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NMR-Based Metabolomics: Quality and Authenticity of Plant-Based Foods

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Modern Magnetic Resonance
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Abstract

Nowadays metabolomics is a widely accepted approach in several scientific disciplines, especially in food science. The possibility to identify a wide range of metabolites (untargeted analysis) allowed to evaluate various food characteristics, regarding quality, adulteration, geographical origin, as well as secondary species-specific metabolites endowed with nutraceutical properties. In the present chapter, latest findings of plant-based foods investigated by NMR-based metabolomics are presented. Almost all of the recent studies were focused on quality assessment and authenticity; different aspects such as geographical origin, metabolic modifications upon stress, nutraceutical properties, and fraud detection are described as well. The here reported plant-based foods are balsamic and traditional balsamic vinegars, cereals, cocoa, coffee, fruits, legumes, spices, vegetables and vegetable oils, wine, beer, and spirits. A brief paragraph is concerning organic and conventional foods, which is a new growing scientific field of interest for researchers encouraged by the increasing consumers’ demand. The results here reported testify the capability and the power of this approach thus endorsing NMR spectroscopy as a valid alternative or complement to the chemical and physical analysis nowadays routinely applied for the quality assessment.

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References

  1. Hall RD. Plant metabolomics: from holistic hope, to type, to hot topic. New Phytol. 2006;169:453–8.

    Article  CAS  Google Scholar 

  2. Turi CE, Finley J, Shipley PR, Murch SJ, Brown PN. Metabolomics for phytochemical discovery: development of statistical approached using a cranberry model system. J Nat Prod. 2015;78:953–66.

    Article  CAS  Google Scholar 

  3. Vallverdù-Queralt A, Lamuela-Raventόs RM. Foodomics: a new tool to differentiate between organic and conventional foods. Elecrophoresis. 2016;37:1784–94.

    Article  CAS  Google Scholar 

  4. Kim S, Kim J, Yun EJ, Kim KH. Food metabolomics: from farm to human. Curr Opin Biotechnol. 2016;37:16–23.

    Article  CAS  Google Scholar 

  5. Consonni R, Gatti A. 1H NMR studies on italian balsamic and traditional balsamic vinegars. J Agric Food Chem. 2004;52:3446–50.

    Article  CAS  Google Scholar 

  6. Papotti G, Bertelli D, Graziosi R, Maietti A, Tedeschi P, Marchetti A, Plessi M. Traditional balsamic vinegar and balsamic vinegar of Modena analyzed by nuclear magnetic resonance spectroscopy coupled with multivariate data analysis. LWT- Food Sci Technol. 2015;50:1017–24.

    Article  CAS  Google Scholar 

  7. Barding Jr GA, Béni S, Fukao T, Bailey-Serres J, Larive CK. Comparison of GC-MS and NMR for metabolite profiling of rice subjected to submergence stress. J Proteome Res. 2013;12:898–909.

    Article  CAS  Google Scholar 

  8. Nam MH, Bang E, Kwon TY, Kim Y, Kim EH, Cho K, Park WJ, Kim BG, Yoon IS. Metabolite profiling of diverse rice germplasm and identification of conserved metabolic markers of rice roots in response to long-term mild salinity stress. Int J Mol Sci. 2015;16:21959–74.

    Article  CAS  Google Scholar 

  9. Monakhova YB, Rutledge DN, Roßmann A, Waiblinger HU, Mahler M, Ilse M, Kuballa T, Lachenmeier DW. Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools. J Chemom. 2014;28:83–92.

    Article  CAS  Google Scholar 

  10. Song EH, Kim HJ, Jeong J, Chung HJ, Kim HY, Bang E, Hong YS. A 1H HR-MAS NMR-based metabolomic study for metabolic characterization of rice grain from various Oryza sativa L. cultivars. J Agric Food Chem. 2016;64:3009–16.

    Article  CAS  Google Scholar 

  11. Caligiani A, Palla L, Acquotti D, Marseglia A, Palla G. Application of 1H NMR for the characterizzation of cocoa beans of different geographical origins and fermentation levels. Food Chem. 2014;157:94–9.

