process. Our analysis uncovers two distinct states in the aggregation of Aβ40 and pinpoints key residues responsible for this seeded growth. To strengthen our findings and enhance their robustness, we validate those using simulated data, thereby highlighting the physical interpretations derived from SVD. Overall, SVD analysis offers a model-free, global kinetic perspective, enabling the selection of optimal kinetic models. This study not only contributes valuable insights into the dynamics but also highlights the versatility of SVD in unravelling complex processes of amyloidogenesis.

Chemometric Combination of Ultrahigh Resolving Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy for a Structural Characterization of Lignin Compounds.

In recent years, the potential of lignins as a resource for material-based applications has been highlighted in many scientific and nonscientific publications. But still, to date, a lack of detailed structural knowledge about this ultracomplex biopolymer undermines its great potential. The chemical complexity of lignin demands a combination of different, powerful analytical methods, in order to obtain these necessary information. In this paper, we demonstrate a multispectroscopic approach using liquid-state and solid-state Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and nuclear magnetic resonance (NMR) spectroscopy to characterize a fractionated LignoBoost lignin. Individual FT-ICR-MS, tandem MS, and NMR results helped to determine relevant information about the different lignin fractions, such as molecular weight distributions, oligomer sizes, linkage types, and presence of specific functional groups. In addition, a hetero spectroscopic correlation approach was applied to chemometrically combine MS, MS/MS, and NMR data sets. From these correlation analyses, it became obvious that a combination of tandem MS and NMR data sets gives the opportunity to comprehensively study and describe the general structure of complex biopolymer samples. Compound-specific structural information are obtainable, if this correlation approach is extended to 1D-MS and NMR data, as specific functional groups or linkages are verifiable for a defined molecular formula. This enables structural characterization of individual lignin compounds without the necessity for tandem MS experiments. Hence, these correlation results significantly improve the depth of information of each individual analysis and will hopefully help to structurally elucidate entire lignin structures in the near future.

Use of carbon-13 NMR to identify known natural products by querying a nuclear magnetic resonance database-An assessment.

The quick identification of known organic low molecular weight compounds, also known as structural dereplication, is a highly important task in the chemical profiling of natural resource extracts. To that end, a method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called acd_lotus, was reported earlier, and its usefulness was illustrated by only three examples. This article presents the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of the biological magnetic resonance bank (BMRB). Two compound retrieval methods were employed. The first one involves searching in the acd_lotus database using commercial software. The second one operates through the freely accessible web interface of the nmrshiftdb2 database, which includes the compounds present in acd_lotus and many others. The two structural dereplication methods have proved to be efficient and can be used together in a complementary way.

An automated multi-order phase correction routine for processing ultra-wideline NMR spectra

Efficient acquisition of wideline solid-state nuclear magnetic resonance (NMR) spectra with patterns affected by large inhomogeneous broadening is accomplished with the use of broadband pulse sequences. These specialized pulse sequences often use frequency-swept pulses, which feature time-dependent phase and amplitude modulations that in turn deliver broad and uniform excitation across large spectral bandwidths. However, the resulting NMR spectra are often affected by complex frequency-dependent phase dispersions, owing to the interplay between the frequency-swept excitations and anisotropic resonance frequencies. Such phase distortions necessitate the use of multi-order non-linear corrections in order to obtain absorptive, distortion-free patterns with uniform phasing. Performing such corrections is often challenging due to the complex interdependence of the linear and non-linear phase contributions, and how these may affect the NMR signal. Hence, processing of these data usually involves calculating the spectra in magnitude mode wherein the phase information is discarded. Herein, we present a fully automated phasing routine that is capable of processing and phase correcting such wideline NMR spectra. Its performance is corroborated via processing of NMR data acquired using both the WURST-CPMG (Wideband, Uniform-Rate, Smooth Truncation with Carr-Purcell Meiboom-Gill acquisition) and BRAIN-CP (BRoadband Adiabatic Inversion Cross Polarization) pulse sequences for a variety of nuclei (i.e., 119Sn, 195Pt, 35Cl, 87Rb, and 14N). Based on both simulated and experimental NMR datasets, it is demonstrated that automatic phase corrections up to and including second order can be readily achieved without a priori information regarding the nature of the phase-distorted NMR datasets, and independently of the exact manner in which time-domain NMR data are collected and subsequently processed. In addition, it is shown that NMR spectra acquired at both single and multiple transmitter frequencies that are processed with this automated phasing routine have improved signal-to-noise properties than those processed with conventional magnitude calculations, along with powder patterns that better match those of ideal NMR spectra, even for datasets possessing low signal-to-noise ratios and/or affected by spectral artifacts.

Recent Advances in NMR Protein Structure Prediction with ROSETTA.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Comparison of Balanites aegyptiaca parts: metabolome providing insights into plant health benefits and valorization purposes as analyzed using multiplex GC-MS, LC-MS, NMR-based metabolomics, and molecular networking.

