# Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with a benchtop NMR spectrometer

Joris Mandral, Simon Roques, Jean-Nicolas Dumez, Patrick Giraudeau, Jonathan Farjon

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## To cite this version:

Joris Mandral, Simon Roques, Jean-Nicolas Dumez, Patrick Giraudeau, Jonathan Farjon. Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with a benchtop NMR spectrometer. Analytical Methods, 2025, 17 (16), pp.3171-3182. 10.1039/d5ay00006h. hal-05185215

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Submitted on 27 Nov 2025

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Analytical
Methods

Evaluation of pure shift NMR methods for the analysis of
complex metabolite mixtures with a benchtop NMR
spectrometer

<table><tr><td>Journal:</td><td>Analytical Methods</td></tr><tr><td>Manuscript ID</td><td>AY-ART-01-2025-000006.R1</td></tr><tr><td>Article Type:</td><td>Paper</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Mandral, Joris; Nantes Université, CEISAM, UMR CNRS 6230<br>Roques, Simon; INRAE<br>Dumez, Jean-Nicolas; CNRS, CEISAM<br>Giraudeau, Patrick; Université de Nantes, CEISAM, UMR CNRS 6230<br>Farjon, Jonathan; CNRS - Nantes Université, CEISAM</td></tr></table>
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Dr. FARJON Jonathan
Faculté des Sciences et Techniques de Nantes
UMR CNRS 6230 - CEISAM,
Equipe MIMM,
BP 92208, 2 Rue de la Houssinière
44322 Nantes Cedex 3, France

Dear Prof. B. Jill Venton,

Please find attached a manuscript entitled "Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with a compact NMR spectrometer" which I wish to be considered for publication in *Analytical Methods*

In this article, we evaluate the potential of ultra-resolved NMR methods with the goal to improve the analysis of metabolite mixtures on a recent compact NMR spectrometer. Six pure shift (PS) NMR methods were compared in terms of resolution, sensitivity, spectral purity and repeatability. Among the different tested experiments, 1D projections of 2D J-resolved double echo spectra, combined with an improved processing strategy is the best compromise based on previously mentioned analytical criteria. In a second step, the potential of this approach was applied on a sample from the food industry as a proof of concept for the detection of metabolites in complex samples. This work shows that PS NMR opens the way for a rapid profiling of complex metabolites mixtures implemented on compact NMR spectrometers.

We hope that this extensive spectroscopic and analytical work will be of interest to a broad community of analytical chemists interested by the characterization of complex mixtures. We also hope you will find it suitable for publication in *Analytical Methods*, and we are looking forward to your constructive remarks and suggestions.

Sincerely,

Jonathan FARJON
Dr. FARJON Jonathan

Faculté des Sciences et Techniques de Nantes

UMR CNRS 6230 - CEISAM,

Equipe MIMM,

BP 92208, 2 Rue de la Houssinière

44322 Nantes Cedex 3, France

Dear Prof B. Jill Venton,

Please find attached the revised version of the manuscript Ref. No: AY-ART-01-2025-000006:
"Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with
a benchtop NMR spectrometer"

We are very grateful to you and the reviewers for the insightful comments and suggestions. We
have carefully revised the manuscript by taking comments into account, as described in the
response to reviews (in red in the following).

In the revised main text, you can find marked changes in red and related answer's number (see
below).

We hope that you will find this revised version suitable for publication in Analytical Methods,
and we thank you in advance for your attention regarding this matter.

Yours sincerely

J. FARJON
Referee 1

The authors report the development of different Pure-Shift (PS) NMR methods (i.e. ¹H-homo-decoupled spectra) on a benchtop NMR system operating at 80 MHz along with a detailed and insightful analytical comparison between the techniques. These results obtained using a model mixture and a 'real' complex biological mixture highlight the potential of this methodology for the NMR analysis of complex mixtures using an (relatively) inexpensive and accessible benchtop spectrometer. This is of interest in many fields, such as metabolomics, pharmaceutical or food analysis, etc. Furthermore, the conclusions established from the comparison of J-resolved based experiments with the modern ones relying on frequency-spatial encoding (ZS and PSYCHE) should be of interest also for high-field NMR spectroscopy. This sheds new light on the efficiency of J-resolved NMR with appropriate processing to yield PS spectra. Thus, I recommend publication of this article with minor revisions. My remarks, questions and suggestions are listed below:

1) At low/moderate magnetic field, second-ordered coupled systems are commonly encountered. In this context, why author did exclude the PS method "TSE-PSYCHE" specially designed to reduce strong coupling unwanted signals? (Ref: Foroozandeh, M., Adams, R. W., Kiraly, P., Nilsson, M., & Morris, G. A. (2015). *Chemical Communications*, 51(84), 15410-15413.)

Even if authors have excluded this method for sensitivity reasons, they should mention it.

R1.1

The TSE-PSYCHE approach was also implemented and tested in an earlier study. This TSE-PSYCHE was already discussed when previously used on a 43 MHz benchtop NMR spectrometer: (Ref: T. Castaing-Cordier, D. Bouillaud, P. Bowyer, O. Gonçalves, P. Giraudeau and J. Farjon, *ChemPhysChem*, 2019, 20, 736-744.). This technique resulted in an even lower sensitivity than the normal PSYCHE approach (approximately 20%). Therefore, we chose to remove it from the present study.

This is now mentioned in the main text as follows: "A version of the PSYCHE specifically designed to reduce unwanted strong coupling signals has been reported in the literature (TSE-PSYCHE)⁴⁹. However, this technique is hampered by even lower sensitivity, as already observed when it was first implemented on a benchtop NMR system²¹. This alternative was therefore not considered in the present study."

2) The three J-resolved based sequences are recorded with the same number of t1-increments and scans per increment, but do these experiments share the same t1max (ie number of t1-increments multiply the dwell-time in indirect dimension)? This is important as the relaxation loss depends on this duration.

R1.2

The J-RES SE and J-RES DE pulse sequences share the same t₁ max.

For the J-RES Chirp, the incremented t₁ delays are the same as the J-RES SE and J-RES DE pulse sequences. However, the fixed duration of the two adiabatic chirp pulses must be considered. Each t₁ increment is in fact 200 ms longer with the J-RES Chirp pulse sequence. This pulse sequence is inspired by the work of Thrippleton et al. (Ref: M. J. Thrippleton, R. A. E. Edden and J. Keeler, *J. Magn. Reson.*
2005, 174, 97-109). In their version, the duration of chirp pulses is subtracted from the duration of t₁ increments, to ensure that the t₁ max is similar between all the versions.

In our version, the spectral width in the indirect dimension is 30 Hz. Consequently, each t₁ increment has a duration of 33.3 ms, and t₁ max is 1.06 seconds. It is technically impossible to reduce 200 ms to 33.3 ms, and thus subtract the duration of the chirp pulses (at least for the six first increments). For this reason, the J-RES CHIRP version pulse sequence is actually longer than the J-RES SE and J-RES DE, because the duration of the chirp pulses cannot be compensated.

Thank you for this comment, which has led us to modify the main text as follows:" To better understand this difference in sensitivity, it is important to keep in mind that each t₁ increment of the J-RES Chirp pulse sequence is 200 ms longer than the J-RES SE and J-RES DE pulse sequences; due to the addition of two 100 ms adiabatic chirp pulses. When the chirp pulses are applied together with pulse field gradients, it is assumed that significant signal losses may occur due to diffusion and relaxation. Actually, by reducing the duration of the chirp pulses, sensitivity could approach that of the J-RES SE and J-RES DE techniques, but at the cost of a less efficient suppression of strong coupling artefacts. In this study, a duration of 100 ms for each of the chirp pulses was found to be the most efficient in reducing the strong coupling artefacts."

In addition, Figure 1 (e) was modified to illustrate the real t₁ incrementation of the pulse sequence.

3) The higher sensitivity of the J-resolved experiments versus those based on frequency-spatial encoding (ZS and PSYCHE) is to be expected. What is less obvious is the loss of a factor 4 in SNR for the J-res chirps versus J-res DE. As a methodological paper, the authors should explain (or at least give assumptions) this sensitivity difference. I guess the loss of signal is due to diffusion when chirp pulses are applied, together with pulse-field-gradients.

R1.3

The lower sensitivity of the J-RES Chirp pulse sequence is now explained in more details in the main text (see R1.2).

4) J-RES DE and SE experiments provide about 25% of the 1D spectrum SNR (Table 3), but the J-RES projections are plot with a 0.56 vertical scaling factor compared to the 1D spectrum to get comparable intensity (as described in the caption of Figure 2). On my mind, this is counterintuitive as I would expect a scaling factor higher than 1 for the J-RES projections.

R1.4

The scaling factors were extracted from MNova. After investigation, it appears that these factors are not derived from the SNR consideration, as some of them have values less than 1, which is indeed counterintuitive. Scaling factors were modified in Figure 2, considering the relative average SNR for analytical performance comparison. These new scaling factors illustrate relative sensitivities with greater fidelity.

5) Regarding the water-suppression signal, authors should comment the potential impact (or not) of the 4x300 ms WET scheme on exchangeable hydrogens (eg metabolites amine protons)
R1.5

In our study, water signal suppression is focused on the best removal of the water peak (since the model sample is in pure water). For now, the potential impact on exchangeable protons has not been examined. The long duration of the WET block might indeed be detrimental to the observation of the exchangeable hydrogens. This is clarified in the main text as follows: "In this study, water suppression is focused on having the best solvent elimination. With the current parameters, the long duration of the WET block might be detrimental to the observation of the exchangeable hydrogens."

6) The powers of shaped-pulses are given in dB, which is apparatus dependent. Can the authors give the power in W or in Hz units (ie gamma*B1/2Pi) ?

R1.6

In Hz units, the power of the shaped pulses are 0.43 Hz, 0.80 Hz, 0.96 Hz and 0.94 Hz. The values in dB were replaced by the values in Hz in the main text as follows: "The resulting shaped pulse angles are 46° (0.43 Hz), 86° (0.80 Hz), 104° (0.96 Hz) and 102° (0.94 Hz)."

7) Typo corrections:

Page 2, line 58: "T1 should be T1" with 1 in index

Page 3, line 39: "2000 1 ns" should be "2000 1 ms"

In Eq (3): "¹D" should be "1D"

Page 8, line 3: 'Table 2' should be "Table 3"

Some redundant references, for examples: Ref 15 and 29 are the same, Ref 12 and 42 are the same. So please check the list of references before publication.

R1.7

Typo and reference corrections were made in the main text.
Referee: 2

In this paper, the authors evaluate the analytical performance of pure shift NMR methods for the analysis of metabolite mixtures on a recent 80 MHz benchtop NMR spectrometer. This article is well described, and the topic is be suitable for publication in Analytical Methods. However, some concerns should be addressed before the publication. Some comments are listed in the following.

1. In this study, six pure shift NMR methods, including single-band ZS, dual-band ZS, PSYCHE, and J-RES Chirp, J-RES DE, as well as J-RES SE, have been compared. However, another typical 1D pure shift NMR and pure shift 2DJ methods, such as: SHARPER (Anal. Chem. 2017, 89 (18), 10013-10021), RASA2DJ (Anal. Chem. 2017, 89 (23), 12646-12651), ATP-2DJ (J. Chem. Phys., 2021, 155, 034202), OPAM-2DJ (IEEE Trans. Instrum. Meas., 2021, 70, 6004509), and so on should be also discussed.

R2.1

The SHARPER approach is now mentioned in the main text: "Another pure shift NMR method called SHARPER has been reported, which does not involve the acquisition of multiple free induction decays in a pseudo-2D manner⁵⁰. This PS strategy is known to achieve greater SNRs compared to the ZS and PSYCHE approaches, because it does not require any gradient-encoding of the NMR tube. However, its band-selective nature makes it mostly suited to monitoring one or two signals as part of reaction monitoring, and is less suitable for the complete elucidation of a complex mixture."

