A field-invariant method for quantitative analysis with benchtop NMR

Polarization transfer methods for quantitative analysis of flowing mixtures with benchtop 13C NMR spectroscopy

Benchtop NMR spectroscopy is attractive for process monitoring; however, there are still drawbacks that often hamper its use, namely, the comparatively low spectral resolution in 1H NMR, as well as the low signal intensities and problems with the premagnetization of flowing samples in 13C NMR. We show here that all these problems can be overcome by using 1H-13C polarization transfer methods. Two ternary test mixtures (one with overlapping peaks in the 1H NMR spectrum and one with well-separated peaks, which was used as a reference) were studied with a 1 T benchtop NMR spectrometer using the polarization transfer sequence PENDANT (polarization enhancement that is nurtured during attached nucleus testing). The mixtures were analyzed quantitatively in stationary as well as in flow experiments by PENDANT enhanced 13C NMR experiments, and the results were compared with those from the gravimetric sample preparation and from standard 1H and 13C NMR spectroscopy. Furthermore, as a proxy for a process monitoring application, continuous dilution experiments were carried out, and the composition of the mixture was monitored in a flow setup by 13C NMR benchtop spectroscopy with PENDANT. The results demonstrate the high potential of polarization transfer methods for applications in quantitative process analysis with benchtop NMR instruments, in particular with flowing samples.

Detection of honey adulteration using benchtop 1H NMR spectroscopy

High magnetic field NMR spectroscopy featuring the use of superconducting magnets is a powerful analytical technique for the detection of honey adulteration. Such high field NMR systems are, however, typically housed in specialised laboratories, require cryogenic coolants, and necessitate specialist training to operate. Benchtop NMR spectrometers featuring permanent magnets are, by comparison, significantly cheaper, more mobile and can be operated with minimal expertise. The lower magnetic fields used in such systems, however, result in limited spectral resolution, which diminishes their ability to perform quantitative composition analysis. These limitations may be overcome by implementing a recently developed field-invariant model-based fitting method which is defined by the underlying quantum mechanical properties of the nuclear spin system; this method is applied here to quantify the sugar composition of honey using benchtop ¹H NMR (43 MHz) spectroscopy. The detection of adulteration of 26 honey samples with brown rice syrup is quantitatively demonstrated to a minimum adulterant concentration of 5 wt%. Honey adulteration with corn syrup, glucose syrup and wheat syrup was also quantitatively detected using this approach. Our NMR detection of adulteration was shown to be invariant with time over 60 days of storage.

Computational simulation of 1 H NMR profiles of complex biofluid analyte mixtures at differential operating frequencies: Applications to low-field benchtop spectra

Estimations of accurate and reliable NMR chemical shift values, coupling patterns and constants within a reasonable timeframe remain significantly challenging, and the unavailability of reliable software strategies for the prediction of low-field (e.g., 60 MHz) spectra from those acquired at higher operating frequencies hampers their direct comparison. Hence, this study explored the applications of accessible software options for predicting these parameters in the ¹ H NMR profiles of analytes as a function of magnetic field strength; this was performed for individual analytes and also for complex biofluid matrices featured in metabolomics investigations. For this purpose, results from the very first successful experimental acquisition and simulation of the ¹ H NMR profiles of intact human salivary supernatant samples on a 60 MHz benchtop spectrometer were evaluated. Using salivary metabolite concentrations determined at 400 MHz, it was demonstrated that simulation of the low-field spectra of five biomolecules with the most prominent ¹ H resonances detectable allowed multiple component fits to be applied to experimental spectra. Hence, these salivary ¹ H NMR profiles could be successfully predicted throughout the 45-600 MHz operating frequency range. With the exception of propionate resonance multiplets, which revealed more complex coupling patterns at low field and required more astute computational and fitting options, valuable quantitative metabolomics data on salivary acetate, formate, methanol and glycine could be attained from low-field spectrometres. These studies are both timely and pertinent in view of the recent advancement of low-field benchtop NMR facilities for diagnostically significant biomarker tracking in biofluids. Experiments performed with added ammonium chloride to facilitate the release of salivary metabolites from biopolymer binding sites provided evidence that a small but nevertheless significant proportion of propionate, but not lactate, was bound to such sites, an observation of much relevance to biomolecule quantification in salivary metabolomics investigations.

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.

