CHAPTER THREE

Quantitative NMR Studies ofMultiple Compound MixturesX. Li*,†,{, K. Hu*,{*State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany,Chinese Academy of Sciences, Kunming, PR China†University of Chinese Academy of Sciences, Beijing, PR China{Yunnan Key Laboratory of Natural Medicinal Chemistry, Kunming Institute of Botany, Chinese Academyof Sciences, Kunming, PR China

Contents

1. Introduction 872. Theoretic Background and Technical Keynotes of Quantitative NMR 89

2.1 qNMR Data Acquisition 892.2 qNMR Data Processing 982.3 Sample Preparation and Other Practical Aspects 102

3. Quantitative NMR Methods 1053.1 Direct 1H 1D qNMR 1053.2 Indirect 1H 1D qNMR 1063.3 Heteronuclear 1D qNMR 1063.4 Homonuclear and Heteronuclear 2D qNMR Methods 109

4. Applications of qNMR 1124.1 Metabolic Studies 1134.2 Natural Products and TCM 1164.3 Pharmaceutical Research 1194.4 Food Analysis 125

5. Prospect 129Acknowledgments 130References 130

Abstract

The unique analytical capabilities of both identification and quantification of com-pounds in complex mixture by NMR technique have been demonstrated. qNMR iswidely applied as routine analytical tool for the mixtures because of the universal exis-tence of NMR-active nuclei. qNMR is one of the few standard-free quantificationmethods. It can quantitatively analyze multiple compound mixtures without require-ment of chemical identical standards. In this review, theoretic background and technicalkeynotes on qNMR data acquisition, spectral processing, and signal deconvolution/

Annual Reports on NMR Spectroscopy, Volume 90 # 2017 Elsevier LtdISSN 0066-4103 All rights reserved.http://dx.doi.org/10.1016/bs.arnmr.2016.08.001

85

integration will be discussed for quantitative analysis of multiple compound mixtures.Sample preparation and the effect of different sample conditions on the assessment ofthe concentration will also be discussed. 1H 1D qNMR is themost often usedmethod forquantitative analysis of multiple compound mixtures, yet heteronuclear 1D and 2DqNMR approaches have been increasingly recognized and exploited for quantitativeassessment or concentration measurement. Some often used quantitative NMRmethods are then summarized. Afterwards, applications of qNMR in the areas of met-abolomics, natural products, traditional Chinese herbal medicine (TCM), pharmaceuticalresearch and food analysis are exemplified. Finally, we prospect the future develop-ments and applications of qNMR.

Key Words: Quantitative analysis, NMR, Mixtures, Deconvolution, Metabolomics

ABBREVIATIONSACE angiotensin-converting enzyme

AGUs anhydroglucose units

APIs active pharmaceutical ingredients

CMC carboxymethyl cellulose

COSY correlation spectroscopy

CPMG Carr–Purcell–Meiboom–GillCSA chemical shift anisotropy

DEPT distortionless enhancement by polarization transfer

DOSY diffusion-ordered NMR spectroscopy

DS dietary supplements

FID free induction decay

FMQ fast metabolite quantification

FT Fourier transform

FWHH full-width-at-half-height

GL generalized Lorentzian

GSD global spectral deconvolution

HPLC high-performance liquid chromatography

HSQC heteronuclear single-quantum coherence

HSQC0 time-zero 2D 1H–13C HSQC

HTFD-ML hybrid time-frequency domain maximum likelihood

INADEQUATE incredible natural-abundance double-quantum transfer experiment

INEPT insensitive nuclei enhanced by polarization transfer

ITOCSY isotope-edited total correlation spectroscopy

JRES J-resolved NMR

MFA metabolic fluxes analysis

NMR nuclear magnetic resonance

PULCON pulse length-based concentration determination

qNMR quantitative NMR

S/N signal-to-noise

SNIF site-specific natural isotope fractionation

TCM traditional Chinese herbal medicine

TOCSY total correlation spectroscopy

86 X. Li and K. Hu

1. INTRODUCTION

Since the emergency of Fourier transform (FT) NMR and the pro-

posal and implementation of two-dimensional (2D) NMR in the 1970s,

nuclear magnetic resonance (NMR) spectroscopy has been used as a routine

tool for structural elucidations of small chemical compounds, such as in nat-

ural products chemistry and organic chemistry. Since the beginning of

1980s, NMR has also been widely used as one of the major tools for struc-

tural determination of biomolecules in structural biology studies. Since then,

NMR has been recognized as a powerful tool for elucidating the molecular

structures and investigating the molecular dynamics of biomolecules, while

its quantitative capability is almost unexploited. Only since the past two

decades, taking advantage of both its qualitative and quantitative nature,

NMR has been brought up as a versatile tool for the analysis of multiple

compound mixtures. Especially, the unique analytical capabilities of

NMR have been demonstrated in the flourishing research area of met-

abolomics. In most metabolomics studies, NMR was used as a semiquanti-

tative profiling technique, with the primary objective to identify individual

chemical component in mixtures and to relate their concentrations to the

precise biological state of the system, such as stress, age, and disease [1].

The unbiased nature of quantitative NMR (qNMR) and the universal exis-

tence of NMR-active nuclei in biological mixture, such as 1H, 31P, and 13C,

render NMR-based techniques some advantages over more sensitive

MS-based methods for absolute quantification. First of all, for one-

dimensional (1D) NMR, the integrated intensities of resolved signals (the

area under the signal curve) are directly proportional to the number of spins

of the nuclei of interest in the mixture. Secondly, this feature essentially

makes the qNMR one of the few standard-free quantification methods, that

is, qNMR can quantitatively analyze multiple compound mixtures without

requirement of chemical identical standards. In addition, for resolved NMR

signals from multiple compounds in the mixture, the 1D qNMR, such as1H, 31P, and 13CNMR can potentially be used for quantification of multiple

analytes simultaneously [2]. Especially, proton 1D qNMR (1H qNMR) is

most widely applied as routine analytical tool for the mixtures because of

its universality, sensitivity, precision, and nondestructive nature. A major

shortcoming for the approach of 1H 1D qNMR for analyzing complex mul-

tiple compound mixtures is the signal overlap, which almost inevitably

87qNMR of Mixtures

occurs for complex mixtures because of relative narrow dispersion of 1H

chemical shifts, large amount of resonance signals, and multiplet patterns

due to homonuclear couplings [3]. Several different strategies can be applied

to address the signal overlap issue in 1H 1D NMR: (a) different types of fil-

tered or edited 1H 1D NMR spectra are developed to clean up the spectra

for quantitative purpose [4,5], for example, relaxation editing by means of

Carr–Purcell–Meiboom–Gill (CPMG) pulse train [6] is often used to sup-

press the signals of proteins, or relaxation editing by generating long-lived

states and coherences is used for analysis of mixtures [7]. (b) A promising

route to enhance spectral resolution is homonuclear broadband decoupling.

It results in greatly simplified 1H 1DNMR spectra with themultiplet pattern

removed, generating the so-called “pure shift” spectra [8,9]. (c) Using

diffusion-ordered NMR spectroscopy (DOSY) is another approach to the

signal overlap issue for 1H 1D NMR spectrum [10,11], which separates

the spectra of different compounds through their respective self-diffusion

coefficient. (d) For complex multiple compound mixtures, 1H-detected

spectrum by DOSY separation may often fail because extensive signal over-

lap remains due to the existence of the homonuclear-coupled multiplet pat-

tern. In this case, combining the concept of “pure shift” with the DOSY

separation, homonuclear broadband-decoupled DOSY spectra can be

acquired [12]. (e) Alternatively, chemometric analytic approach can be used

to resolve multiple compound complex mixtures through decomposition of

trilinear DOSY diffusion data [13]. (f ) 2D NMR spectra of mixtures could

contain higher proportion of resolved peaks, but the signals are usually more

difficult to quantify because of resonance-specific signal attenuation during

the coherence transfer periods as the result of relaxation, imperfect pulses,

and mismatch of the insensitive nuclei enhanced by polarization transfer

(INEPT) delay with specific J-couplings [14,15]. The signal attenuation fac-

tor can be theoretically calculated provided all related parameters are known

for each specific compound [15], or be experimentally determined [16]. The

signal intensities in 2D NMR spectra can also be related to the quantity of

the compound of interest in the mixtures by reference to a standard curve

constructed using concentration reference mixtures of synthetic compounds

[3]. 2D qNMR was increasingly recognized and exploited for analysis of

multiple compound mixtures. The method development and application

of qNMR were recently reviewed [17–19].In this review, we will first discuss the theoretic background and tech-

nical keynotes on the NMR data acquisition, spectral processing, and signal

deconvolution/integration for performing liquid-state NMR in a

88 X. Li and K. Hu

quantitative manner for absolute concentration measurement or for deter-

mination of relative concentrations of multiple compounds in complex mix-

ture. We will also discuss different sample conditions which could affect the

assessment of relative concentrations of the same molecular species among

different samples, such as biomarker identification based on the relative con-

centration change among samples in metabolomics study. 1H 1D qNMR is

the most often used method for quantitative analysis of multiple compound

mixtures, yet heteronuclear 1D and 2D qNMR approaches have been

increasingly recognized and exploited for quantitative assessment or concen-

tration measurement. We thus then summarize those often used qNMR

methods. Afterwards, we exemplify the application of qNMR in the areas

of metabolomics, natural products, traditional Chinese herbal medicine

(TCM), pharmaceutical research and food analysis. Finally, we prospect

the future developments and applications of qNMR.

2. THEORETIC BACKGROUND AND TECHNICALKEYNOTES OF QUANTITATIVE NMR

Quantitative purpose of qNMR is to establish a linear correlation or a

stable working curve between the obtained intensities of NMR signals in

frequency domain and the quantity of specific species in the multiple com-

pound mixtures. Besides the quantity of the compound of interest, yet

the final obtained intensities of NMR signals corresponding to the target

compound can also be affected by the experimental parameters setting up

for the data acquisition and spectral processing, methods for the signal

deconvolution and peak integration, and the sample conditions (such as the

amount and species of the residual proteins, solvent, buffer, and salt concen-

tration). In the following, we will describe the technical key points which

attention should be paid to when setting up the experimental parameters and

preparing the samples.

2.1 qNMR Data AcquisitionGenerally, qNMRdata acquisition includes the following steps: (a) interscan

delay period for the recovery of magnetization to equilibrium,

(b) application of excitation pulse, (c) possible pulses and delays for spectral

editing/filtering or for coherence transfer and chemical shift encoding, and

(e) detection, as shown in Fig. 1. The recovery extent to the equilibrium (fre),

exciting efficiency factor (fe), the NMR signal attenuation factor (fa), and the

89qNMR of Mixtures

receiving efficiency factor (fr) can be affected by the experimental parameters

and sample conditions, and these factors by themselves will affect the final

detected current (free induction decay, FID) in the probe coil. Therefore,

these factors will affect the obtained intensities of NMR signals in frequency

domain which is the FT of the detected FID in time domain. To minimize

the quantification bias caused by the nonuniformity of the above-mentioned

factors, qNMR data acquisition parameters should be properly set up.

2.1.1 Interscan Delay and the Recovery Extent to the Equilibrium (fre)The initial magnetization M0 of specific nucleus at the full equilibrium is

proportional to the number of the nuclei, which is in direct proportion

to the quantity or concentration of the target compound to be quantified.

But the initial magnetization of partially relaxed nuclei depends on the

recovery extent to the equilibrium, fre for nuclei with different longitudinal

relaxation time,T1. Therefore, in multiple scanNMR experiment for quan-

titative purpose, the interscan delay usually should be set long enough to

allow the magnetization to longitudinally relax to full equilibrium. The lon-

gitudinal relaxation time T1 can be determined using the classic “inversion

Fig. 1 (A) Generally, the quantitative NMR pulse sequence can be described as: after therecovery of the magnetization, initial excitation pulse is applied, likely followed by atime period before detection, marked as τ or T, for spectral editing/filtering in 1D qNMRor for coherence transfer and chemical shift encoding of the indirect dimension in 2DqNMR. The recovery extent to the equilibrium (fre), exciting efficiency factor (fe), the NMRsignal attenuation factor (fa), and the receiving efficiency factor (fr) in the rectangle boxesindicate the factors, by which the final detected current (FID) in the probe coil could beaffected. (B) The schematic representation of the transfer path of the initial magnetiza-tion M0 at the full or partial equilibrium state to the final induced current in the probecoil. The curved arrow lines represent the signal weakening at the stages of the r.f. exci-tation, NMR signal evolving (coherence transfer), and the probe coil receiving the signal.

90 X. Li and K. Hu

recovery” experiment, as shown in Fig. 2. The longitudinal magnetization

of the classic “inversion recovery” can be expressed as:

I tð Þ¼ I0 1�2e�t=T1

� �(1)

in which I0 is the longitudinal magnetization at full equilibrium. Thus,

longitudinal relaxation time T1 can be calculated as: T1¼ t0=ln2, in which

t0 is the time point at which the longitudinal magnetization is null.

After 90 degree pulse excitation, the longitudinal magnetization will start

at null point (i.e., the transverse coherence) and relax to equilibrium, which

can be expressed as:

I t0ð Þ¼ I0 1� e�t0=T1

� �(2)

Thus, a delay of approximate 5T1 will allow themagnetization to relax to

reach more than 99% (fre¼1� e�5T1=T1 , when t0 ¼5T1) of the full equilib-

rium. For multiple compound mixtures, the interscan delay usually is set as

5T1,max to minimize the quantification bias due to the nonuniformity of the

fre factor among the compounds. T1,max is the longest longitudinal relaxation

time of the resonances of interest of the compounds to be quantified. For

small molecules, the longest longitudinal relaxation time T1,max is usually

pretty large, which significantly prolongs the total experimental time of

qNMR compared to the conventional NMR spectrum for qualitative

analysis.

In conventional NMR for qualitative purpose only, a small flipping angle

(such as 30 degree) pulse is often proposed to shorten the total NMR

Fig. 2 The effect of full (90 degree)/partial (30 degree) excitation pulse on the accuracyfor quantitative purpose. (A) The schematic representation of the longitudinal andtransverse magnetization after full (90 degree)/partial (30 degree) excitation pulse.(B) The longitudinal relaxation profile of “inversion recovery” (i.e., after 180 degreepulse) and after 90 or 30 degree excitation pulse.

91qNMR of Mixtures

experimental time. However, for the purpose of quantification, the accuracy

obtained by a 90 degree pulse excitation overmatches that obtained by a

30 degree pulse excitation, judging by the signal-to-noise (S/N) ratio

obtained within the same total experimental time. This is counter-intuitive

against the case for qualitative purpose as interpreted in the following.

By a 30 degree pulse excitation, the longitudinal magnetization will

recover fromffiffiffi3

p=2I0 (Fig. 2A). From Eq. (2), I t0ð Þ¼ ffiffiffi

3p

=2I0, that is,ffiffiffi3

p=2I0¼ I0 1� e�t0=T1

�), thus giving t0 � 2:01T1. That is to say, the initial

longitudinal magnetization by a 30 degree pulse excitation,ffiffiffi3

p=2I0, is

equivalent to the longitudinal magnetization by a 90 degree pulse excitation

(null point) after a relaxation delay of about 2.01T1. Thus, to relax to full

equilibrium, a relaxation delay of only about 3T1 is required after a 30 degree

excitation pulse as shown in Fig. 2B. But on the other hand, the signal inten-

sity (transverse coherence) is 1/2 I0 by a 30 degree pulse excitation, com-

pared to I0 by a 90 degree pulse excitation (Fig. 2A). For quantitative

purpose, the measurement accuracy depends on the S/N ratio of the target

signal. Provided that the recovery delay time dominates the total time of

one scan and the pulse sequence time by itself is ignored, the S/N obtained

by a 90 degree pulse excitation with 5T1 recover delay compared to S/N

obtained by a 30 degree pulse excitation with 3T1 recover delay can

written as:

S=N90ð ÞS=N30ð Þ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi15T1=5T1

p � I0ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi15T1=3T1

p �1

2I0

¼ 2ffiffiffi3

pffiffiffi5

p > 1 (3)

in the above equation, the same total experimental time of 15T1 is used to

calculate the number of scan (NS). Partially relaxed initial state and/or par-

tially excited conditions were proposed for data acquisition as compromise

approach to the dilemma of relaxation to full equilibrium for the require-

ment of the measurement accuracy and long T1,max notably prolonging

the experimental time of qNMR, aiming to obtain an optimal balance

between accuracy and measurement time [20]. To shorten the qNMR

experimental time, longitudinal relaxation-enhancing agents can be added

to the samples to obtain shorter T1,max, which will permit shorter interscan

delay while fulfilling the requirement of the measurement accuracy. The

selection of proper relaxation-enhancing agent depends on the aqueous

or organic solvent [21,22], and cautions have to be exercised with regard

to the interaction between the agent and the sample components.

92 X. Li and K. Hu

2.1.2 Off-Resonance Effect and the Exciting Efficiency Factor (fe)With controlled initial equilibrium state, the signal intensity (transverse

coherence) depends on the exciting efficiency factor (fe). The factor fedepends on the flipping angle of the excitation pulse. Meanwhile, it is

also affected by the off-resonance effect of pulse applied. We simulated

the off-resonance effect considering the flip angle of the excitation pulse

and the off-resonance of chemical shift, as shown in Fig. 3.

In the simulation shown in Fig. 3A–C, the pulse phase ϕ is assumed to be

0 (on x axis) without loss of the generality. The excitation profile is calcu-

lated as the ratio (the vertical axis in Fig. 3A–C) of the magnitude of the

coherence in the transverse plane (x–y plane) generated by off-resonance

Fig. 3 Simulation of the inhomogeneous excitation profile of r.f. pulse due to the off-resonance effect. In the simulation shown in (A)–(C), the pulse phase ϕ is assumed to be0 (on x axis) without loss of the generality. The excitation profile is calculated as the ratio(the vertical axis in (A)–(C)) of the magnitude of the coherence in the transverse plane(x–y plane) generated by off-resonance excitation relative to that by the on-resonanceexcitation. In (A), the ratio surface is plotted vs the on-resonance flip angle α and off-resonance angle θ. As shown, the ratio of the on-resonance (θ ¼ 90 degree, indicatedby the dashed vertical line) excitation is defined as 1.00. (B) The ratio of excitation isplotted against the pulse on-resonance flip angle α with off-resonance angle θ of 80,60, 50, and 45, respectively. (C) The ratio of excitation is plotted against the off-resonance angles θ with on-resonance flip angle α of 1, 10, 73, and 90, respectively.(D) Definition of the pulse phase (ϕ), the pulse on-resonance flip angle (α) and off-resonance angle (θ, on-resonance, θ ¼ 90 degree).

