APPLICATION OF NMR SPECTROSCOPY FOR TRACING IMPROVED NUTRITIONAL VALUES IN CACIOTTA-LIKE CHEESES

1. Solovyev,^‡ J. Andersen^‡, M. Bossetti^‡, A. Mancini^‡, T. Nardin^‡, R. Larcher^‡, E. Franciosi^‡, L. Bontempo^‡

^‡ Fondazione Edmund Mach, via Edmund Mach 1 38098, San Michele all’Adige TN, Italy

E-mail: pavel.solovyev@fmach.it

Keywords: solution NMR, small molecules, biomolecules, metabolomics, food

Supplementing the Caciotta-like cheeses with such additives as blackcurrant and Cornelian cherry could improve the food’s health benefactory properties due to increased contents of polyphenols, a class of bioactive compounds [1]. A combination of analytical methods including Folin–Ciocalteu reaction, microbial community determination, organoleptic tests and ¹H NMR spectroscopy was used to differentiate cheeses from different suppliers [2]. PCA score plots of the NMR spectra from aqueous extracts of the cheese samples demonstrate a reasonable clustering appearing according to the enrichment with 25% of variance captured by the first principal component and 23% by the second principal component. Such metabolites as gamma-aminobutyric acid, histamine, and organic acids levels are found somewhat higher in blackcurrant than in control and Cornelian cherry cheeses, and by contrast, lower levels of glucose monosaccharides are found in blackcurrant than in control and Cornelian cherry cheeses (see Fig. 1).

Fig. 1. Scores plot of a two-component PCA model of NMR spectra showing sample clustering according to ingredient addition (left) and loading plot of a two-component PCA model of NMR spectra showing sample clustering according to ingredient addition (right)

As a result, the enrichment was able to improve total polyphenolic contents of the studied products and a metabolite compositional change in cheeses enriched with blueberries indicates a positive effect of this additive on the lactic acid bacteria growth.

References:

Download 1 Solovyev

NMR SPECTROSCOPY TO IMPROVE THE KNOWLEDGE OF INNOVATIVE FOODS CHEMICAL PROFILE: FROM NOVEL FOODS TO POTENTIAL ONES

1. Spano,^1,2 G. Di Matteo,^1,2 C. Ingallina,^1,2 L. Goppa,³ E. Savino,³ C. Totaro Fila,⁴ A. P. Sobolev,⁵ L.Mannina, ^1,2

¹Department of Chemistry and Technology of Drugs, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy

²Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of

Rome, P.le Aldo Moro 5, 00185 Rome, Italy

³ Department of Earth and Environmental Sciences (DSTA), University of Pavia, Via A. Ferrata 9, 27100

Pavia, Italy

⁴ Alia Insect Farm, Via Olmetto, 20123 Milan, Italy

⁵ Institute for Biological Systems, Magnetic Resonance Laboratory “Segre-Capitani”, CNR, Via Salaria Km 29.300, 00015 Monterotondo, Italy

E-mail: mattia.spano@uniroma1.it

Keywords: solution NMR, metabolomics, Novel Foods

Novel Food is defined as food that was not consumed to any significant degree in the EU before 15 May 1997 when the first novel food legislation entered into force. Novel Food can be newly developed, innovative food or food produced using new technologies and production processes, as well as food traditionally eaten outside of the EU [1]. In the last years, dozens of food matrices have been accepted or proposed in the European list of Novel Foods after the demonstration of their nutritional value and safety. Anyway, although a new food matrix is inserted in the list, the improvement of its chemical composition knowledge remains a challenge to carry on, in order to better understand the potential uses, the nutritional value and biological activities. In this context, NMR untargeted approach can be a potential tool that can be used for this purpose. In the present work, some examples of NMR applications to better understand the metabolite composition of Novel Foods or food matrices that can be potentially considered as such are reported. In particular, the recent approved edible insect Acheta domesticus (house cricket) was here characterized, for the first time, by means of NMR spectroscopy, showing the presence of certain metabolites never detected in this matrix. Moreover, the chemical profile of Cannabis sativa L. inflorescences [2] and some species of Wood Decay Fungi was characterized by means of NMR untargeted approach, highlighting the potential nutritional profile of these matrices, thus underlining how they can be proposed as Novel Foods.