The alternative 2DJ methods are now also discussed in the main text: "In the literature, many strategies were tested to try to overcome the phase twist lineshape of 2D J-resolved spectra, as well as the impact of strong coupling artefacts⁴³⁻⁴⁵. There are also alternatives that are more robust against magnetic field inhomogeneities like the UPSIF-2DJ pulse sequence⁴⁶. Such interesting implementation is beyond the scope of this study, which focuses on the performances of the most common and used PS NMR techniques."

2. The decoupling performance of ZS and PSYCHE is affected by experimental parameters, such as the intensity of slice-selective gradients and the duration of the selective pulses in ZS experiments, as well as the angle of chirp pulses in PSYCHE experiments, and so on. Experimental discussion on the performance including SNR, resolution, and spectral purity, for relevant PS methods on benchtop NMR spectrometer should be added, so that a conclusion that "1D projections of 2D J-resolved double-echo spectra (J-RES DE), combined with an improved processing strategy, appeared to offer the best compromise based on these analytical criteria." is drawn.

R2.2

For gradient-encoded PS experiments, it is important to keep in mind that the encoding gradient is orthogonal to the NMR tube, which has a diameter of 4 mm. This drastically reduces the sensitive volume for this kind of experiment, and the NMR sensitivity. This was added in the main text as follows: "In the benchtop NMR setup used for this study, the direction of the encoding gradient is orthogonal to the NMR tube, which considerably reduces the sensitive volume compared to common high-field NMR geometries."
Precisions about the duration of the central selective pulse were given in the main text:

“For single-band and dual-band ZS, the central selective pulse has a width of 40 Hz, which is suitable for selectively exciting all multiplicities. For a Q3 pulse, this corresponds to a duration of 90 ms. This duration proved to be the best compromise between selectivity and sensitivity of the selective pulse.”

Precisions about the intensity of the slice-selective gradients in the ZS experiments were given in the main text:

“In the ZS approaches, a too weak encoding gradient would result in missing (or distorted) signals at the extremities of the NMR tube, and a too strong encoding gradient would result in a lower sensitivity. Consequently, for the ZS approaches, the intensities of the pulse field gradient applied together with the central 180° selective pulse were optimized to encode a spectral width of approximately 10 ppm (± 5 ppm around the central offset frequency). For the dual-band ZS approach, there is a cosine modulation of the central selective pulse, shaping its excitation profile. This new excitation profile enables to encode a similar spectral width (10 ppm) as the one in the single-band ZS, but with a lower encoding gradient intensity, resulting in an improved sensitivity.”

The original parameters for the PSYCHE were inspired from a previous study on a benchtop NMR system: (ref: T. Castaing-Cordier, D. Bouillaud, P. Bowyer, O. Gonçalves, P. Giraudeau and J. Farjon, *ChemPhysChem*, 2019, 20, 736–744.). However, on a benchtop NMR system, the sensitivity of the PSYCHE is also very low, due to the saltire pulses potentially lasting tens of milliseconds (losses due to diffusion). The duration of the saltire pulses was reduced, resulting in greater overall SNRs, while no impact on the decoupling performance were observed. In addition, sensitivity was slightly improved with saltire pulse angle set at 30°, which is a bit higher than the commonly encountered 15° or 20°. This flip angle appeared to be the best compromise between sensitivity and decoupling sidebands intensity for our model sample.

Precisions about the chosen flip angle for the PSYCHE experiment were added in the main text: “In a PSYCHE experiment, the lower the small flip angle, the cleaner the resulting spectrum is, with reduced decoupling sidebands intensities. However, a small flip angle also results in lower sensitivity. In this study, NMR sensitivity was slightly improved with saltire pulse angle set at 30°, which is a bit higher than the commonly encountered 15° or 20° flip angles. This flip angle appeared to be the best compromise between sensitivity and decoupling sidebands intensity for the model sample.”

3. For benchtop NMR spectrometer, possible spatiotemporal variations of magnetic field may also affect the analytical evaluation. The pure shift NMR implementations in inhomogeneous magnetic fields should be also mentioned and discussed.

R2.3

An alternative 2D-J technique which is more robust against magnetic field inhomogeneities is now cited in the main text:

“There are also alternatives that are more robust against magnetic field homogeneities like the UPSIF-2DJ pulse sequence⁴⁶. Such interesting implementation is beyond the scope of this study, which focuses on the performances of the most common and used PS techniques.”
4. Additional samples should be tested to provide the more sufficient evidences and proofs.

R2.4

The aim of this article is to provide an initial assessment of the performance of common solvent-suppressed pure shift NMR techniques on a model mixture. The resulting applications will allow a more thorough assessment of the value of these methods for a wide variety of samples. Additional PS spectra of the fish feed extract used to illustrate the performance of the J-RES DE approach are now provided in the dedicated Supplementary Information section. The additional PS NMR acquisitions performed on a fish feed of commercial formulation appear to us to provide sufficient additional evidence to support our conclusions. More details are provided in R2.5 below.

The additional PS NMR acquisitions are now mentioned in the main text as follows:

"Additional PS NMR acquisitions on a complex sample (fish feed of commercial formulation) are provided in the supplementary information (Fig.S16 and Fig.S17) confirming the superior sensitivity of the J-RES DE approach, which is a critical parameter for the analysis of often dilute complex biological samples."

5. The experimental results of other PS methods on the fish feed extract should also be given

R2.5

Additional PS spectra of the fish feed extract are now provided in the dedicated supplementary information section. They clearly highlight the increased sensitivity provided by the use of J-resolved pulse sequences, which is critical for the analysis of potentially dilute complex biological mixtures. Additional discussions were also added as follows:

"The additional PS spectra shown in Figure S16 clearly shows the lower sensitivity of the J-RES Chirp strategy, with missing singlets and a noisier baseline. The more sensitive J-RES SE and J-RES DE spectra show similar information, but the SNR of the J-RES DE approach are higher in average, which is consistent with the observations made on the model mixture of metabolites."

"The additional PS spectra shown in Figure S17 show the lack of sensitivity of the gradient-encoded PS strategies: especially for the single-band ZS and PSYCHE strategies. SNR on the detected singlets are very low and there is much more noise."

6. Recently, solvent-suppressed pure shift methods proposed and implemented by G. A. Morris et al., may be more suitable to compare with WET 1D 1H.

R2.6

The solvent suppressed pure shift method presented by Gareth Morris et al. could indeed be interesting for an efficient suppression of strong solvent signals, especially when there are some exchangeable hydrogens. However, we observed that the WATERGATE is less straightforward to implement than the WET approach on a benchtop system, and it can harm some very small signals nearby the residual solvent peaks. We also observed that the WET performance for solvent suppression is sufficiently good to envisage complex mixture analysis in non-deuterated solvents on recent compact NMR systems. In
our point of view, both approaches have advantages and inconvenient.

Discussion of the solvent suppressed pure shift method presented recently by Gareth Morris et al. can now be found in the main text: "Recently, Gates et al. presented an elegant solvent suppression strategy for pure shift NMR⁴⁰. They showed that it was possible to add a WATERGATE solvent suppression block within a PSYCHE NMR pulse sequence. This pulse sequence, called PSYCHE-iWG, showed good solvent suppression performance and has the interesting ability to preserve signals undergoing chemical exchange with the solvent. This alternative could be a nice development perspective for solvent-suppressed pure-shift NMR on a benchtop system."

7. Pure shift NMR methodologies and multi-band PS implementations developed by Zhong Chen's group should be not neglected.

R2.7

The dual-band ZS PS NMR strategy developed in the paper has similarities with some previous work from Zhong Chen et al. It is now cited in the paper when first discussing the dual-band ZS strategy.

8. Pseudo-2D pure shift implementations generally requires long experimental times, and this issue would be highlighted due to the increased scan numbers on benchtop NMR spectrometers. Recent PS progresses in DL-accelerated pure shift NMR, such as, Anal. Chem., 2024, 96(4), 1515-1521, and Adv. Sci., 2024, 2309810, may presents prospects in benchtop pure shift NMR.

R2.8

The strategies developed in the above-mentioned papers are indeed interesting prospects for future developments of PS NMR on compact NMR systems.

They are now cited in the main text as follows: "Further improvements of the technique could be focused on accelerating the acquisition of PS NMR spectra for high-throughput studies. Recently, it was demonstrated that non-uniform sampling (NUS) pure shift NMR strategies combined with deep learning (DL) algorithms for missing increment reconstruction provide a robust approach to obtaining highly resolved spectra with time savings of up to a factor of 20⁵¹,⁵²."
Evaluation of pure shift NMR methods for the analysis of complex
metabolite mixtures with a benchtop NMR spectrometer

Joris Mandral,ª Simon Roques,ᵇ,ᶜ Jean-Nicolas Dumez,ª Patrick Giraudeauª* and Jonathan Farjon a*

Compact, low-cost and cryogen-free benchtop nuclear magnetic resonance (NMR) spectrometers have become an attractive option for analysing complex biological mixtures. The implementation of spectral simplification methods such as pure shift spectroscopy is particularly important to circumvent the ubiquitous peak overlaps that occur due to the limited resolution of compact instruments. Pure shift (PS) strategies consist in eliminating the signal multiplicity induced by the homonuclear J-couplings observed in 1D ¹H NMR, thereby simplifying the spectral information. This paper provides an analytical evaluation of optimised pure shift spectroscopy pulse sequences for the analysis of metabolite mixtures on a recent benchtop NMR spectrometer. Six PS pulse sequences were carefully evaluated, based on three families of PS techniques: 1D projections of 2D J-resolved spectra, Zangger-Sterk (ZS) and PSYCHE. Methods were evaluated in terms of resolution, sensitivity, spectral purity and repeatability. Among the strategies we explored, 1D projections of 2D J-resolved double-echo spectra (J-RES DE), combined with an improved processing strategy, appeared to offer the best compromise based on these analytical criteria. The potential of this method was illustrated on a complex sample from the food industry and enabled key metabolites to be detected with improved resolution and sensitivity, showing that PS NMR could be used for a rapid (22 minutes) profiling of complex metabolite mixtures on a benchtop NMR spectrometer.

Received 00th January 20xx,
Accepted 00th January 20xx

DOI: 10.1039/x0xx00000x

# 1. Introduction

A complex mixture can be defined as a sample containing a broad variety of molecules at different concentrations. Liquid-state nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool capable of providing information on molecular structures, molecular dynamics and reliable quantitative data, even in complex mixtures¹,². NMR spectroscopy has found many applications to the analysis of mixtures, in fields such as food science³, metabolomics⁴,⁵ and reaction and process monitoring⁶,⁷.

In order to achieve higher levels of sensitivity and resolution, NMR spectroscopy mostly relies on increasingly powerful superconducting magnets to generate intense magnetic fields⁸, currently up to 1.2 GHz. However, high-field NMR requires large volumes of expensive cryogenic fluids (N₂ and He), expensive NMR magnets and regular maintenance by specialised engineers. In the 2010s, benchtop (or compact) NMR spectrometers using smaller permanent magnets emerged⁹. These spectrometers offer numerous advantages: they are compact enough to be placed under a fume hood, they are almost maintenance-free, and they do not require costly cryogenic fluids. However, due to the weaker magnetic fields of benchtop instruments, their sensitivity is reduced and their ability to separate resonances is limited.

Over the past ten years, benchtop NMR instrumentation has improved and the magnetic fields of benchtop instruments are now stronger and more homogeneous¹⁰,¹¹. Benchtop instruments are also equipped with gradient coils that are essential for pulse sequences development. Many solvent

suppression methods, which are key for the accurate analysis of spectral regions that are usually obscured by intense and broad solvent peaks, were implemented and compared on a benchtop NMR system¹²,¹³. To improve spectral resolution, conventional and ultrafast 2D NMR pulse sequences were also implemented¹⁴,¹⁵. Homodecoupling or pure shift (PS) strategies also form an attractive option to improve resolution and reduce peak overlap¹⁶,¹⁷. Historically, PS spectra were first obtained through the 1D projection of 45° tilted 2D J-resolved experiments¹⁸. Then more sophisticated spatially encoded methods were introduced like the Zangger-Sterk (ZS) or the Pure Shift Yielded by Chirp Excitation (PSYCHE) techniques¹⁹,²⁰. In 2019, our team showed that PS strategies could be implemented on a 43 MHz benchtop NMR spectrometer and enabled efficient homonuclear decoupling²¹. In that work, we showed that PS methods could improve separation between peaks that overlapped in conventional 1D spectra, but we also concluded that further work was needed to address concerns about the intrinsic low sensitivity of these techniques and the frequent occurrence of strong coupling artefacts.

