This, combined with the low natural abundance of 13 C, means that it is much more difficult to observe carbon signals: more sample is required, and often the data from hundreds of scans must be averaged in order to bring the signal-to-noise ratio down to acceptable levels.
This is because the signals for some types of carbons are inherently weaker than for other types — peaks corresponding to carbonyl carbons, for example, are much smaller than those for methyl or methylene CH 2 peaks. Peak integration is generally not useful in 13 C-NMR spectroscopy, except when investigating molecules that have been enriched with 13 C isotope see section 5.
The resonance frequencies of 13 C nuclei are lower than those of protons in the same applied field - in a 7. This is fortunate, as it allows us to look at 13 C signals using a completely separate 'window' of radio frequencies. Chemical shifts for 13 C nuclei in organic molecules are spread out over a much wider range than for protons — up to ppm for 13 C compared to 12 ppm for protons see Table 3 for a list of typical 13 C-NMR chemical shifts. This is also fortunate, because it means that the signal from each carbon in a compound can almost always be seen as a distinct peak, without the overlapping that often plagues 1 H-NMR spectra.
The chemical shift of a 13 C nucleus is influenced by essentially the same factors that influence a proton's chemical shift: bonds to electronegative atoms and diamagnetic anisotropy effects tend to shift signals downfield higher resonance frequency. In addition, sp 2 hybridization results in a large downfield shift.
The 13 C-NMR signals for carbonyl carbons are generally the furthest downfield ppm , due to both sp 2 hybridization and to the double bond to oxygen. How many sets of non-equivalent carbons are there in each of the molecules shown in exercise 5. Because of the low natural abundance of 13 C nuclei, it is very unlikely to find two 13 C atoms near each other in the same molecule, and thus we do not see spin-spin coupling between neighboring carbons in a 13 C-NMR spectrum. There is, however, heteronuclear coupling between 13 C carbons and the hydrogens to which they are bound.
Carbon-proton coupling constants are very large, on the order of — Hz. For clarity, chemists generally use a technique called broadband decoupling , which essentially 'turns off' C-H coupling, resulting in a spectrum in which all carbon signals are singlets. Below is the proton-decoupled 13 C-NMR spectrum of ethyl acetate, showing the expected four signals, one for each of the carbons.
While broadband decoupling results in a much simpler spectrum, useful information about the presence of neighboring protons is lost. For example, a DEPT experiment tells us that the signal at ppm in the ethyl acetate spectrum is a quaternary carbon no hydrogens bound, in this case a carbonyl carbon , that the 61 ppm signal is from a methylene CH 2 carbon, and that the 21 ppm and 14 ppm signals are both methyl CH 3 carbons. These quaternary 13C nuclei may require minutes to relax and this dramatically increases the time required for NMR experiments.
Because running such a lengthy 13C NMR experiment is not practical, it is normal practice to run proton-decoupled 13C NMR using standard conditions and to use the resulting spectra for pattern recognition but not for quantitation. The use of a library of NMR spectra in the analysis of a mixture of natural products requires reproducibility of both chemical shift and peak intensity. Since samples of natural products are often dissolved in either dimethyl sulfoxide or methanol, confining both library and sample data to these two solvents can easily ameliorate the confounding effects of the solvent on the observed chemical shifts.
Modern NMR spectrometers are normally equipped with temperature control so the measurements can be made at a given temperature, which also avoids the temperature dependence of NMR chemical shifts, thus eliminating this problem. The reproducibility of peak intensities, however, requires additional considerations. The robustness of current NMR instrumentation is evident in successful indirect detection methods during which resonances from 1H nuclei bound to 12C are separated from those attached to 13C [ 28 ].
Indirect detection is possible only if the scans or transients are highly reproducible such that these can be added to extract the desired resonances and remove completely the unwanted signals. However, this robustness only entails the reproducibility of an NMR experiment from one transient to the next.
It does not address the reproducibility of NMR experiments among different laboratories. Thus, there is a need to standardize both NMR acquisition conditions and processing parameters. The intensity of an NMR peak depends on the duration of the pulse. Peak intensities are at a maximum with this pulse. For an NMR spectrum to be quantitative, the relative, not the absolute, peak intensities are sufficient. However, the extent of the pulse determines how much time is required for relaxation between transients.
For signal averaging to be effective, one still needs to make sure that the spins have reached equilibrium before applying the next pulse so as to avoid saturation, which leads to loss of signal [ 29 ]. The time required between pulses can be reduced by using a smaller flip angle. This reduces the peak intensity for each scan but reduces the delay time required between scans enabling the acquisition of more scans for the same amount of time.
Another parameter that can affect the appearance of an NMR spectrum is acquisition time, which determines spectral resolution. What is directly acquired from an NMR experiment is a free induction decay FID , which still needs to be processed to produce the frequency spectrum.