93qNMR of Mixtures

excitation relative to that by the on-resonance excitation. In Fig. 3A, the

ratio surface is plotted vs the on-resonance flip angle α and off-resonance

angle θ. As shown, the ratio of the on-resonance (θ¼90 degree, indicated

by the dashed vertical line) excitation is defined as 1.00. In Fig. 3B, the ratio

of excitation is plotted against the pulse on-resonance flip angle α with off-

resonance angle θ of 80, 60, 50, and 45 degree, respectively. It indicates thatthe relative excitation ratio (to the on-resonance excitation) reaches the

minimum when the on-resonance excitation pulse is applied with a flip

angle of around 73 degree, regardless of the extent of the off-resonance effect

(showing similar excitation profile for all off-resonance angles θ of 80, 60,

50, and 45 degree). In Fig. 3C, the ratio of excitation is plotted against

the off-resonance angles θ with on-resonance flip angle α of 1, 10, 73,

and 90 degree, respectively. It shows that with small angle (<10 degree)

excitation pulse it generates more homogeneous excitation profile within

the range of the off-resonance with the effective field tilted angle from

45 to 135 degree. With the generally used excitation pulse of 90 degree

(purple curve), it could bring about 2% (with the excitation ratio of

0.98) error in quantification by NMR because of the inhomogeneous exci-

tation of r.f. pulse due to the off-resonance effect. With an excitation pulse

of 73 degree (brown curve), the error in quantification due to the off-

resonance effect could be more than 3%. Fig. 3D illustrates the definition

of the pulse phase (ϕ), the pulse on-resonance flip angle (α), and off-

resonance angle (θ, on-resonance, θ¼90 degree). The off-resonance effect

of 1H pulse is usually pretty small with effective field tilted angle generally

smaller than 10 degree (θ, 80–100 degree), even at 1H Larmor frequency of

800 MHz and assuming a 90 degree 1H pulse of 10 μs, and 1H chemical

shift dispersion of 10 ppm. From the earlier simulation, it can be seen that

the quantification bias due to the off-resonance effect in direct 1H 1D

qNMR is almost ignorable (<0.2%, Fig. 3B, green curve plotted with

off-resonance angle of 80 degree). In contrast, the effective field tilted angle

of a 13C pulse can be as large as 45 degree (θ, 45–135 degree), assuming a

90 degree 13C pulse of 12.5 μs, and 13C chemical shift dispersion of

200 ppm at 13C Larmor frequency of 200 MHz. Thus, the quantification

bias due to the off-resonance effect of a single 90 degree 13C pulse could

reach 2% as shown in Fig. 3C (purple curve plotted with flip angle of

90 degree). Though the 2% quantification error caused by a single 13C

pulse could be deemed to be acceptable, the quantification error will prop-

agate as the product of the bias caused by every single pulse in a multiple

pulse experiment. For example, cross-peak intensities in 2D 1H–13CHSQC will be modulated by the imperfect excitation profiles of the

94 X. Li and K. Hu

heteronuclear (13C) pulses, in which the peak intensity ratio cannot be

directly translated into the relative concentration. Caution has to be taken

with regard to high-throughput data acquisition across samples with differ-

ent salt concentration, such as urine samples for metabolomics study. In

such a case, uniform 90 degree exciting pulse length (same effective

γB1) with sample-specifically calibrated power level is preferred to balance

the off-resonance effect among different samples, rather than as conven-

tionally, pulse length is adjusted at fixed power level.

2.1.3 NMR Signal Attenuation Factor (fa)In the simplest 1H 1D qNMR, data is acquired immediately after the exci-

tation, in which the generated transverse coherence is directly detected

without any attenuation. However, in this kind of direct 1H 1D qNMR

spectrum, there could be interference from the background signals of pro-

teins or other kind of macromolecules, as very often in the metabolomics

study when there is residual protein in the samples. To remove the back-

ground signals, pulses and delays, such as CPMG pulse train, can be inserted

before detection for spectral editing/filtering to simplify or clean the spec-

trum. For multiple compound mixtures, there could be severe signal overlap

in the 1H 1D qNMR spectra. To alleviate the signal overlap issue by exten-

ding 1D spectra to 2D spectra, pulses and delays can be inserted between

excitation and detection for coherence transfer and chemical shift encoding.

Whether the pulses and delays are inserted for 1D spectral editing/filtering

or for coherence transfer and chemical shift encoding in 2D spectrum, the

detected signal intensities is scaled by the signal attenuation factor, fa, which

is resonance specific and sample condition dependent. Resonance specificity

includes the relaxation by dipole–dipole interaction and chemical shift

anisotropy (CSA), chemical shift-dependent off-resonance effect, and mis-

match of the INEPT delay with specific J-couplings. Viscosity of different

sample solvent and temperature of the sample will affect the molecular rota-

tional tumbling and thus the relaxation rate. The salt concentration will gen-

erally significantly affect the excitation pulse length, and thus the excitation

profile and/or off-resonance effect of all pulses applied.

For the above-mentioned reasons, the integrated intensities of resolved

signals in 2D or edited/filtered 1D spectrum are not proportional to the con-

centration of compound of interest in the mixture and cannot be used

directly for quantification. Though the signal attenuation factor can be the-

oretically calculated [15], it strictly requires that all related resonance-specific

parameters are known, for example, chemical shift, which could affect the

off-resonance effect, J-couplings, which could affect the INEPT transfer

95qNMR of Mixtures

efficiency, related bond lengths and interatom space distance together with

CSA tensor, which affect the relaxation rate by dipole–dipole interaction orby the CSA. More demanding are the sample conditions, such as the viscos-

ity and temperature of the sample, and knowing how they affect the molec-

ular rotational tumbling time and thus also the relaxation rate. In practice,

the predicted intensities back-scaled by the theoretical attenuation factor

can include unacceptable level of error for the quantitative purpose, which

limits the widespread usage of this approach.

Another strategy is to translate the integrated intensities to the concen-

trations by reference to standard curves, which are premade using multiple

compounds with known concentrations individually or in a synthetic mix-

ture [3]. However, the obtained concentrations may be not precisely accu-

rate because the sample conditions of the concentration reference standards

do not perfectly match that of the multiple compound mixtures to be quan-

tified. As stated earlier, the signal attenuation factor, fa is affected not only by

the concentration reference compound itself (such as compound-specific

properties: chemical shift, J-coupling, dipole–dipole interaction, CSA ten-

sor, etc.), but also by its environment, that is, the sample conditions, includ-

ing the solvent viscosity, the buffer composition, salt concentration, the

amount and species of the residual proteins, etc. Ideally, the curves are made

with concentration reference standards in identical sample conditions to that

of the multiple compound mixtures to be quantified. For the approach of

FMQ by NMR, the authors explicitly pointed out that it requires 2D1H–13C NMR spectra of standards have been collected under comparable

conditions [3]. The sample conditions of the target multiple compoundmix-

tures, however, can never be identical to that of the concentration reference

compound, considering the multiple compounds themselves as the matrix of

one specific target compound in the mixture. This may be one of the reasons

leading to the measurement errors.

The signal attenuation factor, fa can in fact be experimentally measured

using a single sample by the approach of time-zero 2D 1H–13CHSQC spec-

trum (HSQC0) without the need to construct any working curve with the

concentration reference standards [16], as shown in Fig. 4. NMR signal

attenuation factor, fa during the HSQC coherence transfer period (Fig. 4,

top panel, from point a, right after the initial 90 degree excitation pulse,

to point f, right before detection, see Fig. 1A) can be obtained from linear

regression. The attenuation factors are signal- (function group-) specific and

could be different due to the different chemical properties of the function

group, T2 relaxation rates, J-coupling, heteronuclear (such as 13C) off-

resonance effect, etc.

96 X. Li and K. Hu

Fig. 4 Quantitative HSQC0 approach records a series of HSQC spectra with incrementedrepetition times of the HSQC coherence transfer unit (middle panel). Peak intensities inthe virtual quantitative HSQC0 spectrum at zero time (bottom panel, bordered by thedashed line) can be derived from linear extrapolation of natural logarithm of the peakintensities of the corresponding peaks in the HSQC1, HSQC2, and HSQC3 spectra.Reprinted with permission from K.F. Hu, W.M.Westler, J.L. Markley, Simultaneous quantifica-tion and identification of individual chemicals in metabolite mixtures by two-dimensionalextrapolated time-zero 1H-13C HSQC (HSQC0), J. Am. Chem. Soc. 133 (2011) 1662–1665.http://pubs.acs.org/doi/full/10.1021/ja1095304. Copyright (2011), American ChemicalSociety.

97qNMR of Mixtures

2.1.4 Receiving Efficiency Factor (fr)The last stage for data acquisition is the receiving. The signal receiving effi-

ciency, fr is affected by the solution permittivity, which changes the coupling

between the receiving coils and the transverse coherence to be detected.

The solution permittivity mainly depends on the salt concentration of sam-

ple solution. According to the principle of reciprocity, on one hand, the

length of the 90 degree pulse for a sample at fixed power level in a given

r.f. coil is inversely proportional to the sensitivity or the receiving efficiency,

fr. On the other hand, the detected current (FID) in the probe coil depends

on the sensitivity, that is, the detected NMR signal strength is proportional

to the receiving efficiency, fr. Therefore, for quantitative purpose, signal

intensity can be adjusted by scaling up by the 90 degree pulse length. This

forms the fundament of the PULCON (pulse length-based concentration

determination) technique [14].

2.2 qNMR Data Processing2.2.1 Spectral Processing and t1 Noise SuppressionTime-domain data can be processed with commercial or third-party soft-

ware, such as Topspin, NMRpipe, etc. For quantitative purpose, prior to

Fourier transform, the time-domain data need to be zero-filled to give at

least five datapoints above the half height for each resonance to allow for

precise and reliable integration [23]. The Fourier transformed spectra can

then be phased manually or automatically [24], polynomial baseline correc-

tion can be applied to improve the accuracy of the integral.

In 2D qNMR spectra, spectrometer instability could result in particularly

severe t1 noise [25–27], especially in heteronuclear correlation spectra,

such as HSQC, HMQC (heteronuclear multiple-quantum coherence),

etc. Instrumental instability, such as fluctuation in phase, pulse power, fre-

quency, etc., results in differences in signal intensities from scan to scan. In

experiments with high dynamic range, imperfect cancellation of phase

cycling will lead to significant artifacts (that is, t1 noise) which may be much

higher than the thermal noise. In such case, t1 noise rather than thermal noise

is the factor limiting the peak identification and the accuracy of the peak

integration. Nevertheless, so far, it is not yet feasible to improve the instru-

ment stability to obtain 2D spectra free of t1 noise. In 2D NMR, artifacts

show as t1 noise bands along f1 dimension at certain f2 frequencies of intense

signals. Ignoring the experimental temperature fluctuation, t1 noises at dif-

ferent f2 frequencies can be assumed to be correlated, which can then be

suppressed by reference deconvolution [27]. Another way to suppress t1noise can be achieved through application of noise-dependent weighted

98 X. Li and K. Hu

smoothing filter, which progressively correct each point to be smoothed

under the influence of both its estimated t1 noise level and the level of t1noise of neighboring points. A t1 noise profile is produced by measuring

the average noise in each column. This profile is then used to determine

weighting coefficients for a sliding weighted smoothing filter. In such a case,

points in the worst t1 noise bands receive the greatest smoothing, whereas

points in low-noise regions remain relatively unaffected [26].

2.2.2 Peak Integration/Peak Height and S/N RatioIn qNMR, the concentration (number) of nuclei (analytes) is proportional

to the peak area at certain frequencies. Therefore, the most common quan-

titationmethod byNMR is to integrate the area under a peak or peak group.

In some cases, for peaks with similar or consistent shapes and peak widths,

peak height of the NMR signals can be used instead for quantitation. For

peaks with inconsistent shapes and peak widths, a postacquisition processing

method employing a Gaussian or line-broadening apodization is proposed to

normalize the shape and width of different peaks [28]. The peak heights can

then be used to calculate the relative concentration of different species.

Quantification based on the peak heights is relatively straightforward, but

prone to errors due to variations in lineshape. Thus, by far, for spectra with-

out significant signal overlap, the peak area ratio is still the most common

approach to calculate the relative concentration of different species. Line

broadening theoretically does not affect the accuracy of quantification

presuming proper integration is carried out. But, in practice, even for well-

resolved resonances, numerical integration may still substantially underesti-

mate the area because of truncation of the long tails of the Lorentzian lin-

eshape [29]. For peaks with inconsistent shapes or with different peak

widths, integration range should be adjusted on the basis of the lineshape

and peak width in order to capture comparable percentage of the peak areas,

otherwise, the relative concentration calculated from the integral ratio could

be biased. Assuming a perfect Lorentzian lineshape in 1D spectra, integration

range of 12.7 times of the linewidth (corresponding to crossing at 1/162.5 of

maximum peak height, linewidth is defined as full-width-at-half-height,

FWHH) will theoretically cover 95% of the area under a peak. For 2D

NMR, the integrated peak intensity (peak volume) was calculated by direct

summation over a rectangular box or ellipse centered on the target peak.

Assuming perfect Lorentzian lineshape along both dimensions, integration

needs to cover even broader range along both dimensions in order to capture

95% of a peak volume. This also implies that, to maximatily capture the peak

volume in the integration (e.g.,>95% of the peak volume), ideally the target

99qNMR of Mixtures

peaks better have sharp and tall lineshapes, that is, with small peak width

(FWHH) but large S/N ratio. Otherwise, broad integration range centered

on the target peak could include the tails from the neighboring peaks, or

for weak peaks (with low S/N ratio), at the level of 1/162.5 ofmaximum peak

height, too much noise will be included in the integration. Under both cases,

inevitably, it will bring errors into the obtained peak area. The errors in the

peak area will propagate into the final calculated relative concentration. In

practice, it is not always the case that the target peaks have narrow line width

and large S/N ratio and are far enough from their neighboring peaks. Very

often, integration over a reasonable range around the target peaks is used

to represent the peak areas, serving as a compromise approach to the dilemma

between the coverage of the peak area and the integration accuracy.

2.2.3 NMR Signal DeconvolutionFor multiple compound mixtures, very often there is signal overlap in 1D

NMR spectra and there are many signal peaks neighboring to each other

in 2D NMR spectra. It is usually very rare that the target peaks have sharp

(narrow line width) and tall (large S/N ratio) lineshapes and meanwhile are

isolated or far enough from their neighboring peaks. As stated earlier, if not

impossible, it is often very hard to integrate peak intensity over a large range

to maximatily capture the peak area, but not capture too much of the tail

from the neighboring peaks or the noisy regions. Thus, the final obtained

peak integrations may cover very different percentage of peak area for peaks

with different peak widths, or include toomuch errors from the neighboring

peaks or noise. These errors will ultimately affect the accuracy of the calcu-

lated relative concentration.

Conventional integration cannot disentangle overlapping peaks or par-

tially overlapping peak pattern, such as the spectral multiplet pattern of spin

A entangles with that of spin B and vice versa. Spectral fitting or

deconvolution is an alternative to integration to obtain the peak area. Fitting

or deconvolution is a model-based approach, attempts to disentangle over-

lapping signals into its individual spectral contributions.

Different spectral fitting strategy mainly lies in using different spectral

database or spectra base of the reference or standard compounds. Most often,

for example, the 1HNMR spectrum of multiple compound mixtures can be

fit with a library of spectra of possible compounds present in the mixture.

Fitting the spectra from the library to the mixture spectrum can be done

by manual or automatic adjustment of chemical shifts and signal intensities.

Simple line fitting algorithms may not be adequate for the spectral complex-

ity of (strongly) coupled spin systems. It was shown that high-resolution 1H

100 X. Li and K. Hu

NMR spectra of rat brain extracts can be reproducibly fit and quantified

with a spectral basis set of 29 compounds, in which each basis set is simulated

with the density matrix formalism using complete prior knowledge of

chemical shifts and scalar couplings [30]. Initial estimates of chemical shifts

and scalar couplings can be obtained manually, their final values were opti-

mized through least-squares fitting using iterative simulation of the spectral

patterns through density matrix calculations. In the simulation, the model

function allows variation and optimization of chemical shifts and scalar

couplings for all spins considering the variations in pH, ionic strength and

composition, and temperature, and the lineshape model can be modified

from Lorentzian to Gaussian [30].

Two important aspects of spectral deconvolution [29–33] are estimation of

the number of signals and proper model function to describe the lineshape.

There is overfitting risk to allow a deconvolution algorithm to determine

the number of peaks and carry out the fit because increasing the number of

peaks will always appear to perfectly fit any spectrum. In such a case, the

experimental data may be mathematically well fit but are physically meaning-

less [29]. Global spectral deconvolution (GSD) is a fast and automatic

deconvolution algorithm for the detection and quantification of all spectral

peaks present in a spectrum. It can extract all resonances from theNMR spec-

trum in the presence of noise, baseline distortions, spikes, and artifacts [31,33].

GSD algorithm can identify physically meaningful peaks and definite the

number of signals for spectral deconvolution by using a fast algorithm based

on knowledge of the zero, first, and second derivatives of the data [29].

With regard to model function to describe the lineshape, a straightfor-

ward approach is to first define individual Lorentzian lineshape. Lorentzian

lineshape can be distorted due to the instrumental instability (thermal noise)

and shift to a Gaussian lineshape. For simulation, the Lorentzian lineshapes

can be modified to Gaussian by changing the decay exponent from�t/T2 to

�(t/T2)2 in the time-domain signal function. To account for suboptimal

shimming (e.g., imperfect magnetic field homogeneity) and other experi-

mental artifacts, lineshape may deviate from the ideal Lorentzian or Gaussian

lineshape. Therefore, some deconvolution algorithms try to make the

lineshape more flexible by using a weighted mixture of Lorentzian and

Gaussian shapes by using decay powers between 1 and 2 (i.e., decay expo-

nent of�(t/T2)m, 1<m<2) to accurately model these non-Lorentzian peak

profiles [32]. For the purpose of accurate signal quantification, a hybrid

time-frequency domain maximum likelihood (HTFD-ML) algorithm was

proposed to perform spectral deconvolution of 1D and 2D NMR spectra,

as shown in Fig. 5.

101qNMR of Mixtures

Aweighted mixture of Lorentzian and Gaussian can only result in shapes

between a pure Lorentzian and a pure Gaussian, which in fact could be insuf-

ficient to accurately describe the real peak lineshape. Recently, to improve

the performance of high-precision qNMRmeasurements, the idea of a gen-

eralized Lorentzian (GL) peak shape was proposed as a more robust model

function to describe the lineshape [29]. GL uses a single extra parameter,

“kurtosis parameter” to describe the peaks shape deviation from the

Lorentzian. The GL shapes cover at least 3 times wider range of lineshape

than a weighted mixture of Lorentzian and Gaussian shapes. Using GL peak

shape model, a hybrid integration approach that combines the traditional

integration with GSD can obtain the edited sum. The edited sum is the

adjusted integration, which can represent more accurate estimation of signal

area for the quantitative purpose.

2.3 Sample Preparation and Other Practical Aspects2.3.1 Relative Quantification or Quantitative Profiling Among SamplesAs stated in the qNMR data acquisition earlier, different sample conditions

may affect the assessment of relative concentrations of the same molecular

species among different samples, such as quantitative profile among samples

in metabolomics study. Attention should be especially paid to the amount

and species of the residual proteins, solvent, and salt concentration. The

residual proteins may interact with the one or more compounds in the mix-

ture, which can change the relaxation properties of the compound of interest

and affect the accuracy of its relative and absolute quantification. Similarly,

the viscosity of the solvent affects the molecular tumbling time which also

affects the relaxation properties and the accuracy of quantification. Salt

Fig. 5 Region of 2D [1H,13C]-HSQC spectrum (left panel) containing resonances from3-hydroxybutryate (BH), MES, and glucose. The HTFD-ML algorithm was applied todeconvolute the spectrum of the mixture. The Newton model reconstructions are dis-played both as contour plots and as surface plots (the middle two panels), the residualspectrum is shown as surface plots in the right panel. Reprinted with permission from R.A.Chylla, K. Hu, J.J. Ellinger, et al., Deconvolution of two-dimensional NMR spectra by fastmaximum likelihood reconstruction: application to quantitativemetabolomics, Anal. Chem.83 (2011) 4871–4880. http://pubs.acs.org/doi/full/10.1021/ac200536b. Copyright (2011),American Chemical Society.

