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Electronic Nose in Combination with Chemometrics for Characterization of Geographical Origin and Agronomic Practices of Table Grape

  • Francesco LongobardiEmail author
  • Grazia Casiello
  • Valentina Centonze
  • Lucia Catucci
  • Angela Agostiano
Article
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Abstract

Nowadays, the protection of food quality attributes (such as geographical origin or method of production) from frauds and adulterations is one of the main concerns of honest producers and aware consumers. In this study, table grape samples were analyzed by using an electronic nose aiming to evaluate the usefulness of sensor data in combination with statistical analysis in discriminating the agronomic practice (conventional vs. organic farming) and the geographical origin of table grape. Principal component analysis (PCA) showed inadequate clustering of samples according to places of production or agronomic practice; thus for classification purpose, a supervised approach was carried out. In particular, linear discriminant analyses (LDA) was used, resulting in mean prediction abilities of 83.6% and 84.6% for the discrimination of farming method and geographical origin, respectively. Considering the results obtained herein, it can be concluded that sensor data combined with chemometrics showed a good potential in discriminating origin as well as method of production of table grapes especially if compared with other analytical techniques both in terms of time and cost of analyses.

Keywords

Electronic nose Table grape Geographical origin Agronomic practices Chemometrics 

Notes

Funding Information

The authors acknowledge the financial support by “Intervento Reti di Laboratori Pubblici di Ricerca cofinanziato dall’Accordo di Programma Quadro in materia di Ricerca Scientifica—II Atto Integrativo—PO Puglia FESR 2007–2013, Asse I, Linea 1.2—PO Puglia FSE 2007–2013 Asse IV” (“Apulian Food Fingerprint” project).

Compliance with Ethical Standards

Conflict of Interest

Francesco Longobardi declares that he has no conflict of interest. Grazia Casiello declares that she has no conflict of interest. Valentina Centonze declares that she has no conflict of interest. Lucia Catucci declares that she has no conflict of interest. Angela Agostiano declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies involving human participants or animals performed by any of the authors.

Informed Consent

Not applicable, as this study does not include any human participants.

Supplementary material

12161_2019_1458_MOESM1_ESM.pdf (178 kb)
ESM 1 (PDF 178 kb)
12161_2019_1458_MOESM2_ESM.pdf (161 kb)
ESM 2 (PDF 161 kb)

