Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics

Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review

Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.

Fast quantification of water content in glycols by compact 1H NMR spectroscopy

Glycols are key chemicals for many applications in different fields of activities. Being highly hydroscopic, glycols contain usually water. The presence of water, even in tiny amounts, can affect their chemical and physical properties. Therefore, the accurate determination of water content is essential for any intended applications. In this context, a novel method using low-field Nuclear Magnetic Resonance (NMR) spectroscopy is introduced. The proposed approach offers a straightforward, fast, low-cost, and versatile solution for water quantification in glycols without the need for reagents or calibration data. It is demonstrated by quantifying the water concentration up to 11 wt% in aqueous ethylene glycol (EG) and triethylene glycol (TEG) mixtures with the help of lineshape analysis of the corresponding proton spectra. The limit of detection, achieved within 1 min of measuring time, was 0.05 wt% for water in EG and 0.15 wt% in TEG. The excellent agreement between the NMR results and those from the Karl-Fischer titration indicates that the proposed NMR-based approach has a great potential to be used as an alternative to the Karl-Fischer method. In addition, it is expected that the same methodology can be applied for water quantification in more complex glycolic solutions and other mixtures.

Metabolite quantification: A fluorescence-based method for urine sample normalization prior to 1H-NMR analysis

INTRODUCTION

Metabolomics is a multi-discipline approach to systems biology that provides a snapshot of the metabolic status of a cell, tissue, or organism. Metabolomics uses mass spectroscopy (MS) and nuclear magnetic resonance (NMR) to analyze biological samples for low molecular weight metabolites.

OBJECTIVE

Normalize urine sample pre-acquisition to perform a targeted quantitative analysis of selected metabolites in rat urine.

METHODS

Urine samples were provided from rats on a control diet (n = 10) and moderate sucrose diet (n = 8) collected in a metabolic cage during an eight hour fast. Urine from each sample was prepared by two different methods. One sample was a non-normalized sample of 1200 µL and the second sample was a variable volume-normalized to the concentration of urobilin in a standard sample of urine. The urobilin concentration in all samples was determined by fluorescence. Ten metabolites for each non-normalized and normalized urine sample were quantified by integration to an internal standard of DSS.

RESULTS

Both groups showed an improvement in pH range going from non-normalized to normalized samples. In the group on the control diet, eight metabolites had significant improvement in range, while the remaining two metabolites had insignificant improvement in range comparing the non-normalized sample to the normalized sample. In the group on the moderate sucrose diet all ten metabolites showed significant improvement in range going from non-normalized to normalized samples.

CONCLUSIONS

These findings describe a pre-acquisition method of urine normalization to adjust for differences in hydration state of each organism. This results in a narrower concentration range in a targeted analysis.

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.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

Fast 1H-NMR Species Differentiation Method for Camellia Seed Oils Applied to Spanish Ornamentals Plants. Comparison with Traditional Gas Chromatography

Camellia genus (Theaceae) is comprised of world famous ornamental flowering plants. C. japonica L. and C. sasanqua Thunb are the most cultivated species due to their good adaptation. The commercial interest in this plant linked to its seed oil increased in the last few years due to its health attributes, which significantly depend on different aspects such as species and environmental conditions. Therefore, it is essential to develop fast and reliable methods to distinguish between different varieties and ensure the quality of Camellia seed oils. The present work explores the study of Camellia seed oils by species and location. Two standardized gas chromatography methods were applied and compared with that of data obtained from proton nuclear magnetic resonance spectroscopy (¹H-NMR) for fatty acids profiling. The principal component analysis indicated that the proposed ¹H-NMR methodology can be quickly and reliably applied to separate specific Camellia species, which could be extended to other species in future works.