    Article  CAS  Google Scholar 

  12. Marseglia A, Acquotti D, Consonni R, Cagliani LR, Palla G, Caligiani A. HR MAS 1H NMR and chemometrics as useful tool to assess the geographical origin of cocoa beans – comparison with HR 1H NMR. Food Res Int. 2016;85:273–81.

    Article  CAS  Google Scholar 

  13. Consonni R, Cagliani LR, Cogliati C. NMR based geographical characterization of roasted coffee. Talanta. 2012;88:420–6.

    Article  CAS  Google Scholar 

  14. Arana VA, Medina J, Alarcon R, Moreno E, Heintz L, Schäfer H, Wist J. Coffee’s country of origin determined by NMR: the Colombian case. Food Chem. 2015;175:500–6.

    Article  CAS  Google Scholar 

  15. Wei F, Furihata K, Miyakawa T, Tanokura M. A pilot of NMR-based sensory prediction of roasted coffee bean extracts. Food Chem. 2014;152:363–9.

    Article  CAS  Google Scholar 

  16. Cagliani LR, Pellegrino G, Giugno G, Consonni R. Quantification of Coffea arabica and Coffea canephora var. robusta in roasted and ground coffee blends. Talanta. 2013;106:169–73.

    Article  CAS  Google Scholar 

  17. Know DJ, Jeong HJ, Moon H, Kim HN, Cho JH, Lee JE, Hong KS, Hong YS. Assessment of green coffee bean metabolites dependent on coffee quality using a 1H NMR-based metabolomics approach. Food Res Int. 2015;67:175–82.

    Article  CAS  Google Scholar 

  18. Sobolev AP, Mannina L, Proietti N, Carradori S, Daglia M, Giusti AM, Antiochia R, Capitani D. Untargeted NMR-based methodology in the study of fruit metabolites. Molecules. 2015;20:4088–108.

    Article  CAS  Google Scholar 

  19. Caligiani A, Coisson JD, Travaglia F, Acquotti D, Palla G, Palla L, Arlorio M. Application of 1H NMR for the characterisation and authentication of “Tonda Gentile Trilobata” hazelnuts from Piedmont (Italy). Food Chem. 2014;148:77–85.

    Article  CAS  Google Scholar 

  20. Goulas V, Minas IS, Kourdoulas PM, Lazaridou A, Molassiotis AN, Gerothanassis IP, Manganaris GA. 1H NMR metabolic fingerprinting to probe temporal postharvest changes on qualitative attributes and phytochemical profile of sweet cherry fruit. Front Plant Sci. 2015;6: article 959.

    Google Scholar 

  21. Jayaprakasha GK, Patil BS. A metabolomics approach to identify and quantify the phytochemicals in watermelons by quantitative 1HNMR. Talanta. 2016;153:268–77.

    Article  CAS  Google Scholar 

  22. Song J, Liu C, Li D, Gu Z. Evaluation of sugar, free amino acid, and organic acid compositions of different varieties of vegetable soybean (Glycine max [L.] Merr). Ind Crop Prod. 2013;50:743–9.

    Article  CAS  Google Scholar 

  23. Yun DY, Kang YG, Yun B, Kim EH, Kim M, Park JS, Lee JH, Hong YS. Distinctive metabolism of flavonoid between cultivated and semiwild soybean unveiled through metabolomics approach. J Agric Food Chem. 2016;64:5773–83.

    Article  CAS  Google Scholar 

  24. Ribeiro AS, Gouveia LR, Barros CJP, Firmino PRA, Silva RO. Discriminating gamma-irradiated soybean seeds by 1H NMR-based metabonomics. Food Control. 2014;36:266–72.

    Article  CAS  Google Scholar 

  25. Rosati A, Cafiero C, Paoletti A, Alfei B, Caporali S, Casciani L. Effect of agronomical practices on carpology, fruit, and oil composition, and oil sensory properties, in olive (Olea europea L.). Food Chem. 2014;159:236–43.

