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Prediction of abnormal wine fermentations using computational intelligent techniques
Keywords:
support vector machines, abnormal, wine fermentations, artificial neural networksAbstract
The early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets.
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References
[1] Executive Report Chilean Wine Production, Servicio Agrı́cola y Ganadero de Chile, 2011.
[2] Informe del Sector, La industria del vino en Chile, Elena Fabiano, 2009.
[3] Bisson, L., Butzke, C., Diagnosis and Rectification of Stuck and Sluggish Fermentations, American Journal of Enology and Viticulture 51 (2), 168-177, 2000.
[4] Blateyron, L., Sablayrolles, J.M., Stuck and Slow Fermentations in Enology: Statical Study of Causes and Effectiveness of Combined Additions of Oxygen and Diammonium Phosphate, Journal of Bioscience and Bioengineering 91 (2), 184-189, 2001.
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[7] Beltran, G., Novo, M., Guillamón, J., Mas, A., Rozés, N., Effect of fermentation temperature and culture media on the yeast lipid composition and wine volatile compounds, International Journal of Food Microbiology 121, 169-177, 2008.
[8] Varela, C., Pizarro, F., Agosin, E., Biomass. Content Govern Fermentation Rate in Nitrogen-Deficient Wine Musts, Applied and Environmental Microbiology 70 (6), 3392-3400, 2004
[9] D’Amatto, D., Corbo, M., Del Nobile, M., Sinigaglia, M., Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system, International Journal of Food Science and Technology 41, 1152-1157, 2006.
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[13] Abe, S., Support Vector Machines for Pattern Classification, Second Edition, Springer, 2010.
[14] Nagata, Y., Chu, K., Optimization of a fermentation medium using neural networks and genetic algorithms, Biotechnology Letters 25, 1837-1842, 2003.
[15] Rocha, M., Cortez, P., Neves, J., Evolution of neural networks for classification and regression, Neurocomputing 70, 2809-2816, 2007.
[16] Capparuccia, R., De Leone, R., Marchitto, E., Integrating support vector machines and neural networks, Neural Networks 20, 590-597, 2007.
[17] Beltran, N., Duarte-Mermoud, M., Bustos, M., Salah, S., Loyola, E., Pena-Neira, A., Jalocha, J., Feature extraction and classification of Chilean wines, Journal of Food Engineering 75, 1-10, 2006.
[18] Marini, F., Bucci, R., Magri, A., Artificial neural networks in chemometrics: History, examples and perspectives, Microchemical. Journal 88, 178-185, 2008.
[19] Penza, M., Cassano, G., Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks, Food Chemistry 86, 283-296, 2004.
[20] Perez-Magariño, S., Ortega-Heras, M., Gonzalez-San Jose, M., Boger, Z., Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines, Talanta 62, 983-990, 2004.
[21] Kruzlicova, D., Mocak, J., Balla, B., Petka, J., Farkova, M., Havel, J., Classification of Slovak white wines using artificial neural networks and discriminant techniques. Food Chemistry 112, 1046-1052, 2009.
[22] Urtubia, A., Hernández, G., Román, C., Prediction of problematic wine fermentations using artificial neural networks, Bioprocess and Biosystems Engineering 34, 1057-1065, 2011.
[23] Urtubia, A., Hernández, G., Roger, J.M., Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques, Journal of Biotechnology 159, 336-341, 2012.
[24] Scholkopf, B., Smola A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2002.
[25] Sánchez, D., Advanced support vector machines and kernel methods, Neurocomputing, 2003.
[26] Yu, H.Y., Niu, X.Y., Lin, H.J., Ying, Y.B., Li, B.B., Pan, X.X., A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines, Food Chemistry 113 (1), 291–296, 2009.
[27] Jurado, M., J., Alcázar, A., Palacios-Morillo, A., de Pablos, F., Classification of Spanish DO white wines according to their elemental profile by means of support vector machines, Food Chemistry 135 (3), 898-903, 2012.
[28] Fagerlund, S., Bird Species Recognition Using Support Vector Machines, EURASIP. Journal on Advances in Signal Processing, 1-8, 2007.
[29] Fernandez Pierna, J.A., Baeten, V., Michotte Renier, A., Cogdill, R.P., Dardenne, P., Combination of Support Vector Machines (SVM) and Near-Infrared (NIR) Imaging Spectroscopy for the Detection of Meat and Bone Meal (MBM) in Compound Feeds, Journal of Chemometrics 18, 341-349, 2004.
[30] Hernández, G., R. León, A. Urtubia, Application of Support Vector Machines to Predict Abnormal Wine Fermentations, Accepted for Presentation in ISUM 2015: International Supercomputing Conference in Mexico, March 9-13, 2015.
[2] Informe del Sector, La industria del vino en Chile, Elena Fabiano, 2009.
[3] Bisson, L., Butzke, C., Diagnosis and Rectification of Stuck and Sluggish Fermentations, American Journal of Enology and Viticulture 51 (2), 168-177, 2000.
