Identification of typical wine aromas by means of an electronic nose

In the field of electronic noses it is not very usual to find many applications in wine detection. Most of them are related to discrimination of wines in order to prevent their illegal adulteration and detection of off-odours but their objective is not the identification of wine aromas. In this paper, an e-nose using headspace as an extraction technique is used for the identification of typical aromatic compounds present in white and red wines. The descriptors of these compounds are: fruity, floral, herbaceous, vegetative, spicy, smoky, and microbiological and they are responsible for the usual aromas in wines; concentrations differ from 2 to 10 times the threshold concentration humans can smell. Some of the measured aromas are: pear, apple, peach, coconut, rose, geranium, cut green grass, mint, vanilla, clove, almond, toast, wood and butter. Principal component analysis (PCA) shows datasets of this group of compounds are clearly separated and radial basis neural networks (RB-NN) show a 98% rate of success in classification.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  C. Natale,et al.  An electronic nose and a mass spectrometry-based electronic nose for assessing apple quality during shelf life , 2004 .

[3]  José Pedro Santos,et al.  Discrimination of different aromatic compounds in water, ethanol and wine with a thin film sensor array , 2004 .

[4]  Markus Lipp,et al.  Characterisation of Italian vinegar by pyrolysis–mass spectrometry and a sensor device (‘electronic nose’) , 1998 .

[5]  M. Penza,et al.  Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks , 2004 .

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  David G. Stork,et al.  Pattern Classification , 1973 .

[8]  José Pedro Santos,et al.  A comparative study of sensor array and GC–MS: application to Madrid wines characterization , 2004 .

[9]  S. van Straten,et al.  Volatile compounds in food. , 1977 .

[10]  Daniel Rodriguez,et al.  Performance of an e-nose in hops classification , 2004 .

[11]  Antonella Macagnano,et al.  Multisensor for fish quality determination , 2004 .

[12]  Ricardo Gutierrez-Osuna,et al.  Pattern analysis for machine olfaction: a review , 2002 .

[13]  Pietro Siciliano,et al.  Recognition of olive oils by means of an integrated sol–gel SnO2 Electronic Nose , 2002 .

[14]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  José Pedro Santos,et al.  Artificial olfactory system for the classification of Iberian hams , 2003 .

[17]  Johanna Smeyers-Verbeke,et al.  Handbook of Chemometrics and Qualimetrics: Part A , 1997 .

[18]  Fritz Vögtle,et al.  Determination of the ripening state of Emmental cheese via quartz microbalances , 2003 .

[19]  Lutgarde M. C. Buydens,et al.  Parallel Processing of Chemical Information in a Local Area Network - II. a Parallel Cross-validation Procedure for Artificial Neural Networks , 1996, Comput. Chem..

[20]  A. Guadarrama,et al.  Array of conducting polymer sensors for the characterisation of wines , 2000 .

[21]  Juan Cacho,et al.  Quantitative determination of the odorants of young red wines from different grape varieties , 2000 .

[22]  Ingemar Lundström,et al.  Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose , 2000 .