Thanks to the strong increase of magnetic fields for benchtop NMR instruments (from 43 to more than 80 MHz in less than a decade), the number of applications has been increasing considerably. Benchtop NMR has been used for a number of applications involving complex biological mixtures, such as food product analysis, the monitoring of bioprocesses and metabolomics²². Benchtop NMR spectrometers are now used for authentication and quality control of beverages²³⁻²⁶, oils and lipids¹⁵,²⁷⁻²⁹, and meat and dairy products³⁰,³¹. In terms of bioprocess monitoring, they have been used to monitor yeast fermentation, hydrolysis or lipid accumulation in microalgae³²⁻³⁵. Metabolomics studies were also recently conducted on urines of diabetic patients³⁶,³⁷, showing that the monitoring of metabolites was possible on compact systems. The prospects for new applications offered by the growing capabilities of benchtop NMR spectrometers motivate the need to further implement and optimise spectral simplification strategies such

<footnote>ª Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France</footnote>

<footnote>ᵇ INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, 64310 Saint Pée sur Nivelle, France</footnote>

<footnote>ᶜ Present address: Université Clermont Auvergne, INRAE, VetAgro Sup, UMR
Herbivores, Saint-Genes-Champanelle, France</footnote>

<footnote>*jonathan.farjon@univ-nantes.fr; patrick.giraudeau@univ-nantes.fr;
Electronic Supplementary Information (ESI) available: [details of any supplementary
information available should be included here]. See DOI: 10.1039/x0xx00000x</footnote>
as PS spectroscopy on compact systems to facilitate the analysis
of complex mixtures.

In this article, we perform a detailed analytical evaluation of different PS strategies combined with solvent signal suppression on complex metabolite mixtures in water. Solvent-suppressed PS pulse sequences were implemented on an 80 MHz benchtop NMR spectrometer which offers a significantly improved resolution and sensitivity compared to previous work at 43 MHz. We evaluate and compare the new analytical possibilities of these PS strategies by comparing their sensitivity, repeatability, resolution and spectral purity on a model mixture of 17 metabolites. Finally, the applicability of the most suitable PS strategy is illustrated on an animal food sample, to illustrate its applicability for the analysis of complex mixtures.

## 2. Material and Methods

### 2.1. Sample preparation

A model metabolite mixture was prepared from 17 raw metabolite powders in MilliQ ultrapure water containing: Histidine [His] = 110 mM, 4-hydroxyphenylacetic acid [4-hydro] = 62 mM, Phenylalanine [Phen] = 63 mM, Valine [Val] = 157 mM, Glycine [Gly] = 279 mM, Citric acid [Cit] = 155 mM, Taurine [Tau] = 228 mM, Serine [Ser] = 220 mM, Glutamine [Glu] = 135 mM, Threonine [Thr] = 162 mM, Arginine [Arg] = 128 mM, Lysine [Lys] = 135 mM, Lactate [Lac] = 234 mM, Alanine [Ala] = 296 mM, Creatine [Cre] = 123 mM, Creatinine [Crea] = 95 mM and Asparagine [Asp] = 63 mM. 3-trimethylsilylpropionate-d4 (TSP-d4) from Eurisotop was used as a reference for calibration ([TSP-d4] = 20 mM). 600 µL of this mixture was analysed in a standard 5 mm NMR tube.

A fish feed extract was prepared by dissolving 222 mg of crude pellet powder (commercial-like fish feed described in the study of Roques et al.³⁸) in 1 mL of D₂O containing 2.25 mM of TSP-d₄ for calibration. The sealed vial was then vortexed for ten min at room temperature. It was then stored in a refrigerator (4°C) overnight, to allow the insoluble part of the fish food to settle. 600 µL of the supernatant were then inserted into a standard 5 mm NMR tube for direct NMR analysis.

### 2.2. NMR apparatus

All the spectra were recorded at 26.5°C using a ¹H, ¹⁹F, ¹³C 80 MHz Spinsolve Ultra benchtop NMR spectrometer from Magritek equipped with a 50 G/cm gradient coil in the direction of the static magnetic field B₀. Pulse sequence implementations, optimisations and final acquisitions were carried out using the Spinsolve Expert software (ver.2.00.23 from Magritek).

### 2.3. NMR acquisition parameters

Key elements of the solvent-suppressed pure shift pulse sequences that were compared are illustrated in Fig. 1.

#### 2.3.1. Solvent-suppressed 1D ¹H experiments

The WET (water suppression enhanced through T₁ effects) solvent suppression scheme shown in Fig. 1a uses four consecutive Gaussian

shaped 300 ms selective pulses on the water signal followed by 2.2 ms spoiler gradients. The amplitude of the 180° Gaussian pulse was manually calibrated and used to calculate the amplitudes of the different angles, as described by Smallcombe et al.³⁹. Pulse angles were refined manually to obtain the best water signal elimination. The resulting shaped pulse angles are 46° (0.43 Hz), 86° (0.80 Hz), 104° (0.96 Hz) and 102° (0.94 Hz). To assess precision on integrals, five consecutive WET 1D ¹H spectra were acquired with 32768 data points and a dwell time of 100 µs. 256 scans were acquired with a repetition time of 5 s for each WET 1D ¹H experiment, resulting in an acquisition time of 21 min and 20 s.

#### 2.3.2. Zangger-Sterk and PSYCHE experiments

The PSYCHE and Zangger-Sterk experiments are based on the homonuclear spin echo shown in Fig. 1b, and differ by the nature of the refocusing block inserted in the red square. For the PSYCHE pulse sequence, a double saltire Chirp pulse of 2 x 2.5 ms was used with a bandwidth of 6.3 kHz and a smoothing of 10% applied simultaneously with a gradient of 11.4 G.cm⁻¹. A small angle of 30° was chosen as a compromise between sensitivity and decoupling sideband intensity.

Two versions of the ZS pulse sequence were tested. The first version uses a single-band 180° Q3 selective pulse (single-band ZS). The second version uses a dual-band 180° Q3 selective pulse (dual-band ZS). In both versions, a 180° selective bandwidth of 40 Hz for Q3 (90 ms) was chosen together with small gradients of adapted intensity for spatial encoding. The intensity of the spatial encoding gradient for the single-band ZS is 0.40 G.cm⁻¹. The intensity of the spatial encoding gradient for the dual-band ZS is 0.16 G.cm⁻¹.

PSYCHE, single-band ZS and dual-band ZS pseudo-2D acquisitions were performed with 32 FID chunks. Each FID chunk lasted 25 ms to ensure a negligible homonuclear J-coupling evolution during the acquisition time, and so that the duration of the reconstructed FID was adapted to the signal decay. Each FID chunk was recorded with 2048 data points and a dwell time of 20 µs. Each FID chunk was recorded in 8 scans with a repetition time of 5 s, making it a total of 256 scans (similar to WET 1D ¹H), resulting in a total acquisition time of 22 min. Five consecutive PSYCHE, single-band and dual-band ZS acquisitions were performed to measure repeatability.

#### 2.3.3. 1D projections of 2D J-resolved spectra

Three different 2D J-resolved pulse sequences were tested: the basic 2D J-resolved experiment with a single spin echo (J-RES SE in Fig. 1c), the 2D J-resolved experiment with a double spin echo (J-RES DE in Fig. 1d) and the 2D J-resolved with two 180° adiabatic chirp pulses (J-RES Chirp in Fig. 1e). All the 2D J-resolved spectra were acquired with 32 t₁ increments and 8 scans per increment, making it a total of 256 scans and resulting in a total acquisition time of 22 min. The J-RES Chirp uses two 100 ms 180° chirp pulses with a bandwidth of 6 kHz applied simultaneously with a gradient of 0.5 G.cm⁻¹. The J-RES SE, J-RES DE and J-RES Chirp acquisitions were repeated five times to measure repeatability.
<figure><img src="image_9.png" /><figcaption>(b) Spatially-encoded pure shift techniques</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(c) J-resolved single echo (J-RES SE)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(d) J-resolved double echo (J-RES DE)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(e) J-resolved chirp (J-RES Chirp)</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 1 Pulse sequence elements used in this work. (a) WET block that was concatenated to the PS pulse sequences, and also used as a reference with a conventional pulse-acquire scheme. (b) Spatially encoded PS pulse sequences with β = 30° small angle saltire pulses for the PSYCHE technique or a 180° Q3 selective pulse for the Zangger-Sterk techniques (ZS). (c) 2D J-resolved pulse sequence with a single spin echo (J-RES SE). (d) 2D J-resolved pulse sequences with a double spin echo to suppress strong coupling terms and related artefacts (J-RES DE). (e) 2D J-resolved pulse sequence with chirp pulses for single-scan suppression of strong coupling artefacts (J-RES Chirp).</figcaption></figure>

2.3.4. Acquisition on ground fish pellets

A WET 1D ¹H and a J-RES DE NMR spectrum of the fish feed pellet extract were acquired using the same acquisition parameters as those presented above. A WET CPMG spectrum, using 2000 1 ms echoes to remove fast relaxing ¹H signals belonging to heavier molecules, was also acquired. The WET CPMG 1D ¹H was acquired with 8192 data points and a dwell time of 100 µs. 256 scans were acquired with a repetition time of 5 s: the total acquisition time was 21 min and 20 s (same as WET 1D ¹H).

2.4. NMR processing parameters

The data processing of all the PS spectra was performed using the MestreNova (MNova) software from Mestrelab. As the acquisition scheme was different between the conventional 2D J-resolved pulse sequences and the pseudo-2D ZS and PSYCHE pulse sequences, the processing was also adapted to yield optimum results for each method.

2.4.1. WET 1D ¹H experiments

The processing of WET 1D ¹H experiments consisted in a 2x zero filling followed by a FT and manual phasing of the spectrum. The same baseline correction (Whittaker Smoother) was applied to the five WET 1D ¹H acquisitions.

2.4.2. Zangger-Sterk and PSYCHE experiments

For the PSYCHE experiments, single-band ZS and dual-band ZS experiments, FID chunks were directly concatenated by the Spinsolve expert software, providing a complete PS FID. The processing consisted in a 4x zero-filling and a Gaussian apodisation (LB = 1 Hz) followed by a FT and manual phasing of the spectrum. The same baseline correction (Whittaker Smoother) was applied to the five single-band ZS, dual-band ZS and PSYCHE acquisitions.

2.4.3. 1D projections of 2D J-resolved spectra

The first step of the processing was a shift of the FID (32 points) in the indirect dimension, artificially doubling the number of lines in the 2D spectrum (from 32 to 64). The 32 first lines were reconstructed by using a backward linear prediction of the FID using the Modified Iterative Soft Thresholding (MIST) algorithm implemented in MNova, generating a pseudo echo in the indirect dimension. Then, a sinusoidal square apodisation was performed in both dimensions. It was followed by a 45° tilt of the 2D data and symmetrisation. The homodecoupled 1D projection was then extracted from the sum of chosen lines in the indirect dimension (lines 8 to 55, thus excluding lines 1 to 7 and 56 to 64) in order to discard the lines that only bring noise, and to increase the resulting signal-to-noise ratio. When extracting a 1D projection, MNova also reconstructs the corresponding time-domain FID, making it possible to apply the
classic processing strategies used in 1D NMR. A 4x zero filling and a Gaussian apodisation (LB = 1 Hz) were performed on the pseudo-FID obtained from the 1D projection in order to recover singlets with sufficient digitisation. The same baseline correction (Whittaker Smoother) was applied to all the 1D projections of 2D J-resolved experiments.