[4] Blateyron, L., Sablayrolles, J.M., Stuck and Slow Fermentations in Enology: Statical Study of Causes and Effectiveness of Combined Additions of Oxygen and Diammonium Phosphate, Journal of Bioscience and Bioengineering 91 (2), 184-189, 2001.
[5] www.techniquesinhomewinemaking.com
[6] Pszczólkowski, P., Carriles, P., Cumsille, M., Maklouf, M., Reflexiones sobre la madurez de cosecha y las condiciones de vinificación, con relación a la Problemática de fermentaciones alcohólicas lentas y/o paralizante en Chile, Facultad de Agronomı́a, Pontificia Universidad Católica de Chile, 2001. 597, 2007.
[7] Beltran, G., Novo, M., Guillamón, J., Mas, A., Rozés, N., Effect of fermentation temperature and culture media on the yeast lipid composition and wine volatile compounds, International Journal of Food Microbiology 121, 169-177, 2008.
[8] Varela, C., Pizarro, F., Agosin, E., Biomass. Content Govern Fermentation Rate in Nitrogen-Deficient Wine Musts, Applied and Environmental Microbiology 70 (6), 3392-3400, 2004
[9] D’Amatto, D., Corbo, M., Del Nobile, M., Sinigaglia, M., Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system, International Journal of Food Science and Technology 41, 1152-1157, 2006.
[10] Engelbrecht A.P., Computational Intelligence: An Introduction, Second Edition, John Wiley & Sons, 2007.
[11] Bishop, C.M., Pattern Recognition and Machine Learning, Springer, 2006
[12] Bishop, C.M., Neural Networks for Pattern
Recognition, Oxford University Press, 1996.
[13] Abe, S., Support Vector Machines for Pattern Classification, Second Edition, Springer, 2010.
[14] Nagata, Y., Chu, K., Optimization of a fermentation medium using neural networks and genetic algorithms, Biotechnology Letters 25, 1837-1842, 2003.
[15] Rocha, M., Cortez, P., Neves, J., Evolution of neural networks for classification and regression, Neurocomputing 70, 2809-2816, 2007.
[16] Capparuccia, R., De Leone, R., Marchitto, E., Integrating support vector machines and neural networks, Neural Networks 20, 590-597, 2007.
[17] Beltran, N., Duarte-Mermoud, M., Bustos, M., Salah, S., Loyola, E., Pena-Neira, A., Jalocha, J., Feature extraction and classification of Chilean wines, Journal of Food Engineering 75, 1-10, 2006.
[18] Marini, F., Bucci, R., Magri, A., Artificial neural networks in chemometrics: History, examples and perspectives, Microchemical. Journal 88, 178-185, 2008.
[19] Penza, M., Cassano, G., Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks, Food Chemistry 86, 283-296, 2004.
[20] Perez-Magariño, S., Ortega-Heras, M., Gonzalez-San Jose, M., Boger, Z., Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines, Talanta 62, 983-990, 2004.
[21] Kruzlicova, D., Mocak, J., Balla, B., Petka, J., Farkova, M., Havel, J., Classification of Slovak white wines using artificial neural networks and discriminant techniques. Food Chemistry 112, 1046-1052, 2009.
[22] Urtubia, A., Hernández, G., Román, C., Prediction of problematic wine fermentations using artificial neural networks, Bioprocess and Biosystems Engineering 34, 1057-1065, 2011.
[23] Urtubia, A., Hernández, G., Roger, J.M., Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques, Journal of Biotechnology 159, 336-341, 2012.
[24] Scholkopf, B., Smola A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2002.
[25] Sánchez, D., Advanced support vector machines and kernel methods, Neurocomputing, 2003.
[26] Yu, H.Y., Niu, X.Y., Lin, H.J., Ying, Y.B., Li, B.B., Pan, X.X., A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines, Food Chemistry 113 (1), 291–296, 2009.
[27] Jurado, M., J., Alcázar, A., Palacios-Morillo, A., de Pablos, F., Classification of Spanish DO white wines according to their elemental profile by means of support vector machines, Food Chemistry 135 (3), 898-903, 2012.
[28] Fagerlund, S., Bird Species Recognition Using Support Vector Machines, EURASIP. Journal on Advances in Signal Processing, 1-8, 2007.
[29] Fernandez Pierna, J.A., Baeten, V., Michotte Renier, A., Cogdill, R.P., Dardenne, P., Combination of Support Vector Machines (SVM) and Near-Infrared (NIR) Imaging Spectroscopy for the Detection of Meat and Bone Meal (MBM) in Compound Feeds, Journal of Chemometrics 18, 341-349, 2004.
[30] Hernández, G., R. León, A. Urtubia, Application of Support Vector Machines to Predict Abnormal Wine Fermentations, Accepted for Presentation in ISUM 2015: International Supercomputing Conference in Mexico, March 9-13, 2015.
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Published
2015-04-01
How to Cite
Hernández, G., León, R., & Urtubia, A. (2015). Prediction of abnormal wine fermentations using computational intelligent techniques. Journal of Computer Science and Technology, 15(01), p. 1–7. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/522
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Invited Articles