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the structure of unknown components and the composition of liquid mixtures. However, these tasks are often tedious and challenging, especially if complex samples are considered. In this work, we introduce automated methods for the identification and quantification of structural groups in pure components and mixtures from NMR spectra using support vector classification. As input, a ¹H NMR spectrum and a ¹³C NMR spectrum of the liquid sample (pure component or mixture) that is to be analyzed is needed. The first method, called group-identification method, yields qualitative information on the structural groups in the sample. The second method, called group-assignment method, provides the basis for a quantitative analysis of the sample by identifying the structural groups and assigning them to signals in the ¹³C NMR spectrum of the sample; quantitative information can then be obtained with readily available tools by simple integration. We demonstrate that both methods, after being trained to NMR spectra of nearly 1000 pure components, yield excellent predictions for pure components that were not part of the training set as well as mixtures. The structural group-specific information obtained with the presented methods can, e.g., be used in combination with thermodynamic group-contribution methods to predict fluid properties of unknown samples.

Drug effects on metabolic profiles of Schistosoma mansoni adult male parasites detected by 1H-NMR spectroscopy

Schistosomiasis is one of the most devastating neglected tropical parasitic diseases caused by trematodes of the genus Schistosoma. Praziquantel (PZQ) is today the only drug used in humans and animals for the treatment of schistosomiasis but unfortunately it is poorly effective on larval and juvenile stages of the parasite. Therefore, it is urgent the discovery of new drug targets and compounds. We have recently showed that the anti-anginal drug perhexiline maleate (PHX) is very active on multiple developmental stages of Schistosoma mansoni in vitro. It is well known that PHX impacts the lipid metabolism in mammals, but the final target on schistosomes still remains unknown. The aim of this study was to evaluate the ability of ¹H nuclear magnetic resonance (NMR) spectroscopy in revealing metabolic perturbations due to PHX treatment of S. mansoni adult male worms. The effects of PHX were compared with the ones induced by vehicle and gambogic acid, in order to detect different metabolic profiles and specificity of the PHX action. Remarkably a list of metabolites associated to PHX-treatment was identified with enrichment in several connected metabolic pathways including also the Kennedy pathway mediating the glycerophospholipid metabolism. Our study represents the first ¹H-NMR metabolomic approach to characterize the response of S. mansoni to drug treatment. The obtained “metabolic fingerprint” associated to PHX treatment could represent a strategy for displaying cellular metabolic changes for any given drug and to compare compounds targeting similar or distinct biochemical pathways.

Schistosomiasis is a chronic and debilitating neglected tropical parasitic disease caused by the helminth Schistosoma. The control and treatment of the disease rely almost exclusively on praziquantel (PZQ), poorly effective on some developmental stage of the parasite. Identification of novel targets and drugs is required. The aim of this study was to use a ¹H-NMR metabolomic approach to characterize the response of Schistosoma mansoni to perhexiline maleate (PHX), a multi-stage schistosomicidal drug previously investigated by our group. Remarkably we identified a metabolic signature specifically associated to drug-treatment in adult male parasites. This approach could contribute to the identification of novel targets and biochemical pathways implicated in parasite development and survival.

Recent Advances in Standardization of Herbal Drugs

Background:

Herbal drugs play a significant role to maintain the human healthiness and to treat the ailments since the dawn of civilization. Moreover, these plants have provided many lead compounds that culminated in modern medicine. A single herb is regarded as mini-combinatorial library of phytoconstituents hence the quality control of herbal drugs in an herbal formulation is not an easy task because a number of factors impact their pharmacological efficiency and consistent therapeutic effects. Hence, to provide consistent beneficial therapeutic effects, standardized herbal products of consistent quality and purity are required.

Methods:

This review is based on publications obtained by a selective search in PubMed using the keywords “Standardized herbal products”, “fingerprinting”, “authentication”, “chemometric, hyphenated techniques”, “quality control of herbal drugs”, “identification”.

Results:

In the era of modernization, chromatographic techniques coupled with sophisticated spectroscopic analytical methods are used in estimating the authenticity, identity and characteristic of herbal products. Further, with the advancement of computer technology, chemometrics methods have become a leading tool with an unsupervised pattern recognition technique for handling multivariate data without prior knowledge about the studied samples and mines more beneficial and valuable information about the chemical entities from the raw data.

