Electronic nose based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for wine prediction

Sensors (Switzerland)

Volumn 12, Issue 6, 2012, Pages 8055-8072

  Aguilera, Teodoro ^a   Lozano, Jesús ^a   Paredes, José A ^a   Álvarez, Fernando J ^a   Suárez, José I ^b  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b Industrial Applications of Artificial Intelligence Research Group   (Spain)

Author keywords

Artificial neural networks; Electronic nose; Independent component analysis; Partial least squares; Wine classification

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; ELECTRONIC NOSE; GENETIC PROCEDURES; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SIGNAL PROCESSING; TIME; WINE;

BIOSENSING TECHNIQUES; ELECTRONIC NOSE; LEAST-SQUARES ANALYSIS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS; WINE;

EID: 84863195088     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120608055     Document Type: Article

References (30)

1. Pearce, T.; Schiffman, S.; Nagle, H.; Gardner, J.W. Handbook of Machine Olfaction; Wiley-VCH: Berlin, Germany, 2006.
2. Gutiérrez-Osuna, R. Pattern analysis for machine olfaction: A review. IEEE Sens. J. 2002, 2, 189-202.
3. Kotseridis, Y.; Razungles, A.; Bertrand, A.; Baumes, R. Differentiation of the aromas of Merlot and Cabernet Sauvignon wines using sensory and instrumental analysis. J. Agric. Food Chem. 2000, 48, 5383-5388.
4. Aenor. 2010. Available online: http://www.en.aenor.es/aenor/aenor/perfil/perfil.asp (accessed on 7 June 2012).
5. Santos, J.; Arroyo, T.; Aleixandre, M.; Lozano, J.; Sayago, I.; Garcia, M.; Fernandez, M.J.; Ares, L.; Gutierrez, J.; Cabellos, J.M.; et al. A comparative study of sensor array and GC-MS: Application to Madrid wines characterization. Sens. Actuators B Chem. 2004, 102, 299-307.
6. Santos, J.; Lozano, J.; Alexaindre, M.; Sayago, I.; Fernández, M.J.; Arés, L.; Guitiérrez, J.; Horrillo, M.C. Discrimination of different aromatic compounds in water, ethanol and wine with a thin film sensor array. Sens. Actuators B Chem. 2004, 103, 98-103.
7. Lozano, J.; Santos, J.; Horrillo, M. Classification of white wine aromas with an electronic nose. Talanta 2005, 67, 610-616.
8. Matlab®. 1984. Available online: http://www.mathworks.com/products/matlab/index.html (accessed on 7 June 2012).
9. Kermit, M.; Tomic, O. Independent component analysis applied on gas sensor array measurement data. IEEE Sens. J. 2003, 3, 218-228.
10. Bermejo, S. Independent component analysis for solid-state chemical sensor arrays. J. Appl. Intell. 2006, 24, 61-73.
11. Unscrambler®. 1987. Available online: http://www.camo.com/rt/Products/Unscrambler/ unscrambler.html (accessed on 7 June 2012).
12. Jutten, C.; Herault, J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process. 91, 24, 1-10.
13. Duda, R.; Hart, P.; Stork, D. Pattern classification. J. Classif. 2007, 24, 115-117.
14. Parastar, H.; Jalali-Heravi, M.; Tauler, R. Is independent component analysis appropriate for multivariate resolution in analytical chemistry? Trends Anal. Chem. 2012, 31, 134-143.
15. Comon, P. Independent component analysis, a new concept? Signal Process. 1994, 36, 287-314.
16. Hyvarinen, A.; Oja, E. Independent component analysis: Algorithms and applications. Neural Netw. 2000, 13, 411-430.
17. Lathauwer, L.D.; Moor, B.D. An introduction to independent component analysis. J. Chemom. 2000, 14, 123-149.
18. Geladi, P.; Kowalski, B. Partial least-squares: A tutorial. Anal. Chim. Acta 1986, 185, 1-17.
19. Lindgren, F.; Geladi, P.; Wold, S. The Kernel algorithm for PLS. J. Chemom. 1986, 7, 45-59.
20. Hierlemann, A.; Weimar, U.; Kraus, G.; Schweizer-Berberich, M.; Göpel, W. Polymer-based sensor arrays and multicomponent analysis for the detection of hazardous organic vapors in the environment. Sens. Actuators B Chem. 1995, 26, 126-134.
21. Sundgren, H.; Winquistn, F. Artificial neural networks and gas sensor arrays: Quantification of individual components in a gas mixture. Meas. Sci. Technol. 1991, 2, 464-469.
22. Wang, X.; Carey, W.; Yee, S.S. Monolithic thin-film metal-oxide gas-sensor arrays with application to monitoring of organic vapors. Sens. Actuators B Chem. 1995, 28, 63-70.
23. Carey, W.; Yee, S.S. Calibration of nonlinear solid-state sensor arrays using multivariate regression techniques. Sens. Actuators B Chem. 1992, 9, 113-122.
24. Vandeginste, B.G.M.; Massart, D.L.; Buydens, L.M.C.; de Jong, S.; Lewi, P.J.; Smeyers-Verbeke, J. Handbook of Chemometrics & Qualimetrics: Part B; Elsevier: New York, NY, USA, 1998; pp. 238-239.
25. Bishop, C.M. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, UK, 1999.
26. Masters, T. Practical Neural Network Recipes in C++; Academic Press: SanDiego, CA, USA, 1993.
27. Eberhart, R.; Dobbins, R. Neural Network PC Tools: A Practical Guide; Academic Press: Waltham, MA, USA, 1990.
28. Specht, D.F. Probabilistic neural network. Neural Netw. 1990, 3, 109-118.
29. Parzen, E. On the estimation of a probability density function and mode. Ann. Math. Stat. 1962, 3, 1065-1076.
30. Lozano, J.; Santos, J.P.; Arroyo, T.; Aznar, M.; Cabellos, J.M.; Gil, M.; Horrillos, M.C. Correlating e-nose responses to wine sensorial descriptors and gas chromatography-mass spectrometry profiles using partial least squares regression analysis. Sens. Actuators B Chem. 2007, 127, 267-276.