    Article  CAS  Google Scholar 

  26. Pacifico D, Casciani L, Ritota M, Mandolino G, Onofri C, Moschella A, Parisi B, Cafiero C, Valentini M. NMR-based metabolomics for organic farming traceability of early potatoes. J Agric Food Chem. 2013;61:11201–11.

    Article  CAS  Google Scholar 

  27. Hohmann M, Christoph N, Wachter H. Holzgrabe. 1H NMR profiling as an approach to differentiate conventionally and organically grown tomatoes. J Agric Food Chem. 2014;62:8530–40.

    Article  CAS  Google Scholar 

  28. Gallo V, Mastrorilli P, Cafagna I, Nitti GI, Latronico M, Longobari F, Minoja AP, Napoli C, Romito VA, Schäfer H, Schütz B, Spraul M, et al. J Food Compos Anal. 2014;35:44–52.

    Article  CAS  Google Scholar 

  29. Petrakis EA, Cagliani LR, Polissiou MG, Consonni R. Evaluation of saffron (Crocus sativus L.) adulteration with plant adulterants by 1H NMR metabolite fingerprinting. Food Chem. 2015;173:890–6.

    Article  CAS  Google Scholar 

  30. Cagliani LR, Culeddu N, Chessa M, Consonni R. NMR investigations for a quality assessment of Italian PDO saffron (Crocus sativus L.). Food Control. 2015;50:342–8.

    Article  CAS  Google Scholar 

  31. Ordoudi SA, Cagliani LR, Lalou S, Naziri E, Tsimidou MZ, Consonni R. 1H NMR-based metabolomics of saffron reveals markers for its quality deterioration. Food Res Int. 2015;70:1–6.

    Article  CAS  Google Scholar 

  32. Consonni R, Ordoudi SA, Cagliani LR, Tsiangali M, Tsimidou MZ. On the traceability of commercial saffron samples using 1H-NMR and FT-IR metabolomics. Metabolomics. 2016;21:286. (13 pp).

    Google Scholar 

  33. Petrakis EA, Cagliani LR, Tarantilis PA, Polissiou MG, Consonni R. Sudan dyes in adulterated saffron (Crocus sativus L.): identification and quantification by 1H NMR. Food Chem. 2017;217:418–24.

    Article  CAS  Google Scholar 

  34. Wei L, Lin M, Han B, Deng X, Hou W, Liao Q, Xie Z. The comparison of cinnamomi cortex and cinnamomun burmannii blume using 1H NMR and GC-MS combined with multivariate data analysis. Food Anal Methods. 2016;9:2419–28.

    Article  Google Scholar 

  35. Lopez MG, Zanor MI, Pratta GR, Stegmayer G, Boggio SB, Conte M, Bermúdez L, Leskow CC, Rodríguez GR, Picardi LA, Zorzoli R, Fernie AR, Milone D, Asís R, Valle EM, Carrari F. Metabolic analyses of interspecific tomato recombinant inbred lines for fruit quality improvement. Metabolomics. 2015;11:1416–31.

    Article  CAS  Google Scholar 

  36. Fatima T, Sobolev AP, Teasdale JR, Kramer M, Bunce J, Handa AK, Mattoo AK. Fruit metabolite networks in engineered and non-engineered tomato genotypes reveal fluidity in a hormone and agroecosystem specific manner. Metabolomics. 2016;12:103. (15 pp).

    Article  CAS  Google Scholar 

  37. Iglesias MJ, García-López J, Collados-Luján JF, López-Ortiz F, Díaz M, Toresano F, Camacho F. Differential response to environmental and nutritional factors of high-quality tomato varieties. Food Chem. 2015;176:278–87.

    Article  CAS  Google Scholar 

  38. Jiang M, Wang C, Zhang Y, Feng Y, Wang Y, Zhu Y. Sparse partial-least-squares discriminant analysis for different geographical origins of Salvia miltiorrhiza by 1H NMR-based metabolomics. Phytochem Anal. 2014;25:50–8.