Conclusion:

Standardization of HDs chromatographic fingerprint is not always a perfect way to present all compounds. To assess the quality of medicinal plants, new ways are regularly being explored such as combination chemical fingerprint with biological methods, biofingerprint and metabolic fingerprint quality metrology, pharmacodynamics and export system of medicinal plants have been researched in some groups but still a significant amount of work is required to achieve a perfect system for quality evaluation of herbal drugs. Further, novel chemometric techniques have been unfolded that mines more beneficial and valuable information about the chemical entities from the raw data. So this review emphasis mainly on hyphenated techniques associated with chemometric method used in herbal drugs for identifying more valuable information and various methods for providing data, among which most commonly used techniques are chemometric resolution method and Principal Component Analysis (PCA) method.

Amino Acid Profile of Fruits as Potential Fingerprints of Varietal Origin

This study aims to assess the capability of the ¹H-NMR profiling of fruits from different genera in combination with multivariate data analysis to provide feasible information for fruit juices’ authenticity in terms of botanical origin. Nine fruit varieties from four genera were selected for the experimental plan. The juice obtained from the fruits was characterized using the ¹H-NMR technique, selecting the obtained amino acid profile of fruits as a potential specific fingerprint. Due to the complex information provided by the NMR spectra, a chemometric approach of the data was further applied to enable the differentiation of the fruit samples, highlighting thus its suitability as a discrimination tool for the varietal origin. The advantage of this analytical approach is given by the relatively simple working procedure, which consists of an easy, fast, and accessible preparation stage while providing complex information on fruit composition.

Inline Reaction Monitoring of Amine-Catalyzed Acetylation of Benzyl Alcohol Using a Microfluidic Stripline Nuclear Magnetic Resonance Setup

We present an in-depth study of the acetylation of benzyl alcohol in the presence of N, N-diisopropylethylamine (DIPEA) by nuclear magnetic resonance (NMR) monitoring of the reaction from 1.5 s to several minutes. We have adapted the NMR setup to be compatible to microreactor technology, scaling down the typical sample volume of commercial NMR probes (500 μL) to a microfluidic stripline setup with 150 nL detection volume. Inline spectra are obtained to monitor the kinetics and unravel the reaction mechanism of this industrially relevant reaction. The experiments are combined with conventional 2D NMR measurements to identify the reaction products. In addition, we replace DIPEA with triethylamine and pyridine to validate the reaction mechanism for different amine catalysts. In all three acetylation reactions, we find that the acetyl ammonium ion is a key intermediate. The formation of ketene is observed during the first minutes of the reaction when tertiary amines were present. The pyridine-catalyzed reaction proceeds via a different mechanism.

1H HR-MAS NMR spectroscopy to study the metabolome of the protozoan parasite Giardia lamblia

Knowledge of the metabolic profile and exchange processes in the protozoan parasite Giardia lamblia is of importance for a better understanding of the biochemical processes and for the development of drugs to control diseases caused by G. lamblia. In the current paper, ¹H High Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy was directly applied to G. lamblia trophozoite suspensions to analyze the detectable small metabolites with a minimum of intervention. Thirty-one components were identified with main contributions from amino acids such as alanine and ornithine. The reproducibility, variability, and stability of the metabolites were investigated. Citrulline was found to be formed as an intermediate and citrulline levels depended on the stage of cell growth. Glucose-1-phosphate was found to be formed in relatively high amounts after cell harvesting if enzymes were not inactivated. In addition, the metabolic footprint of Giardia trophozoites, i.e. changes in the culture medium induced by G. lamblia, was investigated by liquid state NMR spectroscopy of culture media before and after inoculation. A quantitative comparison of the NMR spectra revealed component changes in the culture media during growth. The results suggested that not glucose but rather arginine serves as main energy supply. Biochemical functions of intracellular components and their metabolic exchange with the culture medium are discussed. The results provide an important basis for the design of HR-MAS NMR based metabolomic studies of G. lamblia in particular and any protozoan parasite samples in general.