Balanites aegyptiaca (L.) Delile (Zygophyllaceae), also known as the desert date, is an edible fruit-producing tree popular for its nutritional and several health benefits. In this study, multi-targeted comparative metabolic profiling and fingerprinting approaches were conducted for the assessment of the nutrient primary and secondary metabolite heterogeneity in different parts, such as leaves, stems, seeds, unripe, and ripe fruits of B. aegyptiaca using nuclear magnetic resonance (NMR), ultra-performance liquid chromatography (UPLC-MS), and gas chromatography mass-spectrometry (GC-MS) based metabolomics coupled to multivariate analyses and in relation to its cytotoxic activities. NMR-based metabolomic study identified and quantified 15 major primary and secondary metabolites belonging to alkaloids, saponins, flavonoids, sugars, and amino and fatty acids. Principal component analysis (PCA) of the NMR dataset revealed α-glucose, sucrose, and isorhamnetin as markers for fruit and stem and unsaturated fatty acids for predominated seeds. Orthogonal projections to latent structure discriminant analysis (OPLS-DA) revealed trigonelline as a major distinctive metabolite in the immature fruit and isorhamnetin as a major distinct marker in the mature fruit. UPLC-MS/MS analysis using feature-based molecular networks revealed diverse chemical classes viz. steroidal saponins, N-containing metabolites, phenolics, fatty acids, and lipids as the constitutive metabolome in Balanites. Gas chromatography-mass spectroscopy (GC-MS) profiling of primary metabolites led to the detection of 135 peaks belonging to sugars, fatty acids/esters, amino acids, nitrogenous, and organic acids. Monosaccharides were detected at much higher levels in ripe fruit and disaccharides in predominate unripe fruits, whereas B. aegyptiaca vegetative parts (leaves and stem) were rich in amino acids and fatty acids. The antidiabetic compounds, viz, nicotinic acid, and trigonelline, were detected in all parts especially unripe fruit in addition to the sugar alcohol d-pinitol for the first time providing novel evidence for B. aegyptiaca use in diabetes. In vitro cytotoxic activity revealed the potential efficacy of immature fruit and seeds as cytotoxic agents against human prostate cancer (PC3) and human colorectal cancer (HCT-116) cell lines. Collectively, such detailed profiling of parts provides novel evidence for B. aegyptiaca medicinal uses.

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data.

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

Accurate measurements of self-diffusion coefficients with benchtop NMR using a QM model-based approach.

The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures.

Identifying molecular functional groups of organic compounds by deep learning of NMR data.

We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy to address the imbalance issue frequently encountered in statistical modeling of NMR data set and establish two conventional support vector machine (SVM) and K-nearest neighbor (KNN) models to assess the capability of two feature selections, respectively. Our results in this study show that the models using the selected features of peak sampling outperform those using equidistant sampling. Then we build the recurrent neural network (RNN) model trained by data collected from peak sampling. Furthermore, we illustrate the easier optimization of hyperparameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Learnings From a New Slimhole LWD NMR Technology

This paper presents recent results obtained from a new 4.75-in. logging-while-drilling (LWD) nuclear magnetic resonance (NMR) tool. Data from several wells provided real-time operational insight and new petrophysical learnings. The new LWD technology offers an innovative capability to simultaneously measure NMR T1 and T2 distributions with reduced sensitivity to drilling mud conductivity and a real-time sensor motion-warning system. The real-time T1 and T2 spectra compare favorably with the data set retrieved from the tool memory. The LWD NMR measurements were followed by wireline (WL) NMR logging for comparison in three wells. In carbonate reservoirs, the main objective was to evaluate the new NMR tool’s capability in resolving carbonate pore size with slow NMR relaxation rates. Another set of NMR logs contained time-lapse repeat passes in a well drilled with oil-based mud (OBM) that traversed a clastic reservoir. The sequence of measurements identified rock types and provided a unique insight into the mud filtrate invasion process. In a carbonate reservoir, the LWD NMR real-time partial porosity estimates were in excellent agreement with the core-calibrated WL micro-, meso-, and macropore volumes. Another carbonate formation contained high-permeability layers embedded in a microporous rock, where the real-time LWD NMR logs successfully located the high-quality rock zones, later verified by WL logging. In the clastic reservoir, the while-drilling LWD NMR data indicated native light hydrocarbons, whereas the subsequent LWD reaming and WL NMR passes showed a displacement of native hydrocarbons by oil-based mud filtrate (OBMF). The native hydrocarbon signature observed by the LWD NMR tool was in good agreement with the mud gas log. OBMF invasion occurred shortly after drilling, indicated by the differences observed in the LWD NMR relogged data acquired several hours after the while-drilling pass. The WL NMR data, logged about 4 days after drilling, showed advanced stages of OBMF invasion, including formation water displacement and wettability changes in intermediate and large pores. Finally, the environmental noise remained low in the LWD NMR data set acquired in a well in which the mud salinity changed by several folds, indicating that mud salinity had little effect on the quality of LWD NMR logs in slim holes. The new slimhole LWD NMR technology demonstrated its robust capability to provide T1 and T2 logs through several examples. For the first time, a time-lapse comparison of NMR logs showed that OBMF invasion could occur in silty sands with high capillary-bound fluid fractions.