102 X. Li and K. Hu

concentration can affect the excitation pulse length, and thus the off-

resonance effect and the exciting efficiency factor (fe). It also affects the

detection sensitivity, i.e., the receiving efficiency factor (fr). Therefore,

for high-throughput data acquisition (using identical NMR acquisition

parameters) on large amount of samples, such as for metabolomics study,

strictly parallel sample preparation is very important to improve the reliabil-

ity of the calculated relative quantification among samples.

2.3.2 Absolute Quantification Using Internal StandardAs stated in qNMR data acquisition, signal intensities measured under dif-

ferent sample conditions could be influenced bymany factors, such as the salt

concentration, solvent viscosity, etc. For quantification of multiple com-

pounds in a mixture, compound with known concentration can be added

into the mixture sample as internal standard. The relative concentration

of the compound of interest to the internal standard can be directly deter-

mined from the ratio of peak intensities. However, practically, choosing an

internal concentration reference requires: (a) Considering the precision of

the calculated concentration, the solubility of the internal standard shall

be reasonably high in the sample solution to ensure acceptable S/N ratio

of its NMR signal, which will lead less error propagation into the final

results. (b) The internal standard shall not interact with sample molecules

and the reference signal must not overlap with the signals of the sample.

(c) In addition, for absolute quantification, the interscan delay should be long

enough (approximately 5T1) to allow the magnetization to fully relaxed

(>99%, see interscan delay and the recovery extent to the equilibrium

(fre)) to avoid the quantification bias. Therefore, the NMR relaxation prop-

erties of the reference should be on similar scale to that of the sample mol-

ecules, in other words, T1 value should not be too much longer and the T2

value should not be too much shorter that those values of the sample mol-

ecules. Otherwise, qNMR measurement time may significantly increase or

the peaks of the internal standard may be pretty broad in 1D spectrum or

very weak in 2D spectrum. Finally, relative concentrations determined from

cross-peak intensities can be converted to absolute concentrations by refer-

ence to the known concentration of internal standard or the solvent [34].

2.3.3 Absolute Quantification Using External StandardCompounds used as internal standards must have defined or high purity,

good solubility and stability, and favorable relaxation properties, and be ease

to be accurately weighted, without interaction or signal overlap with any

103qNMR of Mixtures

species in the multiple compound mixtures, etc. On the other hand, sample

preparation for absolute quantification by NMR may become easier and

more efficient if an internal reference is avoided and instead an external stan-

dard is used. External standard means to build an unbiased correlation of the

NMR signal strengths of the sample of interest with a separate concentration

reference sample or samples (premade standard curve). Ideally, all samples,

including the mixture sample to be quantified and all concentration refer-

ence samples, should be under identical conditions to avoid the bias on

the final obtained concentration. This has been specially noted in FMQ,

in which the authors explicitly pointed out those spectra of standards are col-

lected under comparable conditions [3]. Alternatively, the correlation must

include properties of the receiving coil that change with the sample condi-

tions (e.g., salt concentration) and influence the signal intensities. PULCON

technique can be applied to correlate the absolute intensities of two spectra

measured in different solution conditions. The signal intensities can be

adjusted using the PULCON technique or by analysis of the NMR receiv-

ing efficiency [35] for calculating the absolute concentrations. In such a case,

concentrations can be determined from a single general external concentra-

tion reference, and it may serve as an easy and robust method in routine con-

centration measurements.

2.3.4 Other Practical Aspects for Quantification by NMRBesides using internal or external standards, the 1H NMR signals from the

residual protonated fraction of a deuterated solvent can also be exploited as

an internal standard provided that the residual protonated fraction is quan-

tified accurately [36]. Quantification by NMR can also be determined by

reference to a synthesized electronic signal using the ERETIC technique

(electronic reference to assess in vivo concentrations) [37]. In addition,

for quantitative purpose, sample heating issue should be avoided during

NMR data acquisition. For example, in order to avoid sample heating,

the interscan delay should be set long enough to tolerate the heating pro-

duced by continuous 13C decoupling applied during the direct dimension1H data acquisition in HSQC experiment. In case when relaxation-

enhancing agent is added, the interscan delay may be set much longer than

5 times longest T1 (required for accurate quantification) solely to reduce

duty cycle.

104 X. Li and K. Hu

3. QUANTITATIVE NMR METHODS

3.1 Direct 1H 1D qNMR1H 1D qNMR is the most often used method for quantitative analysis of

multiple compoundmixtures. Direct 1H 1D qNMR implies immediate data

acquisition right after the first excitation pulse, that is, there is not any time

gap or coherence manipulating pulse between the first excitation pulse and

data acquisition (Fig. 1A). In case there is any time gap or coherence manip-

ulating pulse in front of data acquisition, including 1D NMR with signal

filtration element (e.g., CPMG) or 2D NMR intrinsically requiring coher-

ence transfer elements, the NMR signal will attenuate and the attenuation

factor fa is strongly molecule dependent. Direct 1H 1D qNMR by far the

most straightforward and sensitive method for relative or absolute quantifi-

cation, in which the NMR signal attenuation can be ignored and thus factor

fa¼1. Assuming the initial fully recovered magnetization (fre�1 with inter-

scan delay larger than 5T1,max, Fig. 2) is excited by 90 degree excitation pulse

(fe�1, the off-resonance effect of 1H can generally be ignored, Fig. 3), under

this case, the integrated intensities of resolved signals (the area under the sig-

nal curve) are directly proportional to quantity the compounds of interest in

the mixture. If an external concentration reference is used, the signal inten-

sities can be adjusted using the PULCON technique [14,38]. Its analytical

precision usually does not depend on the chemical properties of target

molecules.

In order to optimize the receiver gain and improve the sensitivity, the pro-

tonated solvent signal usually needs to be suppressed adequately to prevent

overflowing the digitizer dynamic range, such as by a presaturation pulse train.

Low receiver gain often limits the observation of small signals of minor com-

pounds against large background signals of major abundant compounds. In

such a case, soft pulses can be applied to selectively excite the signals of minor

compound, which allows larger receiver gain and can significantly increase the

sensitivity. Themain drawback of direct 1H 1D qNMR spectrum is that there

may be interference from the background signal of protein or other kind of

macromolecules, as very often in the metabolomics study when there is resid-

ual protein in the samples. The signal interference from protein or other kind

of macromolecules will affect the integration of the target peak intensities and

thus the accuracy of the quantification.

105qNMR of Mixtures

3.2 Indirect 1H 1D qNMRIndirect 1H 1D qNMR implies there are time gaps or coherence manipu-

lating pulses between the excitation pulse and the data acquisition for signal

filtration, suppression, or spectral simplification. Different types of spectral

filtering or editing techniques can be applied to acquire 1H 1D qNMR spec-

tra, such as using relaxometric and diffusometric spectral editing techniques

[12,13,39,40]. Most often used spectral editing method is the T2 relaxation

editing by means of CPMG pulse train [5,6], which suppresses the back-

ground signal interference from protein or other kind of macromolecules

in the indirect 1H 1D qNMR, thus enabling quantitative assessment of

low-molecular weight metabolites. Though much flatter baseline can be

obtained, it is meanwhile inevitable that all NMR signals will attenuate,

probably with different signal attenuation factors fa. In such a case, for

relative or absolute quantification using either internal or external standard,

signal attenuation factors fa should be comparable or be considered as

signal intensity adjusting coefficient. It is worthy to note, in metabolomics

study, very often there are different amount and species of the residual

proteins across samples. These proteins may selectively interact with some

compounds and affect their relaxation properties. Thus, the signal attenua-

tion factors fa for these compounds may be different across samples. In

this case, their quantitative profile among these metabolomics samples

may not be correctly represented by their peak intensities without taking

into consideration of their different signal attenuation factors fa across these

samples.

3.3 Heteronuclear 1D qNMR1H qNMR generally has better sensitivity than heteronuclear 1D qNMR.

Besides, the universal existence of 1H in many compounds or metabolites

represents another advantage, which makes 1H 1D qNMR most widely

applied as routine analytical tool for the mixtures. On the other hand, the

universal existence of 1H turns into a disadvantage of either direct or indirect1H 1D qNMR, that is, for complex multiple compound mixtures, it is

almost inevitable that there is the signal overlap in 1H 1D qNMR spectra

because of large amount of resonance signals (almost from every compound

in the mixture) and narrow dispersion of 1H chemical shifts. That is to say,1H qNMR is less selective or specific approach compared to other

heteronuclear 1D qNMRmethods. In addition, for regular (nondeuterated)

solvent, heteronuclear 1D qNMR usually does not suffer from large

106 X. Li and K. Hu

protonated solvent signal and background signal from many irrelevant mol-

ecules. Therefore, heteronuclear 1D qNMR can usually have an optimal

dynamic ranger of the receiver and much cleaner spectrum with flatter base-

line. These will greatly simplify the quantitative analysis and improve the

analytical accuracy.

Heteronuclear 1D qNMR is often used to selectively quantify com-

pounds with specific type of heteronuclei, such as 31P, 19F, 13C, and also2H. As mentioned earlier, the off-resonance effect of 1H pulses can usually

be ignored. But attention has to be paid to the off-resonance effect of het-

eronuclear pulses on the inhomogeneous excitation profile (Fig. 3). For

example, assuming setting the carrier frequency at the center of 200 ppm

spectral width of 13C on 800 MHz spectrometer, the off-resonance angle

θ of a 12.5 μs 90 degree pulse may be up to 45 degree. Inhomogeneous

exciting efficiency factor (fe) due to the off-resonance effect may bias the

result if the peak intensity ratio is directly used to calculate the relative

quantification.

3.3.1 13C 1D qNMRChemical shift dispersion of 13C is much wider than that of 1H. With 13C at

natural abundance, usually in 13C spectra there is not multiplet pattern due to

homonuclear couplings. In many cases, using 13C NMR spectra for quan-

titative analysis may be beneficial because there is much less signal overlap in13C NMR spectra compared to that in 1H NMR spectra. But on the other

hand, low natural abundance (about 1.1% 13C) and intrinsic low sensitivity

of 13C NMR and long T1 relaxation times make the acquisition of 13C

qNMR with adequate S/N ratio time consuming. This limits application

of 13C 1D qNMR mostly to quantify large amount or highly concentrated

material. INEPT or DEPT (distortionless enhancement by polarization

transfer) technique allows increased experimental repetition rate and can

be used to improve 13C NMR sensitivity, but the sensitivity enhancement

factor strongly depends on the off-resonance effect of 13C pulses, and the

relevant J-couplings and relaxation parameters. Integrated intensities of non-

uniformly enhanced signals are not linearly proportional to quantity the

compounds of interest any more. DEPT and INEPT can be optimized by

using adiabatic pulses to minimize off-resonance effects [41]. Q-INEPT-CT

(quantitative constant-time INEPT) based on constant time INEPT polar-

ization transfer is capable of producing 13C qNMR spectra with better sen-

sitivity than traditional 13C qNMR [42].

107qNMR of Mixtures

3.3.2 31P 1D qNMRPhosphorus-31 is a spin 1/2 nucleus with high gyromagnetic ratio, 100%

natural abundance, and broad chemical shift range (430 ppm) [43]. These

features are favorable to quantitative analysis using 31P NMR. Nonetheless,

there are still some factors affecting quantification of 31P NMR, such as long

spin-lattice relaxation time T1 and the variable NOE with adjacent proton

when gated proton decoupling is applied. The measures taken to overcome

these disadvantage factors include (a) addition of paramagnetic relaxation

agents to accelerate the relaxation up to the T1 values less than 5 s and

(b) reference to internal standard with known concentration [44]. In addi-

tion, appropriate pulse sequence, e.g., inverse-gated decoupling technique

can be used to reduce NOE effects [45]. A systematic study on validating

quantitative 31P NMR has showed that there is no significant difference

between 31P qNMR and chromatographic methods with respect to

analytical capability [46]. Apart from the direct quantitative analysis of

common phosphorus containing compounds such as ATP (adenosine-50-triphosphate), ADP (adenosine-50-diphosphate), and AMP (adenosine-50-monophosphate) using 31P NMR [47], in order to quantify compounds

with various types of hydroxyl groups including aliphatic, phenolic, and

carboxylic acids, a strategy of making use of derivatizing reaction with these

hydroxyl groups to give phosphitylated products and associating with 31P

qNMR has been widely employed [48–50]. Inevitably, this strategy will

increase economical and time costs.

3.3.3 19F 1D qNMRQuantitative 1D 19F NMR is widely applied in pharmaceutical, polymer,

and organic industries [51–53]. Fluorine (19F) nucleus is similar to hydrogen

(1H) with nuclear spin I¼1/2, a large gyromagnetic ratio of 40.05 MHz/T,

and natural abundance of 100% [54], which opens a way to selective, sen-

sitive, and quantitative detection of fluorinated compounds in complex

matrices. Furthermore, the 19F nucleus has larger chemical shift range

(�300 ppm) than proton, thus being favorable to obtain signal resolved

spectrum. Another strength of 19F NMR is that the relative short longitu-

dinal relaxation times of 19F compared to 1H allow shorter repetition times

(5 s for 19F NMR vs 15 s for 1H NMR, respectively), thus saving the exper-

imental time. 19F NMR is usually employed to investigate the fate of exog-

enously administered fluorinated agents because of the absence of

radioactivity and interfering background signal in the body. In addition

to quantification compounds containing fluorine at specific chemical shift,

108 X. Li and K. Hu

some interesting parameters such as pO2, pH, metal ion concentrations,

transgene/enzyme activity, etc., can also be analyzed by 19F NMR on

the basis of relaxation processes (R1 and R2) and chemical exchange [51].

In principle, more equivalent fluorines incorporated into compound, stron-

ger signals can be obtained, which is helpful to reduce quantification errors.

However, the extent of fluorine incorporation can influence molecular

properties including acidity, basicity, the shielding of adjacent nuclei, hydro-

phobicity, ability of molecules to cross-membranes, etc., mainly due to the

strong electronegativity of fluorine atom. These are also important points

needed to be taken into account for quantification. The chemical shift stan-

dard of 19F NMR specified by the International Union of Pure and Applied

Chemistry (IUPAC) [55] is fluorotrichloromethane (CFCl3), which is a vol-

atile solvent and not suitable for biomedical studies. An alternative to CFCl3is sodium trifluoroacetate (CF3CO2Na or NaTFA; Δδ vs CFCl3 is

�76.530 ppm), which has the advantages of ready availability, cheap cost,

nontoxicity, enabling it used as external or internal chemical shift standard

in biological investigations [56,57]. It should be noted that quantitative anal-

ysis using nucleus with wide chemical shift range, such as 19F, 13C, requires

broadband excitation to cover the full spectral width with constant ampli-

tude and phase (more strictly, phase with a linear offset dependence). This is

often a problem in that the available radiofrequency power for pulse exci-

tation is limited and off-resonance effects (Fig. 3) distort both signal inten-

sities and phases, thus compromising the accuracy of quantification.

Recently, a new pulse sequence, termed CHORUS (chirped, ordered

pulses for ultra-broadband spectroscopy) [58], has been proposed to increase

the bandwidth of pulse excitation, which is based on chirp pulses and can

achieve uniform excitation over very large bandwidth range. Thus, it can

obtain accurate integral result over wide chemical shift range.

3.4 Homonuclear and Heteronuclear 2D qNMR Methods1H 1D qNMR is widely applied because there is 1H nuclei in almost all nat-

ural products, metabolites. For a complex mixture of natural products or

metabolites, there are almost always very severe signal overlap in 1H 1D

qNMR. Therefore, quantitative analysis by 1D qNMR has been restricted

to relatively simple mixtures with minimal peak overlap. 2D NMR spec-

trum of mixtures spreads signals along an additional dimension, so it usually

contains more resolved peaks. To acquire 2D NMR spectrum, it intrinsi-

cally requires coherence transfer elements, during which NMR signals

109qNMR of Mixtures

will attenuate and the attenuation factor fa is strongly molecule dependent,

yielding quantitatively uninformative peaks. 2D cross-peak intensities (or

volumes) are influenced by more variables than those of 1H 1D qNMR

peaks, including excitation profile, relaxation parameters, transfer efficiency,

evolution times, mixing times, etc. This nonuniform signal attenuation fac-

tor fa makes it difficult to directly translate peak intensities into the quantity

or concentration. But the signal attenuation factor can be theoretically cal-

culated [15], or experimentally measured [16], or calibrated using standards

with known concentrations [3].

3.4.1 ITOCSYIsotope-edited total correlation spectroscopy (ITOCSY) separates 2D1H–1H total correlation spectroscopy (TOCSY) spectra from 12C- and13C-containing molecules, respectively, into two quantitatively equivalent

spectra [59]. It can thus quantify 12C-isotopic (nonlabeled) compounds rel-

atively to 13C-containing counterpart. Associating with the idea of tradi-

tional isotope dilution method, ITOCSY can derive the absolute

concentrations of metabolites in complex solutions from the measured ratio

of a metabolite’s signal to the signal from 13C-labeled internal standards

added into the solution. Multiplying the measured isotope ratio by the

amount of 13C-labeled internal standards added will give the amount of

metabolite present in the mixture sample.

3.4.2 FMQ by NMRCompared to 1H 1D qNMR, generally much longer experimental time of

2D NMR prevents its general use for high-throughput quantitative appli-

cations [19]. But with substantial amount of starting material, 2D 1H–13CHSQC spectrum can be collected in as little as 12 min. 2D 1H–13C HSQC

strategy, such as FMQ by NMRmethod, can be applied for absolute quan-

tification of metabolite mixture by reference to the standard curve con-

structed using mixtures with known compound concentrations.

Generally, cross-peak intensities in 2D 1H–13C HSQC spectrum are mod-

ulated by many factors, including the INEPT magnetization transfer, which

depends on compound-specific heteronuclear J-coupling (specific to each

metabolite and each functional group), the inhomogeneous excitation pro-

files of the heteronuclear pulses (Fig. 3), the relaxation properties of the rel-

evant coherences, etc. FMQ by NMR first records 2D 1H–13C HSQC

calibration spectra on synthetic concentration reference mixtures and con-

structs the standard curve [3]. The signal intensities in 2D 1H–13C HSQC

110 X. Li and K. Hu

spectrum of the metabolite mixture can then be related to the quantity of the

metabolite of interest by reference to the standard curve.

It should be emphasized that FMQ by NMR requires that 2D 1H–13CHSQC spectra of standards are collected under comparable conditions [3].

