European Food Research and Technology

, Volume 242, Issue 2, pp 259–270 | Cite as

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

  • Luís G. Dias
  • Nuno Rodrigues
  • Ana C. A. Veloso
  • José A. Pereira
  • António M. Peres
Original Paper


Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactory–gustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.


Single-cultivar extra-virgin olive oil Sensory analysis Potentiometric electronic tongue Linear multivariate methods Simulated annealing algorithm 



This work was co-financed by FCT/MEC and FEDER under Program PT2020 (Project UID/EQU/50020/2013); by Fundação para a Ciência e Tecnologia under the strategic funding of UID/BIO/04469/2013 unit; and by Project POCTEP through Project RED/AGROTEC—Experimentation network and transfer for development of agricultural and agro industrial sectors between Spain and Portugal.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Compliance with Ethics Requirements

This article does not contain any studies with human or animal subjects.


  1. 1.
    Sinelli N, Cerretani L, Di Egidio V, Bendini A, Casiraghi E (2010) Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity. Food Res Int 43:369–375CrossRefGoogle Scholar
  2. 2.
    Garcia R, Martins N, Cabrita MJ (2013) Putative markers of adulteration of extra virgin olive oil with refined olive oil: prospects and limitations. Food Res Int 54:2039–2044CrossRefGoogle Scholar
  3. 3.
    Dias LG, Fernandes A, Veloso ACA, Machado AASC, Pereira JA, Peres AM (2014) Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue. Food Chem 160:321–329CrossRefGoogle Scholar
  4. 4.
    Lerma-García MJ, Simó-Alfonso EF, Bendini A, Cerretan L (2009) Metal oxide semiconductor sensors for monitoring of oxidative status evolution and sensory analysis of virgin olive oils with different phenolic content. Food Chem 117:608–614CrossRefGoogle Scholar
  5. 5.
    Rotondi A, Beghè D, Fabbri A, Ganino T (2011) Olive oil traceability by means of chemical and sensory analyses: a comparison with SSR biomolecular profiles. Food Chem 129:1825–1831CrossRefGoogle Scholar
  6. 6.
    Lauri I, Pagano B, Malmendal A, Sacchi R, Novellino E, Randazzo A (2013) Application of “magnetic tongue” to the sensory evaluation of extra virgin olive oil. Food Chem 140:692–699CrossRefGoogle Scholar
  7. 7.
    Carrasco-Pancorbo A, Gómez-Caravaca AM, Cerretani L, Bendini A, Segura-Carretero A, Fernández-Gutiérrez A (2006) Rapid quantification of the phenolic fraction of Spanish virgin olive oils by capillary electrophoresis with UV detection. J Agric Food Chem 54:7984–7991CrossRefGoogle Scholar
  8. 8.
    Matos LC, Cunha SC, Amaral JS, Pereira JA, Andrade PB, Seabra RM, Oliveira BPP (2007) Chemometric characterization of three varietal olive oils (cvs. Cobrançosa, Madural and Verdeal Transmontana) extracted from olives with different maturation indices. Food Chem 102:406–414CrossRefGoogle Scholar
  9. 9.
    Bakhouche A, Lozano-Sánchez J, Beltrán-Debón R, Joven J, Segura-Carretero A, Fernández-Gutiérrez A (2013) Phenolic characterization and geographical classification of commercial Arbequina extra-virgin olive oils produced in southern Catalonia. Food Res Int 50:401–408CrossRefGoogle Scholar
  10. 10.
    Karabagias I, Michos Ch, Badeka A, Kontakos S, Stratis I, Kontominas MG (2013) Classification of Western Greek virgin olive oils according to geographical origin based on chromatographic, spectroscopic, conventional and chemometric analyses. Food Res Int 54:1950–1958CrossRefGoogle Scholar
  11. 11.
    Longobardi F, Ventrella A, Napoli C, Humpfer E, Schütz B, Schäfer H, Kontominas MG, Sacco A (2012) Classification of olive oils according to geographical origin by using 1H NMR fingerprinting combined with multivariate analysis. Food Chem 130:177–183CrossRefGoogle Scholar
  12. 12.
    Romero C, Brenes M (2012) Analysis of total contents of hydroxytyrosol and tyrosol in olive oils. J Agric Food Chem 60:9017–9022CrossRefGoogle Scholar
  13. 13.
    Ruiz-Samblás C, Tres A, Koot A, van Ruth SM, González-Casado A, Cuadros-Rodríguez L (2012) Proton tranfer reaction-mass spectrometry volatile organic compound fingerprint for monovarietal extra virgin olive oil identification. Food Chem 134:589–596CrossRefGoogle Scholar
  14. 14.