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During processing, apodization, zero-filling, and baseline and phase corrections are normally applied. All of these can significantly alter the integrated areas under the peaks of an NMR spectrum. For processing, careful manual phase and baseline corrections are recommended since automated features of popular NMR processing software packages are unreliable. With 13C, relaxation times are appreciably longer so relaxation agents such as paramagnetic compounds have been used as in the earlier work on petroleum distillates [ 31 ].
For the unbiased profiling of natural products extracts, one needs to consider the problems of sensitivity and dynamic range. A natural product extract typically contains major and minor components. Oftentimes, in order to detect minor components, it is necessary to employ separation techniques, such as successive fractionation and chromatography which have the effect of increasing sensitivity to minor constituents and improving dynamic range.
However, this introduces bias. Limits of detection and quantification are often given in terms of signal to noise ratios. The International Conference on Harmonization of Technical Requirements recommends a signal to noise ratio of 3 and 10 for the detection limit and quantification limit, respectively ICH Expert Working Group, Considering both magnetogyric ratio and natural abundance, one can therefore estimate that the detection limit for 13C will be orders of magnitude higher than that of 1H. In an analysis of diesel fuel, detection limits of 0.
Another consideration is dynamic range.
This can be alleviated by suppressing solvent resonances, but this introduces the problem of reproducibility between runs and remains a problem for components which have signals near the solvent. For the application of 1H NMR for pattern recognition, the use of the magnitude spectrum has been suggested [ 34 ]. The standard 1H NMR spectrum utilizes the phase-corrected real component of the Fourier transform of the free induction decay FID , discarding the imaginary component. This yields the absorption spectrum which is useful for normal qualitative analysis due to its good peak resolution.
However, this procedure sacrifices reproducibility. The use of the magnitude spectrum, which utilizes the absolute value of both the real and imaginary components of the FID improves the reproducibility of the spectra thereby improving its accuracy for pattern recognition. This method is applicable to one-dimensional 1H NMR. Peak integrals in an NMR spectrum unfortunately are also sensitive to data processing. Apodization, zero-filling, phase and baseline corrections, and the integration itself can affect the signal-to-noise ratio of an NMR spectrum.
Thus, the current limit in the sensitivity of NMR-based metabolomics is not due to magnetic field strength, but is due to the current data processing methodology which uses spectral binning alternatively called bucketing and PCA. The usual bin size for 1H NMR is 0. A smaller bin size can be used if the variability in the chemical shift can be minimized.
Another problem observed is the effect of different solvent see below to move the position of chemical shifts, which will make identification using database comparisons difficult [ 35 ]. Because plant samples contain a wide variety of compounds with corresponding differences in polarity, the solvent used for extraction and the NMR analysis is very important. The solvent system must balance the ability to perform a comprehensive extraction with solvent complexity and reproducibility. In particular, multi-component solvent systems are prone to variation, and if there is a wide difference in vapor pressures boiling points , the solvent composition may change if care is not taken.
Acetone and acetonitrile are effective solvents but their use is limited by their low boiling points. The use of methanol-D 4 in combination with deuterated water have been reported. By using these deuterated solvents, the extracts can be measured directly after extraction without need for evaporation and reconstitution. However, use of water will introduce a strong water peak in the 1H NMR spectrum that must be irradiated.
This becomes a source of variability around the water peak across different operators and instruments. NMR is capable of providing simultaneous access to both qualitative chemical structure and quantitative information. Fan pointed out that comprehensive metabolite profiling of complex food products can be done using one- and two-dimensional NMR analysis [ 37 ].
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However, it is in the use of NMR combined with chemometric methods that the extraordinary potential of both the qualitative and quantitative applications have been realized [ 38 ]. In view of its ability to be used as an exhaustive molecular fingerprinting technique, 1H NMR has been found to be a suitable method for the identification, quality control, and fraud detection of essential oils, a function normally reserved for GC-MS [ 39 ]. NMR fingerprinting involves obtaining 1H or 13C spectra of whole solvent extracts under standardized conditions and ignoring, at least initially, the assignment of peaks.
Multivariate statistical methods, such as PCA, are used to compare spectra from the samples to identify clusters so that inferences can be drawn about the classification of individual plant samples. The identities of metabolites responsible for differences between groups can be investigated from loadings plots generated by PCA [ 40 ]. The earliest use of 1D HNMR for the profiling of complex extracts had the objective of monitoring the major components of exudates of plants, such as its root system.
The relative increase or decrease of primary metabolites, such as lactate, ethanol, and certain amino acids, could be observed [ 41 ]. However, its application to natural product compounds is more challenging due to their more complex structures and lower concentrations.