References

2 Spano Mattia_Abstract Poster

1. Di Matteo,^‡,ⱡ M. Spano,^‡,ⱡ M. Bruschi,^† S. Zamboni,^± L. Mannina^‡,ⱡ

^‡Department of Chemistry and Technology of Drugs, Laboratory of Food Chemistry, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy

^ⱡNMR-Based Metabolomics Laboratory of Sapienza (NMLab), Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy

^† Bioniks srl, Via della Segheria 1/H, 37141 Verona, Italy

^± Legendary Drink srl, Via della Segheria 1/H, 37141 Verona, Italy

E-mail: giacomo.dimatteo@uniroma1.it

Keywords: solution NMR, metabolomics, food, kombucha, fermentation, fermented beverages, NMR Benchtop

Kombucha is a sweetened tea fermented with a symbiotic culture of yeasts and bacteria. The beverage present a balanced taste among sweet, due to the residual sugars, and sour, due to organic acids produced by acetic acid bacteria. Due to the complex fermentation process, the determination of its quality requires a complete characterization. In order to evaluate the stages of kombucha fermentation objectively an NMR-based metabolomics characterization was performed. Kombucha samples were collected every 5 days for 25 days of fermentation, freeze-dried and prepared in 400 mM phosphate buffer/D2O, containing a 2 mM 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt (TSP), as an internal standard, and EDTA-d16. The levels of sugars (sucrose, glucose, fructose, trehalose), organic acids (acetic acid, lactic acid, succinic acid, malic acid, citric acid, formic acid and gluconic acid), ethanol and glycerol were quantified in all the kombucha stages. The merged results were analyzed by principal component analysis (PCA). A 60 MHz benchtop NMR instrument was also used monitoring the quantity of sucrose, lactic acid, acetic acid and succinic acid, which represent the total signals not overlapped in the spectra. The Benchtop NMR spectrometer can be readily used as an on-line process monitoring tool in the kombucha production. Moreover, this fast analysis method could be applied on different kombucha industrial productions around the world to expand the knowledge about the dynamics of the substrate consumption and metabolite production.

3 GIDRM-DAY_Di Matteo

 Elisabetta Schievano^a, Marco Tessari^b

^a Department of Chemical Sciences, University of Padova, via Marzolo 1, 35131 Padova, Italy, E-mail: elisabetta.schievano@unipd.it

^bMagnetic Resonance Research Center, Radboud University, Nijmegen, the Netherlands

NMR analysis of organic extracts of honey is a powerful technique to verify its authenticity and to confirm its botanical, entomological and geographical origin. This method relies on the identification of signals that are specific to floral or entomological markers in the ¹H NMR spectra. A quantitative analysis of these marker signals can also reveal direct or indirect dilution of unifloreal honeys with sugar syrups.

Here we present an automatic NMR method for fast screening honeys of declared unifloral origin that can reveal different forms of product adulteration based on the deviations of the levels of floral and entomological markers relative to a set of genuine samples. This approach was tested on honeys of six different floral origins.

4 Abstract PosterSchievano-NMRday16giugnoBari

1. Palmioli,¹ C. Ciaramelli, ¹ L. Zucconi,² S. Tosi,³ C. Airoldi¹

¹BioOrgNMR Lab, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy ²Soil and applied mycology Lab, Department of Ecological and Biological Sciences, University of Tuscia, Viterbo (Italy)

³Mycology Laboratory, Department of Earth and Environmental Sciences, University of Pavia, via S. Epifanio 14, 27100, Pavia, Italy

E-mail: alessandro.palmioli@unimib.it

Being recognized as the oldest, coldest, driest, and most uninhabited area of our planet, Antarctica represents one of the most extreme environments for life on Earth. The conditions that characterize the Antarctic continent, which include frequent freeze-thaw cycles, low nutrient availability, severe drought, and local high salinity levels, seem indeed very hostile for the development of life forms. Despite the above conditions, microbial life is surprisingly abundant and ubiquitous, being distributed across the aquatic environments (lakes and ice) as well as arid mineral soils apparently lacking water or organic matter.^1-2 Victoria Land, a region spanning from the Darwin Glacier in the South to Cape Adare in the North, can be considered the area of Antarctica most studied from a microbiological point of view and among the most uncontaminated ecosystems that represent the ideal Earth model to study the sensitivity of ecosystem processes to climate variability. Nevertheless, data on the biodiversity of these communities are still rare and scattered. In particular, there is a lack of knowledge on the structure and functional diversity of soil-associated microbial communities and their capability to adapt to biotic and abiotic factors. The aim of the project was to fill this gap exploiting a multidisciplinary and integrated approach, including metabolomic and proteomic.