### 2.4.4. WET CPMG experiment

The processing of the WET CPMG experiment consisted in a 4x zero filling followed by a FT and manual phasing of the spectrum. A Whittaker Smoother baseline correction was then applied.

### 2.5. Analytical evaluation criteria

The analytical performance of PS spectroscopy methods was characterised considering the following criteria: resolution, spectral purity, sensitivity and repeatability. Performances were characterised on a common set of 17 singlets with none to moderate overlap. Each experiment was repeated five times, enabling average performances and standard deviations to be calculated for each criterion. In addition, to obtain the most accurate measurements possible, the deconvolution tool of MNova was used to perform a Lorentzian-Gaussian fitting of the singlets, via the Global Spectra Deconvolution (GSD) method implemented in MNova.

#### 2.5.1. Resolution

To assess the resolution, the linewidth at 10% maximum intensity ($LW_{10}$) was measured on each selected singlet. The average value of $LW_{10}$ was calculated and used to characterise the performance in terms of resolution for each PS techniques. The smallest the average $LW_{10}$, the better the resolution. However, to facilitate the graphical representation, a resolution score (R) was calculated as follows:

$$R (\%) = \frac{\langle LW_{10}^{opt} \rangle}{\langle LW_{10} \rangle} \times (1)$$

where $\langle LW_{10} \rangle$ is the average 10% linewidth for a given method, and $\langle LW_{10}^{opt} \rangle$ is the average 10% linewidth for the method with the best resolution.

#### 2.5.2. Spectral purity

Spectral regions impacted by artefacts and/or baseline distortions arising from strongly coupled spin systems were defined and investigated. The total absolute area of these spectral regions was measured. The absolute area(s) of the signal corresponding to real chemical shift(s) (pure signal(s)) were also measured (with the help of GSD). The spectral purity (SP) was quantitatively assessed by comparing the integral of the pure signal(s) with the integral of the whole spectral regions in which artefacts and/or baseline distortions were present, and an average SP value was calculated for each PS method to characterise the performance in terms of spectral purity:

$$SP (\%) = \frac{\text{Integral of the pure signal(s)}}{\text{Integral of the impacted region}} \times 100(2)$$

#### 2.5.3. Sensitivity

Site-by-site signal-to-noise ratios (SNRs) were measured for each PS technique. The same approach was performed for the corresponding signals in the WET 1D $^{1}$H spectra. Sensitivity (S) was characterised by measuring the ratio between the site-by-site SNRs and their corresponding signal in the WET 1D $^{1}$H. Then, an average S value was calculated for each PS techniques to characterise the performance in terms of sensitivity:

$$S (\%) = \left( \frac{\text{SNRs in the PS spectrum}}{\text{SNRs in the WET 1D $^{1}$H}} \right) \times 100(3)$$

#### 2.5.4. Repeatability

Repeatability was determined on relative peak areas, by dividing each singlet area by the area of the lactate singlet at 1.35 ppm. The standard deviations and coefficient of variation (CV) of each singlet relative area were calculated over the five repetitions of each PS technique. An average CV value was calculated and used to characterise the performance in terms of repeatability for each PS technique. In order to facilitate the representation, a repeatability score (RS) was also calculated as follows:

$$RS (\%) = 100 - \text{Average CV value}(4)$$

## 3. Results and discussion

The aim of this work is to evaluate and compare the performance of pure shift NMR strategies on an 80 MHz benchtop spectrometer, relying on relevant analytical criteria in order to find the most suitable homodecoupling strategy for the analysis of complex metabolite mixtures. Six WET PS pulse sequences were tested on a model mixture of 17 metabolites in pure water. Regular WET 1D $^{1}$H acquisitions were also performed for comparison of the sensitivity. All the solvent-suppressed acquisitions were carried out with a similar acquisition time of 22 min, guaranteeing the comparability of techniques. In addition, 22 min is a good compromise for obtaining sufficient sensitivity in a short time to consider future metabolic profiling with PS.

### 3.1. Pure shift pulse sequences and developments

This section explains how the PS pulse sequences work, and highlights the optimisations that were carried out to improve their performance in terms of sensitivity, resolution and spectral purity, in order to make them compatible with the analysis of complex metabolite mixtures.

#### 3.1.1. WET solvent elimination

The WET solvent suppression block shown in Fig. 1a was concatenated to all the PS pulse sequences and also to a pulse acquire 1D pulse sequence. Water signal suppression is essential for the analysis of biological mixtures to focus the receiver gain on the small signals of interest. The WET scheme was chosen because it reduces the SNR of the water by more than 99%, is easy to concatenate with other pulse sequences, does not interfere with
signals close to the residual water peak and presents no phase
distortions. Previous work showed that WET-based schemes were
optimal for water suppression on metabolite samples studied with
benchtop NMR¹². In this study, water suppression is focused on
providing the best solvent elimination. With the current parameters,
the long duration of the WET block might be detrimental to the
observation of the exchangeable hydrogens. Recently, Gates et al.
presented an elegant solvent suppression strategy for pure shift
NMR⁴⁰. They showed that it was possible to add a WATERGATE
solvent suppression block within a PSYCHE NMR pulse sequence. This
pulse sequence, called PSYCHE-iWG, showed good solvent
suppression performance and has the interesting ability to preserve
signals undergoing chemical exchange with the solvent. This
alternative could be a nice development perspective for solvent-
suppressed pure-shift NMR on a benchtop system.

**3.1.2. 1D projections of 2D J-resolved spectra**

2D J-resolved techniques are conventional 2D techniques, in which
several t₁ increments are acquired. A 2D J-resolved pulse sequence is
a homonuclear spin echo refocusing the chemical shift in the
incremented indirect (t₁) dimension¹⁸. After a 45° tilt and a
symmetrisation of the 2D data, it is possible to extract a
homodecoupled 1D horizontal projection.

One drawback of 2D J-resolved techniques is that they are
hampered by a phase twist lineshape induced by the magnitude
acquisition mode along the indirect dimension, resulting into
distorted and broad signals. This drawback can be overcome by
applying a strong sinusoidal square apodisation in both dimensions.
(before tilting and symmetrisation) at the expense of signal intensity.
In order to recover narrow signals while maintaining maximum
sensitivity, we applied a processing strategy inspired by the work of
Sakhaii et al. published in 2014⁴¹. A sinusoidal square apodisation
was performed in both dimensions after a pseudo-echo filtering to
preserve the primary signal-to-noise ratio at the start of the FID in
the indirect dimension. The pseudo-echo filtering preserves the
sensitivity by matching the maximum amplitude of the FID to that of
the sinusoidal square apodisation window. Thanks to this improved
processing strategy, 1D projections of 2D J-resolved spectra are
expected to show highly resolved singlets with greater SNR for
improved sensitivity.

Another drawback is that the basic 2D J-resolved technique (J-
RES SE in Fig. 1c) is affected by strongly coupled systems, responsible
for the appearance of artefactual signals due to undesired coherence
transfers that cannot simply be erased by phase cycling or pulse field
gradients. The J-RES DE (Fig. 1d) and J-RES Chirp (Fig. 1e) pulse
sequences are inspired from the work of Thrippleton et al. in 2005.,
in which they showed to be more robust against the appearance of
unwanted strong coupling signals (artefacts)⁴². Consequently, the
spectral purity of the 1D projections extracted from the acquisitions
of these 2D J-resolved pulse sequences should be improved over
those obtained from the original J-RES SE pulse sequence.

In the literature, many strategies were tested to try to overcome
the phase twist lineshape of 2D J-resolved spectra, as well as the
impact of strong coupling artefacts. Strategies such as RASA2DJ, ATP-
2DJ and OPAM-2DJ are hybrid between conventional 2D J-resolved
techniques and gradient-encoded PS techniques, and enable to

acquire absorption mode 2D J-resolved spectra with reduced strong
coupling artefacts⁴³⁻⁴⁵. There are also alternatives that are more
robust against magnetic field inhomogeneities like the UPSIF-2DJ
pulse sequence⁴⁶. Such interesting implementation is beyond the
scope of this study, which focuses on the performances of the most
common and used PS NMR techniques.

**3.1.3. Gradient encoded pure shift strategies**

The single-band ZS¹⁹, dual-band ZS⁴⁷,⁴⁸ and PSYCHE²⁰ experiments are based on the homonuclear spin echo shown in Fig. 1b, in which families of coupled active and passive spins (of the same nucleus) are differentiated. For the ZS method, the distinction between passive and active spins is determined by the position within the sample, thanks to a low intensity encoding gradient applied simultaneously with the central selective 180° pulse. For the PSYCHE method, the distinction between passive and active spins is statistical, thanks to the small flip angles of the saltire pulses. This discrimination is used to refocus the coupling between active and passive spins at the end of the homonuclear spin echo.

The single-band ZS, dual-band ZS and PSYCHE are pseudo 2D PS
pulse sequences in which several short parts (chunks) of FID are
acquired and concatenated, in order to reconstruct a full FID without
coupling evolution. The single-band ZS uses a Q3 shaped pulse. The
dual-band ZS uses a cosine-modulated dual-band version of the Q3
shaped pulse. Assuming that there is no coupling between aliphatic
and aromatic protons (which is the case for most metabolites), one
band can be used to refocus coupling in the aliphatic region and the
other to refocus coupling in the aromatic region of the spectrum,
enabling a wider spectral region to be encoded with a less intense
encoding gradient amplitude. This multi-band (or multi-offset)
approach was already applied in the past to improve the SNR of
spatially-encoded NMR experiments⁴⁷. As a result, the dual-band ZS
should have a better signal-to-noise ratio than the single-band ZS,
thus improving the sensitivity of ZS at 80 MHz.

**3.2. Description of the PS spectra on the model mixture**

This section provides a qualitative description of the WET PS spectra,
and the spectral simplifications they provide compared to the WET
1D ¹H spectrum of the model mixture. A stacked representation of all
solvent-suppressed acquisitions performed on the model mixture of
17 metabolites for the aliphatic region is provided in Fig. 2. An
equivalent figure for the aromatic region is shown in the
supplementary information (Fig. S1). A full elucidation and
annotation of the model mixture was carried out with the help of the
Human Metabolome Database (HMDB) (supplementary information,
Fig. S2) and of additional 2D NMR experiments (supplementary
information, Fig. S3, Fig. S4 and Table S1).

**3.2.1. Spectral simplifications provided by PS spectra**

The aliphatic region of the WET 1D ¹H spectrum of the model mixture shown in Fig. 2a is very complex as it is constituted of 39 different signals with various multiplicities. Among these 39 signals, only the citric acid signal at 2.73 ppm is completely isolated as it is not overlapped with other signals. The signals of valine at 1.00 and 1.04 ppm, despite being overlapped with each other, are also isolated from the other signals, making the detection of valine quite
straightforward. The other 36 signals are all mixed together in large
and highly complex spectral regions. The spectral region ranging
from 2.83 to 4.41 ppm is the most complex spectral region of the
WET 1D ¹H spectrum, as it features a continuous overlap of 23 signals
from various metabolites. Such strong overlap is highly detrimental
to the determination of individual mixture components.

Overall, the different PS acquisitions on the model mixture (from
Fig 2b to Fig 2g) provide spectra with similar spectral information
(same signals and similar fingerprints). PS methods provide
considerable spectral simplification compared to the WET 1D ¹H
spectrum. For example, initially overlapping signals, such as alanine

at 1.49 ppm, asparagine at 2.95 ppm or threonine at 4.29 ppm, are
now well isolated from other signals. Similarly, in PS spectra,
originally large and complex spectral regions with continuous overlap
between signals are divided into smaller and simpler spectral
regions. The spectral region ranging from 3.77 to 4.10 ppm is the
most complex spectral region of the PS spectra containing 9
continuously overlapping signals. The most complex spectral region
in PS spectra is 4.6 times narrower than the most complex spectral
region in the WET 1D ¹H spectrum, demonstrating the higher
separation power of PS NMR.