Application of CRAFT (complete reduction to amplitude frequency table) in nonuniformly sampled (NUS) 2D NMR data processing

The recently published CRAFT (complete reduction to amplitude frequency table) technique converts the raw FID data (i.e., time domain data) into a table of frequencies, amplitudes, decay rate constants, and phases. It offers an alternate approach to decimate time-domain data, with minimal preprocessing step. It has been shown that application of CRAFT technique to process the t₁ dimension of the 2D data significantly improved the detectable resolution by its ability to analyze without the use of ubiquitous apodization of extensively zero-filled data. It was noted earlier that CRAFT did not resolve sinusoids that were not already resolvable in time-domain (i.e., t₁ max dependent resolution). We present a combined NUS-IST-CRAFT approach wherein the NUS acquisition technique (sparse sampling technique) increases the intrinsic resolution in time-domain (by increasing t₁ max), IST fills the gap in the sparse sampling, and CRAFT processing extracts the information without loss due to any severe apodization. NUS and CRAFT are thus complementary techniques to improve intrinsic and usable resolution. We show that significant improvement can be achieved with this combination over conventional NUS-IST processing. With reasonable sensitivity, the models can be extended to significantly higher t₁ max to generate an indirect-DEPT spectrum that rivals the direct observe counterpart.

NMR Analysis of Fecal Samples

Fecal analysis can generate data that is relevant for the exploration of gut microbiota and their relationship with the host. Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for the profiling of fecal extracts as it enables the simultaneous detection of various metabolites from a broad range of chemical classes including, among others, short-chain fatty acids, organic acids, amino acids, bile acids, carbohydrates, amines, and alcohols. Compounds present at low μM concentrations can be detected and quantified with a single measurement. Moreover, NMR-based profiling requires a relatively simple sample preparation. Here we describe the three main steps of the general workflow for the NMR-based profiling of feces: sample preparation, NMR data acquisition, and data analysis.

Use of qNMR for speciation of flaxseeds (Linum usitatissimum) and quantification of cyanogenic glycosides

This report describes a routine method taking less than 20 min to quantify cyanogenic glycosides such as linustatin and neolinustatin from flaxseeds (Linum usitatissimum L.) using ¹H nuclear magnetic resonance. After manual dehulling, a higher linustatin content was shown in the almond fraction, while neolinustatin and total cyanogenic glycoside contents were significantly higher in hulls. Linustatin and neolinustatin were quantified in seven cultivars grown in two locations in three different years. Linustatin, neolinustatin, and total cyanogenic glycosides ranged between 91 and 267 mg/100 g, 78-272 mg/100 g, and 198-513 mg/100 g dry weight flaxseeds, respectively. NMR revealed differences of up to 70% between samples with standard deviation variations lower than 6%. This study shows that NMR is a very suitable tool to perform flaxseed varietal selection for the cyanogenic glycoside content. Graphical abstract qNMR can be used to perform flaxseed varietal selection for the cyanogenic glycoside content.

Multivariate Analysis of Two-Dimensional 1H, 13C Methyl NMR Spectra of Monoclonal Antibody Therapeutics To Facilitate Assessment of Higher Order Structure

Two-dimensional (2D) 1H-13C methyl NMR provides a powerful tool to probe the higher order structure (HOS) of monoclonal antibodies (mAbs), since spectra can readily be acquired on intact mAbs at natural isotopic abundance, and small changes in chemical environment and structure give rise to observable changes in corresponding spectra, which can be interpreted at atomic resolution. This makes it possible to apply 2D NMR spectral fingerprinting approaches directly to drug products in order to systematically characterize structure and excipient effects. Systematic collections of NMR spectra are often analyzed in terms of the changes in specifically identified peak positions, as well as changes in peak height and line widths. A complementary approach is to apply principal component analysis (PCA) directly to the matrix of spectral data, correlating spectra according to similarities and differences in their overall shapes, rather than according to parameters of individually identified peaks. This is particularly well-suited for spectra of mAbs, where some of the individual peaks might not be well resolved. Here we demonstrate the performance of the PCA method for discriminating structural variation among systematic sets of 2D NMR fingerprint spectra using the NISTmAb and illustrate how spectral variability identified by PCA may be correlated to structure.

Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: A tutorial

Metabolomics analysis of body fluids as well as cells is depended on many factors. While several well-accepted standard operating procedures for the analysis of body fluids are available, the NMR based quantitative analysis of cellular metabolites is less well standardized. Experimental designs depend on the cell type, the quenching protocol and the applied post-acquisition workflow. Here, we provide a tutorial for the quantitative description of the metabolic phenotype of mammalian cells using NMR spectroscopy. We discuss all key steps of the process, starting from the selection of the appropriate culture medium, quenching techniques to arrest metabolism in a reproducible manner, the extraction of the intracellular components and the profiling of the culture medium. NMR data acquisition and methods for both qualitative and quantitative analysis are also provided. The suggested methods cover experiments for adherent cells and cells in suspension. We ultimately describe the application of the discussed workflow to a thyroid cancer cell line. Although this tutorial focuses on mammalian cells, the given guidelines and procedures may be adjusted for the analysis of other cell types.

Automated quantification of metabolites in blood-derived samples by NMR

NMR is widely applied in the field of metabolomics due to the quantitative nature of the technology and the reproducible data generated. However, because of severe spectral crowding, quantifying individual metabolites in body fluids such as serum and plasma remains a challenge. In this study, a method to automatically annotate and quantify a number of small metabolites in human serum and EDTA plasma is introduced. It combines the superior signal-to-noise ratio of the commonly applied CPMG and NOESY1D pulse sequences with the superior resolution of the 2D JRES experiment to construct a model that extracts the metabolite concentrations directly from the 1D spectra without tedious deconvolution. The performance of the method was assessed by comparing the calculated areas of the various glucose peaks with known clinical values, by comparing several peaks of the same metabolite (extracted versus non-extracted), and by comparing areas obtained from various NMR pulse sequences. Additionally, the models were tested on independent datasets. It was found that for many metabolites peaks could be assigned that show a consistent behavior, indicating a precise quantification. The same method should be applicable to other biofluids with a stable composition and pH, such as CSF fluid, cell extracts, and cell media.

Fast profiling of metabolite mixtures using chemometric analysis of a speeded-up 2D heteronuclear correlation NMR experiment

One-dimensional (1D) NMR spectra of mixtures of metabolites suffer from severe overlap of spectral resonances and hence recent research in NMR-based metabolomics focuses on using two-dimensional (2D) NMR experiments for metabolite fingerprinting.

NMR Techniques in Metabolomic Studies: A Quick Overview on Examples of Utilization

Metabolomics is a rapidly developing branch of science that concentrates on identifying biologically active molecules with potential biomarker properties. To define the best biomarkers for diseases, metabolomics uses both models (in vitro, animals) and human, as well as, various techniques such as mass spectroscopy, gas chromatography, liquid chromatography, infrared and UV-VIS spectroscopy and nuclear magnetic resonance. The last one takes advantage of the magnetic properties of certain nuclei, such as ¹H, ¹³C, ³¹P, ¹⁹F, especially their ability to absorb and emit energy, what is crucial for analyzing samples. Among many spectroscopic NMR techniques not only one-dimensional (1D) techniques are known, but for many years two-dimensional (2D, for example, COSY, DOSY, JRES, HETCORE, HMQS), three-dimensional (3D, DART-MS, HRMAS, HSQC, HMBC) and solid-state NMR have been used. In this paper, authors taking apart fundamental division of nuclear magnetic resonance techniques intend to shown their wide application in metabolomic studies, especially in identifying biomarkers.