Predicting Mandarin Fruit Acceptability: From High-Field to Benchtop NMR Spectroscopy

Recent advances in nuclear magnetic resonance (NMR) have led to the development of low-field benchtop NMR systems with improved sensitivity and resolution suitable for use in research and quality-control laboratories. Compared to their high-resolution counterparts, their lower purchase and running costs make them a good alternative for routine use. In this article, we show the adaptation of a method for predicting the consumer acceptability of mandarins, originally reported using a high-field 400 MHz NMR spectrometer, to benchtop 60 MHz NMR systems. Our findings reveal that both instruments yield comparable results regarding sugar and citric acid levels, leading to the development of virtually identical predictive linear models. However, the lower cost of benchtop NMR systems would allow cultivators to implement this chemometric-based method as an additional tool for the selection of new cultivars.

Quantification of mixtures of analogues of illicit substances by benchtop NMR spectroscopy

This paper investigates the possibility of using benchtop NMR spectroscopy for quantification of illicit drugs (methamphetamine) in binary and ternary mixtures with impurities and cutting agents (N-isopropylbenzylamine, phenethylamine and dimethylsulfone). To avoid handling regulated substances, methamphetamine in our experiments is substituted with amino-2-propanol, which has similar functional groups and chemical structure to methamphetamine and hence a related NMR spectrum. Binary and ternary mixtures at concentrations from 30 mmol/L up to 500 mmol/L for each of these species were measured using a 60 MHz benchtop spectrometer. The spectra were analysed using both integration and a model-based algorithm that relies on a full quantum mechanical description of the studied spin systems. Both techniques were able to quantify the composition of the mixtures. The root mean squared error in the measured concentration using the model-based algorithm was < 10 mmol/L, whereas the error using integration was typically > 20 mmol/L. Thus, we conclude benchtop NMR is viable for quantitative measurements of mixtures of illicit substances, particularly when coupled with a quantum mechanical model for the analysis.

Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. "Chemputer" which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.

Quantitative analysis of wine and other fermented beverages with benchtop NMR

We present a fully automated approach for quantitative compositional analysis of fermented beverages using benchtop nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy, renowned for its applications in chemical structure elucidation, is gaining attention as a quantitative analytical technique due to its inherent linearity and the ability to obtain comprehensive quantitative information with a single simple experiment. Recently developed benchtop NMR spectrometers offer the quantitative capabilities of NMR to a wide range of potential users in industry, but their applicability has been limited by the reduced effective spectral resolution and the need for more advanced data processing. We address this problem with a model-based algorithm that hinges on the well-understood description of quantum mechanical phenomena in NMR spectroscopy. We demonstrate the effectiveness of our approach on a challenging problem of analysing the composition of wine and related fermented beverages - an important potential niche application of quantitative NMR. We successfully quantify more than 15 major components in the wine matrix and enable the quantification of species whose analysis is generally not possible with established methods. The average discrepancy of the obtained concentrations, when compared to the traditional methods of analysis, usually does not exceed 10% and is lower for the most abundant species (e.g. below 5% for ethanol).

Quantitative frother analysis on coal mine process water with a benchtop NMR spectrometer

This paper investigates the use of a benchtop NMR for quantification of a commonly used frothing agent, methyl isobutyl carbinol (MIBC) in the process water of a coal preparation facility. Solid phase extraction is used to increase the concentration of MIBC in the sample so that it is quantifiable by a benchtop NMR. A polymeric, reversed phase column with methanol as solvent gives a MIBC recovery rate of 67 ± 4% as determined using 400 MHz high-field NMR. The recovery rate consistently falls in the above narrow range even in the presence of diesel and inorganic electrolytes which are likely present as background chemicals in the process water. Using the average MIBC recovery rate, we use a quantum mechanical model to analyse the intensity of MIBC in the benchtop spectra. The quantum mechanical modelling algorithm effectively excludes the effect of the diesel on the measured NMR signal. The quantification error when the inlet concentration of MIBC is between 1 and 12 mg/L (1.2-15 ppm v/v), is within 0.5 mg/L (0.6 ppm v/v).

Recent Applications of Benchtop Nuclear Magnetic Resonance Spectroscopy

Benchtop nuclear magnetic resonance (NMR) spectroscopy uses small permanent magnets to generate magnetic fields and therefore offers the advantages of operational simplicity and reasonable cost, presenting a viable alternative to high-field NMR spectroscopy. In particular, the use of benchtop NMR spectroscopy for rapid in-field analysis, e.g., for quality control or forensic science purposes, has attracted considerable attention. As benchtop NMR spectrometers are sufficiently compact to be operated in a fume hood, they can be efficiently used for real-time reaction and process monitoring. This review introduces the recent applications of benchtop NMR spectroscopy in diverse fields, including food science, pharmaceuticals, process and reaction monitoring, metabolomics, and polymer materials.