Our group is involved in the NMR-based metabolic profiling of the soil samples and here we present our preliminary results on the optimization of sample extraction and on the set-up of NMR analysis. 

MicroBiomaS – “La diversità dei microrganismi del suolo e delle loro biomolecole nella Terra Vittoria” project (PNRA18_00015) was founded by MIUR – Ministero dell’Istruzione, dell’Università e della Ricerca – Programma Nazionale di Ricerca in Antartide 2018

Fig. 1. Workflow of NMR metabolomic analysis of Antarctica soils

References

5 GIDRMday_abstract_Bari2023_PALMIOLI

Vadym Samukha,^‡ Francesca Fantasma,^‡ Gilda D’Urso,^† Gianluigi Lauro,^† Agostino Casapullo,^† Giuseppe Bifulco,^† Maria Giovanna Chini,^‡ Maria Iorizzi^‡

^‡Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy

^†Department of Pharmacy, University of Salerno, Fisciano (SA), Italy

v.samukha@studenti.unimol.it

Keywords:

solid state NMR, solution NMR, small molecules, biomolecules, metabolomics, food.

Phaseolus vulgaris L., known as common bean, is one of the most consumed legumes around the world. Seeds of this species are a source of proteins, carbohydrates, phenolic compounds and flavonoids making beans a functional food [1]. This study is based on the optimization of the method for the metabolomic analysis of some Italian commercial bean varieties by NMR spectroscopy coupled with the multivariate analysis. The first step provides a specific extraction with CH₂Cl₂/H₂O/MeOH in order to obtain a hydrophilic extract and a lipophilic extract [2] which have been analyzed through 1D and 2D-NMR. Spectra are processed with TOPSPIN software and analyzed with NMRProcFlow software. An untargeted approach is carried out by comparing each NMR spectrum with the PCA technique with the aim of assessing variations in the chemical profile of the samples, which will allow an understanding of differences or similarities in the analyzed varieties of P. vulgaris. Metabolites with the most intense signals are identified and quantified with NMRProcFlow software.

Fig. 1. Workflow of the extraction and analysis through NMR

Moreover, high-resolution magic angle spinning (HR-MAS) NMR analysis will be performed on plant samples to obtain a chemical profile in the complete lack of any chemical manipulation of the sample.

References

6 Abstract Vadym Samukha Bari

Carmen Marino^1,2, Enza Napolitano^1,2, Manuela Grimaldi², Arianna Polverino³, Pierpaolo Sorrentino^3,4, Giuseppe Sorrentino^3,4 and Anna Maria D’Ursi^2,*

1            Department of Pharmacy and PhD Program in Drug Discovery and Development, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy. cmarino@unisa.it; enapolitano@unisa.it

2            Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy. magrimaldi@unisa.it; dursi@unisa.it

3            Institute of Diagnosis and Treatment Hermitage Capodimonte, Naples, Cupa delle Tozzole, 2, 80131, Italy. arianna.polverino@collaboratore.uniparthenope.it; giuseppe.sorrentino@uniparthenope.it

4            Institute of Applied Sciences and Intelligent Systems of National Research Council, Pozzuoli, Via Campi Flegrei 34, 8007, Italy. giuseppe.sorrentino@uniparthenope.it; ppsorrentino@gmail.com

Human perceptions, cognitive processes, motor activity, and social interaction are based on the accurate timing of the generation of neuronal currents ranging from sub-milliseconds to seconds. The genesis of neuronal currents is determined by the potential actions that originate at pre- and post-synaptic levels and, therefore, ions and neurotransmitters’ concentrations. Neuronal currents produce weak magnetic fields that can be detected from the head with sensors. Magneto-Encephalography (MEG) is based on the measurement of neuromagnetic signals from the brain. [1] The MEG recording of brain magnetic activity enables the study of neuronal activity implicated in cognitive processes, language perception, memory encoding, and so on.[2]

Metabolomics is an omic science that aims to identify the metabolites generated by an individual’s metabolism at a given time. [3, 4]. The brain is characterized by specific metabolomic profiles that depend on brain activity related to specific physiological and pathological states.

