Next Article in Journal
Bridge Displacement Monitoring Method Based on Laser Projection-Sensing Technology
Previous Article in Journal
Research on Joint Parameter Inversion for an Integrated Underground Displacement 3D Measuring Sensor
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(4), 8429-8443; doi:10.3390/s150408429

A Wireless and Portable Electronic Nose to Differentiate Musts of Different Ripeness Degree and Grape Varieties

1
GRIDSEN, Instituto de Tecnologías Físicas y de la Información (ITEFI-CSIC), Madrid 28006, Spain
2
Dpto. Investigación Agroalimentaria, Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), Madrid 28800, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: W. Rudolf Seitz
Received: 26 February 2015 / Revised: 3 April 2015 / Accepted: 3 April 2015 / Published: 13 April 2015
(This article belongs to the Section Chemical Sensors)
View Full-Text   |   Download PDF [2326 KB, uploaded 13 April 2015]   |  

Abstract

Two novel applications using a portable and wireless sensor system (e-nose) for the wine producing industry—The recognition and classification of musts coming from different grape ripening times and from different grape varieties—Are reported in this paper. These applications are very interesting because a lot of varieties of grapes produce musts with low and similar aromatic intensities so they are very difficult to distinguish using a sensory panel. Therefore the system could be used to monitor the ripening evolution of the different types of grapes and to assess some useful characteristics, such as the identification of the grape variety origin and to prediction of the wine quality. Ripening grade of collected samples have been also evaluated by classical analytical techniques, measuring physicochemical parameters, such as, pH, Brix, Total Acidity (TA) and Probable Grade Alcoholic (PGA). The measurements were carried out for two different harvests, using different red (Barbera, Petit Verdot, Tempranillo, and Touriga) and white (Malvar, Malvasía, Chenin Blanc, and Sauvignon Blanc) grape musts coming from the experimental cellar of the IMIDRA at Madrid. Principal Component Analysis (PCA) and Probabilistic Neural Networks (PNN) have been used to analyse the obtained data by e-nose. In addition, and the Canonical Correlation Analysis (CCA) method has been carried out to correlate the results obtained by both technologies. View Full-Text
Keywords: electronic nose; degree of ripeness; must; analytical parameters; PCA; PNN; CCA electronic nose; degree of ripeness; must; analytical parameters; PCA; PNN; CCA
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Aleixandre, M.; Santos, J.P.; Sayago, I.; Cabellos, J.M.; Arroyo, T.; Horrillo, M.C. A Wireless and Portable Electronic Nose to Differentiate Musts of Different Ripeness Degree and Grape Varieties. Sensors 2015, 15, 8429-8443.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top