    Bazakos C, Dulger AO, Uncu AT, Spaniolas S, Spano T, Kalaitzis P (2012) A SNP-based PCR–RFLP capillary electrophoresis analysis for the identification of the varietal origin of olive oils. Food Chem 134:2411–2418CrossRefGoogle Scholar
  15. 15.
    Dais P, Hatzakis E (2013) Quality assessment and authentication of virgin olive oil by NMR spectroscopy: a critical review. Anal Chim Acta 765:1–27CrossRefGoogle Scholar
  16. 16.
    Nunes CA (2013) Vibrational spectroscopy and chemometrics to assess authenticity, adulteration and intrinsic quality parameters of edible oils and fats. Food Res Int 60:255–261CrossRefGoogle Scholar
  17. 17.
    Apetrei IM, Apetrei C (2013) Voltammetric e-tongue for the quantification of total polyphenol content in olive oils. Food Res Int 54:2075–2082CrossRefGoogle Scholar
  18. 18.
    Escuderos ME, Sánchez S, Jiménez A (2011) Quartz Crystal Microbalance (QCM) sensor arrays selection for olive oil sensory evaluation. Food Chem 124:857–862CrossRefGoogle Scholar
  19. 19.
    Haddi Z, Alami H, El Bari N, Tounsi M, Barhoumi H, Maaref A, Jaffrezic-Renault N, Bouchikhi B (2013) Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Res Int 54:1488–1498CrossRefGoogle Scholar
  20. 20.
    Peres AM, Veloso ACA, Pereira JA, Dias LG (2014) Electrochemical multi-sensors device coupled with heuristic or meta-heuristic selection algorithms for single-cultivar olive oil classification. Proced Eng 87:192–195CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Casale M, Sinelli N, Oliveri P, Di Egidio V, Lanteri S (2010) Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification. Talanta 80:1832–1837CrossRefGoogle Scholar
  23. 23.
    Casale M, Oliveri P, Casolino C, Sinelli N, Zunin P, Armanino C, Forina M, Lanteri S (2012) Characterisation of PDO olive oil Chianti Classico by non-selective (UV–visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques. Anal Chim Acta 712:56–63CrossRefGoogle Scholar
  24. 24.
    Pizarro C, Rodríguez-Tecedor S, Pérez-del-Notario N, Esteban-Díez I, González-Sáiz JM (2013) Classification of Spanish extra virgin olive oils by data fusion of visible spectroscopic fingerprints and chemical descriptors. Food Chem 138:915–922CrossRefGoogle Scholar
  25. 25.
    Gutiérrez JM, Haddi Z, Amari A, Bouchikhi B, Mimendia A, Cetó X, del Valle M (2013) Hybrid electronic tongue based on multisensor data fusion for discrimination of beers. Sens Actuators B 177:989–996CrossRefGoogle Scholar
  26. 26.
    Vera L, Aceña L, Guasch J, Boqué R, Mestres M, Busto O (2011) Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools. Anal Bioanal Chem 399:2073–2081CrossRefGoogle Scholar
  27. 27.
    Bruwer M-J, MacGregor JF, Bourg WM Jr (2007) Fusion of sensory and mechanical testing data to define measurements of snack food texture. Food Qual Prefer 18:890–900CrossRefGoogle Scholar
  28. 28.
    Haddi Z, Mabrouk S, Bougrini M, Tahri K, Sghaier K, Barhoumi H, El Bari N, Maaref A, Jaffrezic-Renault N, Bouchikhi B (2014) E-nose and e-tongue combination for improved recognition of fruit juice samples. Food Chem 150:246–253CrossRefGoogle Scholar
  29. 29.
    Banerjee R, Modak A, Mondal S, Tudu B, Bandyopadhyay R, Bhattacharyya N (2013) Fusion of electronic nose and tongue response using fuzzy based approach for black tea classification. Procedia Technol 10:615–622CrossRefGoogle Scholar
  30. 30.
    Apetrei C, Apetrei IM, Villanueva S, de Saja JA, Gutierrez-Rosales F, Rodriguez-Mendez ML (2010) Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Anal Chim Acta 663:91–97CrossRefGoogle Scholar
  31. 31.
    International Olive Council (2013) Sensory analysis of olive oil—method for the organoleptic assessment of virgin olive oil. COI/T.20/Doc. No. 15/Rev. 6 November 2013.
  32. 32.
    International Olive Council (2014) IOC Mario Solinas quality award—rules of the international competition for extra virgin olive oils. T.30/Doc. No. 17 June 2014.
  33. 33.
    Dias LG, Peres AM, Veloso ACA, Reis FS, Vilas Boas M, Machado AASC (2009) An electronic tongue taste evaluation: identification goat milk adulterations with bovine milk. Sens Actuators B 136:209–217CrossRefGoogle Scholar
  34. 34.
    Sousa MEBC, Dias LG, Veloso ACA, Estevinho L, Peres AM, Machado AASC (2014) Practical procedure for discriminating monofloral honeys with a broad pollen profile variability using an electronic tongue. Talanta 128:284–292CrossRefGoogle Scholar
  35. 35.