For FMQ by NMR using the same pulse program with identical acquisition

parameters, the cross-peak intensities modulated by the INEPT transfer, can

be adjusted by reference to the calibration curve. But the relaxation prop-

erties (such as T1 and T2 relaxation rates) of each metabolite are function of

the molecular tumbling time, which is dependent on the buffer composition

and the sample temperature, and all pulse width and 13C off-resonance effect

may also be affected by the sample composition, such as the salt concentra-

tion. That is to say, the sample condition will affect the overall signal atten-

uation factor (fa) and the final cross-peak intensities in 2D 1H–13C HSQC

spectrum. Therefore, it requires that 2D 1H–13C HSQC spectra of standard

mixtures are collected under comparable (ideally identical) sample condi-

tions to that of the metabolite mixture to be measured. As the composition

of the complex mixture itself (rather than the buffer composition) is almost

always different from one or the mixture of the standards, thus strictly, it is

inevitable that the signal attenuation factor (fa) of the metabolite in the mix-

ture to be measured is different from that of the same metabolite standard in

the calibration mixtures solely due to the different sample conditions, con-

sidering the compositions of both the sample buffer and mixture itself (anal-

ogous to the matrix effect in mass spectrum). The difference of sample

conditions between the metabolite mixtures to be measured and the refer-

ence mixtures may partially contribute to the measurement error of FMQ

by NMR.

3.4.3 HSQC0As stated earlier, the cross-peak intensities in 2D 1H–13C HSQC spectrum

are modulated bymany variables. Thought the overall signal attenuation fac-

tor for different compounds can be theoretically [15] calculated, its accuracy

heavily depends on correct relaxation parameters and J-couplings (specific to

each metabolite and each functional group) used, and also the excitation

profile and the off-resonance effect of the 13C pulses. It can also be exper-

imentally calibrated [3], but strictly only under identical sample conditions

even if the same pulse program with identical acquisition parameters is used.

HSQC0 was proposed to experimentally determine signal attenuation fac-

tor [16]. A series of HSQC spectra acquired with incremented repetition

times of the HSQC coherence transfer unit (from the end of the first 1H

111qNMR of Mixtures

excitation pulse to the beginning of data acquisition) can be extrapolated

back to zero time to yield a HSQC0 in which signal intensities are propor-

tional to concentrations of individual metabolites. The signal attenuation

factors can be derived from the slope of the natural logarithm linear

regression.

The main drawback of HSQC0 is that it is time consuming, especially

with an interscan delay long enough for magnetization to relax to fully equil-

ibrated state. Relaxation enhancing agents can be added into the sample to

partially alleviate the experimental time requirement. Choice of proper

relaxation-enhancing agents depends on the aqueous or organic solvent

[21,22], and cautions have to be exercised with regard to the interaction

between the agents and the sample components. For example, in our

previous study, for sample in the solvent of CDCl3, a 40 μL amount of

4% Cr(AcAc)3 in CDCl3 can be added to the sample of 500 μL as a

relaxation-enhancing agent [22], while for aqueous sample, 12 μL of

100 mM Fe(III)EDTA (ethylenediaminetetraacetic acid) can be added to

the sample of 500 μL as a relaxation-enhancing agent [21].

4. APPLICATIONS OF qNMR

NMR is one of the most powerful analytical tools for identifying and

quantifying molecular species due to its capability of providing structure

information and its inherent direct proportional relationship between reso-

nance strength and number of nucleus, respectively. Since the initial stage of

development of NMR, it has been considered as a valuable analytical tool for

analysis of complexmixtures. For instance, in 1981 by Stilbs [60] made use of

molecular self-diffusion coefficients to analyze complex mixtures by

FT-NMR. With the rapidly progress of NMR in the past decades, such

as emergence of gigahertz magnets and cryogenic probes, some of its original

drawbacks have been increasingly overcame and its application range has

been expanding. Atkinson et al. [61] proposed a new hyperpolarization tech-

nique, named as signal amplification by reversible exchange (SABRE) to

address low sensitivity problem. This technique has been successfully applied

in quantitative trace analysis of complex mixtures [62]. In the past decade,

the use of NMR for complex mixture analysis gained great attention due to

its unbiased and robust nature, as well as its employment in high-throughput

mode, particularly in metabolomics area [63]. In this section, we summarize

the applications of qNMR in typical mixture analysis over the past 5 years

112 X. Li and K. Hu

including metabolic studies, natural products, TCM, pharmaceutical

research and food analysis.

4.1 Metabolic Studies4.1.1 MetabolomicsMetabolomics (also called metabonomics) is defined as comprehensive anal-

ysis of all small molecular weight metabolites (<1500 Da) in complex bio-

logical systems [64–66] under different state, such as biofluids, cells, and

tissues. Identification and detection of differential metabolites with biolog-

ical significance are the main purposes of metabolomics [67]. Since the

inception of metabolomics, 1D 1H NMR has been considered as a main

analytical tool for metabolomics studies. The usual procedure of NMR-

based metabolomics is that datasets are preprocessed with bucketing or

binning strategy followed by multivariate statistical analysis which reports

relative quantitative changes. In spite of rapid, simplicity, this nontargeting

method has some drawbacks including susceptibility to solvent and matrix

effects, high rates of false positives, poor reproducibility, and limited data

transferability between different platforms. Given these limitations, there

is a growing trend toward NMR-based targeted profiling or quantitative

metabolomics.

qNMR, whose quantitative capability of complex mixtures has been

comprehensively described in some recent reviews [68–70], is one of the

most employed techniques for quantitative metabolomics. Among various

qNMR techniques, 1D 1H qNMR is extensively used for quantitative met-

abolomics in complex biological samples (see Table 1), which is mainly clas-

sified as: (a) biofluids, such as plasma [71], serum [72–77], urine

[71–73,76,78–80], and cerebral spinal fluid [72]; (b) cell [83,86] and culturemedia [86,87]; (c) tissue extracts [30,73,88]; (d) plants [89,90]; and

(e) microorganisms [73,91]. In spite of a good number of strengths for quan-

titatively detecting multiple components in complex mixture by 1H 1D

qNMR, its critical drawback is severe spectral overlap. To alleviate peak

overlap problem in 1H NMR spectra of complex mixtures and obtain

reliable and accurate quantitative results, there are two main strategies used

to separate or extract overlapping resonance signals. The first one is data

processing technique, mainly including spectral line fitting [30,72,78,91,93]

and deconvolutionmethod [33,94–97]. The second strategy is 2D- or higher-

dimensional NMR techniques. For example, 1H–13C HSQC [81], 2D

J-resolved NMR (JRES) [82,92], 2D 1H incredible natural-abundance

double-quantum transfer experiment (INADEQUATE) [85], and 1H–1H

113qNMR of Mixtures

Table 1 Overview of qNMR Applications in Metabolomics Over the Past 5 YearsSamples NMR Experiments References (Years)

Biofluids

Plasma 1H NMR [71] (2012)

Serum 1H NMR [72] (2011)

[71] (2012)

[73,74] (2013)

[75,76] (2015)

[77] (2016)

Urine 1H NMR1H–13C HSQC1H 2D JRES

[72] (2011)

[71] (2012)

[73,78,79] (2013)

[76,80] (2015)

[81] (2012)

[82] (2013)

Cerebral spinal fluid 1H NMR [72] (2011)

Cell and culture media

Glioblastoma cell 1H NMR [83] (2011)

Breast cancer cell COSY1H INADEQUATE

[84] (2012)

[85] (2012)

Culture media 1H NMR [86] (2014)

[87] (2016)

Tissue extracts

Rat brain 1H NMR [30] (2011)

Earthworm 1H NMR [73] (2013)

Wide fish 1H NMR [88] (2016)

Plants

Mungbean seeds 1H NMR [89] (2014)

Pelargonium sidoides

P. reniforme

1H NMR [90] (2015)

Microorganisms

Yeast (Pichia pastoris) 1H NMR [91] (2011)

Pseudomonas aeruginosa 1H NMR [73] (2013)

Others

Multiple samples 1H 2D JRES [92] (2016)

114 X. Li and K. Hu

correlation spectroscopy (COSY) [84] have been employed to quantitative

metabolomics. However, conventional quantitative 2D NMR experiments

usually require long experimental time (usually from 30 min to several hours).

To overcome this limitation, spatially encoded pulse sequence [84] and

nonlinear sampling schemes [81] have been incorporated into quantitative

2D NMR, which have been successfully applied in metabolomics studies.

4.1.2 Metabolic FluxAnother important aspect of metabolic studies is metabolic fluxes analysis

(MFA). MFA aims at evaluating the distribution of label within metabolites

extracted from biological samples (such as biomass hydrolyzates, biofluids,

cells, tissues, or organisms) for understanding the dynamic behavior during

metabolic processes. Accurate characterization of metabolic isotopomers

and quantification of 13C labeling ratios within those isotopomers are the

two key aspects of MFA [98]. NMR technique can offer two different types

of information (specific enrichments and isotopic isomers) [99]. Combined

with 13C-labeling, it is often applied to determine metabolic fluxes in bio-

logical systems [100,101]. The metabolic flux in complex network can

be evaluated from the labeling pattern of metabolites of biological samples

incubated with 13C-labeled substrates.

Detailed site-specific enrichment information within a metabolite of

interest can be obtained from NMR spectroscopy. Integrating the 13C-

satellite peaks in 1H NMR spectra is one of the simplest ways to measure

the 13C-enrichment [102–104]. However, due to the presence of serious

overlapping peaks in 1D NMR spectra of complex metabolic mixtures,

application of this method in MFA is limited. To overcome this limitation,

several approaches, including spectral fitting/deconvolution [105] and 2D

NMR techniques [59,106], have been developed, but the spectral fitting

method requires the exact knowledge of 1D spectrum of individual metab-

olite at a given pH. A comparative evaluation of 2D COSY and TOCSY

were reported for isotopomer analysis, and the results showed TOCSY

was preferred to provide accurate measurements of 13C-enrichments [99].

This quantitative potential was demonstrated by application of 2D-TOCSY

NMR to the measurement of specific 13C-enrichments in a biomass hydro-

lyzate from Escherichia coli cells grown on a mixture of 20% [U-13C]-glucose

and 80% [1-13C]-glucose [107]. Considering the long experimental time of

conventional 2D NMR, ultrafast 2D NMR proposed in 2002 by Frydman

and coworkers [108] has recently been applied to metabolic flux analysis.

Giraudeau et al. [109] presented the first application of ultrafast 2D NMR

115qNMR of Mixtures

to measurement of 13C-enrichments in a real biological extract, which was a

biomass hydrolyzate. For reason of still existing overlapping problem in 2D

NMR, acquisition of 3D NMR spectrum has been increasingly used for

MFA. For instance, ultrafast spatially encoded J-resolved COSY

(UFJCOSY) [110,111] and nonuniform sampling (NUS)-based 3D

TOCSY-HSQC [112] have been successfully applied to metabolic flux

analysis.

4.2 Natural Products and TCM4.2.1 Natural ProductsGenerally, natural products mainly referred as to naturally biosynthesized

small molecules, existing in the form of complex extracts or isolated chem-

ical entities. Natural products have a close relation with our everyday life and

play an important role in modern life science [113] and are extensively

applied for drug discovery [114,115], food [116], and cosmetic development

[117]. In traditional natural products research, several restrictive factors may

slow down the discovery and identification of new single chemical entities

(SCEs), such as time consuming and cumbersome separation, purification

and structural elucidation steps, redundant work such as identifying already

known compounds, limited detection sensitivity, lacking systematic regis-

tration of origin organisms and derived compounds. Recently, this situation

has been increasingly changed owing to the improvements of NMR tech-

nique and the application of dereplication- and metabolomics-based strate-

gies [118–121].Aside from as a major analytical tool for structure elucidation of

unknown natural products, NMR is also widely used to quantitatively ana-

lyze natural products [122] (see Table 2), including (a) small molecular

weight chemical compounds such as alcohols [133,134], phenolic acids

[125,126], glycosides [124,129], alkaloids [127,130], flavonolignans [131],

alkylene [132], heterocyclic compounds [128,141], and terpenoids

[123,132] and (b) high molecular weight natural substances such as polysac-

charides [142] and lignins [135–140]. To obtain accurate quantitative results,it is usually required to optimize qNMR toward given class of compounds.

For example, in the case of determining hydrogen peroxide and phenolic

compounds in plant extracts, it requires using DMSO-d6 as solvent together

with picric acid at low temperature near the freezing point of the solution, in

order to maximatily minimize proton exchange rate [126]. For lignins,

HSQC is often applied for quantitative evaluation of different interunit

linkages [135,137,140]. In addition, due to the presence of phenolic,

116 X. Li and K. Hu

Table 2 Overview of qNMR Applications in Natural Products Over the Past 5 Years

Extracts/Materials NMR Experiments ComponentsReferences(Years)

Arnica montana 1H NMR Sesquiterpene

lactones

[123] (2011)

Stevia rebaudiana 1H NMR Glycosides [124] (2011)

Rosmarinus officinalis,

Salvia officinalis,Origanum

vulgare, Ligustrum lucidum,

leaves

1H NMR Phenolic acids,

flavonoids

[125] (2011)

Origanum vulgare 1H NMR Hydrogen peroxide,

phenolic acids

[126] (2012)

Annona species 1H NMR Trigonelline [127] (2013)

Rosmarinus officinalis 1H NMR Rosmarinic acids,

carnosic acids

[122] (2013)

Ginkgo biloba 1H NMR Ginkgotoxin [128] (2014)

Prunus serotina Ehrh. 1H NMR Amygdalin, prunasin [129] (2014)

Berberidis radix, Coptidis

rhizoma, Phellodendri

chinensis cortex

1H NMR Protoberberine [130] (2014)

Silybum marianum fruit 1H NMR Silychristin,

silydianin, silybin A,

silybin B, isosilybin

A, isosilybin B

[131] (2016)

Eucalyptus, Corymbia

essentials oils

1H NMR α-Pinene [132] (2016)

Argyrolobium roseum 1H NMR Pinitol [133] (2016)

Euphorbia, Morus,

Araujia, leaves

13C NMR Nonpolar metabolites [134] (2011)

Birch 13C NMR Lignins [135] (2013)

Wheat straw 31P NMR1H–13C HSQC

Lignins [136] (2014)

Picea abies, Picea mariana,

beech, wheat straw

1H–13C HSQC Lignins [137] (2011)

Bambusa rigida sp. 1H–13C HSQC Lignins [138] (2012)

Continued

117qNMR of Mixtures

carboxylic, and aliphatic hydroxyl groups, 31P NMR can also be used to

quantify lignin after derivatizing with 2-chloro-4,4,5,5-tetramethyl-1,3,2-

dioxaphospholane [136].

4.2.2 TCMTCM has been widely used in China for thousands of years for preventing

and curing human diseases, because of their less side effects, recognized effi-

cacy, and low cost. It is well known that multicomponents, multichannels,

and multitargets are typical characteristics of TCM, which exert effects syn-

ergistically. Accumulative clinical practices of TCM have increasingly

prompted their rational and safe usage. However, due to complex compo-

nents and adulterations made by unconscientious manufacturers, quality

control of TCMs has become the main concern, which seriously impedes

its application and development. Thus, comprehensive methods, such as

the fingerprint and the multicomponents quantification, are in urgent need

for quality control of TCM. qNMRhas been increasingly regarded as one of

the critical analytical tools for addressing problems usually encountering in

quality control of TCM. For instance, Fan et al. [143] adopted qNMR to

quantitatively analyze six alkaloids in Rhizoma coptidis for quality evaluation

and species differentiation. The applications of qNMR in TCM research

Table 2 Overview of qNMR Applications in Natural Products Over the Past 5Years—cont’d

Extracts/Materials NMR Experiments ComponentsReferences(Years)

Picea abies, Picea mariana 1H–13C HSQC Lignins [139] (2013)

Paulownia elongata, aspen,

sugar maple, southern

hardwood mixture,

willow

1H–13C HSQC Lignins [140] (2015)

Ground coffee 2D SEPP-HSQC Pyrazines and

pyridines derivatives

[141] (2016)

Haemophilus influenzae

type b

1H NMR

ZQF-TOCSY,

COSY, 1H

INADEQUATE,1H–13C HSQC,1H–31P HSQC

Capsular

polysaccharide PRP

[142] (2015)

118 X. Li and K. Hu

mainly include purity estimation of either the major active components’

reference material or preparations derived from TCMs and determination

of the content of major active components in TCMs extracts [144].

Yao et al. [145] employed high-performance liquid chromatography

(HPLC) and qNMR to evaluate the purity of baicalein reference material.

Recently, a study of quantitative control of the quality of TCM preparation

of salvianolate lyophilized injection has been reported [146]. This prepara-

tion is composed of multiple salvianolic acids (Sal) with highly similar struc-

ture, which are difficult to be quantitatively analyzed by traditional HPLC.

Quantitative analysis of the content of major active components in TCM

products is the main application of qNMR in TCMs research, which is

summarized in Table 3 over the past 5 years.

4.3 Pharmaceutical ResearchPharmaceutical drug development involves purity check and stability test of

the starting materials (active substance, excipients), intermediates, and the

end products. NMR can qualitatively elucidate structural information of

and simultaneously quantify the components in complex mixtures, with less

or almost no sample preparation and without the need of beforehand sep-

aration and authentication of reference standard. Meanwhile, NMR is avail-

able for most pharmaceutical laboratories. Therefore, it is widely applied and

plays an important role in pharmaceutical analysis. qNMR has been increas-

ingly accepted as one of the main analytical methods in pharmaceutical

industry by the International Conference on Harmonization (ICH),

European and US Pharmacopeia, and all work in pharmaceutical industry

must meet some appropriate regulatory standards. Although solid-state

NMR can also be applied in pharmaceutical industry [159,160], we mainly

focus on the application of liquid-state qNMR in pharmaceutical analysis in

this review. The application of qNMR in pharmaceutical research and

development predominantly includes three aspects (see Table 4):

(a) purity determination of pharmaceutical reference materials, (b) quality

evaluation of the active pharmaceutical ingredients (APIs) and excipients,

and (c) measurement of the level of impurities.

Pharmaceutical reference standards play an essential role in drug quality

control because the obtained absolute concentrations directly depend on

their purities either as an external or internal standard. Therefore, the purity

of the reference standards needs to be determined in very high accuracy. The

purity is traditionally quantified by the mass balance method on the basis of

119qNMR of Mixtures

Table 3 Overview of qNMR Applications in TCM Products Over the Past 5 Years

Materials/PreparationsNMRExperiments Components

References(Years)

Andrographis paniculata

CHUANXINLIAN

tablets

CHUANXINLIAN

capsules

XIAOYANLIDAN

tablets

FUKEQIANJIN

tablets

1H NMR Andrographolide,

dehydroandrographolide,

deoxyandrographolide,

neoandrographolide

[147] (2012)

Angelica sinensis 1H NMR Ligustilide, polyynes, alkyl

phthalides, phenylpropanoids,

poly-unsaturated fatty acids

[148] (2012)

Angelica sinensis,

Angelica gigas (ROK),

Angelica gigas (DPRK)

1H NMR Glucose, fructose, threonine,

ferulic acid, other common

metabolites

[149] (2014)

Rhizoma coptidis 1H NMR Berberine, coptisine,

jatrorrhizine, palmatine,

epiberberine, columbamine

[143,150]

(2012)

Dictamni cortex 1H NMR Dictamin [151] (2013)

Reum palmatum 1H NMR Rhein, emodin, aloe-emodin,

physcion, chrysophanol

[152] (2013)

Leonurus cardiaca,

Leonurus japonicus,

Leonotis leonurus

1H NMR Stachydrine [153] (2013)

[154] (2014)

Peucedani radix 1H NMR Praeruptorin A,

praeruptorin B

[155] (2014)

Pueraria lobata,

Pueraria thomsonii

1H NMR Puerarin, isoflavones [156] (2014)

Mahoniae caulis 1H NMR Berberine, columbamine,

jatrorrhizine, palmatine

[157] (2015)

Breviscapine preparations 1H NMR Scutellarin [158] (2016)

Salvianolate lyophilized

injection

1H NMR PRO, Sal B, Sal D, Sal E,

DSal E, Sal Y, LA, DLA, RA

[146] (2016)

DLA, diastereomer of lithospermic acid; DSal E, diastereomer of salvianolic acid E; LA, lithospermic acid;PRO, protocatechualdehyde; RA, rosmarinic acid; Sal, salvianolic acid.