A comparison of non-uniform sampling and model-based analysis of NMR spectra for reaction monitoring

Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex reaction mixtures are studied, NMR spectra often suffer from low resolution and overlapping peaks, which places high demands on the method used to acquire or to analyse the NMR spectra. This work presents two NMR methods that help overcome these challenges: 2D non-uniform sampling (NUS) and a recently proposed model-based fitting approach for the analysis of 1D NMR spectra. We use the reaction of glycerol with acetic acid as it produces five reaction products that are all chemically similar and, hence, challenging to distinguish. The reaction was measured on a high-field 400 MHz NMR spectrometer with a 2D NUS-heteronuclear single quantum coherence (HSQC) and a conventional 1D ¹ H NMR sequence. We show that comparable results can be obtained using both 2D and 1D methods, if the 2D volume integrals of the 2D NUS-HSQC NMR spectra are calibrated. Further, we monitor the same reaction on a low-field 43 MHz benchtop NMR spectrometer and analyse the acquired 1D ¹ H NMR spectra with the model-based approach and with partial least-squares regression (PLS-R), both trained using a single, calibrated data set. Both methods achieve results that are in good quantitative agreement with the high-field data. However, the model-based method was found to be less sensitive to the training data set used than PLS-R and, hence, was more robust when the reaction conditions differed from that of the training data.

Real-Time Reaction Monitoring with In Operando Flow NMR and FTIR Spectroscopy: Reaction Mechanism of Benzoxazole Synthesis

In operando observation of reaction intermediates is crucial for unraveling reaction mechanisms. To address the sensitivity limitations of commercial ReactIR, a flow cell was integrated with a Fourier transform infrared (FTIR) spectrometer yielding a "flow FTIR" device coupled with an NMR spectrometer for the elucidation of reaction mechanisms. The former device detects the low-intensity IR peaks of reaction intermediates by adjusting the path length of the FTIR sample cell, whereas the flow NMR allows the quantitative analysis of reaction species, thus offsetting the limitations of IR spectroscopy resulting from different absorption coefficients of the normal modes. Using the flow NMR and FTIR device, the controversial mechanism of benzoxazole synthesis was conclusively determined by spectroscopic evaluation of the reaction intermediates. This system enabled the accurate acquisition of previously elusive kinetic data, such as the reaction time and rate-determining step. The implementation of reaction flow cells into NMR and FTIR systems could be widely applied to study various reaction mechanisms, including dangerous and harsh reactions, thus avoiding contact with potentially harmful reaction intermediates.

Low-field benchtop NMR spectroscopy: status and prospects in natural product analysis†

INTRODUCTION

Since a couple of years, low-field (LF) nuclear magnetic resonance (NMR) spectrometers (40-100 MHz) have re-entered the market. They are used for various purposes including analyses of natural products. Similar to high-field instruments (300-1200 MHz), modern LF instruments can measure multiple nuclei and record two-dimensional (2D) NMR spectra.

OBJECTIVE

To review the commercial availability as well as applications, advantages, limitations, and prospects of LF-NMR spectrometers for the purpose of natural products analysis.

METHOD

Commercial LF instruments were compared. A literature search was performed for articles using and discussing modern LF-NMR. Next, the articles relevant to natural products were read and summarised.

RESULTS

Seventy articles were reviewed. Most appeared in 2018 and 2019. Low costs and ease of operation are most often mentioned as reasons for using LF-NMR.

CONCLUSION

As the spectral resolution of LF instruments is limited, they are not used for structure elucidation of new natural products but rather applied for quality control (QC), forensics, food and health research, process control and teaching. Chemometric data handling is valuable. LF-NMR is a rapidly developing niche and new instruments keep being introduced.

Bayesian approach for automated quantitative analysis of benchtop NMR data

Low-cost, user-friendly benchtop NMR instruments are often touted as a "one-click" solution for data acquisition, however insufficient peak dispersion in their spectra often reduces the accuracy of quantification and requires user expertise with sophisticated processing tools. Our work aims to facilitate the wide acceptance of benchtop NMR instruments as a viable and effective substitute for cryogenic magnets. We propose an algorithmic approach that completely automates the routine analysis of sets of samples with similar compositions - the problem that often underlies many industrial applications concerned with reaction and process monitoring and quality control. Our solution is rooted in the idea of parametric modelling formulated in terms of Bayesian statistics, which effectively incorporates prior knowledge about the studied system (such as concentration-dependent chemical shift changes) that is usually available in industrial applications. Furthermore, the use of quantum mechanical models for chemical species makes our approach invariant to the spectrometer field strength - a necessary prerequisite for the successful analysis of benchtop data. We demonstrate the performance of our method with two representative sets of samples: mixtures of alcohols and acetates, and aqueous mixtures of biologically relevant species. In these examples, our fully automated analysis of benchtop spectra achieves average errors in concentrations of 0.01 mol/mol and 0.02 mol/mol respectively. Our method is competitive with the traditional processing approaches of well resolved high-field data and has the potential to bring the benefits of NMR even to a small chemistry laboratory.