<figure><img src="image_14.png" /><figcaption>Fig. 2 Spectra acquired on the model mixture of 17 metabolites. (a) WET 1D ¹H spectrum. WET-based (b) J-RES SE PS spectrum. (c) J-RES DE PS spectrum. (d) J-RES Chirp PS spectrum. (e) Single-band ZS PS spectrum. (f) Dual-band ZS PS spectrum. (g) PSYCHE PS spectrum. Red stars symbolise artefact(s)/baseline distortion(s) arising from strongly coupled systems. Spectra were scaled vertically to show comparable maximum intensity, by a factor indicated on the right of each spectrum.</figcaption></figure>

**3.2.2. Deconvolution, strong coupling and spectral purity**

PS spectroscopy simplifies spectral information for complex mixtures
because of its greater separation power. However, in the case of very

complex mixtures, significant overlap can still occur between
singlets. But even in these cases, PS spectroscopy could be
advantageous, as it can more easily be coupled with spectral
deconvolution algorithms. Actually, the deconvolution of singlets in
complex spectral regions is facilitated by the absence of signal multiplicity, making it possible to deconvolute all the signals with only one peak per chemical shift. A peak-picking step is necessary before deconvolution. This procedure is further facilitated in the case of J-resolved methods, where the 2D spectrum which has been recorded to provide a homodecoupled projection contains additional multiplicity information that helps identifying individual singlets on the PS spectra (see supplementary Figure S5 and S6).

In PS spectroscopy, spectral artefacts can arise from different sources like decoupling sidebands and/or strong coupling. On benchtop NMR spectrometers, PS spectroscopy artefacts mostly come from strongly coupled spin systems, inducing unwanted coherence transfers that give rise to additional signal. In the case of the model mixture, solvent-suppressed PS spectra are particularly impacted by three strongly coupled spin systems: citric acid (2.56 to 2.87 ppm), taurine (3.15 to 3.52 ppm) and 4-hydroxyphenylacetic acid (6.70 to 7.25 ppm). The impact of the strongly coupled spin systems on the spectral purity was assessed for each method, relying on the SP parameter described in Equation 2 (see details in the next section).

### 3.3. Analytical comparison of PS strategies

In this section, the analytical performance of solvent-suppressed PS strategies was measured by evaluating and comparing their resolution, sensitivity, repeatability and spectral purity. Performances were then summarised to highlight the PS strategy that represents the best compromise.

#### 3.3.1. Resolution

Site-by-site average linewidth values at 10% intensity are provided with their variability in the supplementary information (from Fig. S7 to Fig. S12 and Table S2 to Table S6). As summarised in Table 1, the overall average $LW_{10}$ values do not vary significantly from one PS technique to another, with a minimum of $4.0 \pm 0.2$ Hz for the ZS single band and a maximum of $5.0 \pm 0.7$ Hz for PSYCHE. The small standard deviations of this parameter show that the homodecoupling performance does not depend on the multiplicity of the signals. In order to facilitate the graphical representation, the average $LW_{10}$ values were normalised to the average $LW_{10}$ value of the single-band ZS method (which provides the best resolution) in order to calculate the resolution scores R (see equation (1) in part 2.5.1). As shown in Table 1, all methods are characterised by R values higher or equal to 90% except for the PSYCHE technique.

<table><caption>Table 1 Average linewidth at 10% intensity ($LW_{10}$) and resolution score (R) of the singlets for each PS strategy.</caption><thead><tr><td>PS technique</td><td>LW<sub>10</sub></td><td>R</td></tr></thead><tbody><tr><td>J-RES SE</td><td>4.4 ± 0.3 Hz</td><td>90%</td></tr><tr><td>J-RES DE</td><td>4.1 ± 0.3 Hz</td><td>97%</td></tr><tr><td>J-RES Chirp</td><td>4.2 ± 0.2 Hz</td><td>95%</td></tr><tr><td>Single-band ZS</td><td>4.0 ± 0.2 Hz</td><td>100%</td></tr><tr><td>Dual-band ZS</td><td>4.1 ± 0.3 Hz</td><td>97%</td></tr><tr><td>PSYCHE</td><td>5.0 ± 0.7 Hz</td><td>83%</td></tr></tbody></table>

#### 3.3.2. Spectral purity

The baseline of the single-band ZS and dual-band ZS spectra shown in Fig. 2e and Fig. 2f are mostly impacted by distortions in the citric acid and 4-hydroxyphenylacetic acid spectral regions. The citric acid singlet is missing in both single-band and dual-band ZS spectra. Small baseline distortions can also be observed in the taurine spectral region. The PSYCHE spectrum (Fig. 2g) has the noisiest baseline and many baseline distortions are observed, particularly in the taurine spectral region. In a PSYCHE experiment, the lower the small flip angle, the cleaner the resulting spectrum, with reduced decoupling sidebands intensities. However, a small flip angle also results in lower sensitivity. In this study, NMR sensitivity was slightly improved with saltire pulse angle set at 30°, which is a bit higher than the commonly encountered 15° or 20° flip angles. This flip angle appeared to be the best compromise between sensitivity and decoupling sidebands intensity for the model sample. A version of the PSYCHE specifically designed to reduce unwanted strong coupling signals has been described in the literature (TSE-PSYCHE)⁴⁹. However, this technique is hampered by even lower sensitivity, as already discussed when it was first implemented on a benchtop NMR system²¹. This alternative was therefore not considered in the present study. The 1D projections of 2D J-resolved spectra, shown in Fig. 2b, Fig.2c and Fig. 2d have a flat baseline free from signal distortions. The J-RES DE spectrum is of especially high quality with only two very small baseline distortions observable in the taurine spectral regions. The spectral purities were calculated according to equation (2) and are summarised in Table 2.

<table><caption>Table 2 Spectral purities (SP in %) of the PS techniques for the spectral regions of taurine (Tau), 4-hydroxyphenylacetic acid (4-hydro) and citric acid (Cit). Average SP values are also provided.</caption><thead><tr><td>PS technique</td><td>Tau</td><td>4-hydro</td><td>Cit</td><td>Average SP</td></tr></thead><tbody><tr><td>J-RES SE</td><td>37%</td><td>69%</td><td>66%</td><td>57%</td></tr><tr><td>J-RES DE</td><td>68%</td><td>72%</td><td>89%</td><td>76%</td></tr><tr><td>J-RES Chirp</td><td>54%</td><td>72%</td><td>76%</td><td>67%</td></tr><tr><td>Single-band ZS</td><td>43%</td><td>92%</td><td>0%</td><td>45%</td></tr><tr><td>Dual-band ZS</td><td>38%</td><td>66%</td><td>0%</td><td>35%</td></tr><tr><td>PSYCHE</td><td>24%</td><td>52%</td><td>84%</td><td>53%</td></tr></tbody></table>

As expected from the qualitative observations, the J-RES DE and J-RES Chirp PS techniques have the highest average spectral purities, as they are more immune to perturbations from strongly coupled spin systems. The most impacted PS pulse sequences are the single-band ZS and dual-band ZS pulse sequences, with average SP values of just 45% and 35% respectively. This poor result is mainly due to the fact that PS ZS techniques were unable to deliver the expected singlet of citric acid at 2.73 ppm.

#### 3.3.3. Sensitivity

Site-by-site average SNR values are provided with their variability in the supplementary information (from Fig. S7 to Fig. S12 and Table S2 to Table S6). The overall average SNR of the different PS techniques are summarised in Table.3. The average SNR of the various PS techniques relative to the average SNR of the WET 1D ¹H spectra was also calculated, following equation (3) in section 2.5.3.
<table><caption>Table 3 Average SNR of PS techniques for the selected set of 17 signals. The average SNR relative to the average SNR of the WET 1D ¹H spectra (S) is also shown.</caption><thead><tr><td>PS technique</td><td>Average SNR</td><td>S</td></tr></thead><tbody><tr><td>J-RES SE</td><td>536</td><td>26%</td></tr><tr><td>J-RES DE</td><td>597</td><td>27%</td></tr><tr><td>J-RES Chirp</td><td>136</td><td>6%</td></tr><tr><td>Single-band ZS</td><td>67</td><td>3%</td></tr><tr><td>Dual-band ZS</td><td>274</td><td>12%</td></tr><tr><td>PSYCHE</td><td>117</td><td>5%</td></tr></tbody></table>

According to **Table 2**, the J-RES SE and J-RES DE pulse sequences show the best performance in terms of sensitivity. The J-RES Chirp is significantly less sensitive than the J-RES SE and the J-RES DE pulse sequences. **To better understand this difference in sensitivity, it is important to keep in mind that each t₁ increment of the J-RES Chirp pulse sequence is 200 ms longer than the J-RES SE and J-RES DE pulse sequences; due to the addition of two 100 ms adiabatic chirp pulses. When the chirp pulses are applied together with pulse field gradients, it is assumed that significant signal losses may occur due to diffusion and relaxation. Actually, by reducing the duration of the chirp pulses, sensitivity could approach that of the J-RES SE and J-RES DE techniques, but at the cost of a less efficient suppression of strong coupling artefacts. In this study, a duration of 100 ms for the chirp pulses was found to be the most efficient in reducing the strong coupling artefacts. If the processing of the J-RES SE, J-RES DE and J-RES Chirp data is performed without backward linear prediction in the indirect dimension to preserve the initial intensity of the FID during the sine square apodisation, the average SNR are 463, 430 and 107 respectively. This means that the pseudo-echo processing approach respectively improves the SNR by factors of 1.17, 1.39 and 1.12 for each above-mentioned PS method.**

The PSYCHE and single-band ZS pulse sequences are the least sensitive, with lower average SNR. **In the benchtop NMR setup used for this study, the direction of the encoding gradient is orthogonal to the NMR tube, which considerably reduces the sensitive volume compared to common high-field NMR geometries. For ZS approaches, sensitivity depends on the duration of the central selective pulse and the intensity of the encoding gradient applied simultaneously with the selective pulse. For single-band and dual-band ZS, the central selective pulse has a width of 40 Hz, which is suitable for selectively exciting all multiplicities. For a Q3 pulse, this corresponds to a duration of 90 ms. This duration proved to be the best compromise between selectivity and sensitivity of the selective pulse. In the ZS approaches, a too weak encoding gradient would result in missing (or distorted) signals at the extremities of the NMR tube, and a too strong encoding gradient would result in a lower sensitivity. Consequently, for the ZS approaches, the intensities of the pulse field gradient applied together with the central 180° selective pulse were optimized to encode a spectral width of approximately 10 ppm (± 5 ppm around the central offset frequency). For the dual-band ZS approach, there is a cosine modulation of the central selective pulse, shaping its excitation profile. This new excitation profile enables to encode a similar spectral width (10 ppm) as the one in the single-band ZS, but with a lower encoding gradient intensity, resulting in an improved sensitivity.**

The dual-band ZS method is four times more sensitive than the single-band ZS, demonstrating that the use of a dual-band cosine-modulated Q3 pulse improves the sensitivity of the ZS. Despite this sensitivity boost, the J-RES-DE PS techniques remain the most sensitive among all the PS techniques. **Another pure shift NMR method called SHARPER has been reported, which does not involve the acquisition of multiple free induction decays in a pseudo-2D manner⁵⁰. This PS strategy is known to achieve greater SNRs compared to the ZS and PSYCHE approaches, because it does not require any gradient-encoding of the NMR tube. However, its band-selective nature makes it mostly suited to monitoring one or two signals as part of reaction monitoring, and is less suitable for the complete elucidation of a complex mixture.**

### 3.3.4. Repeatability

Site-by-site relative area values are provided with their variability in the supplementary information (from Fig. S7 to Fig. S12 and Table S2 to Table S6). The average coefficients of variation of the relative area values range from 4% for the J-RES DE technique to 14% for the PSYCHE technique. In order to facilitate the discussion and the graphical representation, a repeatability score was calculated for each technique as described in equation (2) (see part 2.5.3). The repeatability scores are summarised in **Table 3**.