120 X. Li and K. Hu

Table 4 Overview of qNMR Applications in Pharmaceutical Research Over the Past 5Years

ProductsNMRExperiments Quantitative Compositions

References(Years)

Purity determination of pharmaceutical reference material

Sodium diclofenac 1H NMR Sodium diclofenac [161] (2013)

Ibandronic acid,

Amantadine, Ambroxol,

Lercanidipine

1H NMR Ibandronic acid,

amantadine, ambroxol,

lercanidipine

[162] (2014)

Acetylsalicylic acid,

Fumaric acid

1H NMR Acetylsalicylic acid, fumaric

acid

[163] (2014)

Acetanilide, 3,4,5-

Trichloropyridine,

Dimethylterephthalate,

Maleic acid, 3-sulfolene,

1,4-bis(trimethylsilyl)

benzene, 1,3,5-

trimethoxybenzene

1H NMR Acetanilide, 3,4,5-

trichloropyridine,

dimethylterephthalate,

maleic acid, 3-sulfolene,

1,4-bis(trimethylsilyl)

benzene, 1,3,5-

trimethoxybenzene

[164] (2014)

ACE inhibitors 1H NMR Imidapril hydrochloride,

benazepril hydrochloride,

lisinopril, enalapril maleate,

quinapril hydrochloride,

captopril

[165] (2015)

SRM 350b

SRM 84 L

SRM 194a

31P NMR Benzoic acid, potassium

hydrogen phthalate,

ammonium dihydrogen

phosphate

[166] (2015)

Content determination of drugs (APIs)

Diquertin 1H NMR Taxifolin [167] (2011)

Unibiotic capsules

Flagyl tablets

Sutrim tablets

1H NMR Levofloxacin,

metronidazole,

sulfamethoxazole

[168] (2012)

AVB1a tablets1H NMR AVB1a [169] (2014)

CLP tablets 1H NMR Clindamycin phosphate [170] (2014)

Iobitridol injection 1H NMR Iobitridol [171] (2014)

Rectal suppositories 1H NMR Umifenovirum, ibuprofen,

imunofan

[172] (2014)

Continued

121qNMR of Mixtures

water, inorganic impurity, and chromatographic purity. But these conven-

tional quantitative analytical methods for purity determination are relatively

cumbersome and time consuming. qNMR has been increasingly employed

for purity evaluation of reference standards [185], which is usually much

Table 4 Overview of qNMR Applications in Pharmaceutical Research Over the Past 5Years—cont’d

ProductsNMRExperiments Quantitative Compositions

References(Years)

RLM API 1H NMR Rilmenidine dihydrogen

phosphate

[173] (2014)

API Prota powders,

Herring Prota powders,

Prota injection USP

1H NMR Ala βH/Arg δH, Gly αH/

Arg δH, Arg αH/Arg δH[174] (2015)

MEM (USP grade)

MEM tablets

1H NMR Memantine hydrochloride [175] (2015)

STG tablets 19F NMR Sitagliptin phosphate [176] (2015)

FOSF calcium tablets,

FOSF sodium tablets

31P NMR Fosfomycin [177] (2015)

Ibuprofen 1H–13CHSQC

Ibuprofen [178] (2013)

Excipients analysis

Amorphous lactose 1H NMR β/α anomer [179] (2012)

Pharmaceutical lipids 1H NMR Acid value [180] (2014)

2-HP-β-CD 1H NMR 2-HP-β-CD [181] (2015)

Sodium CMC 13C NMR AGUs [182] (2016)

Impurities detection

Heparin sodium 1H NMR O-acetylation product of

heparin

[183] (2011)

RLM API 1H NMR Relative impurity-B [173] (2014)

FOSF calcium tablets,

FOSF sodium tablets

31P NMR Impurity A [177] (2015)

Oligonucleotide 31P NMR Monophosphate

substituted impurity

[184] (2016)

2-HP-β-CD, 2-hydroxypropyl-β-cyclodextrin; CLP, clindamycin phosphate; FOSF, fosfomycin;MEM, memantine hydrochloride; Prota, protamine sulfate; RLM, rilmenidine dihydrogen phosphate;SRM, standard reference material; STG, sitagliptin phosphate monohydrate; USP, United StatePharmacopoeia.

122 X. Li and K. Hu

easier and faster than traditional mass balance approach including quantifi-

cation of all impurities (as well as moisture and ash). For example, Nogueira

et al. [161] adopted mass balance approach and 1H qNMR to determine the

certified property value of sodium diclofenac certified reference material

(CRM), and achieved satisfactory results. Similarly, qNMR was established

for purity determination of pharmaceutical reference materials by Shen et al.

[165], who investigated the accuracy and precision of the qNMR method

for purity determination of six types of angiotensin-converting enzyme

(ACE) inhibitors reference standards. They made a comparison between

qNMR and mass balance approach and obtained good accuracy and preci-

sion which satisfied the requirements for quantitative analysis of chemical

reference standards. Usually, the quantification is based on the signal from

a chemical reference standard added to the sample (internal standard). Nev-

ertheless, the internal standards must be met some requirements, such as no

interaction with target compounds, no signal overlap, etc. Recently, an

alternative to the routine qNMR on the basis of the internal reference cal-

ibrationwas proposed byMonakhova et al. [162], who applied 1H qNMR in

combination with PULCON methodology for purity determinations of

pharmaceutical reference materials. Besides 1H qNMR, 31P qNMR can also

be used for quantitative analysis of the purity of reference standards con-

taining phosphorus element [166].

The quality of APIs and excipients greatly influences the potency (or

efficacy) of the given drug formulations. The quality assurance and quality

control of APIs and excipients are the major tasks of pharmaceutical analysis,

which is another main application of qNMR in pharmaceutical industry.

Recently, pharmaceutical application of qNMR has obtained great

attention, which is described by two excellent reviews [186,187] and is also

demonstrated by lots of studies reported. In order to secure the drug quality,1H qNMR is generally exploited for content determination of APIs, such as

diquertin [167], iobitridol [171], and for quantitative analysis of excipients,

such as lactose [179], pharmaceutical lipids [180], and 2-hydro-

xypropyl-β-cyclodextrin (2-HP-β-CD) [181]. Liang et al. [170] proposed

a qNMR method for reliable content determination of clindamycin phos-

phate (CLP) tablets, which contained about 45% APIs. Aiming to demon-

strate the capability of qNMR for low-content determination of a drug,

Hou et al. [169] developed a validated qNMR approach for quantification

of avermectin B1a (AVB1a) tablets, whose content was less than 5%. Besides

tablets, qNMR is also employed for quantitative analysis of injection [171],

suppositories [172], etc. Apart from 1H qNMR, 19F qNMR has also been

considered as an important analytical tool in pharmaceutical analysis [176]

123qNMR of Mixtures

because of the wide presence of fluorine in many drugs, 100% natural abun-

dance of fluorine and generally low background interference. For example,

disposition of fluoropyrimidine during the drug development process was

investigated by 19F qNMR [188]. Very often, in order to improve the phar-

macological and/or absorption, disposition, metabolism, and excretion

(ADME) properties of lead drug candidates, 19F is incorporated in lead com-

pounds in pharmaceutical research [189]. Furthermore, 13C qNMR can also

be employed in pharmaceutical filed. For instance, Kono et al. [182] utilized13C qNMR to analyze a series of sodium carboxymethyl cellulose (CMC)

samples, aiming to determine the anhydroglucose units (AGU) and the sub-

stitution distribution. Despite 1D qNMR is the most common used

method, signal overlaps 1D NMR may often be encountered for complex

solutions or mixtures, resulting in poorly feasible quantification. Therefore,

in some cases, it is necessary to adopt 2D qNMR for quantitative analysis.

For example, Martineau et al. [178] employed fast quantitative 1H–13CHSQC to determine ibuprofen. Likewise, a quantitative 2DCOSY has been

used to identify and to quantify pharmaceutical excipients cyclodextrins in

plasma medium [190].

Identification and quantification of impurities are very importance for

drug quality control, because pharmaceutical impurities may influence the

safety and/or potency (or efficacy) of the pharmaceutical products [191].

One requirement of current standards and regulations is to thoroughly

understand the source and formation mechanism of impurities arising from

the manufacturing and/or storing process of drug substances, which renders

qNMR a suitable analytical tool for impurity analysis due to its qualitative

and quantitative ability. Lee et al. [183] identified an impurity, namely

O-acetylation product of heparin, which produces the extra signals at

2.17 and 2.25 ppm in the 1H NMR spectrum of pharmaceutical-grade hep-

arin sodium, and quantified the level of this impurity using three orthogonal

techniques, including 1H NMR, ion chromatography, and headspace gas

chromatography/mass spectrometry. The results of their work showed good

agreement among these three approaches. Similarly, a study was reported on

qualitative and quantitative analysis of rilmenidine dihydrogen phosphate

(RLM) and its relative impurity-B by using 1H qNMR [173]. In addition,31P qNMR was also used to determine fosfomycin (FOSF) and impurity

A in pharmaceutical products of FOSF sodium or calcium [177], and

coeluting impurity in a modified oligonucleotide [184]. Besides the afore-

mentioned main applications of qNMR in pharmaceutical research, qNMR

can also be applied to counterfeit analysis [192] and evaluation of drug

124 X. Li and K. Hu

delivery systems, such as in vitro drug release test [193], analysis of skin

penetration of active drug [194,195].

4.4 Food AnalysisThe safety, authenticity, and quality of food are of public concern and

present a challenging task for food analysis. NMR is a typical nondestructive,

but powerful qualitative and quantitative tool for food analysis. NMR has a

wide range of applications in food industry, including identification and

quantification of the chemical compositions of food, detection of food

authentication, monitoring food processing, differentiating the origins of

food ingredients [196,197]. These topics are well reviewed by Spyros

[198] through exemplification according to food type. In addition,

Duynhoven et al. [18] have also summarized the application of qNMR

in food ingredients and products. In this section, we mainly review the

application of qNMR in food analysis reported since 2013–2016, whichis summarized in Table 5.

Among all qNMR techniques applied in food analysis, 1D 1H qNMR is

most often used. It is employed to determine chemical compositions in a

variety of food matrices for quality control, such as pork meat [232], energy

drink [212], apple fruit juice [214], cider [215,216], honey [236], and beer

[201]. It is also applied to evaluate food processing, for example, monitoring

lactic acid production during milk fermentation [230], estimating nonther-

mal processing of orange juice [217]. In addition, food additives [246] and

packaging material [245] can also be evaluated by 1H qNMR. Food authen-

ticity detection or adulteration analysis is an important application field of

qNMR, which is well demonstrated by recent published literatures

[235,239,248]. Pages et al. [238] identified and quantified adulterants in sex-

ual enhancement and weight loss dietary supplements (DS) with a benchtop

cryogen-free low-field 1H NMR spectrometer. To control the authenticity

of vinegar [206–208] and rice spirits [203], a method named SNIF (site-

specific natural isotope fractionation)–NMR has been proposed to monitor

adulteration. SNIF–NMR can distinguish acetic acid or ethyl alcohol from

different raw materials on the basis of significant differences in the site-

specific ratio of deuterium to hydrogen. 1H NMR-based metabolomics

can also be employed to investigate different origins of food, such as soybean

[223] and green coffee bean [227]. Caligiani et al. [197] utilized 1HNMR for

characterizing geographical origins and fermentation levels of cocoa

beans based on the metabolic profiling. Similar to 1H qNMR, 13C

125qNMR of Mixtures

Table 5 Overview of qNMR Applications in Food Analysis Over the Past 3 Years

ProductsNMRExperiments Quantitative Compositions

References(Years)

Alcoholic beverages

Wine 1H–13C HSQC Free and sulfite-bound

carbonyl compounds

[199] (2015)

13C NMR Fructose isomer [200] (2015)

Beer 1H NMR Benzoxazinoids [201] (2016)

1H–13C HSQC Glucans [202] (2013)

Spirits 1H NMR Ethanol [203] (2014)

Tobacco liqueur 1H NMR Nicotine [204] (2014)

Egg liqueur 1H NMR Ethanol, cholesterol, total

sugar

[205] (2015)

Vinegar 1H NMR Acetic acid [206,207]

(2013)

[208] (2014)

1H NMR Sugars, organic acids,

5-hydroxymethyl-furfural

(HMF)

[209] (2015)

2H NMR Acetic acid, ethanol [210] (2015)

Nonalcoholic beverages

Green tea 1H NMR Catechins [211] (2014)

Energy drinks 1H NMR Taurine [212] (2014)

Cola drinks 31P NMR Phosphate [213] (2013)

Apple fruit juice 1H NMR Sugars, amino acids, organic

acids, acetoin, arbutin, HMF,

ethanol, methanol,

benzaldehyde, acetaldehyde,

[214] (2014)

Cider 1H NMR Lactic acid, acetic acid [215] (2015)

1H NMR Ethanol [216] (2015)

Orange juice 1H NMR Sucrose, β-glucose, fructose,malic acid, citric acid

[217] (2016)

Coffee 19F NMR Coffeine [218] (2016)

126 X. Li and K. Hu

Table 5 Overview of qNMR Applications in Food Analysis Over the Past 3 Years—cont’d

ProductsNMRExperiments Quantitative Compositions

References(Years)

Oils

Vegetable oils 1H NMR Fatty acids, oleic acid, linoleic

acid, linolenic acid

[219] (2015)

1H NMR Cyclic dimer fatty acid [220] (2015)

Green coffee oil 1H NMR,13C NMR

Glycerides, fatty acids,

caffeine, cafestol, kahweol,

16-O-methylcafestol

[221] (2013)

Edible oils 19F NMR OH-molecules [222] (2015)

Vegetables

Soybean 1H NMR Amino acids, organic acids,

sugars, choline, p-cresol

O-acetylcholine, trigonelline,

dimethylamine

[223] (2015)

Edible onion species 1H NMR Metabolites [224] (2014)

Tomato UF-COSY Metabolites [225] (2015)

Cocoa bean 1H NMR Amino acids, organic acids,

sugars

[197] (2014)

1H NMR Amino acids, polyalcohols,

organic acids, sugars,

methylxanthines, catechins

[226] (2016)

Coffee bean 1H NMR Quinic acid, choline, acetic

acid, sucrose

[227] (2015)

Milk and dairy products

Human milk 1H NMR Metabolites [228] (2016)

Butter, cheese,

margarine

1H NMR Dehydroacetic acid [229] (2013)

Cow milk 1H NMR Lactic acid [230] (2013)

Milk, halloumi

cheese

1D TOCSY

2D TOCSY

Lipids [231] (2015)

Meat

Pork 1H NMR Fatty acid chain [232] (2013)

Beef 1H NMR Conjugated linoleic acid [233] (2015)

Salmon by-products 1H NMR Metabolites [234] (2016)

Continued

127qNMR of Mixtures

Table 5 Overview of qNMR Applications in Food Analysis Over the Past 3 Years—cont’d

ProductsNMRExperiments Quantitative Compositions

References(Years)

Honey

1H NMR Glucose, fructose, sucrose,

HMF

[235] (2015)

1H NMR Carboxylic acids, amino acids,

carbohydrates, ethanol, HMF

[236] (2016)

13C NMR Carbohydrate [237] (2015)

Food or dietary supplements

Sexual enhancement

DS

Weight loss DS

1H NMR Sildenafil and its analogs,

tadalafil, vardenafil

[238] (2014)

Weight loss FS 1H NMR Sibutramine,

phenolphthalein, orlistat,

lorcaserin, fluoxetine,

sildenafil, caffeine, DMAA,

p-synephrine

[239] (2016)

Fish oil DS 1H NMR,13C NMR,31P NMR

Fatty acids [240] (2015)

Others

Sugar products 13C NMR Sucrose [241] (2016)

Soy sauce 13C NMR Glutamate, sucrose, glucose [242] (2016)

Multiple types of

food

2D JRES Methyleugenol, estragole [243] (2013)

Sugar beet flakes,

pea hulls, olive

pomace, grape

pomace, apple cake

1H NMR Methylation, acetylation, and

feruloylation degree of pectin

[244] (2014)

Food packaging 1H NMR 12 Additives in three types of

polylactide

[245] (2015)

Twelve processed

foods

1H NMR Acesulfame potassium [246] (2015)

Dark chocolate,

banana, gouda

cheese

1H NMR Biogenic amines [247] (2016)

DMAA, 1,3-dimethylamylamine; FS, food supplements; HMF, 5-hydroxymethyl-furfural

128 X. Li and K. Hu

[200,237,241], 19F [218,222], and 31P [213] qNMR techniques can also be

used to simultaneously qualitatively and quantitatively analyze foods and

their relevant products. Apart from the conventional 1D qNMR, 1D

TOCSY NMR spin-chromatography method can become an alternative

for food analysis [231]. This method has been successfully applied to rapid

identification and quantification of minor components in the lipid fraction

of milk and dairy products. Besides 1D qNMR, 2D qNMR can also be

employed to quantitative analysis of chemical compositions from food or

dietary product matrices. For instance, an optimized 1H–13C HSQC has

been used to identify and to quantify linear and branched starch fragments

in beer and has obtained the limits of detection (LOD) on the order of

10 μg/mL with experimental time of about 15 min [202]. The comparison

with quantitation using chromatographic methods shows a strong linear cor-

relation with correlation coefficients (R2) of above 0.99. Similarly,

Nikolantonaki et al. [199] have implemented direct analysis of free and

sulfite-bound carbonyl compounds in wine using 2D quantitative 1H–1HCOSY and 1H–13C HSQC. Moreover, the recent burgeoning ultrafast

2D NMR has also been applied to food analysis, which is demonstrated

by absolute quantification of metabolites in tomato fruit extracts using ultra-

fast COSY [225].

5. PROSPECT

For quantitative analysis of multiple compound mixtures, even its 2D

NMR spectra contain more resolved peaks than 1DNMR, direct numerical

integration by running summation may substantially underestimate the area

because of truncation of the long tails of the Lorentzian lineshape [29], or

otherwise may overestimate the area if the integration boundaries are set sig-

nificantly large to capture higher percentage of the target peak area because it

may at the same time include the tails of the neighboring peaks. Robust data

processing and analysis procedure have been used to automatically extract

the qNMR data [249]. To accurately obtain the peak areas, spectra can

be deconvoluted and peak areas are automatically determined for quantifi-

cation, preferably in combination with algorithms for their identification

based on the standard NMR spectra base or database. A keypoint to improve

the capability of the deconvolution algorithms for accurate signal identifica-

tion and quantification is to develop a proper model function to describe the

real experimental lineshape. In practice, due to suboptimal shimming and

other experimental artifacts, lineshape may deviate from the ideal Lorentzian

129qNMR of Mixtures

or Gaussian lineshape. To improve the performance spectral deconvolution,

weighted mixture of Lorentzian and Gaussian shapes and/or a GL peak

shape have been proposed to describe more flexible lineshape. In some cases,

a signal may be more properly modeled as a Gaussian distribution of

Lorentzian profile.