Quantum mechanical NMR full spin analysis in pharmaceutical identity testing and quality control

Issues related to pharmaceutical quality are arising at an alarming rate. Pharmaceutical quality concerns both the Active Pharmaceutical Ingredients (APIs) and the Finished Drug Product/ Formulation. Recently, there has been a significant increase in the number of reports of harmful impurities in marketed drug formulations. Impurities range from solvents, reactants, adulterants, and catalysts to synthetic byproducts. Quality concerns in commercial preparations may also arise due to shelf life stability. Furthermore, a number of falsified and substandard drug cases have been reported. Most of the techniques which are currently in place can, at best, detect the impurities, but cannot identify them unless they are already known and can be compared to a standard. On the other hand, ¹H NMR spectroscopy detects all the hydrogen containing species, typically provides information to elucidate structures partially or even completely, and through its absolute quantitative capabilities even can detect the presence hydrogen-free species indirectly. The structural properties that produce ¹H NMR signals as characteristic representations of a given molecule are the chemical shifts (δ in ppm) and coupling constants (J in Hz). Along with the line widths (ω_1/2 in Hz), these parameters are bound to both the molecule and the NMR experimental conditions by quantum mechanical (QM) principles. This means that the ¹H NMR spectra of APIs can be precisely calculated and compared to the experimental data. This review explains how ¹H NMR spectroscopy coupled with Full Spin Analysis can contribute towards the quality control of pharmaceuticals by improving structural dereplication and achieving simultaneous quantification of both APIs and their contaminants.

Feasibility of precise and reliable glucose quantification in human whole blood samples by 1 tesla benchtop NMR

The standard procedure for blood glucose measurements is enzymatic testing. This method is cheap, but requires small samples of open blood with direct contact to the test medium. In principle, NMR provides non-contact analysis of body fluids, but high-field spectrometers are expensive and cannot be easily utilized under clinical conditions. Low-field NMR systems with permanent magnets are becoming increasingly smaller and more affordable. The studies presented here aim at exploring the capabilities of low-field NMR for measuring glucose concentrations in whole blood. For this purpose, a modern 1 T benchtop NMR spectrometer was used. Challenges arise from broad spectral lines, the glucose peak locations close to the water signal, low SNR and the interference with signals from other blood components. Whole blood as a sample comprises even more boundary conditions: crucial for reliable results are avoiding the separation of plasma and cells by gravitation and reliable reference values. First, the accuracy of glucose levels measured by NMR was tested using aqueous glucose solutions and commercially available bovine plasma. Then, 117 blood samples from oral glucose tolerance testing were measured with minimal preparation by simple pulse-acquire NMR experiments. The analysis itself is the key to achieve high precision, so several approaches were investigated: peak integration, orthogonal projection to latent structure analysis and support vector machine regression. Correlations between results from the NMR spectra and the routine laboratory automated analyzer revealed an RMSE of 7.90 mg/dL for the best model. 91.5% of the model output lies within the limits of the German Medical Association guidelines, which require the glucose measurement to be within 11% of the reference method. It is concluded that spectral quantification of glucose in whole blood samples by high-quality NMR spectrometers operating at 1 T is feasible with sufficient accuracy.

Improving the accuracy of model-based quantitative nuclear magnetic resonance

Low spectral resolution and extensive peak overlap are the common challenges that preclude quantitative analysis of nuclear magnetic resonance (NMR) data with the established peak integration method. While numerous model-based approaches overcome these obstacles and enable quantification, they intrinsically rely on rigid assumptions about functional forms for peaks, which are often insufficient to account for all unforeseen imperfections in experimental data. Indeed, even in spectra with well-separated peaks whose integration is possible, model-based methods often achieve suboptimal results, which in turn raises the question of their validity for more challenging datasets. We address this problem with a simple model adjustment procedure, which draws its inspiration directly from the peak integration approach that is almost invariant to lineshape deviations. Specifically, we assume that the number of mixture components along with their ideal spectral responses are known; we then aim to recover all useful signals left in the residual after model fitting and use it to adjust the intensity estimates of modelled peaks. We propose an alternative objective function, which we found particularly effective for correcting imperfect phasing of the data - a critical step in the processing pipeline. Application of our method to the analysis of experimental data shows the accuracy improvement of 20 %-40 % compared to the simple least-squares model fitting.