The number of such studies is limited because of the presence of overlapping signals and the need for high magnetic fields. Spectral data were reduced by binning using 0. Chemometric methods, in particular PCA and partial least squares PLS , enabled the identification of compounds that contributed to differences between berries, due to the sugars glucose, fructose, and sucrose , organic acids tartaric, malic, citric, and succinic acids , and amino acids proline, arginine, gamma-aminobutyric acid, valine, alanine, leucine, and isoleucine [ 43 ].
PCA metabolomics profiling revealed a separation between the high- and the low-quality green teas. The taste marker compounds contributing to the discrimination of tea quality were identified from 1D HNMR as caffeine, theanine, epigallocatechingallate, epigallocatechin, epicatechingallate, and epicatechin [ 44 ].
The use of the magnitude spectrum showed good reproducibility in the analysis of 4 diverse natural product samples 12 tea extracts, 8 liquor samples, 9 hops extracts, and 25 cannabis extracts using 1D HNMR at MHz and various statistical tools [ 45 ].
The highest quality Chinese tea showed higher levels of theanine, gallic acid, caffeine, epigallocatechin gallate, and epicatechin gallate and lower levels of epigallocatechin when compared with other teas. These new markers were suggested to be useful for the authentication of tea [ 47 ]. In another study, the effects of climatic conditions temperature, sun exposure, and precipitation and plucking positions on the tea plant were investigated using 1D HNMR profiling combined with multivariate pattern recognition methods.
The variations in the composition of specific tea compounds were obtained [ 48 , 49 ]. The following compounds were identified: theanine, alanine, threonine, succinic acid, aspartic acid, lactic acid, caffeine, and derivatives of epigallocatechin [ 50 ]. The same strategy was used for chemotaxonomic classification of 11 South American Ilex species. The combined use of 1D- and 2D-NMR and chemometric analysis enabled unambiguous chemotaxonomic discrimination of the Ilex species and varieties [ 51 ].
In this way, the major metabolites—glutamine, arginine, sucrose, malate, and myo-inositol—were identified as chemical markers for quality assurance [ 52 ]. In a study on Indian ginseng, Withania somnifera L. PCA and hierarchical cluster analysis HCA were performed to group samples which were collected from six different regions of India. The ratio of two withanolides was found to be a key discriminating feature of W. This NMR-based metabolomic strategy was applied to analyze seven spices used in traditional Mediterranean cuisine and to detect metabolic changes over different seasons.
Both primary and secondary metabolites were identified and quantified.
The major secondary metabolites identified were polyphenols, including flavonoids apigenin, quercetin, and kaempferol derivatives and phenylpropanoid derivatives chlorogenic and rosmarinic acid. The application of NMR-based metabolomics method in plant breeding has been reported. This approach was able to successfully profile foliar metabolites without inoculation tests which would have required a significant amount of time and effort. In this study, field-grown leaves which had different levels of resistance were collected from 12 sugar beet genotypes at 4 growth time points.
PCA of the NMR data revealed clear differences among the growth stages, in terms of the content of sugar, glycine betaine, and choline [ 55 ]. However, 13C NMR spectra are simpler, have less severe problems with overlapping peaks, are more comparable across different magnetic field strengths, and are less susceptible to solvent effects.
In addition, the singlet nature of 13C NMR signals makes it easier to determine the identity of individual compounds in a mixture. It was able to simultaneously detect specific unsaturated acyl chains according to their positions on the glycerol backbone through carboxylic, olefinic, and methylene carbons [ 56 ]. However, at this time, its use was not specifically identified as a profiling method. Later, 13C NMR was applied to the fingerprinting of lipids for the authentication of marine and fish oils.
In this work, 13C NMR was combined with chemometrics and database information and compared with relevant authentic samples [ 57 ].
5.6: 13C-NMR spectroscopy
In one application, this method was used to discriminate between farmed and wild Atlantic salmon Salmo salar , L. The whole extract was first separated into fractions of simpler composition, which were then analyzed by 13C NMR. The 13C spectra of all the fractions were aligned and subjected to pattern recognition by HCA.
This yielded correlations among 13C signals within each fraction which were visualized as chemical shift clusters, which were assigned to specific compounds in a 13C database. Chemical profiling and standardization of the methanol extract from the leaves of Vitex negundo , L. This was able to successfully differentiate samples that were deliberately allowed to degrade.
The multivariate control chart, which is analogous to the analytical control chart method, classified samples whose quality exceeded the upper control limit UCL. The plant samples were also analyzed by quantitative thin layer chromatography qTLC using agnuside as marker compound. Comparison of the univariate qTLC results with the multivariate control chart showed poor correspondence: some samples that gave high agnuside values exceeded the UCL while others that had low agnuside values were below the UCL.