ACKNOWLEDGMENTSPart of this work was supported by Yunnan Provincial Science and Technology Department

(2012HA015), the Recruitment Program of Global Youth Experts, and the National Natural

Science Foundation of China (21505142).

REFERENCES[1] D.S. Wishart, Quantitative metabolomics using NMR, TrAC Trends Anal. Chem.

27 (2008) 228–237.[2] L. Xu, X.H. Shi, K.F. Hu, Quantification of multiple compounds containing hetero-

geneous elements in the mixture by one-dimensional nuclear magnetic resonancespectroscopy of different nuclei using a single universal concentration reference, Magn.Reson. Chem. 52 (2014) 779–782.

[3] I.A. Lewis, S.C. Schommer, B. Hodis, et al., Method for determining molar concen-trations of metabolites in complex solutions from two-dimensional 1H-13C NMRspectra, Anal. Chem. 79 (2007) 9385–9390.

[4] C.G. Tsiafoulis, V. Exarchou, P.P. Tziova, et al., A newmethod for the determinationof free L-carnitine in serum samples based on high field single quantum coherence fil-tering 1H-NMR spectroscopy, Anal. Bioanal. Chem. 399 (2011) 2285–2294.

[5] K. Skidmore, D. Hewitt, Y.H. Kao, Quantitation and characterization of processimpurities and extractables in protein-containing solutions using proton NMR as ageneral tool, Biotechnol. Prog. 28 (2012) 1526–1533.

[6] H.Y. Carr, E.M. Purcell, Effects of diffusion on free precession in nuclear magneticresonance experiments, Phys. Rev. 94 (1954) 630–638.

[7] M. Singh, V.K. Soni, R. Mishra, et al., Relaxation editing using long-lived states andcoherences for analysis of mixtures, Anal. Chem. 88 (2016) 3004–3008.

[8] J.A. Aguilar, S. Faulkner, M. Nilsson, et al., Pure shift 1H NMR: a resolution of theresolution problem, Angew. Chem. Int. Ed. 49 (2010) 3901–3903.

[9] J.A. Aguilar, M. Nilsson, G.A. Morris, Simple proton spectra from complex spin sys-tems: pure shift NMR spectroscopy using BIRD, Angew. Chem. Int. Ed. 50 (2011)9716–9717.

[10] G.A. Morris, Systematic sources of signal irreproducibility and t1 noise in high-fieldNMR spectrometers, J. Magn. Reson. 100 (1992) 316–328.

[11] C.S. Johnson, Diffusion ordered nuclear magnetic resonance spectroscopy: principlesand applications, Prog. Nucl. Magn. Reson. Spectrosc. 34 (1999) 203–256.

[12] S. Glanzer, K. Zangger, Directly decoupled diffusion-ordered NMR spectroscopy forthe analysis of compound mixtures, Chemistry 20 (2014) 11171–11175.

[13] J. Bjorneras, A. Botana, G.A. Morris, et al., Resolving complex mixtures: trilinear dif-fusion data, J. Biomol. NMR 58 (2014) 251–257.

[14] L. Dreier, G. Wider, Concentration measurements by PULCON using X-filtered or2D NMR spectra, Magn. Reson. Chem. 44 (2006) S206–S212.

130 X. Li and K. Hu

[15] R.K. Rai, P. Tripathi, N. Sinha, Quantification of metabolites from two-dimensionalnuclear magnetic resonance spectroscopy: application to human urine samples, Anal.Chem. 81 (2009) 10232–10238.

[16] K.F. Hu, W.M. Westler, J.L. Markley, Simultaneous quantification and identificationof individual chemicals in metabolite mixtures by two-dimensional extrapolated time-zero 1H-13C HSQC (HSQC0), J. Am. Chem. Soc. 133 (2011) 1662–1665.

[17] U. Holzgrabe, Quantitative NMR spectroscopy in pharmaceutical applications, Prog.Nucl. Magn. Reson. Spectrosc. 57 (2010) 229–240.

[18] J. Duynhoven, E. Velzen, D.M. Jacobs, Quantification of complex mixtures byNMR,in: G.A. Webb (Ed.), Annual Reports on NMR Spectroscopy, vol. 80, AcademicPress, Elsevier, Oxford, 2013, pp. 181–236.

[19] P. Giraudeau, Quantitative 2D liquid-state NMR, Magn. Reson. Chem. 52 (2014)259–272.

[20] E.J. Saude, C.M. Slupsky, B.D. Sykes, Optimization of NMR analysis of biologicalfluids for quantitative accuracy, Metabolomics 2 (2006) 113–123.

[21] K.F. Hu, J.J. Ellinger, R.A. Chylla, et al., Measurement of absolute concentrations ofindividual compounds in metabolite mixtures by gradient-selective time-zero 1H-13CHSQC with two concentration references and fast maximum likelihood reconstruc-tion analysis, Anal. Chem. 83 (2011) 9352–9360.

[22] K.F. Hu, T.P. Wyche, T.S. Bugni, et al., Selective quantification by 2D HSQC0

spectroscopy of thiocoraline in an extract from a sponge-derived Verrucosispora sp.,J. Nat. Prod. 74 (2011) 2295–2298.

[23] F. Malz, H. Jancke, Validation of quantitative NMR, J. Pharm. Biomed. Anal.38 (2005) 813–823.

[24] G. Balacco, C. Cobas, Automatic phase correction of 2DNMR spectra by a whiteningmethod, Magn. Reson. Chem. 47 (2009) 322–327.

[25] A.F. Mehlkopf, D. Korbee, T.A. Tiggelman, et al., Sources of t1 noise in two-dimensional NMR, J. Magn. Reson. 58 (1984) 315–323.

[26] N. Manoleras, R.S. Norton, Spectral processing methods for the removal of t1 noiseand solvent artifacts from NMR spectra, J. Biomol. NMR 2 (1992) 485–494.

[27] A. Gibbs, G.A.Morris, A.G. Swanson, et al., Suppression of t1 noise in 2DNMR spec-troscopy by reference deconvolution, J. Magn. Reson. Ser. A 101 (1993) 351–356.

[28] P.A. Hays, R.A. Thompson, A processing method enabling the use of peak height foraccurate and precise proton NMR quantitation, Magn. Reson. Chem. 47 (2009)819–824.

[29] T. Schoenberger, S. Menges, M.A. Bernstein, et al., Improving the performance ofhigh-precision qNMR measurements by a double integration procedure in practicalcases, Anal. Chem. 88 (2016) 3836–3843.

[30] R.A. de Graaf, G.M.I. Chowdhury, K.L. Behar, Quantification of high-resolution 1HNMR spectra from rat brain extracts, Anal. Chem. 83 (2011) 216–224.

[31] C. Cobas, F. Seoane, S. Sykora, Global spectral deconvolution (GSD) of 1D-NMRspectra, in: SMASH Conference, Santa Fe, NM, 2008, pp. 2–10.

[32] R.A. Chylla, K. Hu, J.J. Ellinger, et al., Deconvolution of two-dimensional NMRspectra by fast maximum likelihood reconstruction: application to quantitative met-abolomics, Anal. Chem. 83 (2011) 4871–4880.

[33] C. Cobas, F. Seoane, S. Domı́nguez, et al., A new approach to improving automatedanalysis of proton NMR spectra through global spectral deconvolution (GSD),Spectrosc. Eur. 23 (2011) 26–30.

[34] H.P. Mo, D. Raftery, Solvent signal as an NMR concentration reference, Anal.Chem. 80 (2008) 9835–9839.

[35] H.P. Mo, J. Harwood, S.C. Zhang, et al., R: a quantitative measure of NMR signalreceiving efficiency, J. Magn. Reson. 200 (2009) 239–244.

131qNMR of Mixtures

[36] G.K. Pierens, A.R. Carroll, R.A. Davis, et al., Determination of analyte concentrationusing the residual solvent resonance in 1HNMR spectroscopy, J. Nat. Prod. 71 (2008)810–813.

[37] S. Akoka, L. Barantin, M. Trierweiler, Concentration measurement by proton NMRusing the ERETIC method, Anal. Chem. 71 (1999) 2554–2557.

[38] G.Wider, L. Dreier, Measuring protein concentrations by NMR spectroscopy, J. Am.Chem. Soc. 128 (2006) 2571–2576.

[39] C.C. Su, R. Hopson, P.G. Williard, Isotopically enriched 13C diffusion-ordered NMRspectroscopy: analysis of methyllithiunn, J. Org. Chem. 78 (2013) 11733–11746.

[40] G. Dal Poggetto, D.C. Favaro, M. Nilsson, et al., 19F DOSY NMR analysis for spinsystems with nJFF couplings, Magn. Reson. Chem. 52 (2014) 172–177.

[41] C. Thibaudeau, G. Remaud, V. Silvestre, et al., Performance evaluation of quantita-tive adiabatic 13C NMR pulse sequences for site-specific isotopic measurements, Anal.Chem. 82 (2010) 5582–5590.

[42] A.V. Makela, I. Kilpelainen, S. Heikkinen, Quantitative 13C NMR spectroscopy usingrefocused constant-time INEPT, Q-INEPT-CT, J.Magn. Reson. 204 (2010) 124–130.

[43] H.S. Gutowsky, D.W. Mccall, Electron distribution in molecules. IV. Phosphorusmagnetic resonance shifts, J. Chem. Phys. 22 (1954) 162–164.

[44] A. Spyros, P. Dais, 31PNMR spectroscopy in food analysis, Prog. Nucl. Magn. Reson.Spectrosc. 54 (2009) 195–207.

[45] C. Lucas-Torres, A. Perez, B. Cabanas, et al., Study by 31P NMR spectroscopy of thetriacylglycerol degradation processes in olive oil with different heat-transfer mecha-nisms, Food Chem. 165 (2014) 21–28.

[46] G. Maniara, K. Rajamoorthi, S. Rajan, et al., Method performance and validation forquantitative analysis by 1H and 31PNMR spectroscopy. Applications to analytical stan-dards and agricultural chemicals, Anal. Chem. 70 (1998) 4921–4928.

[47] Y.K. Lian, H. Jiang, J.Z. Feng, et al., Direct and simultaneous quantification of ATP,ADP and AMP by 1H and 31P nuclear magnetic resonance spectroscopy, Talanta150 (2016) 485–492.

[48] Y.Q. Pu, S.L. Cao, A.J. Ragauskas, Application of quantitative 31P NMR in biomasslignin and biofuel precursors characterization, Energy Environ. Sci. 4 (2011)3154–3166.

[49] F.Melone, R. Saladino, H. Lange, et al., Tannin structural elucidation and quantitative31P NMR analysis. 2. Hydrolyzable tannins and proanthocyanidins, J. Agric. FoodChem. 61 (2013) 9316–9324.

[50] H.X. Ben, J.R. Ferrell, In-depth investigation on quantitative characterization ofpyrolysis oil by 31P NMR, RSC Adv. 6 (2016) 17567–17573.

[51] J.X. Yu, R.R. Hallac, S. Chiguru, et al., New frontiers and developing applications in19F NMR, Prog. Nucl. Magn. Reson. Spectrosc. 70 (2013) 25–49.

[52] A.Moghimi, I. Omrani, R.H. Khanmiri, et al., Determination of NCO content of theurethane prepolymers by 19F NMR spectroscopy, Polym. Test. 33 (2014) 30–33.

[53] J.D. Seitz, J.G. Vineberg, L.F.Wei, et al., Design, synthesis and application of fluorine-labeled taxoids as 19F NMR probes for the metabolic stability assessment of tumor-targeted drug delivery systems, J. Fluor. Chem. 171 (2015) 148–161.

[54] W.R. Dolbier, Guide to Fluorine NMR for Organic Chemists, Wiley, Hoboken, NJ,2009.

[55] R.K. Harris, E.D. Becker, S.M.C. De Menezes, et al., NMR nomenclature. Nuclearspin properties and conventions for chemical shifts-(IUPAC Recommendations2001), Pure Appl. Chem. 73 (2001) 1795–1818.

[56] W.N. Cui, P. Otten, Y.M. Li, et al., Novel NMR approach to assessing genetransfection: 4-fluoro-2-nitrophenyl-β-D-galactopyranoside as a prototype reportermolecule for β-galactosidase, Magn. Reson. Med. 51 (2004) 616–620.

132 X. Li and K. Hu

[57] L. Liu, V.D. Kodibagkar, J.X. Yu, et al., 19F-NMR detection of lacZ gene expressionvia the enzymic hydrolysis of 2-fluoro-4-nitrophenyl β-D-galactopyranoside in vivoin PC3 prostate tumor xenografts in the mouse, FASEB J. 21 (2007) 2014–2019.

[58] J.E. Power, M. Foroozandeh, R.W. Adams, et al., Increasing the quantitative band-width of NMR measurements, Chem. Commun. 52 (2016) 2916–2919.

[59] I.A. Lewis, R.H. Karsten, M.E. Norton, et al., NMR method for measuring carbon-13 isotopic enrichment of metabolites in complex solutions, Anal. Chem. 82 (2010)4558–4563.

[60] P. Stilbs, Molecular self-diffusion coefficients in Fourier transform nuclear magneticresonance spectrometric analysis of complex mixtures, Anal. Chem. 53 (1981)2135–2137.

[61] K.D. Atkinson,M.J. Cowley, S.B. Duckett, et al., Para-hydrogen induced polarizationwithout incorporation of para-hydrogen into the analyte, Inorg. Chem. 48 (2009)663–670.

[62] N. Eshuis, B.J.A. van Weerdenburg, M.C. Feiters, et al., Quantitative trace analysis ofcomplex mixtures using SABRE hyperpolarization, Angew. Chem. Int. Ed. 54 (2015)1481–1484.

[63] G.A.N. Gowda, D. Raftery, Can NMR solve some significant challenges in met-abolomics, J. Magn. Reson. 260 (2015) 144–160.

[64] O. Fiehn, Combining genomics, metabolome analysis, and biochemical modelling tounderstand metabolic networks, Comp. Funct. Genomics 2 (2001) 155–168.

[65] O. Fiehn, Metabolomics: the link between genotypes and phenotype, Plant Mol. Biol.48 (2002) 155–171.

[66] J.C. Lindon, J.K.Nicholson, E.Holmes, et al.,Metabonomics: metabolic processes stud-ied by NMR spectroscopy of biofluids, Concept Magn. Reson. A 12 (2000) 289–320.

[67] S.L. Robinette, R. Bruschweiler, F.C. Schroeder, et al., NMR in metabolomics andnatural products research: two sides of the same coin, Acc. Chem. Res. 45 (2012)288–297.

[68] G.A. Barding, R. Salditos, C.K. Larive, Quantitative NMR for bioanalysis and met-abolomics, Anal. Bioanal. Chem. 404 (2012) 1165–1179.

[69] S.K. Bharti, R. Roy, Quantitative 1H NMR spectroscopy, Trends Anal. Chem.35 (2012) 5–26.

[70] C. Simmler, J.G. Napolitano, J.B. McAlpine, et al., Universal quantitative NMR anal-ysis of complex natural samples, Curr. Opin. Biotechnol. 25 (2014) 51–59.

[71] R. Schicho, R. Shaykhutdinov, J. Ngo, et al., Quantitative metabolomic profiling ofserum, plasma, and urine by 1H NMR spectroscopy discriminates between patientswith inflammatory bowel disease and healthy individuals, J. Proteome Res.11 (2012) 3344–3357.

[72] P. Mercier, M.J. Lewis, D. Chang, et al., Towards automatic metabolomic profiling ofhigh-resolution one-dimensional proton NMR spectra, J. Biomol. NMR 49 (2011)307–323.

[73] M. Liebeke, J. Hao, T.M.D. Ebbels, et al., Combining spectral ordering with peakfitting for one-dimensional NMR quantitative metabolomics, Anal. Chem.85 (2013) 4605–4612.

[74] S. Srivastava, R. Roy, S. Kumar, et al., Cholesterol and its esters as serum biomarkers inmalignant obstructive jaundice: a single step 1H NMR metabolomic approach,Metabolomics 9 (2013) 1181–1191.

[75] G.A.N. Gowda, Y.N. Gowda, D. Raftery, Expanding the limits of human bloodmetabolite quantitation using NMR spectroscopy, Anal. Chem. 87 (2015) 706–715.

[76] Y.R. Liu, B.K. Xiao, J.Y. Yang, et al., 1H-NMR and HPLC-MS/MS-basedglobal/targeted metabolomic evaluation of Hypericum perforatum L. intervention formenopause, J. Funct. Foods 17 (2015) 722–741.

133qNMR of Mixtures

[77] N.K. Srivastava, S. Annarao, N. Sinha, Metabolic status of patients with musculardystrophy in early phase of the disease: in vitro, high resolution NMR spectroscopybased metabolomics analysis of serum, Life Sci. 151 (2016) 122–129.

[78] T.J. Athersuch, S. Malik, A. Weljie, et al., Evaluation of 1H NMRmetabolic profilingusing biofluid mixture design, Anal. Chem. 85 (2013) 6674–6681.

[79] S. Sokolenko, R. McKay, E.J.M. Blondeel, et al., Understanding the variability ofcompound quantification from targeted profiling metabolomics of 1D-1H-NMRspectra in synthetic mixtures and urine with additional insights on choice of pulsesequences and robotic sampling, Metabolomics 9 (2013) 887–903.

[80] C.W. Lam, C.Y. Law, K.H. Sze, et al., Quantitative metabolomics of urine for rapidetiological diagnosis of urinary tract infection: evaluation of a microbial-mammalianco-metabolite as a diagnostic biomarker, Clin. Chim. Acta 438 (2015) 24–28.

[81] R.K.Rai, N. Sinha, Fast and accurate quantitative metabolic profiling of body fluids bynonlinear sampling of 1H-13C two-dimensional nuclear magnetic resonance spectros-copy, Anal. Chem. 84 (2012) 10005–10011.

[82] L. Da Silva, M. Godejohann, F.P.J. Martin, et al., High-resolution quantitativemetabolome analysis of urine by automated flow injection NMR, Anal. Chem.85 (2013) 5801–5809.

[83] O. Robert, J. Sabatier, D. Desoubzdanne, et al., pH optimization for a reliable quan-tification of brain tumor cell and tissue extracts with 1H NMR: focus on choline-containing compounds and taurine, Anal. Bioanal. Chem. 399 (2011) 987–999.

[84] A. Le Guennec, I. Tea, I. Antheaume, et al., Fast determination of absolute metaboliteconcentrations by spatially encoded 2D NMR: application to breast cancer cellextracts, Anal. Chem. 84 (2012) 10831–10837.