    Kobayashi Y, Habara M, Ikezazki H, Chen R, Naito Y, Toko K (2010) Advanced taste sensors based on artificial lipids with global selectivity to basic taste qualities and high correlation to sensory scores. Sensors 10:3411–3443CrossRefGoogle Scholar
  36. 36.
    Liu Y, Brown SD (2004) Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches. Anal Bioanal Chem 380:445–452CrossRefGoogle Scholar
  37. 37.
    Kuhn M, Johnson K (2013) Applied predictive modeling, features. Springer, 17 May 2013Google Scholar
  38. 38.
    Chun H, Keleş S (2010) Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J R Stat Soc B 72(Part 1):3–25CrossRefGoogle Scholar
  39. 39.
    Cadima J, Cerdeira JO, Minhoto M (2004) Computational aspects of algorithms for variable selection in the context of principal components. Comput Stat Data Anal 47:225–236CrossRefGoogle Scholar
  40. 40.
    Dias LG, Sequeira C, Veloso ACA, Sousa MEBC, Peres AM (2014) Evaluation of healthy and sensory indexes of sweetened beverages using an electronic tongue. Anal Chim Acta 848:32–42CrossRefGoogle Scholar
  41. 41.
    Söderström C, Rudnitskaya A, Legin A, Krantz-Rülcker C (2005) Differentiation of four Aspergillus species and one Zygosaccharomyces with two electronic tongues based on different measurement techniques. J Biotechnol 119:300–308CrossRefGoogle Scholar
  42. 42.
    Rudnitskaya A, Kirsanov D, Legin A, Beullens K, Lammertyn J, Nicolaï BM, Irudayaraj J (2006) Analysis of apples varieties—comparison of electronic tongue with different analytical techniques. Sens Actuators B 116:23–28CrossRefGoogle Scholar
  43. 43.
    Kuhn M (Contributions from Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, and the R Core Team) (2014) caret: classification and regression training. R package version 6.0-24.
  44. 44.
    Cerdeira JO, Silva PD, Cadima J, Minhoto M (2012) subselect: selecting variable subsets. R package version 0.12-2.
  45. 45.
    Mevik B-H, Wehrens R, Liland KH (2011) pls: partial least squares and principal component regression. R package version 2.3-0.
  46. 46.
    Chung D, Chun H, Keles S (2012) spls: sparse partial least squares (SPLS) regression and classification. R package version 2.1-2.
  47. 47.
    Venables WN, Ripley BD (2002) Modern applied statistics with S (Statistics and Computing), 4th edn. Springer, New York. ISBN 978-0-387-21706-2CrossRefGoogle Scholar
  48. 48.
    Correia DPA, Magalhães JMCS, Machado AASC (2005) Array of potentiometric sensors for simultaneous analysis of urea and potassium. Talanta 67:773–782CrossRefGoogle Scholar
  49. 49.
    Cimato A, Dello Monaco D, Distante C, Epifani M, Siciliano P, Taurino AM, Zuppa M, Sani G (2006) Analysis of single-cultivar extra virgin olive oils by means of electronic nose and HS-SPME/GC/MS methods. Sens Actuators B 114:674–680CrossRefGoogle Scholar
  50. 50.
    Agiomyrgianaki A, Petrakis PV, Dais P (2012) Influence of harvest year, cultivar and geographic origin on Greek extra virgin olive oils composition: a study by NMR spectroscopy and biometric analysis. Food Chem 135:2561–2568CrossRefGoogle Scholar
  51. 51.
    Pouliarekou E, Badeka A, Tasioula-Margari M, Kontakos S, Longobardi F, Kontominas MG (2011) Characterization and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds. J Chromatogr A 1218:7534–7542CrossRefGoogle Scholar
  52. 52.
    Uncu AT, Frary A, Doganlar S (2015) Cultivar origin and admixture detection in Turkish olive oils by SNP-based CAPS assays. J Agric Food Chem 63:2284–2295CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Luís G. Dias
    • 1
    • 2
  • Nuno Rodrigues
    • 3
  • Ana C. A. Veloso
    • 4
    • 5
  • José A. Pereira
    • 3
  • António M. Peres
    • 6
  1. 1.Escola Superior AgráriaInstituto Politécnico de BragançaBragançaPortugal
  2. 2.CQ-VR, Centro de Química – Vila RealUniversity of Trás-os-Montes e Alto DouroVila RealPortugal
  3. 3.CIMO - Mountain Research Centre, Escola Superior AgráriaInstituto Politécnico de BragançaBragançaPortugal
  4. 4.Instituto Politécnico de Coimbra, ISEC, DEQBCoimbraPortugal
  5. 5.CEB - Centre of Biological EngineeringUniversity of MinhoBragaPortugal
  6. 6.LSRE - Laboratory of Separation and Reaction Engineering - Associate Laboratory LSRE/LCM, Escola Superior AgráriaInstituto Politécnico de BragançaBragançaPortugal

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