European Food Research and Technology

, Volume 243, Issue 6, pp 941–953 | Cite as

Assessing wine sensory attributes using Vis/NIR

  • José Antonio CayuelaEmail author
  • Belén Puertas
  • Emma Cantos-Villar
Original Paper


The quality of wine involves the presence and intensity of many flavors and nuances. The assessment by tasting panels of the sensory quality of wine and by measuring the intensity of their attributes or defects is more difficult than assessing the global score of wine quality, which is a single value for each wine. This research focused on the feasibility of the visible and near-infrared spectroscopy for assessing wine sensory attributes. Predictive models of sensory attributes such as positive and negative for red and white wines have been developed, based on their spectra and reference values from a specialized tasting panel. The results indicate that the technique is feasible for predicting some of the most characteristic sensory attributes of red and white wines such as Flavor intensity, Astringency, Color intensity, Length, Persistency, Pleasantness and Balance. Their correlation coefficients of validations were about 0.9 in most cases. The technique was also suitable for predicting some important defects such as Oxidation, Unclean, Ethyl acetate and Acetic acid. The main potential use of this technique is for contrasting or confirming the assessment of wine for the positive and negative sensory attributes, determined by the sensory analysis of tasting panels. This can be particularly useful in cases where there is discrepancy in the assessments of a sensory panel. It is reasonable to consider the possibility of tuning the technique for detection in routine analysis of some negative attributes.


Attributes Sensory analysis Tasting panel Vis/NIR Wine 



Spanish Council for Scientific Research


Institute Research and Training Agricultural and Fisheries


Mean normalization


Near-infrared spectroscopy


Principal components


Coefficient of calibration


Correlation coefficient between the analyzed and predicted values in the external validation exercises


Coefficient of cross-validation


Residual predictive deviation


Standard deviation


Standard error of calibration


Standard error of performance


Visible and near infrared


Visible and near-infrared spectroscopy


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 subject.