Isotope-filtered nD NMR spectroscopy of complex mixtures to unravel the molecular structures of phenolic compounds in tagged soil organic matter

Unravelling structures of molecules contained in complex, chromatographically inseparable mixtures is a challenging task. Due to the number of overlapping resonances in NMR spectra of these mixtures, unambiguous chemical shift correlations attributable to individual molecules cannot be achieved and thus their structure determination is elusive by this technique. Placing a tag carrying an NMR active nucleus onto a subset of molecules enables (i) to eliminate signals from the non-tagged molecules, and (ii) to obtain a set of correlated chemical shifts and coupling constants belonging to a single molecular type. This approach provides an opportunity for structure determination without the need for compound separation. Focusing on the most abundant functional groups of natural organic matter molecules, the carboxyl and hydroxyl groups were converted into esters and ethers, respectively by introducing (13)CH3O groups. A set of (13)C-filtered nD NMR experiments was designed yielding structures/structural motives of tagged molecules. The relative sensitivity of these experiments was compared and a step-by-step guide how to use these experiments to analyse the structures of methylated phenolics is provided. The methods are illustrated using an operational fraction of soil organic matter, fulvic acid isolated from a Scottish peat bog. Analysis of 33 structures identified in this sample revealed a correlation between the position of the methoxy cross-peaks in the (1)H, (13)C HSQC spectra and the compound type. This information enables profiling of phenolic compounds in natural organic matter without the need to acquire a full set of experiments described here or access to high field cryoprobe NMR spectrometers.

Untargeted NMR-based methodology in the study of fruit metabolites

In this review, fundamental aspects of the untargeted NMR-based methodology applied to fruit characterization are described. The strategy to perform the structure elucidation of fruit metabolites is discussed with some examples of spectral assignments by 2D experiments. Primary ubiquitous metabolites as well as secondary species-specific metabolites, identified in different fruits using an untargeted ¹H-NMR approach, are summarized in a comprehensive way. Crucial aspects regarding the quantitative elaboration of spectral data are also discussed. The usefulness of the NMR-based metabolic profiling was highlighted using some results regarding quality, adulteration, varieties and geographical origin of fruits and fruit-derived products such as juices.

Innovative methods in soil phosphorus research: A review

Phosphorus (P) is an indispensable element for all life on Earth and, during the past decade, concerns about the future of its global supply have stimulated much research on soil P and method development. This review provides an overview of advanced state-of-the-art methods currently used in soil P research. These involve bulk and spatially resolved spectroscopic and spectrometric P speciation methods (1 and 2D NMR, IR, Raman, Q-TOF MS/MS, high resolution-MS, NanoSIMS, XRF, XPS, (µ)XAS) as well as methods for assessing soil P reactions (sorption isotherms, quantum-chemical modeling, microbial biomass P, enzymes activity, DGT, ³³P isotopic exchange, ¹⁸O isotope ratios). Required experimental set-ups and the potentials and limitations of individual methods present a guide for the selection of most suitable methods or combinations.

Structural characterization of dissolved organic matter: a review of current techniques for isolation and analysis

Natural dissolved organic matter (DOM) in aquatic systems plays many environmental roles: providing building blocks and energy for aquatic biota, acting as a sunscreen in surface water, and interacting with anthropogenic compounds to affect their ultimate fate in the environment. Such interactions are a function of DOM composition, which is difficult to ascertain due to its heterogeneity and the co-occurring matrix effects in most aquatic samples. This review focuses on current approaches to the chemical structural characterization of DOM, ranging from those applicable to bulk samples and in situ analyses (UV-visible spectrophotometry and fluorescence spectroscopy) through the concentration/isolation of DOM followed by the application of one or more analytical techniques, to the detailed separation and analysis of individual compounds or compound classes. Also provided is a brief overview of the main techniques used to characterize isolated DOM: mass spectrometry (MS), nuclear magnetic resonance mass spectrometry (NMR) and Fourier transform infrared spectroscopy (FTIR).

Profiling convoluted single-dimension proton NMR spectra: a Plackett-Burman approach for assessing quantification error of metabolites in complex mixtures with application to cell culture

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.