[85] E. Martineau, I. Tea, S. Akoka, et al., Absolute quantification of metabolites in breastcancer cell extracts by quantitative 2D 1H INADEQUATE NMR, NMR Biomed.25 (2012) 985–992.

[86] S. Sokolenko, E.J.M. Blondeel, N. Azlah, et al., Profiling convoluted single-dimension proton NMR spectra: a Plackett-Burman approach for assessing quantifi-cation error of metabolites in complex mixtures with application to cell culture, Anal.Chem. 86 (2014) 3330–3337.

[87] L. Goldoni, T. Beringhelli, W. Rocchia, et al., A simple and accurate protocol forabsolute polar metabolite quantification in cell cultures using quantitative nuclear mag-netic resonance, Anal. Biochem. 501 (2016) 26–34.

[88] T. Cappello, F. Brandao, S. Guilherme, et al., Insights into the mechanisms underlyingmercury-induced oxidative stress in gills of wild fish (Liza aurata) combining 1HNMRmetabolomics and conventional biochemical assays, Sci. Total Environ. 548 (2016)13–24.

[89] X.Y. Wu, N. Li, H.D. Li, et al., An optimized method for NMR-based plant seedmetabolomic analysis with maximized polar metabolite extraction efficiency, signal-to-noise ratio, and chemical shift consistency, Analyst 139 (2014) 1769–1778.

[90] A.M. Viljoen, J.P. Zhao, M. Sandasi, et al., Phytochemical distinction between Pelar-gonium sidoides (“Umckaloabo”) and P. reniforme through 1H-NMR and UHPLC-MSmetabolomic profiling, Metabolomics 11 (2015) 594–602.

[91] G.D. Tredwell, V. Behrends, F.M. Geier, et al., Between-person comparison ofmetabolite fitting for NMR-based quantitative metabolomics, Anal. Chem.83 (2011) 8683–8687.

[92] J. Kikuchi, Y. Tsuboi, K. Komatsu, et al., Spin couple: development of a web tool foranalyzing metabolite mixtures via two-dimensional J-resolved NMR database, Anal.Chem. 88 (2016) 659–665.

[93] D.J. Crockford, H.C. Keun, L.M. Smith, et al., Curve-fitting method for direct quan-titation of compounds in complex biological mixtures using 1H NMR: application inmetabonomic toxicology studies, Anal. Chem. 77 (2005) 4556–4562.

134 X. Li and K. Hu

[94] K.R. Metz, M.M. Lam, A.G. Webb, Reference deconvolution: a simple and effectivemethod for resolution enhancement in nuclear magnetic resonance spectroscopy,Concepts Magn. Reson. A 12 (2000) 21–42.

[95] F.L. Zhang, R. Bruschweiler, Spectral deconvolution of chemical mixtures by covari-ance NMR, ChemPhysChem 5 (2004) 794–796.

[96] K. Bingol, R. Bruschweiler, Deconvolution of chemical mixtures with high complex-ity by NMR consensus trace clustering, Anal. Chem. 83 (2011) 7412–7417.

[97] F.L. Zhang, R. Bruschweiler, Robust deconvolution of complex mixtures by covari-ance TOCSY spectroscopy, Angew. Chem. Int. Ed. 46 (2007) 2639–2642.

[98] M.R. Antoniewicz, Methods and advances in metabolic flux analysis: a mini-review,J. Chromatogr. A 42 (2015) 317–325.

[99] S.Massou, C. Nicolas, F. Letisse, et al., NMR-based fluxomics: quantitative 2DNMRmethods for isotopomers analysis, Phytochemistry 68 (2007) 2330–2340.

[100] A.I. Amaral, A.P. Teixeira, S. Martens, et al., Metabolic alterations induced by ische-mia in primary cultures of astrocytes: merging 13C NMR spectroscopy and metabolicflux analysis, J. Neurochem. 113 (2010) 735–748.

[101] B. Tiret, A.A. Shestov, J. Valette, et al., Metabolic modeling of dynamic 13C NMRisotopomer data in the brain in vivo: fast screening of metabolic models using auto-mated generation of differential equations, Neurochem. Res. 40 (2015) 2482–2492.

[102] D. Rontein, M. Dieuaide-Noubhani, E.J. Dufourc, et al., The metabolic architectureof plant cells. Stability of central metabolism and flexibility of anabolic pathways duringthe growth cycle of tomato cells, J. Biol. Chem. 277 (2002) 43948–43960.

[103] A.P. Alonso, H. Vigeolas, P. Raymond, et al., A new substrate cycle in plants. Evi-dence for a high glucose-phosphate-to-glucose turnover from in vivo steady-stateand pulse-labeling experiments with [13C] glucose and [14C] glucose, Plant Physiol.138 (2005) 2220–2232.

[104] W. Eisenreich, J. Slaghuis, R. Laupitz, et al., 13C isotopologue perturbation studies ofListeria monocytogenes carbon metabolism and its modulation by the virulence regulatorPrfA, Proc. Natl. Acad. Sci. U.S.A. 103 (2006) 2040–2045.

[105] M. Tiainen, H. Maaheimo, M. Niemitz, et al., Spectral analysis of 1H coupled13C spectra of the amino acids: adaptive spectral library of amino acid 13C isotopomersand positional fractional 13C enrichments, Magn. Reson. Chem. 46 (2008) 125–137.

[106] C. Goudar, R. Biener, C. Boisart, et al., Metabolic flux analysis of CHO cells in per-fusion culture by metabolite balancing and 2D [13C, 1H] COSY NMR spectroscopy,Metab. Eng. 12 (2010) 138–149.

[107] S. Massou, C. Nicolas, F. Letisse, et al., Application of 2D-TOCSYNMR to the mea-surement of specific 13C-enrichments in complex mixtures of 13C-labeled metabolites,Metab. Eng. 9 (2007) 252–257.

[108] L. Frydman, T. Scherf, A. Lupulescu, The acquisition of multidimensional NMRspectra within a single scan, Proc. Natl. Acad. Sci. U.S.A. 99 (2002) 15858–15862.

[109] P. Giraudeau, S. Massou, Y. Robin, et al., Ultrafast quantitative 2DNMR: an efficienttool for the measurement of specific isotopic enrichments in complex biological mix-tures, Anal. Chem. 83 (2011) 3112–3119.

[110] P. Giraudeau, E. Cahoreau, S. Massou, et al., UFJCOSY: a fast 3D NMRmethod formeasuring isotopic enrichments in complex samples, ChemPhysChem 13 (2012)3098–3101.

[111] R. Boisseau, B. Charrier, S. Massou, et al., Fast spatially encoded 3D NMR strategiesfor 13C-based metabolic flux analysis, Anal. Chem. 85 (2013) 9751–9757.

[112] P.N. Reardon, C.L. Marean-Reardon, M.A. Bukovec, et al., 3D TOCSY-HSQCNMR for metabolic flux analysis using non-uniform sampling, Anal. Chem.88 (2016) 2825–2831.

[113] L. Bohlin, U. Goransson, C. Alsmark, et al., Natural products in modern life science,Phytochem. Rev. 9 (2010) 279–301.

135qNMR of Mixtures

[114] A.G. Atanasov, B. Waltenberger, E.M. Pferschy-Wenzig, et al., Discovery andresupply of pharmacologically active plant-derived natural products: a review,Biotechnol. Adv. 33 (2015) 1582–1614.

[115] M.G. Moloney, Natural products as a source for novel antibiotics, Trends Pharmacol.Sci. 37 (2016) 689–701.

[116] J. Perez-Jimenez, M.E. Diaz-Rubio, F. Saura-Calixto, Obtainment and characteriza-tion of a potential functional ingredient from olive, Int. J. Food Sci. Nutr. 66 (2015)749–754.

[117] F. Rodrigues, F.B. Pimentel, M.B.P.P. Oliveira, Olive by-products: challenge appli-cation in cosmetic industry, Ind. Crop. Prod. 70 (2015) 116–124.

[118] D.G. Cox, J. Oh, A. Keasling, et al., The utility of metabolomics in natural product andbiomarker characterization, Biochim. Biophys. Acta 1840 (2014) 3460–3474.

[119] M. Halabalaki, K. Vougogiannopoulou, E. Mikros, et al., Recent advances and newstrategies in the NMR-based identification of natural products, Curr. Opin. Bio-technol. 25 (2014) 1–7.

[120] J.L. Wolfender, G. Marti, A. Thomas, et al., Current approaches and challenges for themetabolite profiling of complex natural extracts, J. Chromatogr. A 1382 (2015) 136–164.

[121] A. Mohamed, C.H. Nguyen, H. Mamitsuka, Current status and prospects of compu-tational resources for natural product dereplication: a review, Brief. Bioinform.17 (2016) 309–321.

[122] M. Ben Jemia, R. Tundis, A. Maggio, et al., NMR-based quantification of rosmarinicand carnosic acids, GC-MS profile and bioactivity relevant to neurodegenerativedisorders of Rosmarinus officinalis L. extracts, J. Funct. Foods 5 (2013) 1873–1882.

[123] J. Staneva, P. Denkova, M. Todorova, et al., Quantitative analysis of sesquiterpene lac-tones in extract of Arnica montana L. by 1H NMR spectroscopy, J. Pharm. Biomed.Anal. 54 (2011) 94–99.

[124] V. Pieri, A. Belancic, S. Morales, et al., Identification and quantification of majorsteviol glycosides in Stevia rebaudiana purified extracts by 1H NMR spectroscopy,J. Agric. Food Chem. 59 (2011) 4378–4384.

[125] A.A. Nerantzaki, C.G. Tsiafoulis, P. Charisiadis, et al., Novel determination of thetotal phenolic content in crude plant extracts by the use of 1HNMR of the –OH spec-tral region, Anal. Chim. Acta 688 (2011) 54–60.

[126] P. Charisiadis, C.G. Tsiafoulis, V. Exarchou, et al., Rapid and direct low micromolarNMR method for the simultaneous detection of hydrogen peroxide and phenolics inplant extracts, J. Agric. Food Chem. 60 (2012) 4508–4513.

[127] A.R.T. Machado, G.A. Lage, F.D.S. Medeiros, et al., Quantitative analysis oftrigonelline in some Annona species by proton NMR spectroscopy, Nat. Prod. Bio-prospect. 3 (2013) 158–160.

[128] Y. Liu, S.N. Chen, J.B. McApine, et al., Quantification of a botanical negative markerwithout an identical standard: ginkgotoxin in Ginkgo biloba, J. Nat. Prod. 77 (2014)611–617.

[129] L.P. Santos Pimenta, M. Schilthuizen, R. Verpoorte, et al., Quantitative analysis ofamygdalin and prunasin in Prunus serotina Ehrh. using 1H-NMR spectroscopy,Phytochem. Anal. 25 (2014) 122–126.

[130] Y. Wu, Y. He, W. He, et al., Application of quantitative 1H NMR for the calibrationof protoberberine alkaloid reference standards, J. Pharm. Biomed. Anal. 90 (2014)92–97.

[131] A. Cheilari, S. Sturm, D. Intelmann, et al., Head-to-head comparison of ultra-high-performance liquid chromatography with diode array detection versus quantitativenuclear magnetic resonance for the quantitative analysis of the silymarin complex inSilybum marianum fruit extracts, J. Agric. Food Chem. 64 (2016) 1618–1626.

136 X. Li and K. Hu

[132] C.I. Cerceau, L.C.A. Barbosa, C.A. Filomeno, et al., An optimized and validated1H NMR method for the quantification of α-pinene in essentials oils, Talanta150 (2016) 97–103.

[133] N. Sharma, M.K. Verma, D.K. Gupta, et al., Isolation and quantification of pinitol inArgyrolobium roseum plant, by 1H-NMR, J. Saudi Chem. Soc. 20 (2016) 81–87.

[134] M. Palomino-Schatzlem, P.V. Esrig, H. Boira, et al., Evaluation of nonpolar metab-olites in plant extracts by 13C NMR spectroscopy, J. Agric. Food Chem. 59 (2011)11407–11416.

[135] J.L. Wen, B.L. Xue, S.L. Sun, et al., Quantitative structural characterization andthermal properties of birch lignins after auto-catalyzed organosolv pretreatment andenzymatic hydrolysis, J. Chem. Technol. Biotechnol. 88 (2013) 1663–1671.

[136] F. Zikeli, T. Ters, K. Fackler, et al., Successive and quantitative fractionation andextensive structural characterization of lignin from wheat straw, Ind. Crop. Prod.61 (2014) 249–257.

[137] M. Sette, R. Wechselberger, C. Crestini, Elucidation of lignin structure by quantita-tive 2D NMR, Chemistry 17 (2011) 9529–9535.

[138] J.L. Wen, B.L. Xue, F. Xu, et al., Unveiling the structural heterogeneity of bamboolignin by in situ HSQC NMR technique, Bioenerg. Res. 5 (2012) 886–903.

[139] M. Sette, H. Lange, C. Crestini, Quantitative HSQC analyses of lignin: a practicalcomparison, Comput. Struct. Biotechnol. J. 6 (2013) e201303016.

[140] J.P. Yan, D. Kiemle, S.J. Liu, Quantification of xylooligomers in hot water woodextract by 1H-13C heteronuclear single quantum coherence NMR, Carbohydr.Polym. 117 (2015) 903–909.

[141] N.K.J. Hermkens, N. Eshuis, B.J.A. van Weerdenburg, et al., NMR-basedchemosensing via p-H2 hyperpolarization: application to natural extracts, Anal. Chem.88 (2016) 3406–3412.

[142] E. Martineau, K. El Khantache, M. Pupier, et al., Non-linear effects in quantitative 2DNMR of polysaccharides: pitfalls and how to avoid them, J. Pharm. Biomed. Anal.108 (2015) 78–85.

[143] G. Fan, M.Y. Zhang, X.D. Zhou, et al., Quality evaluation and species differentiationof Rhizoma coptidis by using proton nuclear magnetic resonance spectroscopy, Anal.Chim. Acta 747 (2012) 76–83.

[144] X.L. Ma, P.P. Zou, W. Lei, et al., Optimization of experimental parameters forquantitative NMR (qNMR) and its application in quantitative analysis of traditionalChinese medicines, Acta Pharm. Sin. 49 (2014) 1248–1257.

[145] H.F. Yao, C. Quan, J. Liu, et al., Certification and uncertainty evaluation of baicaleinpurity certified reference material, Chem. Anal. Meterage 21 (2012) 4–8.

[146] X.L. Chen, Y.J. Guo, Y.J. Hu, et al., Quantitative analysis of highly similar salvianolicacids with 1H qNMR for quality control of traditional Chinese medicinal preparationsalvianolate lyophilized injection, J. Pharm. Biomed. Anal. 124 (2016) 281–287.

[147] M. Yang, J. Wang, L. Kong, Quantitative analysis of four major diterpenoids inAndrographis paniculata by 1H NMR and its application for quality control of commer-cial preparations, J. Pharm. Biomed. Anal. 70 (2012) 87–93.

[148] T. Godecke, P. Yao, J.G. Napolitano, et al., Integrated standardization concept forAngelica botanicals using quantitative NMR, Fitoterapia 83 (2012) 18–32.

[149] P.H. Chan, W.L. Zhang, C.H. Lau, et al., Metabonomic analysis of water extractsfrom different angelica roots by 1H-nuclear magnetic resonance spectroscopy,Molecules 19 (2014) 3460–3470.

[150] P.L. Ding, L.Q. Chen, Y. Lu, et al., Determination of protoberberine alkaloids inRhizoma coptidis by ERETIC 1H NMR method, J. Pharm. Biomed. Anal.60 (2012) 44–50.

137qNMR of Mixtures

[151] P.P. Zou, P.F. Tu, Y. Jiang, A simple and specific quantitative method for determi-nation of dictamnine in Dictamni cortex by 1H NMR spectroscopy, Anal. Methods5 (2013) 1062–1067.

[152] Z.W.Wang, J.S.Wang,M.H. Yang, et al., Developmental changes in the compositionof five anthraquinones from Rheum palmatum as quantified by 1H-NMR, Phytochem.Anal. 24 (2013) 329–335.

[153] K. Kuchta, R.B. Volk, H.W. Rauwald, Stachydrine in Leonurus cardiaca, Leonurusjaponicus, Leonotis leonurus: detection and quantification by instrumental HPTLC and1H-qNMR analyses, Pharmazie 68 (2013) 534–540.

[154] K. Kuchta, J. Ortwein, L. Hennig, et al., 1H-qNMR for direct quantification ofstachydrine in Leonurus japonicus and L. cardiaca, Fitoterapia 96 (2014) 8–17.

[155] Y.L. Song, W.H. Jing, Y.G. Chen, et al., 1H nuclear magnetic resonance based-metabolomic characterization of Peucedani Radix and simultaneous determination ofpraeruptorin A and praeruptorin B, J. Pharm. Biomed. Anal. 93 (2014) 86–94.

[156] Y.G. Chen, Y.L. Song, Y.Wang, et al., Metabolic differentiations of Pueraria lobata andPueraria thomsonii using 1H NMR spectroscopy and multivariate statistical analysis,J. Pharm. Biomed. Anal. 93 (2014) 51–58.

[157] W. Wang, X. Ma, X. Guo, et al., A series of strategies for solving the shortage of ref-erence standards for multi-components determination of traditional Chinese medicine,Mahoniae Caulis as a case, J. Chromatogr. A 1412 (2015) 100–111.

[158] Z.Z. Jiang, J. Yang, Y.J. Jiao, et al., Determination of scutellarin in breviscapine prep-arations using quantitative proton nuclear magnetic resonance spectroscopy, J. FoodDrug Anal. 24 (2016) 392–398.

[159] M. Nasu, T. Nemoto, H. Mimura, et al., Development of qualitative and quantitativeanalysis methods in pharmaceutical application with new selective signal excitationmethods for 13C solid-state nuclear magnetic resonance using 1H T1rho relaxationtime, J. Pharm. Sci. 102 (2013) 154–161.

[160] K. Paradowska, I. Wawer, Solid-state NMR in the analysis of drugs and naturallyoccurring materials, J. Pharm. Biomed. Anal. 93 (2014) 27–42.

[161] R. Nogueira, B.C. Garrido, R.M. Borges, et al., Development of a new sodiumdiclofenac certified reference material using the mass balance approach and 1H qNMRto determine the certified property value, Eur. J. Pharm. Sci. 48 (2013) 502–513.

[162] Y.B. Monakhova, M. Kohl-Himmelseher, T. Kuballa, et al., Determination of thepurity of pharmaceutical reference materials by 1H NMR using the standardless PUL-CON methodology, J. Pharm. Biomed. Anal. 100 (2014) 381–386.

[163] Y. Huang, B.N. Su, Q. Ye, et al., Improving the efficiency of quantitative 1H NMR:an innovative external standard-internal reference approach, J. Pharm. Biomed. Anal.88 (2014) 1–6.

[164] T. Rundlof, I. McEwen, M. Johansson, et al., Use and qualification of primary andsecondary standards employed in quantitative 1H NMR spectroscopy of pharmaceu-ticals, J. Pharm. Biomed. Anal. 93 (2014) 111–117.

[165] S. Shen, X. Yang, Y. Shi, Application of quantitative NMR for purity determinationof standard ACE inhibitors, J. Pharm. Biomed. Anal. 114 (2015) 190–199.