<table><caption>Table 4 Average repeatability scores (RS in %) of the different PS techniques.</caption><thead><tr><td>PS technique</td><td>RS</td></tr></thead><tbody><tr><td>J-RES SE</td><td>92%</td></tr><tr><td>J-RES DE</td><td>96%</td></tr><tr><td>J-RES Chirp</td><td>96%</td></tr><tr><td>Single-band ZS</td><td>95%</td></tr><tr><td>Dual-band ZS</td><td>92%</td></tr><tr><td>PSYCHE</td><td>86%</td></tr></tbody></table>

The J-RES DE, J-RES Chirp and single-band ZS techniques are the most repeatable. They are followed by the J-RES SE and the dual-band ZS, and PSYCHE is the least repeatable technique. Actually, as shown in the detailed values shown in supplementary information, the biggest variations arise from the spectral regions impacted by strong couplings, demonstrating that the repeatability performance is directly linked to the robustness of PS against strong coupling artefacts and baseline distortions.

### 3.3.5. Overall performance comparison

The overall performances of the PS methods in terms of sensitivity, resolution, repeatability and spectral purity are summarised in Fig. 3. With this representation, the determination of the best PS strategy is quite straightforward because the bigger the area of the coloured surface, the better overall performance of the corresponding PS strategy. The first observation is that the PSYCHE PS technique does not perform as well as the other PS techniques, with insufficient sensitivity, the worst repeatability score and resolution score, and a spectral purity that is not optimal. Apart from the PSYCHE, all the PS strategies are comparable in terms of repeatability (RS above 92%) and resolution score (R above 90%). Finally, the analytical criteria lower encoding gradient intensity, resulting in an improved sensitivity. The dual-band ZS method is four times more sensitive than the single-band ZS, demonstrating that the use of a dual-band cosine-modulated Q3 pulse improves the sensitivity of the ZS. Despite this sensitivity boost, the J-RES-DE PS techniques remain the most sensitive among all the PS techniques. Another pure shift NMR method called SHARPER has been reported, which does not involve the acquisition of multiple free induction decays in a pseudo-2D manner⁵⁰. This PS strategy is known to achieve greater SNRs compared to the ZS and PSYCHE approaches, because it does not require any gradient-encoding of the NMR tube. However, its band-selective nature makes it mostly suited to monitoring one or two signals as part of reaction monitoring, and is less suitable for the complete elucidation of a complex mixture.
that clearly discriminate the PS strategies in terms of overall
performance are the sensitivity and the spectral purity, because they
vary significantly from one strategy to another.

This evaluation of the analytical performances of the PS
techniques demonstrates that the 1D projection of 2D J-resolved
double echo method is the best option to perform PS spectroscopy
of small molecule mixtures on an 80 MHz benchtop NMR
spectrometer. Overall, this PS strategy is the best in terms of
sensitivity, spectral purity and repeatability. Its resolution factor is
not the best among the PS strategies but the difference with the
single-band ZS (only 0.1 Hz) is not significant. Therefore, the J-RES DE
PS strategy is the best suited for applications on biologically relevant
complex mixtures, especially because of its high sensitivity, which is
often a critical requirement for the analysis of dilute samples at lower
magnetic fields. Additional PS NMR acquisitions on a complex sample
(fish feed of commercial formulation) are provided in the
supplementary information (Fig.S16 and Fig.S17) confirming the
superior sensitivity of the J-RES DE approach, which is a critical
parameter for the analysis of often dilute complex biological
samples.

**3.4. Application on a fish feed of commercial formulation**

In 2018, Roques et al. carried out a metabolite profiling of different
fish feed formulations and investigated the effect of each feed on
rainbow trout growth, using a 500 MHz NMR spectrometer³⁸.
Commercial fish feed formulations consist of fish meal and fish oil,

which are rich in proteins and lipids. They also contain high levels of
essential low molecular weight compounds like free amino acids as
well as peptides and organic acids. Several of these smaller
compounds are known to have a direct impact on fish metabolism
and could therefore potentially help explain differences in growth
performance. Such complex samples are a good example to illustrate
the relevance of PS methods and their ability to decipher individual
mixture components. The J-RES DE PS strategy was applied on a
commercial fish food sample containing the above-mentioned
biologically relevant metabolites.

The WET 1D ¹H spectrum of the commercial fish food formulation (Fig. 4a) is very complex with many overlapping signals from both small and large molecules. The PS spectrum in Fig.4c offers significant spectral simplification, and is sensitive enough to highlight many small metabolite signals with fewer overlap. The J-RES DE spectrum benefits from the suppression of the multiplicity of signals such as lactate, alanine, lysine and threonine, but also from the filtering of broad signals from large molecules due to the T₂ filtering effect provided by the relatively long PS pulse sequence. Nevertheless, the CPMG spectrum of the same sample (Fig. 4b) recorded for the sake of comparison, shows that the spectral simplification offered by PS NMR clearly goes beyond a simple T₂ filtering effect. This experiment shows that the J-RES DE PS strategy is a promising tool for the metabolic profiling of complex biological mixtures. Additional spectra were acquired to validate the assignments made in Fig. 4 and are displayed in the supplementary information (see Fig.S13, Fig.S14, Fig.S15, Fig S16, Fig S17 and Table S7).
<figure><img src="image_15.png" /><figcaption>Fig. 3 Evaluation of the analytical performance of the different PS strategies based on the comparison of their relative sensitivity (normalised with the SNR of WET 1D ¹H spectra), repeatability score, resolution score and spectral purity. The four axes are in percentage and range from 0% to 100%. For each analytical criterion, the bigger the percentage value, the better the performance. The larger the coloured surface are the better the overall performances of the PS strategy</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 4 Spectra acquired on a fish feed extract. (a) WET 1D ³H spectrum. (b) WET CPMG spectrum. (c) 1D projection of a WET 2D J-resolved double echo spectrum. Signals of sucrose (suc), lactate (lac), threonine (thr), choline (cho), succinic acid (succ), methionine (meth), sodium acetate (ace), alanine (ala), betaine (bet), dimethylglycine (dimeth), lysine (lys) and valine (val) could be annotated thanks to the J-RES DE PS approach.</figcaption></figure>
## 4. Conclusions

We have shown that solvent-suppressed PS acquisitions could be performed on complex metabolite mixtures with an 80 MHz benchtop NMR spectrometer. The higher separation power of PS NMR enables significant spectral simplifications for highly overlapped 1D ¹H spectra. Multiplicity suppression allows previously overlapping signals to be isolated, facilitating future chemical shift assignments for profiling and peaks integration for quantification.

The analytical performance evaluation of the different PS strategies showed that the J-RES DE PS strategy outperforms other methods for the analysis of complex metabolite mixtures. It proved to be the best suited in terms of sensitivity, repeatability and spectral purity. The potential of this J-RES DE PS strategy was illustrated on a real commercial fish food formulation sample, containing small key metabolites involved in the growth performance of fishes. The quality of the resulting PS spectrum opens promising perspectives for the profiling of complex biological mixtures on compact NMR systems.

Further improvements of the technique could be focused on accelerating the acquisition of PS NMR spectra for high-throughput studies. Recently, it was demonstrated that non-uniform sampling (NUS) pure shift NMR strategies combined with deep learning (DL) algorithms for missing increment reconstruction provide a robust approach to obtaining highly resolved spectra with time savings of up to a factor of 20⁵¹,⁵².

## Author Contributions

JM, JND, PG, JF: conceptualisation, methodology, writing – review & editing. JM: investigation, data curation, formal analysis, validation, writing – original draft. SR: investigation, review and editing. JND, PG, JF: funding acquisition, supervision, project administration.

## Conflicts of interest

There are no conflicts to declare.

## Data availability

Data supporting this article have been included as part of the Supplementary Information. In addition, all spectral data and calculations used for this manuscript have been deposited online at: https://doi.org/10.57745/QHKEXQ

## Acknowledgements

This work has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreements no 814747/SUMMIT and 801774/DINAMIX) and the Region Pays de la Loire (ConnectTalent/HPNMR). We acknowledge the French National Infrastructure for Metabolomics and Fluxomics MetaboHUB-ANR-11-INBS-0010 (www.metabohub.fr) and the Corsaire metabolomics core facility (Biogenouest). This work includes NMR experiments carried out on the CEISAM NMR platform.

We thank Frédéric Terrier and Dr Benoît Fauconneau from NuMeA for the preparation and formulation of the fish feed. We thank Dr. Annick Moing from the French National Institute for Agriculture, Food, and Environment (INRAE) for providing us with the fish feed formulation and for her expert advice. JM acknowledge the French ministry of higher education and research for funding his PhD thesis. JF warmly thank Sandrine Bouchet for a permanent assistance.

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(a) WET block for solvent suppression

<figure><img src="image_17.png" /></figure>

(b) Spatially-encoded pure shift techniques

<figure><img src="image_18.png" /></figure>

(c) J-resolved single echo (J-RES SE)

<figure><img src="image_19.png" /></figure>

(d) J-resolved double echo (J-RES DE)

<figure><img src="image_20.png" /></figure>

(e) J-resolved chirp (J-RES Chirp)

<figure><img src="image_21.png" /><figcaption>Fig. 1</figcaption></figure>

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<figure><img src="image_30.png" /><figcaption>Optimised pure shift NMR for biological mixtures on a benchtop spectrometer</figcaption></figure>

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DATA AVAILABILITY STATEMENT

Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with a
benchtop NMR spectrometer

Joris Mandral,ª Simon Roques,ᵇ,ᶜ Jean-Nicolas Dumez,ª Patrick Giraudeauª* and Jonathan Farjonª*

a. Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France

b. INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, 64310 Saint Pée sur Nivelle, France

c. Present adress : Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genes-Champanelle, France

*jonathan.farjon@univ-nantes.fr; patrick.giraudeau@univ-nantes.fr;

Data supporting this article have been included as part of the supplementary Information.

In addition, all spectral data and calculations used for this manuscript have been deposited online on the
recherche.data.gouv.fr repository under https://doi.org/10.57745/QHKEXQ
Electronica Supplementary Material (ESI) for Analytical Methods

This journal is © The Royal Society of Chemistry 2023

Evaluation of pure shift NMR methods for the analysis of complex metabolite mixtures with a benchtop NMR spectrometer

Joris Mandral,ª Simon Roques,ᵇᶜ Jean-Nicolas Dumez,ª Patrick Giraudeauª* and Jonathan Farjonª*

a. Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France

b. INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, 64310 Saint Pée sur Nivelle, France

c. Present adress : Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genes-Champanelle, France

*jonathan.farjon@univ-nantes.fr; patrick.giraudeau@univ-nantes.fr;

Index

<table><tr><td>1.</td><td>Spectral simplification in the aromatic region</td><td>2</td></tr><tr><td>2.</td><td>Annotation of the model mixture</td><td>3</td></tr><tr><td>3.</td><td>Chemical shifts assignment</td><td>6</td></tr><tr><td>4.</td><td>Fitting of the PS spectra</td><td>7</td></tr><tr><td>5.</td><td>Site-by-site performances of the J-RES SE</td><td>9</td></tr><tr><td>6.</td><td>Site-by-site performances of the J-RES DE</td><td>10</td></tr><tr><td>7.</td><td>Site-by-site performances of the J-RES Chirp</td><td>11</td></tr><tr><td>8.</td><td>Site-by-site performances of the single-band ZS</td><td>12</td></tr><tr><td>9.</td><td>Site-by-site performances of the dual-band ZS</td><td>13</td></tr><tr><td>10.</td><td>Site-by-site performances of the PSYCHE</td><td>14</td></tr><tr><td>11.</td><td>Additional spectra of the fish feed of commercial formulation</td><td>15</td></tr></table>
<figure><img src="image_31.png" /><figcaption>1. Spectral simplification in the aromatic region</figcaption></figure>

Fig.S1: Spectra acquired on the model mixture of seventeen metabolites in the aromatic spectral regions. WET based: (a) 1D ¹H spectrum. (b) J-RES SE PS spectrum. (c) J-RES DE PS spectrum. (d) J-RES Chirp PS spectrum. (e) Single-band ZS PS spectrum. (f) Dual-band ZS PS spectrum. (g) PSYCHE PS spectrum. Strong coupling artefact(s)/baseline distortion(s) are symbolized by the red stars.
## 2. Annotation of the model mixture

<figure><img src="image_32.png" /><figcaption>Fig.S2: Stack representation of the full WET based: (a) 1D ¹H spectrum. (b) J-RES DE PS spectrum. Both spectra intensities were normalized for a clear visualization of the spectral information. The J-RES DE PS spectrum was fully annotated. Signal annotation was made with the help of Human Metabolome Database (HMDB).</figcaption></figure>
In order to validate the elucidation of the model mixture of seventeen metabolites, additional 2D NMR acquisitions were performed on the 80 MHz benchtop spectrometer.