  1. 1.
    Vannier A, Bruna OX, Feinberg MH (1999) Application of sensory analysis to champagne wine characterisation and discrimination. Food Qual Prefer 10:101–107CrossRefGoogle Scholar
  2. 2.
    Cozzolino D, Smith HE, Lattey KA, Cynkar W, Janik L, Dambergs RG, Francis IL, Gishen M (2006) Combining mass spectrometry based electronic nose, visible-near infrared spectroscopy and chemometrics to assess the sensory properties of Australian Riesling wines. Anal Chim Acta 563:319–324CrossRefGoogle Scholar
  3. 3.
    Tempere S, Hamtat ML, Bougeant JC, de Revel GM, Sicard G (2014) Learning odors: the impact of visual and olfactory mental imagery training on odor perception. J Sens Stud 29:435–449CrossRefGoogle Scholar
  4. 4.
    Vidal L, Antúnez L, Giménez A, Ares G (2016) Evaluation of palate cleansers for astringency evaluation of red wines. J Sens Stud 31:93–100CrossRefGoogle Scholar
  5. 5.
    Le Cloirec P, Gueux M, Paillard H, Anselme C (1991) In: Martin G, Laport P (eds) Odeurs and desodorisation dans l’Environnement. Cachan, LavoisierGoogle Scholar
  6. 6.
    Berglund B, Berglund U, Lindwall T, Svensson LT (1973) A quantitative principle of perceived intensity summation in odours mixtures. J Exp Psychol 100:29–38CrossRefGoogle Scholar
  7. 7.
    Forina M, Oliveri P, Bagnasco L, Simonetti R, Casolino MC, Nizzi Grifi F, Casale M (2015) Artificial nose, NIR and UV–visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil. Talanta 144:1070–1078CrossRefGoogle Scholar
  8. 8.
    Downey G, Sheehan E, Delahunty C, Callaghan O, Guinee T, Howard V (2005) Prediction of maturity and sensory attributes of Cheddar cheese using near-infrared spectroscopy. Int Dairy J 15:701–709CrossRefGoogle Scholar
  9. 9.
    Woodcock T, Fagan C, O’Donell C, Downey G (2008) Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food Bioprocess Technol 1(2):117–129CrossRefGoogle Scholar
  10. 10.
    Cattaneo TMP, Tornelli C, Erini S, Panarelli EV (2008) Relationship between sensory scores and near infrared absorptions in characterising Bitto, an Italian protected denomination of origin cheese. J NIRS 16:173–178Google Scholar
  11. 11.
    Meulemans A, Dotreppe O, Leroy B, Istase L, Clinquart A (2003) Prediction of organoleptic and technological characteristics of pork meat by near infrared spectroscopy. Sci Aliment 23:159–162CrossRefGoogle Scholar
  12. 12.
    Andrés S, Murray I, Navajas EA, Fisher AV, Lambe NR, Bünger L (2007) Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Sci 76:509–516CrossRefGoogle Scholar
  13. 13.
    Ripoll G, Albertí P, Panea B, Olleta JL, Sañudo S (2008) Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Sci 80:697–702CrossRefGoogle Scholar
  14. 14.
    Ortiz MC, Sarabia L, García-Rey R, Luque de Castro MD (2006) Sensitivity and specificity of PLS-class modelling for five sensory characteristics of dry-cured ham using visible and near infrared spectroscopy. Anal Chim Acta 558:125–131CrossRefGoogle Scholar
  15. 15.
    Harker FR, Marsh KB, Young H, Murray SH, Gunson FA, Walker SB (2002) Sensory interpretation of instrumental measurement. 2. Sweet and acid taste of apple fruit. Post Bioltechnol 24:241–250Google Scholar
  16. 16.
    Piers A, Desmet M, Nicolai B, Buyssens S (2003) Relations between sensory analysis, instrumental quality and NIR measurements of tomato quality. Acta Hortic 600:471–477CrossRefGoogle Scholar
  17. 17.
    Francois I, Wins H, Buysens S, Godts C, Van Pee E, Nicoläi B, De Proft M (2008) Predicting sensory attributes of different chicory hybrids using physico-chemical measurements and visible/near infrared spectroscopy. Post Biol Technol 49:366–373CrossRefGoogle Scholar
  18. 18.
    Yan SH (2005) Evaluation of the composition and sensory properties of tea using near infrared spectroscopy and principal component analysis. J NIRS 13:313–325Google Scholar
  19. 19.
    Cozzolino D, Smith HE, Lattey KA, Cynkar W, Janik L, Dambergs RG, Francis IL, Gishen M (2005) Relationship between sensory analysis and near infrared spectroscopy in Australian Riesling and Chardonnay wines. Anal Chim Acta 539:341–348CrossRefGoogle Scholar
  20. 20.
    Cozzolino D, Cowey G, Lattey KA, Godden P, Cynkar W, Dambergs RG, Janik L, Gishen M (2008) Relationship between wine scores and visible–near-infrared spectra of Australian red wines. Anal Bioanal Chem 391:975–981CrossRefGoogle Scholar
  21. 21.
    AENOR (1992) Análisis sensorial. Metodología. Método para establecer el perfil olfato-gustativo.Google Scholar
  22. 22.
    Nobel AC, Arnold RA, Masuda BM, Pecore SD, Schmidt JO, Sterm PM (1984) Progress towards a standardized system of wine aroma terminology. Am J Enol Vitic 35:42–52Google Scholar
  23. 23.
    Nobel AC, Arnold RA, Buechsenstein J, Leach EJ, Schmidt JO, Sterm PM (1987) Modification of a standardized system of wine aroma terminology. Am J Enol Vitic 38:143–146Google Scholar
  24. 24.
    Brown B, Aaron M (2001) In: Smith J (ed) The rise of modern genomics, 3rd edn. Wiley, New YorkGoogle Scholar
  25. 25.
    Williams P, Sobering D (1996) In: Davies AMC, Williams P (eds) Near infrared spectroscopy: the future waves. NIR Publications, ChichesterGoogle Scholar
  26. 26.
    Minasny B, McBratney A (2013) Why you don’t need to use RPD. Pedometron 33:14–15Google Scholar
  27. 27.
    Andersson M, Nørgaard L. A procedure to determine when NIR is better than its reference method. NIR2013 Proceedings. 2013; P. 618–620.Google Scholar
  28. 28.
    Fearn T (2002) Assessing calibration: SEP, RPD, RER and R2. NIR News 13(6):12–14CrossRefGoogle Scholar
  29. 29.
    Williams P (2014) The RPD statistic: a tutorial note. NIR News 25(1):22Google Scholar
  30. 30.
    Fearn T (2015) The library of Babel (validation). NIR News 26(8):23CrossRefGoogle Scholar
  31. 31.
    Esbensen KH, Geladi P, Larsen A (2014) The RPD myth. NIR News 25(5):24CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • José Antonio Cayuela
    • 1
    Email author
  • Belén Puertas
    • 2
  • Emma Cantos-Villar
    • 2
  1. 1.CSIC-IG, Fisilogía y Tecnología de Productos VegetalesSevilleSpain
  2. 2.Instituto de Investigación y Formación Agraria y Pesquera (IFAPA) Rancho de la MercedJerez de la FronteraSpain

Personalised recommendations