Although it has long been known that pre- and post-synaptic transmission is affected by the metabolic conditions of nerve cells and that different brain regions respond differently to the presence or absence of metabolites that influence ion currents, correlation studies between data collected by MEG and metabolomic data have never been performed.[2, 5] Based on this evidence, this study aims to correlate MEG data to ¹H-NMR metabolomics serum profile to understand if the variations of the cerebral magnetic fields, due to variation of potential and therefore of ionic flow, are influenced by the metabolomic profile. Specifically, we analyzed MEG data and NMR-based metabolite concentrations of 57 healthy human subjects, to understand whether MEG parameters could be predicted by circulating serum concentrations of metabolites detected by NMR in the basal state and under normal conditions. Our data show that preliminary linear regression models are effective for predicting the global MEG parameters. In particular, MEG parameters calculated in the presence of alpha, beta, and delta waves correlated with serum amino acid concentrations. Similarly, significant correlations are identified between centrality parameters calculated on the 90 regions of the AAL atlas [6] and the metabolites belonging  to amino acids and energy pathways.

Taken together, our data provide preliminary results on the possibility that serum metabolic concentrations can predict the state of conduction and integration of the neural networks.

1. Adjamian, P., The Application of Electro- and Magneto-Encephalography in Tinnitus Research – Methods and Interpretations. 2014. 5(228).
2. Tschirner, S.K., et al., Neurotransmitter and their metabolite concentrations in different areas of the HPRT knockout mouse brain. J Neurol Sci, 2016. 365: p. 169-74.
3. Wishart, D.S., et al., NMR and Metabolomics—A Roadmap for the Future. 2022. 12(8): p. 678.
4. Wishart, D.S.J.P.r., Metabolomics for investigating physiological and pathophysiological processes. 2019. 99(4): p. 1819-1875.
5. Nartsissov, Y.R.J.C.-., Neuroimmunology and N. Function, Amino Acids as Neurotransmitters. The Balance between Excitation and Inhibition as a Background for Future Clinical Applications. 2022: p. 81.
6. Rolls, E.T., et al., Automated anatomical labelling atlas 3. 2020. 206: p. 116189.

7 abstractBari2023CMEN300523

In this study, we provided detailed information on the compositional
differences of seven varieties of coffee (C. arabica) from batches of
green and roasted coffee beans sourced from agricultural companies in
Nicaragua, using a metabolomic approach based on Nuclear Magnetic
Resonance (NMR) spectroscopy.
Furthermore, we assessed the effect of different post-harvest procedures
(i.e., fermentation time and drying methods), on the coffee composition,
suggesting that different post-harvest procedures may be responsible for
distinct flavors.

tenori@cerm.unifi.it

1. Petrella,^‡ G. Ciufolini,^‡ F. Cortese,^‡ C. Montesano,^† G. Di Francesco,^† M. Sergi,^† M. Marti,^⸹ D.O. Cicero^‡

^‡Department of Chemical Science and Technology, University of Rome “Tor Vergata,” Via della Ricerca Scientifica 1, 00133, Rome, Italy

^†Department of Chemistry, University of Rome “La Sapienza,” Piazzale Aldo Moro 5, 00185, Rome, Italy

^⸹Department of Experimental and Clinical Medicine, University of Ferrara, Via Fossato di Mortara 70

44121, Ferrara, Italy

E-mail: petrella@scienze.uniroma2.it

The value of combining NMR and MS, the two most commonly used techniques in metabolomics, is widely recognized [1]. So far, few studies have linked these two techniques, and often they were dedicated to developing statistical methods for weighing the two datasets [2] and for the discovery of new components of biofluids [3]. Given the high complementarity of the two techniques, combining the data obtained separately with NMR and MS would be beneficial to improve the ability to classify the “metabotypes” under investigation [1]. Urine is the best test bench to measure the potential value of the MS-NMR combination, as it represents one of the most challenging biofluids to characterize due to complexity and variability.

We employ the metabolite concentrations determined iteratively by NMR and UHPLC-HRMS, obtaining a significant increase in identified and quantified compounds using a new approach called SYNHMET [4]. Metabolite identification and quantification by this method result in higher accuracy than using the two techniques separately.

This approach was applied to the study of the urinary metabolic profile of mice treated with two opioids: morphine and fentanyl, to discover endogenous metabolites influenced by drug use. Using the SYNHMET approach, 82 metabolites were quantified in urine for 43 samples, yielding a more extensive and complete dataset than that obtainable using NMR alone. The results obtained confirmed the advantages of applying SYNHMET for metabolomic analysis. This poster will discuss the main workflow of the method, the complementary characteristics of the two datasets, and the relevant synergy between NMR and MS quantitative data.

References

9 Abstract_GPtrella_Bari_GIDRM