<figure><img src="image_33.png" /><figcaption>Fig.S3: WET TOtal Correlation SpectroscopY (WET TOCSY) spectrum at 80 MHz of the model mixture of seventeen metabolites in the aliphatic region. Signal annotation was made with the help of Human Metabolome Database (HMDB).</figcaption></figure>
<figure><img src="image_34.png" /><figcaption>Fig.S4: Heteronuclear Single Quantum Correlation Multiplicity-Edited (HSQC-ME) spectrum at 80 MHz of the model mixture of seventeen metabolites in the aliphatic region. Full chemical shift annotations are summarized in **Table S1**. Signal annotation was made with the help of Human Metabolome Database (HMDB) and the WET TOCSY spectrum presented in Fig. S2.</figcaption></figure>
3. Chemical shifts assignment

<table><caption>Table S1: Full <sup>1</sup>H and <sup>13</sup>C annotations of the model mixture of seventeen metabolites for the HSQC-ME presented in Fig.S4. Annotations in bold correspond to the signals appearing in the aromatic region.</caption><thead><tr><th>N°</th><th><sup>1</sup>H (ppm)</th><th><sup>13</sup>C (ppm)</th><th>Annotation</th><th>N°</th><th><sup>1</sup>H (ppm)</th><th><sup>13</sup>C (ppm)</th><th>Annotation</th></tr></thead><tbody><tr><td>1</td><td>0</td><td>0</td><td>TSP</td><td>24</td><td>3.44</td><td>38.35</td><td>Tau</td></tr><tr><td>2</td><td>1</td><td>19.59</td><td>Val</td><td>25</td><td>3.5</td><td>45.33</td><td>4-hydro</td></tr><tr><td>3</td><td>1.04</td><td>20.75</td><td>Val</td><td>26</td><td>3.59</td><td>44.5</td><td>Gly</td></tr><tr><td>4</td><td>1.35</td><td>22.34</td><td>Thr</td><td>27</td><td>3.63</td><td>63.17</td><td>Val/ Thr</td></tr><tr><td>5</td><td>1.35</td><td>22.91</td><td>Lac</td><td>28</td><td>3.82</td><td>57.28</td><td>Arg/ Lys</td></tr><tr><td>6</td><td>1.49</td><td>19.09</td><td>Ala</td><td>29</td><td>3.82</td><td>53.46</td><td>Ala</td></tr><tr><td>7</td><td>1.53</td><td>24.24</td><td>Lys</td><td>30</td><td>3.88</td><td>59.28</td><td>Ser</td></tr><tr><td>8</td><td>1.62</td><td>26.73</td><td>Arg</td><td>31</td><td>3.94</td><td>56.62</td><td>Cre</td></tr><tr><td>9</td><td>1.72</td><td>29.22</td><td>Lys</td><td>32</td><td>3.95</td><td>63.09</td><td>Ser</td></tr><tr><td>10</td><td>1.84</td><td>30.38</td><td>Arg</td><td>33</td><td>3.99</td><td>54.13</td><td>Asp</td></tr><tr><td>11</td><td>1.88</td><td>32.71</td><td>Lys</td><td>34</td><td>4</td><td>58.77</td><td>His</td></tr><tr><td>12</td><td>2.03</td><td>28.06</td><td>Glu</td><td>35</td><td>4.04</td><td>59.27</td><td>Crea</td></tr><tr><td>13</td><td>2.26</td><td>31.88</td><td>Val</td><td>36</td><td>4.12</td><td>71.06</td><td>Lac</td></tr><tr><td>14</td><td>2.39</td><td>32.54</td><td>Glu</td><td>37</td><td>4.16</td><td>57.61</td><td>Phen</td></tr><tr><td>15</td><td>2.73</td><td>47.65</td><td>Cit</td><td>38</td><td>4.19</td><td>61.1</td><td>Glu</td></tr><tr><td>16</td><td>2.95</td><td>37.53</td><td>Asp</td><td>39</td><td>4.29</td><td>68.74</td><td>Thr</td></tr><tr><td>17</td><td>3.03</td><td>39.85</td><td>Cre</td><td>40</td><td>6.85</td><td>118.21</td><td>4-hydro</td></tr><tr><td>18</td><td>3.03</td><td>42.01</td><td>Lys</td><td>41</td><td>7.17</td><td>133.32</td><td>4-hydro</td></tr><tr><td>19</td><td>3.09</td><td>33.54</td><td>Phen</td><td>42</td><td>7.37</td><td>132.16</td><td>Phen</td></tr><tr><td>20</td><td>3.2</td><td>43.33</td><td>Arg</td><td>43</td><td>7.37</td><td>130.33</td><td>Phen</td></tr><tr><td>21</td><td>3.23</td><td>39.18</td><td>Phen</td><td>44</td><td>7.37</td><td>131.83</td><td>Phen</td></tr><tr><td>22</td><td>3.28</td><td>50.31</td><td>Tau</td><td>45</td><td>7.4</td><td>120.37</td><td>His</td></tr><tr><td>23</td><td>3.31</td><td>28.72</td><td>His</td><td>46</td><td>8.64</td><td>137.14</td><td>His</td></tr></tbody></table>
4. Fitting of the PS spectra

The removal of multiplicity provided by PS strategies divides complex spectral regions into narrower and less complex spectral regions. For very complex mixtures (like the model of seventeen metabolites), it remains difficult to fully annotate PS spectra. The following Fig.S4 and Fig.S5 show how the use of the 2D J-resolved data and deconvolution algorithms can facilitate the annotation/processing of complex regions inside PS spectra.

<figure><img src="image_35.png" /><figcaption>Fig.S5: Annotated 2D J-resolved spectrum (J-RES DE) after a 45° tilt and symmetrization on the spectral region ranging from 2.9 to 4.35 ppm. The most complex spectral region observed in PS spectra is highlighted in the blue square.</figcaption></figure>
As illustrated in Fig.S5, the more in depth 2D data provided by the acquisition of J-resolved spectra facilitates the recovering of sometimes hidden chemical shifts in the PS spectra. These chemical shifts can then be used to perform spectral deconvolution of the singlets. PS spectroscopy is easy to combine with deconvolution since it only requires the knowledge of the chemical shifts. An example of a deconvoluted PS spectrum by GSD is illustrated in Fig.S6.

<figure><img src="image_36.png" /><figcaption>Fig.S6: Fitting of a 1D projection of a 2D J-RES DE acquisition. Fitted singlets were annotated according to Table S1 and with the help of Human Metabolome Database (HMDB). The original PS spectrum (from J-RES DE) is in red. The deconvoluted singlets are in green. The sum of the deconvoluted singlets is in indigo.</figcaption></figure>
5. Site-by-site performances of the J-RES SE experiment

Analytical performance of the J-RES SE experiment

<table><caption>Table S2: Average site-by-site SNR, CV of relative areas and LW<sub>10</sub> of the J-RES SE pure shift strategy over five repetitions.</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>257</td><td>4</td><td>3.8</td></tr><tr><td>7.17</td><td>90</td><td>13</td><td>4.6</td></tr><tr><td>6.84</td><td>156</td><td>9</td><td>4.2</td></tr><tr><td>4.29</td><td>194</td><td>9</td><td>4.6</td></tr><tr><td>4.16</td><td>427</td><td>7</td><td>4.2</td></tr><tr><td>3.63</td><td>364</td><td>6</td><td>4.4</td></tr><tr><td>3.59</td><td>1216</td><td>6</td><td>3.5</td></tr><tr><td>3.44</td><td>292</td><td>11</td><td>5.4</td></tr><tr><td>3.28</td><td>296</td><td>13</td><td>4.4</td></tr><tr><td>3.09</td><td>588</td><td>9</td><td>4.9</td></tr><tr><td>3.03</td><td>519</td><td>5</td><td>4.0</td></tr><tr><td>2.93</td><td>130</td><td>11</td><td>3.9</td></tr><tr><td>2.73</td><td>428</td><td>12</td><td>5.1</td></tr><tr><td>1.49</td><td>1462</td><td>2</td><td>3.6</td></tr><tr><td>1.35</td><td>1332</td><td>-</td><td>4.5</td></tr><tr><td>1.04</td><td>670</td><td>7</td><td>5.4</td></tr><tr><td>1.00</td><td>699</td><td>5</td><td>4.8</td></tr><tr><td><strong>Average</strong></td><td><strong>536</strong></td><td><strong>8</strong></td><td><strong>4.4</strong></td></tr></tbody></table>

<figure><img src="image_37.png" /><figcaption>Fig.S7: Average site-by-site characterization of the analytical performance of the J-RES SE pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
## 6. Site-by-site performances of the J-RES DE experiment

### Analytical performance of the J-RES DE experiment

<table><caption>Table S3: Average site-by-site SNR, CV of relative areas and LW<sub>10</sub> of the J-RES DE pure shift strategy over five repetitions.</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>297</td><td>6</td><td>3.6</td></tr><tr><td>7.17</td><td>70</td><td>4</td><td>4.7</td></tr><tr><td>6.84</td><td>125</td><td>4</td><td>4.6</td></tr><tr><td>4.29</td><td>177</td><td>2</td><td>4.8</td></tr><tr><td>4.16</td><td>484</td><td>4</td><td>4.0</td></tr><tr><td>3.63</td><td>363</td><td>6</td><td>4.2</td></tr><tr><td>3.59</td><td>1548</td><td>6</td><td>2.9</td></tr><tr><td>3.44</td><td>318</td><td>4</td><td>4.6</td></tr><tr><td>3.28</td><td>304</td><td>2</td><td>4.1</td></tr><tr><td>3.09</td><td>624</td><td>6</td><td>4.5</td></tr><tr><td>3.03</td><td>627</td><td>6</td><td>3.4</td></tr><tr><td>2.93</td><td>141</td><td>5</td><td>4.1</td></tr><tr><td>2.73</td><td>383</td><td>3</td><td>4.8</td></tr><tr><td>1.49</td><td>1688</td><td>3</td><td>3.3</td></tr><tr><td>1.35</td><td>1598</td><td>-</td><td>3.8</td></tr><tr><td>1.04</td><td>675</td><td>4</td><td>4.6</td></tr><tr><td>1.00</td><td>735</td><td>5</td><td>4.3</td></tr><tr><td><strong>Average</strong></td><td><strong>597</strong></td><td><strong>4</strong></td><td><strong>4.1</strong></td></tr></tbody></table>

<figure><img src="image_38.png" /><figcaption>Fig.S8: Average site-by-site characterization of the analytical performance of the J-RES DE pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
7. Site-by-site performances of the J-RES Chirp experiment

Analytical performance of the J-RES Chirp experiment

<table><caption>Table S3: Average site-by-site SNR, CV of relative areas and LW<sub>10</sub> of the J-RES Chirp pure shift strategy over five repetitions.</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>85</td><td>3</td><td>3.7</td></tr><tr><td>7.17</td><td>6</td><td>8</td><td>4.8</td></tr><tr><td>6.84</td><td>19</td><td>15</td><td>4.4</td></tr><tr><td>4.29</td><td>45</td><td>4</td><td>4.8</td></tr><tr><td>4.16</td><td>118</td><td>5</td><td>4.3</td></tr><tr><td>3.63</td><td>121</td><td>3</td><td>4.5</td></tr><tr><td>3.59</td><td>320</td><td>1</td><td>3.4</td></tr><tr><td>3.44</td><td>71</td><td>7</td><td>4.2</td></tr><tr><td>3.28</td><td>57</td><td>4</td><td>4.3</td></tr><tr><td>3.09</td><td>117</td><td>2</td><td>4.7</td></tr><tr><td>3.03</td><td>166</td><td>1</td><td>3.8</td></tr><tr><td>2.93</td><td>28</td><td>8</td><td>3.9</td></tr><tr><td>2.73</td><td>152</td><td>2</td><td>4.9</td></tr><tr><td>1.49</td><td>348</td><td>1</td><td>3.4</td></tr><tr><td>1.35</td><td>367</td><td>-</td><td>4.0</td></tr><tr><td>1.04</td><td>134</td><td>4</td><td>4.7</td></tr><tr><td>1.00</td><td>153</td><td>2</td><td>4.5</td></tr><tr><td><strong>Average</strong></td><td><strong>136</strong></td><td><strong>4</strong></td><td><strong>4.2</strong></td></tr></tbody></table>