References

  1. Benedetti S, Buratti S, Spinardi A, Mannino S, Mignani I (2008) Electronic nose as a non-destructive tool to characterise peach cultivars and to monitor their ripening stage during shelf-life. Postharvest Biol Technol 47:181–188CrossRefGoogle Scholar
  2. Bernal LJ, Melo LA, Díaz Moreno C (2014) Evaluation of the antioxidant properties and aromatic profile during maturation of the blackberry (Rubus glaucus Benth) and the bilberry (Vaccinium meridionale Swartz). Rev Fac Nal Agr Medellín 67:7209–7218CrossRefGoogle Scholar
  3. Berrueta LA, Alonso-Salces RM, Héberger K (2007) Supervised pattern recognition in food analysis. J Chromatogr A 1158:196–214CrossRefGoogle Scholar
  4. Bonte A, Neuweger H, Goesmann A, Thonar C, Mäder P, Langenkämper G, Niehaus K (2014) Metabolite profiling on wheat grain to enable a distinction of samples from organic and conventional farming systems. J Sci Food Agric 94:2605–2612CrossRefGoogle Scholar
  5. Buratti S, Casiraghi A, Minghetti P, Giovanelli G (2013) The joint use of electronic nose and electronic tongue for the evaluation of the sensorial properties of green and black tea infusions as related to their chemical composition. Food Nutr Sci 4:605–615Google Scholar
  6. Capuano E, Boerrigter-Eenling R, van der Veer G, van Ruth SM (2013) Analytical authentication of organic products: an overview of markers. J Sci Food Agric 93:12–28CrossRefGoogle Scholar
  7. Casale M, Casolino C, Oliveri P, Forina M (2010) The potential of coupling information using three analytical techniques for identifying the geographical origin of Liguria extra virgin olive oil. Food Chem 118:163–170CrossRefGoogle Scholar
  8. Ceglie F, Amodio M, Colelli G (2016) Effect of organic production systems on quality and postharvest performance of horticultural produce. Horticulturae 2:4CrossRefGoogle Scholar
  9. Cubero-Leon E, Peñalver R, Maquet A (2014) Review on metabolomics for food authentication. Food Res Int 60:95–107CrossRefGoogle Scholar
  10. Dani C, Oliboni LS, Vanderlinde R, Bonatto D, Salvador M, Henriques JAP (2007) Phenolic content and antioxidant activities of white and purple juices manufactured with organically- or conventionally-produced grapes. Food Chem Toxicol 45:2574–2580CrossRefGoogle Scholar
  11. Defernez M, Kemsley EK (1997) The use and misuse of chemometrics for treating classification problems. Trends Anal Chem 16:216–221CrossRefGoogle Scholar
  12. del Amor FM, Serrano-Martínez A, Fortea I, Núñez-Delicado E (2008) Differential effect of organic cultivation on the levels of phenolics, peroxidase and capsidiol in sweet peppers. J Sci Food Agric 88:770–777CrossRefGoogle Scholar
  13. Di Rosa AR, Leone F, Cheli F, Chiofalo V (2017) Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment - a review. J Food Eng 210:62–75CrossRefGoogle Scholar
  14. FAOSTAT (2016) Food and agriculture organization of the United Nations, http://www.fao.org/faostat/en/#data/QC. Updated on August 2018
  15. Gallo V, Mastrorilli P, Cafagna I, Nitti GI, Latronico M, Longobardi F, Minoja AP, Napoli C, Romito VA, Schäfer H, Schütz B, Spraul M (2014) Effects of agronomical practices on chemical composition of table grapes evaluated by NMR spectroscopy. J Food Compos Anal 35:44–52CrossRefGoogle Scholar
  16. Gancarz M, Wawrzyniak J, Gawrysiak-Witulska M, Wiącek D, Nawrocka A, Tadla M, Rusinek R (2017) Application of electronic nose with MOS sensors to prediction of rapeseed quality. Measurement 103:227–234CrossRefGoogle Scholar
  17. Giungato P, Laiola E, Nicolardi V (2017) Evaluation of industrial roasting degree of coffee beans by using an electronic nose and a stepwise backward selection of predictors. Food Anal Methods 10:3424–3433CrossRefGoogle Scholar
  18. Gliszczyńska-Świgło A, Chmielewski J (2017) Electronic nose as a tool for monitoring the authenticity of food. A review. Food Anal Methods 10:1800–1816CrossRefGoogle Scholar
  19. Gómez AH, Wang J, Hu G, Pereira AG (2006) Electronic nose technique potential monitoring mandarin maturity. Sensors Actuators B Chem 113:347–353CrossRefGoogle Scholar
  20. Granato D, Koot A, Schnitzler E, van Ruth SM (2015a) Authentication of geographical origin and crop system of grape juices by phenolic compounds and antioxidant activity using chemometrics. J Food Sci 80:C584–C593CrossRefGoogle Scholar
  21. Granato D, Koot A, van Ruth SM (2015b) Geographical provenancing of purple grape juices from different farming systems by proton transfer reaction mass spectrometry using supervised statistical techniques. J Sci Food Agric 95:2668–2677CrossRefGoogle Scholar
  22. Granato D, Magalhães Carrapeiro M, Fogliano V, van Ruth SM (2016) Effects of geographical origin, varietal and farming system on the chemical composition and functional properties of purple grape juices: a review. Trends Food Sci Technol 52:31–48CrossRefGoogle Scholar