Automatic NMR-based identification of chemical reaction types in mixtures of co-occurring reactions

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the ¹H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the ¹H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of the molecules in the mixtures.

Characterization of heroin samples by 1H NMR and 2D DOSY 1H NMR

Twenty-four samples of heroin from different illicit drug seizures were analyzed using proton Nuclear Magnetic Resonance ((1)H NMR) and two-dimensional diffusion-ordered spectroscopy (2D DOSY) (1)H NMR. A careful assignment and quantification of (1)H signals enabled a comprehensive characterization of the substances present in the samples investigated: heroin, its main related impurities (6-acetylmorphine, acetylcodeine, morphine, noscapine and papaverine) and cutting agents (caffeine and acetaminophen in nearly all samples as well as lactose, lidocaine, mannitol, piracetam in one sample only), and hence to establish their spectral signatures. The good agreement between the amounts of heroin, noscapine, caffeine and acetaminophen determined by (1)H NMR and gas chromatography, the reference method in forensic laboratories, demonstrates the validity of the (1)H NMR technique. In this paper, 2D DOSY (1)H NMR offers a new approach for a whole characterization of the various components of these complex mixtures.

VIZR--an automated chemometric technique for metabolic profiling

A chemometric technique, visual interpretation of z-score ratios (VIZR), written in the open source code R, has been developed to identify metabolic differences between individual biosamples and a control group. To demonstrate the capabilities of VIZR, 49 urine samples were collected from healthy volunteers: 41 samples were collected randomly following a normal dietary routine and 7 test samples were collected after dietary supplementation with either ibuprofen or alcoholic beverages. An eighth test sample was prepared by 50% dilution of a control sample. Sample analysis was conducted by (1)H nuclear magnetic resonance (NMR) spectroscopy and the collected data were subjected to VIZR analysis, which successfully discriminated each of the 8 test samples from the 41 control samples. In addition, VIZR analysis revealed the NMR spectral regions responsible for the disparity between the individual test samples and the control group. The self-normalizing nature of the VIZR calculation provides a robust analysis independent of dilution effects, which is especially important in urine analyses. Potential applications of VIZR include high-throughput data analysis for toxicological profiling, disease diagnosis, and biomarker identification in any type of biosample for which a control dataset can be established. Although demonstrated herein for the statistical analysis of (1)H NMR data, the VIZR program is platform independent and could be applied to digitized metabolic datasets acquired using other techniques including hyphenated mass spectrometry measurements.

Applications of NMR metabolomics to the study of foodstuffs: truffle, kiwifruit, lettuce, and sea bass

In this review, four examples of the NMR metabolomic approach to foodstuff investigation are reported. Different types of foodstuff of different origin (namely truffle, kiwifruit, lettuce, and sea bass), with different metabolite composition, processing, and storage procedures have been chosen to demonstrate the versatility and potentiality of NMR in the foodstuff analysis. Fundamental aspects of NMR methodology such as sample preparation, metabolites extraction, quantitative elaboration of spectral data, and statistical analysis have been described. Metabolic profilings of aqueous and/or organic extracts as obtained by one- and two-dimensional NMR experiments have been reported together with the results obtained from their statistical elaboration. Discrimination between wild and farmed sea bass and between genetically modified and wild lettuces as well as changes in the kiwifruit metabolic profiles monitored over the season have been investigated. For each foodstuff, some complementary findings provided by other analytical methods are also described to underline the importance of different analytical approaches to explore specific aspects related to foodstuff.

The application of high resolution diffusion NMR to the analysis of manuka honey

The application of DOSY (Diffusion Ordered SpectroscopY) NMR as a technique for the virtual separation of key components of manuka honey and the implications for future discriminatory analysis of honey types is reported for the first time. The scope and the limitations of DOSY NMR are considered using the recently conceived DOSY Tool Box processing software and preliminary anti-bacterial data for the different honey types is reported.