[166] M.Weber, C. Hellriegel, A. Rueck, et al., Method development in quantitative NMRtowards metrologically traceable organic certified reference materials used as 31PqNMR standards, Anal. Bioanal. Chem. 407 (2015) 3115–3123.

[167] V.V. Podgorskii, A.S. Mikhalev, G.A. Kalabin, Quantitative NMR spectroscopy forquality control of drugs and pharmaceuticals, Pharm. Chem. J. 45 (2011) 194–197.

[168] A.A. Salem, H.A. Mossa, Method validation and determinations of levofloxacin, met-ronidazole and sulfamethoxazole in an aqueous pharmaceutical, urine and bloodplasma samples using quantitative nuclear magnetic resonance spectrometry, Talanta88 (2012) 104–114.

138 X. Li and K. Hu

[169] Z. Hou, X. Liang, L. Du, et al., Quantitative determination and validation of aver-mectin B1a in commercial products using quantitative nuclear magnetic resonancespectroscopy, Magn. Reson. Chem. 52 (2014) 480–485.

[170] X. Liang, L. Du, F. Su, et al., The application of quantitative NMR for the facile, rapidand reliable determination of clindamycin phosphate in a conventional tablet formu-lation, Magn. Reson. Chem. 52 (2014) 178–182.

[171] A. Borioni, G. Gostoli, E. Bossu, et al., Quantitative analysis of iobitridol in aninjectable preparation by 1H NMR spectroscopy, J. Pharm. Biomed. Anal.94 (2014) 19–22.

[172] G.A. Kalabin, S.V. Goryainov, V.A. Ivlev, et al., Identification and quantitative deter-mination of active pharmaceutical ingredients in suppositories by a combination of 1HNMR spectroscopy and desorption mass spectrometry, Russ. Chem. Bull. 63 (2014)1848–1855.

[173] K. Parmar, A.T. Mahato, J.M. Shah, et al., Qualitative and quantitative application ofNMR in rilmenidine dihydrogen phosphate API and its related impurity-B and cor-relation with alternate technique, Int. J. Pharm. Sci. Rev. Res. 26 (2014) 6–12.

[174] A.C. Gucinski, M.T. Boyne 2nd, D.A. Keire, Modern analytics for naturally derivedcomplex drug substances: NMR andMS tests for protamine sulfate from chum salmon,Anal. Bioanal. Chem. 407 (2015) 749–759.

[175] A. Sahu, M. Narayanam, M. Kurmi, et al., Quantitation of memantine hydrochloridebulk drug and its tablet formulation using proton nuclear magnetic resonancespectrometry, Magn. Reson. Chem. 54 (2016) 632–636.

[176] F.F. Zhang, M.H. Jiang, L.L. Sun, et al., Quantitative analysis of sitagliptin using the19F-NMRmethod: a universal technique for fluorinated compound detection, Analyst140 (2015) 280–286.

[177] H. Jiang, H. Chen, N. Cai, et al., Quantitative 31P-NMR spectroscopy for the deter-mination of fosfomycin and impurity A in pharmaceutical products of fosfomycinsodium or calcium, Magn. Reson. Chem. 53 (2015) 454–459.

[178] E. Martineau, S. Akoka, R. Boisseau, et al., Fast quantitative 1H-13C two-dimensionalNMR with very high precision, Anal. Chem. 85 (2013) 4777–4783.

[179] R. Jawad, C. Elleman, L. Vermeer, et al., The measurement of the β/α anomer com-position within amorphous lactose prepared by spray and freeze drying using a simple1H-NMR method, Pharm. Res. 29 (2012) 511–524.

[180] C. Skiera, P. Steliopoulos, T. Kuballa, et al., Determination of free fatty acids in phar-maceutical lipids by 1H NMR and comparison with the classical acid value, J. Pharm.Biomed. Anal. 93 (2014) 43–50.

[181] G. Dufour, B. Evrard, P. de Tullio, Rapid quantification of 2-hydroxypropyl-β-cyclo-dextrin in liquid pharmaceutical formulations by 1H nuclear magnetic resonance spec-troscopy, Eur. J. Pharm. Sci. 73 (2015) 20–28.

[182] H. Kono, K. Oshima, H. Hashimoto, et al., NMR characterization of sodium carbo-xymethyl cellulose: substituent distribution and mole fraction of monomers in thepolymer chains, Carbohydr. Polym. 146 (2016) 1–9.

[183] S.E. Lee, E.K. Chess, B. Rabinow, et al., NMR of heparin API: investigation of uni-dentified signals in the USP-specified range of 2.12–3.00 ppm, Anal. Bioanal. Chem.399 (2011) 651–662.

[184] M. Smith, T. Beck, Quantitation of a low level coeluting impurity present in a mod-ified oligonucleotide by both LC-MS and NMR, J. Pharm. Biomed. Anal. 118 (2016)34–40.

[185] G.F. Pauli, S.N.Chen,C. Simmler, et al., Importance of purity evaluation and the poten-tial of quantitative 1H NMR as a purity assay, J. Med. Chem. 57 (2014) 9220–9231.

[186] G.K. Webster, S. Kumar, Expanding the analytical toolbox: pharmaceutical applica-tion of quantitative NMR, Anal. Chem. 86 (2014) 11474–11480.

139qNMR of Mixtures

[187] U. Holzgrabe, Quantitative NMR spectroscopy in pharmaceutical R&D, eMagRes4 (2015) 45–56.

[188] H. Hu,N. Huang, P. Yi, et al., Utilizing 19FNMR to investigate drug disposition earlyin drug discovery, Xenobiotica 45 (2015) 1081–1091.

[189] B.K. Park, N.R. Kitteringham, P.M. O’Neill, Metabolism of fluorine-containingdrugs, Annu. Rev. Pharmacol. Toxicol. 41 (2001) 443–470.

[190] G. Dufour, B. Evrard, P. de Tullio, 2D-COSY NMR spectroscopy as a quantitativetool in biological matrix: application to cyclodextrins, AAPS J. 17 (2015) 1501–1510.

[191] R.M. Maggio, N.L. Calvo, S.E. Vignaduzzo, et al., Pharmaceutical impurities anddegradation products: uses and applications of NMR techniques, J. Pharm. Biomed.Anal. 101 (2014) 102–122.

[192] U. Holzgrabe, M. Malet-Martino, Analytical challenges in drug counterfeiting andfalsification—the NMR approach, J. Pharm. Biomed. Anal. 55 (2011) 679–687.

[193] C. Zhang, T. Zhang, N.A. Oyler, et al., Direct and real-time quantification oftenofovir release from pH-sensitive microparticles into simulated biological fluidsusing 1H nuclear magnetic resonance, J. Pharm. Sci. 103 (2014) 1170–1177.

[194] J.C. Schwarz, M. Hoppel, H. Kahlig, et al., Application of quantitative 19F nuclearmagnetic resonance spectroscopy in tape-stripping experiments with natural micro-emulsions, J. Pharm. Sci. 102 (2013) 2699–2706.

[195] D. Mahrhauser, M. Hoppel, J. Scholl, et al., Simultaneous analysis of skin penetrationof surfactant and active drug from fluorosurfactant-based microemulsions, Eur. J.Pharm. Biopharm. 88 (2014) 34–39.

[196] M.F. Marcone, S.A. Wang, W. Albabish, et al., Diverse food-based applications ofnuclear magnetic resonance (NMR) technology, Food Res. Int. 51 (2013) 729–747.

[197] A. Caligiani, L. Palla, D. Acquotti, et al., Application of 1H NMR for the character-isation of cocoa beans of different geographical origins and fermentation levels, FoodChem. 157 (2014) 94–99.

[198] A. Spyros, Application of NMR in food analysis, Nucl. Magn. Reson. 45 (2016)269–307.

[199] M. Nikolantonaki, P. Magiatis, A.L. Waterhouset, Direct analysis of free and sulfite-bound carbonyl compounds in wine by two-dimensional quantitative proton and car-bon nuclear magnetic resonance spectroscopy, Anal. Chem. 87 (2015) 10799–10806.

[200] C. Colombo, C. Aupic, A.R. Lewis, et al., In situ determination of fructose isomerconcentrations in wine using 13C quantitative nuclear magnetic resonance spectros-copy, J. Agric. Food Chem. 63 (2015) 8551–8559.

[201] P. Manoukian, E. Melliou, M. Liouni, et al., Identification and quantitation ofbenzoxazinoids in wheat malt beer by qNMR and GC-MS, LWT Food Sci. Technol.65 (2016) 1133–1137.

[202] M. Bojstrup, B.O. Petersen, S.R. Beeren, et al., Fast and accurate quantitation of glu-cans in complex mixtures by optimized heteronuclear NMR spectroscopy, Anal.Chem. 85 (2013) 8802–8808.

[203] C.H. Lai, C.W. Hsieh, W.C. Ko, Detection limit of molasses spirits mixed in ricespirits using the SNIF-NMR method, J. Food Drug Anal. 22 (2014) 197–201.

[204] D.W. Lachenmeier, T.A. Breaux, T. Kuballa, et al., Composition of distilled Periquetobacco liqueur: a connoisseur’s spirit or a health risk due to nicotine, Food Chem.159 (2014) 230–235.

[205] M. Hohmann, V. Koospal, C. Bauer-Christoph, et al., Quantitative 1HNMR analysisof egg yolk, alcohol, and total sugar content in egg liqueurs, J. Agric. Food Chem.63 (2015) 4112–4119.

[206] C.W. Hsieh, P.H. Li, J.Y. Cheng, et al., Using SNIF-NMR method to identify theadulteration of molasses spirit vinegar by synthetic acetic acid in rice vinegar, Ind.Crop. Prod. 50 (2013) 904–908.

140 X. Li and K. Hu

[207] W.C. Ko, J.Y. Cheng, P.Y. Chen, et al., Optimized extraction method of acetic acidin vinegar and its effect on SNIF-NMR analysis to control the authenticity of vinegar,Food Bioprocess Technol. 6 (2013) 2202–2206.

[208] M. Perini, M. Paolini, M. Simoni, et al., Stable isotope ratio analysis for verifyingthe authenticity of balsamic and wine vinegar, J. Agric. Food Chem. 62 (2014)8197–8203.

[209] S. Lalou, E. Hatzidimitriou, M. Papadopoulou, et al., Beyond traditional balsamicvinegar: compositional and sensorial characteristics of industrial balsamic vinegarsand regulatory requirements, J. Food Compos. Anal. 43 (2015) 175–184.

[210] R.A. Werner, A. Rossmann, Multi element (C, H, O) stable isotope analysis for theauthentication of balsamic vinegars, Isotopes Environ. Health Stud. 51 (2015) 58–67.

[211] J.G. Napolitano, T. Godecke, D.C. Lankin, et al., Orthogonal analytical methods forbotanical standardization: determination of green tea catechins by qNMR andLC-MS/MS, J. Pharm. Biomed. Anal. 93 (2014) 59–67.

[212] M. Hohmann, C. Felbinger, N. Christoph, et al., Quantification of taurine in energydrinks using 1H NMR, J. Pharm. Biomed. Anal. 93 (2014) 156–160.

[213] M. Kollo, M. Kudrjasova, M. Kulp, et al., Methylphosphonic acid as a 31P-NMR stan-dard for the quantitative determination of phosphorus in carbonated beverages, Anal.Methods 5 (2013) 4005–4009.

[214] Y.B. Monakhova, B. Schutz, H. Schafer, et al., Validation studies for multicomponentquantitative NMR analysis: the example of apple fruit juice, Accredit. Qual. Assur.19 (2014) 17–29.

[215] A. Zuriarrain, J. Zuriarrain, A.I. Puerias, et al., Quantitative determination of lactic andacetic acids in cider by 1H NMR spectrometry, Food Control 52 (2015) 49–53.

[216] A. Zuriarrain, J. Zuriarrain, M. Villar, et al., Quantitative determination of ethanol incider by 1H NMR spectrometry, Food Control 50 (2015) 758–762.

[217] E.G. Alves, F.D.L. Almeida, R.S. Cavalcante, et al., 1H NMR spectroscopy andchemometrics evaluation of non-thermal processing of orange juice, Food Chem.204 (2016) 102–107.

[218] Y.C. Zhao, L. Chen, T.M. Swager, Simultaneous identification of neutral and anionicspecies in complex mixtures without separation, Angew. Chem. Int. Ed. 55 (2016)917–921.

[219] R. Popescu, D. Costinel, O.R. Dinca, et al., Discrimination of vegetable oils usingNMR spectroscopy and chemometrics, Food Control 48 (2015) 84–90.

[220] K.J. Park, M. Kim, S. Seok, et al., Quantitative analysis of cyclic dimer fatty acid con-tent in the dimerization product by proton NMR spectroscopy, Spectrochim. Acta A149 (2015) 402–407.

[221] N. D’Amelio, E. De Angelis, L. Navarini, et al., Green coffee oil analysis by high-resolution nuclear magnetic resonance spectroscopy, Talanta 110 (2013) 118–127.

[222] M. Vlasiou, C. Drouza, 19F NMR for the speciation and quantification of theOH-molecules in complex matrices, Anal. Methods 7 (2015) 3680–3684.

[223] G.G. Harrigan, K. Skogerson, S. MacIsaac, et al., Application of 1HNMR profiling toassess seed metabolomic diversity. A case study on a soybean era population, J. Agric.Food Chem. 63 (2015) 4690–4697.

[224] T.H. Soininen, N. Jukarainen, S.O.K. Auriola, et al., Quantitative metabolite pro-filing of edible onion species by NMR and HPLC-MS, Food Chem. 165 (2014)499–505.

[225] T. Jezequel, C. Deborde, M. Maucourt, et al., Absolute quantification of metabolitesin tomato fruit extracts by fast 2D NMR, Metabolomics 11 (2015) 1231–1242.

[226] A.Marseglia, D. Acquotti, R. Consonni, et al., HRMAS 1HNMR and chemometricsas useful tool to assess the geographical origin of cocoa beans -comparison with HR 1HNMR, Food Res. Int. 85 (2016) 273–281.

141qNMR of Mixtures

[227] D.J. Kwon, H.J. Jeong, H. Moon, et al., Assessment of green coffee bean metabolitesdependent on coffee quality using a 1H NMR-based metabolomics approach, FoodRes. Int. 67 (2015) 175–182.

[228] J.F. Wu, M. Domellof, A.M. Zivkovic, et al., NMR-based metabolite profiling ofhuman milk: a pilot study of methods for investigating compositional changes duringlactation, Biochem. Biophys. Res. Commun. 469 (2016) 626–632.

[229] T. Ohtsuki, K. Sato, N. Furusho, et al., Absolute quantification of dehydroacetic acidin processed foods using quantitative 1H NMR, Food Chem. 141 (2013) 1322–1327.

[230] R. Bouteille, M. Gaudet, B. Lecanu, et al., Monitoring lactic acid production duringmilk fermentation by in situ quantitative proton nuclear magnetic resonance spectros-copy, J. Dairy Sci. 96 (2013) 2071–2080.

[231] C. Papaernmanouil, C.G. Tsiafoulis, D. Alivertis, et al., Selective one-dimensionaltotal correlation spectroscopy nuclear magnetic resonance experiments for a rapididentification of minor components in the lipid fraction of milk and dairy products:toward spin chromatography, J. Agric. Food Chem. 63 (2015) 5381–5387.

[232] C. Siciliano, E. Belsito, R. De Marco, et al., Quantitative determination of fatty acidchain composition in pork meat products by high resolution 1H NMR spectroscopy,Food Chem. 136 (2013) 546–554.

[233] D. Prema, T.D. Turner, J. Jensen, et al., Rapid determination of total conjugated lin-oleic acid concentrations in beef by 1H NMR spectroscopy, J. Food Compos. Anal.41 (2015) 54–57.

[234] E. Shumilina, R. Slizyte, R. Mozuraityte, et al., Quality changes of salmonby-products during storage: assessment and quantification by NMR, Food Chem.211 (2016) 803–811.

[235] M. Spiteri, E. Jamin, F. Thomas, et al., Fast and global authenticity screening of honeyusing 1H-NMR profiling, Food Chem. 189 (2015) 60–66.

[236] G. del Campo, J. Zuriarrain, A. Zuriarrain, et al., Quantitative determination of car-boxylic acids, amino acids, carbohydrates, ethanol and hydroxymethylfurfural in honeyby 1H NMR, Food Chem. 196 (2016) 1031–1039.

[237] A. Kazalaki, M. Misiak, A. Spyros, et al., Identification and quantitative determinationof carbohydrate molecules in Greek honey by employing 13C NMR spectroscopy,Anal. Methods 7 (2015) 5962–5972.

[238] G. Pages, A. Gerdova, D. Williamson, et al., Evaluation of a benchtop cryogen-freelow-field 1H NMR spectrometer for the analysis of sexual enhancement and weightloss dietary supplements adulterated with pharmaceutical substances, Anal. Chem.86 (2014) 11897–11904.

[239] R. Hachem, G. Assemat, N. Martins, et al., Proton NMR for detection, identificationand quantification of adulterants in 160 herbal food supplements marketed for weightloss, J. Pharm. Biomed. Anal. 124 (2016) 34–47.

[240] P. Dais, M. Misiak, E. Hatzakis, Analysis of marine dietary supplements using NMRspectroscopy, Anal. Methods 7 (2015) 5226–5238.

[241] Y.B. Monakhova, B.W.K. Diehl, Authentication of the origin of sucrose-based sugarproducts using quantitative natural abundance 13CNMR, J. Sci. Food Agric. 96 (2016)2861–2866.

[242] G.M. Kamal, X. Wang, Y. Bin, et al., Compositional differences among Chinese soysauce types studied by 13C NMR spectroscopy coupled with multivariate statisticalanalysis, Talanta 158 (2016) 89–99.

[243] S. Grosch, Y.B. Monakhova, T. Kuballa, et al., Comparison of GC/MS and NMR forquantification of methyleugenol in food, Eur. Food Res. Technol. 236 (2013)267–275.

142 X. Li and K. Hu

[244] J. Muller-Maatsch, A. Caligiani, T. Tedeschi, et al., Simple and validated quantitative1H NMRmethod for the determination of methylation, acetylation, and feruloylationdegree of pectin, J. Agric. Food Chem. 62 (2014) 9081–9087.

[245] A. Gratia, D. Merlet, V. Ducruet, et al., A comprehensive NMR methodology toassess the composition of biobased and biodegradable polymers in contact with food,Anal. Chim. Acta 853 (2015) 477–485.

[246] T. Ohtsuki, K. Sato, Y. Abe, et al., Quantification of acesulfame potassium inprocessed foods by quantitative 1H NMR, Talanta 131 (2015) 712–718.

[247] T. Chatzimitakos, V. Exarchou, S.A. Ordoudi, et al., Ion-pair assisted extractionfollowed by 1H NMR determination of biogenic amines in food and biological matri-ces, Food Chem. 202 (2016) 445–450.

[248] Y.B. Monakhova, B.W.K. Diehl, Quantitative analysis of sunflower lecithin adulter-ation with soy species by NMR spectroscopy and PLS regression, J. Am. Oil Chem.Soc. 93 (2016) 27–36.

[249] M.A. Bernstein, S. Sykora, C. Peng, et al., Optimization and automation of quanti-tative NMR data extraction, Anal. Chem. 85 (2013) 5778–5786.

143qNMR of Mixtures