  23. Hai Z, Wang J (2006) Electronic nose and data analysis for detection of maize oil adulteration in sesame oil. Sensors Actuators B Chem 119:449–455CrossRefGoogle Scholar
  24. Istat (2014) Italian National Institute of Statistics, http://www.istat.it/en/archive/183622. Accessed 24 July 2018
  25. Karabagias IK, Casiello G, Kontakos S, Louppis AP, Longobardi F, Kontominas MG (2016) Investigating the impact of botanical origin and harvesting period on carbon stable isotope ratio values (13C/12C) and different parameter analysis of Greek unifloral honeys: a chemometric approach for correct botanical discrimination. Int J Food Sci Technol 51:2460–2467CrossRefGoogle Scholar
  26. Krejčová A, Návesník J, Jičínská J, Černohorský T (2016) An elemental analysis of conventionally, organically and self-grown carrots. Food Chem 192:242–249CrossRefGoogle Scholar
  27. Longobardi F, Casiello G, Cortese M, Perini M, Camin F, Catucci L, Agostiano A (2015a) Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectrometry combined with chemometrics. Food Chem 188:343–349CrossRefGoogle Scholar
  28. Longobardi F, Casiello G, Ventrella A, Mazzilli V, Nardelli A, Sacco D, Catucci L, Agostiano A (2015b) Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries. Food Chem 170:90–96CrossRefGoogle Scholar
  29. Longobardi F, Casiello G, Centonze V, Catucci L, Agostiano A (2017a) Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape. J Sci Food Agric 97:3173–3180CrossRefGoogle Scholar
  30. Longobardi F, Innamorato V, di Gioia A, Ventrella A, Lippolis V, Logrieco AF, Catucci L, Agostiano A (2017b) Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses. Food Chem 237:743–748CrossRefGoogle Scholar
  31. Luykx DMAM, van Ruth SM (2008) An overview of analytical methods for determining the geographical origin of food products. Food Chem 107:897–911CrossRefGoogle Scholar
  32. Ma Y, Guo B, Wei Y, Wei S, Zhao H (2014) The feasibility and stability of distinguishing the kiwi fruit geographical origin based on electronic nose analysis. Food Sci Technol Res 20:1173–1181CrossRefGoogle Scholar
  33. Maggio A, De Pascale S, Paradiso R, Barbieri G (2013) Quality and nutritional value of vegetables from organic and conventional farming. Sci Hortic 164:532–539CrossRefGoogle Scholar
  34. Medicalxpress (2017) Europe’s tainted food scandals. https://medicalxpress.com/news/2017-08-europe-tainted-food-scandals.html. Accessed 24 July 2018
  35. Meng W, Xu X, Cheng K-K, Xu J, Shen G, Wu Z, Dong J (2017) Geographical origin discrimination of oolong tea (TieGuanYin, Camellia sinensis (L.) O. Kuntze) using proton nuclear magnetic resonance spectroscopy and near-infrared spectroscopy. Food Anal Methods 10:3508–3522CrossRefGoogle Scholar
  36. Rossi F, Godani F, Bertuzzi T, Trevisan M, Ferrari F, Gatti S (2008) Health-promoting substances and heavy metal content in tomatoes grown with different farming techniques. Eur J Nutr 47:266–272CrossRefGoogle Scholar
  37. Szczurek A, Krawczyk B, Maciejewska M (2013) VOCs classification based on the committee of classifiers coupled with single sensor signals. Chemom Intell Lab Syst 125:1–10CrossRefGoogle Scholar
  38. Talavera-Bianchi M, Chambers Iv E, Carey EE, Chambers DH (2010) Effect of organic production and fertilizer variables on the sensory properties of pac choi (Brassica rapa var. Mei Qing Choi) and tomato (Solanum lycopersicum var. Bush Celebrity). J Sci Food Agric 90:981–988Google Scholar
  39. Torri L, Sinelli N, Limbo S (2010) Shelf life evaluation of fresh-cut pineapple by using an electronic nose. Postharvest Biol Technol 56:239–245CrossRefGoogle Scholar
  40. Willer H, Lernoud J (2015) Organic viticulture worldwide 2015. http://www.sinab.it/sites/default/files/Organic%20Viticulture%20Worldwide%202015.pdf. Accessed 02 February 2019
  41. Wojnowski W, Majchrzak T, Dymerski T, Gębicki J, Namieśnik J (2017) Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors 17:2715CrossRefGoogle Scholar
  42. Wu H, Yue T, Yuan Y (2018) Authenticity tracing of apples according to variety and geographical origin based on electronic nose and electronic tongue. Food Anal Methods 11:522–532CrossRefGoogle Scholar
  43. Zhao Y, Yang S, Wang D (2016) Stable carbon and nitrogen isotopes as a potential tool to differentiate pork from organic and conventional systems. J Sci Food Agric 96:3950–3955CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di ChimicaUniversità di Bari “Aldo Moro”BariItaly
  2. 2.Consiglio Nazionale delle RicercheIstituto per i Processi Chimico-Fisici (IPCF-CNR)BariItaly

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