This means that a univariate analysis of a plant sample using a marker compound does not adequately represent the overall plant profile [ 61 ]. This approach is being enhanced by availability of 13C NMR databases and predictive software which list compounds that are most likely to be present in the extract. The combined use of high-resolution 1H and 13C NMR analysis has the potential to reveal more details that are not available using only one technique. This combined approach was employed to detect and quantify a wide range of triacylglycerols and their component fatty acids in marine cod liver oil supplements.
The combination of 1H and 13C spectra permitted the detailed analysis of components, including sn-1 monoacylglycerols, sn-1,2- and sn-1,3-diacylglycerol adducts, and other minor components, such as trans-fatty acids, free glycerol and cholesterol, and added vitamins A and E and synthetic compounds, such as ethyl docosahexaenoate or eicosopentaenoate. The use of 1H and 13C NMR for the profiling of natural products extracts is a rapidly growing branch of metabolomics.
It will further accelerate with the increasing use of NMR in quality management, the growth of NMR databases, the development of portable and benchtop NMR instrumentation, and advances in the use of statistical analysis. Despite its considerable potential, the routine application of this method is limited by the lack of expertise to run sophisticated NMR experiments and the lack of computational tools for NMR spectral deconvolution, in particular of 1H spectra [ 64 ].
NMR has been used for the monitoring and quality management of foods, beverages, cosmetics, and pharmaceuticals. The same can be done for the profiling of natural products. In order to ensure reproducibility and reliability and to minimize experimental artifacts, the entire process—from sample collection and storage, extraction, NMR measurement and data processing, and statistical analysis—should be optimized and standardized [ 65 , 66 ].
The NMR solvent is of particular importance because of its influence on the chemical shift positions of protons in phenolic compounds [ 67 ] and other solvent effects.
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It has been claimed that periodic calibration can deliver accuracy as high as The various experimental parameters are listed below: Sample preparation: homogeneity of sample, extraction solvent, extraction method, and NMR solvent. Acquisition parameters: temperature, acquisition time, pulse angle, number of data points, time delay relaxation time , and electronic amplification. NMR data processing: smoothing, phase correction, baseline correction, and signal integration. The usefulness of NMR databases is premised on the reproducibility of the NMR experiment—starting with sample preparation, NMR acquisition, and processing—across different laboratories.
It is important to avoid conditions that alter the position of chemical shifts, which will make identification using database comparisons difficult. Open-access and user-contributed 1H and 13C NMR spectral databases have a high potential as a useful tool for natural products researchers provided that sample preparation, instrumentation, and acquisition parameters are standardized. For sample preparation, only selected NMR solvents should be used. Acquisition and processing parameters should be standardized. To further promote participation by researchers, the entire process, from data acquisition, conversion of vendor-specific raw data files, and data deposition have to be simplified and standardized [ 69 ].
NMR is usually considered to be an expensive analytical technique which is used for research purposes only. However, for NMR to become more useful for the natural products industry where many of the companies are small to medium in size, more affordable instrumentation is needed. Although these are limited in capability and reproducibility compared with a full laboratory NMR instrument, they can be used in the field or production site where cryogenic liquids and stable power are not available.
Because there is a demand for such instrumentation for other purposes, such as forensic investigation, detection of explosives, and medical diagnostics, their development is certain to accelerate. This will expand the use of NMR for the profiling of natural products.
Basic 1H- and 13C-NMR Spectroscopy - 1st Edition
Although the use of NMR in the analysis of biological extracts was already being done in the s, it was the application of statistical methods that enabled researchers to make use the large amount of NMR data to find patterns and correlations. The first step usually involves the simplification of large NMR data sets to find relationships, groupings, or dependencies using PCA.
Second, the groups can be classified with or without a training set which has known information or characteristics against which other sample sets are compared. For quantitative analysis of constituents, in particular for strongly overlapping peaks, principal component regression PCR or PLS regression can be used [ 71 ]. Although these statistical techniques are now commonly used, new ones continue to be developed and reported. One of the most exciting areas of development is the use of statistical methods to correlate NMR signals with biological activity.
Since the NMR signals can be related to specific compounds, this in effect allows one to correlate specific compounds with biological activity. Although it has to be emphasized that correlation is not proof of biological activity, this strategy nevertheless allows one to shortcut the process of discovering bioactive compounds in a complex natural product mixture. This also allows one to detect multiple active compounds. A major enabler for the use of NMR for metabolomic studies is the application of various statistical techniques which are able to find patterns and correlations in the large NMR data sets.
The continued expansion of the use of NMR for the metabolomic profiling of natural product extracts will likely depend on the further development of statistical methods and the availability of NMR databases for both 1H and 13C nuclei. It is likely that more compounds will be identified as techniques are improved.
An NMR spectrum is quantitative.