<figure><img src="image_39.png" /><figcaption>Fig.S9: Average site-by-site characterization of the analytical performance of the J-RES CHIRP pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
## 8. Site-by-site performances of the single-band ZS experiment

<table><caption>Analytical performance of the single-band ZS experiment</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>11</td><td>7</td><td>3.9</td></tr><tr><td>7.17</td><td>6</td><td>3</td><td>2.6</td></tr><tr><td>6.84</td><td>8</td><td>5</td><td>2.9</td></tr><tr><td>4.29</td><td>32</td><td>5</td><td>4.2</td></tr><tr><td>4.16</td><td>89</td><td>5</td><td>4.2</td></tr><tr><td>3.63</td><td>74</td><td>8</td><td>4.2</td></tr><tr><td>3.59</td><td>147</td><td>7</td><td>2.5</td></tr><tr><td>3.44</td><td>32</td><td>8</td><td>4.2</td></tr><tr><td>3.28</td><td>36</td><td>8</td><td>4.2</td></tr><tr><td>3.09</td><td>134</td><td>1</td><td>3.1</td></tr><tr><td>3.03</td><td>91</td><td>4</td><td>3.3</td></tr><tr><td>2.93</td><td>20</td><td>11</td><td>5.5</td></tr><tr><td>2.73</td><td>0</td><td>0</td><td>-</td></tr><tr><td>1.49</td><td>151</td><td>1</td><td>4.2</td></tr><tr><td>1.35</td><td>165</td><td>-</td><td>4.7</td></tr><tr><td>1.04</td><td>69</td><td>7</td><td>4.4</td></tr><tr><td>1.00</td><td>71</td><td>6</td><td>5.9</td></tr><tr><td><strong>Average</strong></td><td><strong>67</strong></td><td><strong>5</strong></td><td><strong>4.0</strong></td></tr></tbody></table>

<figure><img src="image_40.png" /><figcaption>Fig.S10: Average site-by-site characterization of the analytical performance of the single-band ZS pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
9. Site-by-site performances of the dual-band ZS experiment

Analytical performance of the dual-band ZS experiment

<table><caption>Table S5: Average site-by-site SNR, CV of relative areas and LW<sub>10</sub> of the dual-band ZS pure shift strategy over five repetitions.</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>122</td><td>3</td><td>3.0</td></tr><tr><td>7.17</td><td>39</td><td>7</td><td>2.2</td></tr><tr><td>6.84</td><td>36</td><td>6</td><td>2.6</td></tr><tr><td>4.29</td><td>46</td><td>24</td><td>4.9</td></tr><tr><td>4.16</td><td>185</td><td>10</td><td>4.1</td></tr><tr><td>3.63</td><td>292</td><td>8</td><td>4.6</td></tr><tr><td>3.59</td><td>567</td><td>3</td><td>2.8</td></tr><tr><td>3.44</td><td>137</td><td>5</td><td>4.2</td></tr><tr><td>3.28</td><td>162</td><td>25</td><td>4.5</td></tr><tr><td>3.09</td><td>596</td><td>6</td><td>3.5</td></tr><tr><td>3.03</td><td>414</td><td>5</td><td>3.5</td></tr><tr><td>2.93</td><td>103</td><td>7</td><td>5.7</td></tr><tr><td>2.73</td><td>0</td><td>0</td><td>-</td></tr><tr><td>1.49</td><td>752</td><td>1</td><td>4.2</td></tr><tr><td>1.35</td><td>753</td><td>0</td><td>4.8</td></tr><tr><td>1.04</td><td>231</td><td>11</td><td>5.2</td></tr><tr><td>1.00</td><td>219</td><td>8</td><td>6.6</td></tr><tr><td><strong>Average</strong></td><td><strong>274</strong></td><td><strong>8</strong></td><td><strong>4.1</strong></td></tr></tbody></table>

<figure><img src="image_41.png" /><figcaption>Fig.S11: Average site-by-site characterization of the analytical performance of the dual-band pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
10. Site-by-site performances of the PSYCHE experiment

Analytical performance of the PSYCHE experiment

<table><caption>Table S6: Average site-by-site SNR, CV of relative areas and LW<sub>10</sub> of the PSYCHE pure shift strategy over five repetitions.</caption><thead><tr><th>Chemical shift (ppm)</th><th>Average SNR</th><th>CV of relative areas (%)</th><th>LW<sub>10</sub> (Hz)</th></tr></thead><tbody><tr><td>8.64</td><td>37</td><td>20</td><td>4.1</td></tr><tr><td>7.17</td><td>21</td><td>12</td><td>3.7</td></tr><tr><td>6.84</td><td>32</td><td>19</td><td>6.1</td></tr><tr><td>4.29</td><td>0</td><td>0</td><td>-</td></tr><tr><td>4.16</td><td>114</td><td>13</td><td>5.9</td></tr><tr><td>3.63</td><td>90</td><td>18</td><td>6.1</td></tr><tr><td>3.59</td><td>160</td><td>11</td><td>4.0</td></tr><tr><td>3.44</td><td>41</td><td>30</td><td>5.3</td></tr><tr><td>3.28</td><td>50</td><td>34</td><td>5.0</td></tr><tr><td>3.09</td><td>168</td><td>7</td><td>4.8</td></tr><tr><td>3.03</td><td>124</td><td>5</td><td>4.6</td></tr><tr><td>2.93</td><td>29</td><td>16</td><td>3.5</td></tr><tr><td>2.73</td><td>218</td><td>11</td><td>4.5</td></tr><tr><td>1.49</td><td>262</td><td>3</td><td>5.9</td></tr><tr><td>1.35</td><td>313</td><td>-</td><td>6.9</td></tr><tr><td>1.04</td><td>159</td><td>14</td><td>4.6</td></tr><tr><td>1.00</td><td>166</td><td>19</td><td>5.4</td></tr><tr><td><strong>Average</strong></td><td><strong>117</strong></td><td><strong>14</strong></td><td><strong>5.0</strong></td></tr></tbody></table>

<figure><img src="image_42.png" /><figcaption>Fig.S12: Average site-by-site characterization of the analytical performance of the PSYCHE pure shift strategy over five repetitions. Top panel: average SNR compared to WET 1D <sup>1</sup>H. Middle panel: average relative areas values normalized with the signal of lactate at 1.35 ppm. Bottom panel: average linewidth at 10% of the maximum intensity of the signal.</figcaption></figure>
<figure><img src="image_43.png" /><figcaption>11. Additional spectra of the fish feed of commercial formulation</figcaption></figure>

Fig.S13: WET TOCSY spectrum of the fish feed of commercial formulation acquired on an 80 MHz NMR spectrometer. Signal annotation was made with the help of Human Metabolome Database (HMDB).
<table><caption>Table S7: Full ¹H and ¹³C annotations of the fish feed of commercial formulation for the HSQC-ME presented in Fig.S14. Signal annotation was made with the help of Human Metabolome Database (HMDB).</caption><thead><tr><th></th><th><sup>1</sup>H (ppm)</th><th><sup>13</sup>C (ppm)</th><th>Annotation</th></tr></thead><tbody><tr><td>6</td><td>5.42</td><td>94.76</td><td>Sucrose</td></tr><tr><td>7</td><td>4.18</td><td>79.15</td><td>Sucrose</td></tr><tr><td>8</td><td>3.81</td><td>62.88</td><td>Sucrose</td></tr><tr><td>9</td><td>3.81</td><td>65.04</td><td>Sucrose</td></tr><tr><td>10</td><td>3.69</td><td>64.04</td><td>Sucrose</td></tr><tr><td>11</td><td>3.57</td><td>73.79</td><td>Sucrose</td></tr><tr><td>12</td><td>3.49</td><td>71.85</td><td>Sucrose</td></tr><tr><td>13</td><td>3.28</td><td>56.04</td><td>Betaine</td></tr><tr><td>14</td><td>3.2</td><td>56.57</td><td>Choline</td></tr><tr><td>15</td><td>3.04</td><td>41.8</td><td>Lysine</td></tr><tr><td>16</td><td>2.92</td><td>47.27</td><td>Dimethylglycine</td></tr><tr><td>17</td><td>2.73</td><td>37.14</td><td>Succinate</td></tr><tr><td>18</td><td>2.16</td><td>16.57</td><td>Methionine</td></tr><tr><td>19</td><td>1.95</td><td>25.86</td><td>Sodium acetate</td></tr><tr><td>20</td><td>1.46</td><td>18.88</td><td>Alanine</td></tr><tr><td>21</td><td>1.3</td><td>22.87</td><td>Lactate</td></tr><tr><td>22</td><td>1.3</td><td>22.04</td><td>Threonine</td></tr><tr><td>23</td><td>0</td><td>0</td><td>TSP</td></tr></tbody></table>
<figure><img src="image_44.png" /><figcaption>Fig.S14: Heteronuclear Single Quantum Correlation Multiplicity-Edited (HSQC-ME) spectrum of fish feed of commercial formulation acquired on an 80 MHz NMR spectrometer. Signal annotation was made with the help of Human Metabolome Database (HMDB).</figcaption></figure>
<figure><img src="image_45.png" /><figcaption>Fig.S15: T2 project zTOCSY spectrum of the fish feed of commercial formulation acquired on a 700 MHz NMR spectrometer. Signal annotation was made with the help of Human Metabolome Database (HMDB).</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>Fig.S16: Additional PS NMR spectra of the commercial fish feed extract (COM). (a) 1D ¹H NMR spectrum (b) 1D projection of a WET 2D J-resolved single echo spectrum. (c) 1D projection of a WET 2D J-resolved double echo spectrum. (d) 1D projection of a WET 2D J-resolved chirp spectrum. Acquisition and processing parameters are similar to those used for the analytical comparison of the model mixture.</figcaption></figure>

The additional PS spectra shown in Figure S16 clearly shows the lower sensitivity of the J-RES Chirp strategy, with missing singlets and a noisier baseline. The more sensitive J-RES SE and J-RES DE spectra show similar information, but the SNR of the J-RES DE approach are higher in average, which is consistent with the observations made on the model mixture of metabolites. SNRs of the J-RES SE approach range from 7 to 38 while SNRs of the J-RES DE approach range from 13 to 86.
<figure><img src="image_47.png" /></figure>

Fig.S17: Additional PS NMR spectra of the commercial fish feed extract (COM). (a) 1D ¹H NMR spectrum (b) WET single-band Zangger-Sterk spectrum. (c) WET double-band Zangger-Sterk spectrum. (d) WET PSYCHE spectrum. Acquisition and processing parameters are similar to those used for the analytical comparison of the model mixture.

The additional PS spectra shown in Figure S17 show the lack of sensitivity of the gradient-encoded PS strategies: especially for the single-band ZS and PSYCHE strategies. SNR on the detected singlets are very low and there is much more noise.
## Statement of societal impact

The work described in this paper is of great importance in facilitating the analysis of different complex mixtures containing molecules with various concentrations and sizes. It is expected to be applied to the analysis of various complex samples such as food (wine, oils ...), biofluids (urine), environmental samples (soil, polluted water...) and to address problems related to quality control, food adulteration, identification of biomarkers in biofluids or authentication, which are socially relevant issues.