    Article  CAS  Google Scholar 

  39. Kim J, Jung Y, Song B, Bong YS, Ryu DH, Lee KS, Hwang GS. Discrimination of cabbage (Brassica rapa ssp. pekinensis) cultivars grown in different geographical areas using 1H NMR-based metabolomics. Food Chem. 2013;137:68–75.

    Article  CAS  Google Scholar 

  40. Nguyen HT, Lee DK, Choi YG, Min YE, Yoon SJ, Yu YH, Lim J, Lee J, Kwon SW, Park JH. A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: a case study of Panax ginseng. Metabolomics for the authenticity of herbal medicine. J Pharm Biomed Anal. 2016;124:120–8.

    Article  CAS  Google Scholar 

  41. http://lipidlibrary.aocs.org/OilsFats/. Last accessed 30 Nov 2016.

  42. Sacchi R, Patumi M, Fontanazza G, Barone P, Fiordiponti P, Mannina L, Rossi E, Segre AL. A high-field 1H nuclear magnetic resonance study of the minor components in virgin olive oils. J Am Oil Chem Soc. 1996;73:747–58.

    Article  CAS  Google Scholar 

  43. Del Coco L, De Pascali SA, Iacovelli V, Cesari G, Schena FP, Fanizzi FP. Following the olive oil production chain: 1D and 2D NMR study of olive paste, pomace, and oil. Eur J Lipid Sci Technol. 2014;116:1513–21.

    Article  CAS  Google Scholar 

  44. Piccinonna S, Ragone R, Stocchero M, Del Coco L, De Pascali SA, Schena FP, Fanizzi FP. Robustness of NMR-based metabolomics to generate comparable data sets for olive oil cultivar classification. An inter-laboratory study on Apulian olive oils. Food Chem. 2016;199:675–83.

    Article  CAS  Google Scholar 

  45. Fang G, Goh JY, Tay M, Lau HF, Li SFY. Characterization of oils and fats by 1H NMR and GC/MS fingerprinting: classification, prediction and detection of adulteration. Food Chem. 2013;138:1461–9.

    Article  CAS  Google Scholar 

  46. Godelmann R, Fang F, Humpfer E, Schütz B, Bansbach M, Schäfer H, Spraul M. Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage. J Agric Food Chem. 2013;61:5610–9.

    Article  CAS  Google Scholar 

  47. Hu B, Yue Y, Zhu Y, Wen W, Zhang F, Hardie JW. Proton nuclear magnetic resonance-spectroscopic discrimination of wines reflects genetic homology of several different grape (V. vinifera L.) cultivars. PLoS One. 2015;10:e0142840.

    Article  CAS  Google Scholar 

  48. Fotakis C, Zervou M. NMR metabolic fingerprinting and chemometrics driven authentication of Greek grape marc spirits. Food Chem. 2016;196:760–8.

    Article  CAS  Google Scholar 

  49. Mazzei P, Spaccini R, Francesca N, Moschetti G, Piccolo A. Metabolomic by 1H NMR spectroscopy differentiates “Fiano di Avellino” white wines obtained with different yeast strains. J Agric Food Chem. 2013;61:10816–22.

    Article  CAS  Google Scholar 

  50. Spraul M, Link M, Schaefer H, Fang F, Schuetz B. Wine analysis to check quality and authenticity by fully-automated 1H–NMR. In 38th World Congress of Vine and Wine. Edited by JeanMarie A. EDP Sciences. BIO Web of Conferences. 2015;5. https://doi.org/10.1051/bioconf/20150502022.

    Article  Google Scholar 

  51. Spevacek AR, Benson KH, Bamforth CW, Slupsky CM. Beer metabolomics: molecular details of the brewing process and the differential effects of late and dry hopping an yeast purine metabolism. J Inst Brew. 2016;122:21–8.

    Article  CAS  Google Scholar 

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Correspondence to R. Consonni .

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Cagliani, L.R., Scano, P., Consonni, R. (2018). NMR-Based Metabolomics: Quality and Authenticity of Plant-Based Foods. In: Webb, G. (eds) Modern Magnetic Resonance. Springer, Cham. https://doi.org/10.1007/978-3-319-28388-3_1

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