Recoupled-STOCSY-based co-expression network analysis to extract phenotype-driven metabolite modules in NMR-based metabolomics dataset

Nuclear magnetic resonance (NMR)-based metabolomics study usually involves spectral preprocessing, identification of biomarkers and interpretation of biological processes and pathogenesis, however, the traditional procedure is bound to inborn defects. In this study, a new analytical frame was proposed to assist spectral alignment and dimensionality reduction, screen the differential metabolites and get biological explanation of the metabolic network by combing weighted gene co-expression network analysis (WGCNA) and recoupled statistical total correlation spectroscopy (RSTOCSY). The performance of RSTOCSY-based WGCNA method was evaluated by the NMR dataset of serum from coronary heart disease with diabetes mellitus (CHDDM) patients. The statistical recoupling of variables (SRV) was successfully used to categorize the whole dataset into a number of superclusters of signals and served to spectral alignment, and its effectiveness was confirmed by the wine dataset with a larger spectral drift. Three phenotype-driven metabolite modules related to CHDDM were identified from the dataset by WGCNA, and 22 metabolites were further identified from the three modules according to the metabolic correlations within or between modules, and 40 significant metabolic correlations were observed from the intra- and inter-metabolites in the 2D pseudospectrum. These modules involve amino acid metabolism, microbial metabolism and glucose metabolism, and their analysis of metabolite network diffusion revealed a new discovery that the ferroptosis pathway is related to CHDDM. This RSTOCSY-based WGCNA approach provides an effective analysis workflow for information recovery and structure identification of metabolites and improving interpretability and understanding of the disease pathogenesis.

INCA 2.0: A tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.

Affine Transformation of Negative Values for NMR Metabolomics Using the mrbin R Package

Data from untargeted metabolomics studies employing nuclear magnetic resonance (NMR) spectroscopy oftentimes contain negative values. These negative values hamper data processing and analysis algorithms and prevent the use of such data in multiomics integration settings. New methods to deal with such negative values are thus an urgent need in the metabolomics community. This study presents affine transformation of negative values (ATNV), a novel algorithm for replacement of negative values in NMR data sets. ATNV was implemented in the R package mrbin, which features interactive menus for user-friendly application and is available for free for various operating systems within the free R statistical programming language. The novel algorithms were tested on a set of human urinary NMR spectra and were able to successfully identify relevant metabolites.

13C NMR Dataset Qualitative Analysis of Grecian Wines

The development of analytical techniques for characterizing food samples, especially for the wine industry, is a main topic of research. Regarding the classification of wines based on their geographical origin, nuclear magnetic resonance (NMR) spectroscopy represents a fast and effective tool for determining chemical fingerprints. Herein, a 13C NMR dataset, which was acquired for classification of Grecian wines through multivariate statistics, is reported and described. Thus, the main qualitative differences between grapes of the same geographical origin, observable by the visual analysis of the 13C NMR data, are discussed.

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization.

Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time–frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T2* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

1D "Spikelet" Projections from Heteronuclear 2D NMR Data-Permitting 1D Chemometrics While Preserving 2D Dispersion.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this application note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This application note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers.

1H NMR based metabolite profiling for optimizing the ethanol extraction of Wolfiporia cocos.

Metabolite profiling of Wolfiporia cocos (family: Polyporaceae) had been much advancement in recent days, and its analysis by nuclear magnetic resonance (NMR) spectroscopy has become well established. However, the highly important trait of W. cocos still needs advanced protocols despite some standardization. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the 1H NMR data set. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of different ethanol W. cocos extract. 1H NMR spectroscopy of W. cocos identified 33 chemically diverse metabolites in D2O, consisting of 13 amino acids, 11 organic acids 2 sugars, 3 sugar alcohols, 1 nucleoside, and 3 others. Among these metabolites, the levels of tyrosine, proline, methionine, sarcosine, choline, acetoacetate, citrate, 4-aminobutyrate, aspartate, maltose, malate, lysine, xylitol, lactate threonine, leucine, valine, isoleucine, uridine, guanidoacetate, arabitol, mannitol, glucose, and betaine were increased in the 95% ethanol extraction sample compared with the levels in other samples, whereas level of acetate, phenylalanine, alanine, succinate, and fumarate were significantly increased in the 0% ethanol extraction sample. A biological triterpenoid, namely pachymic acid, was detected from different ethanol P. cocos extract using 1H-NMR spectra were found in CDCl3. This is the first report to perform the metabolomics profiling of different ethanol W. cocos extract. These researches suggest that W. cocos can be used to obtain substantial amounts of bioactive ingredients for use as potential pharmacological and nutraceuticals agents.