Abstract
The early detection of problematic fermentations is one of the main problems that appear in winemaking processes, due to the significant impacts in wine quality and utility. This situation is specially important in Chile because is one of the top ten wine production countries. In last years, different methods coming from Multivariate Statistics and Computational Intelligence have been applied to solve this problem. In this work we detect normal and problematic (sluggish and stuck) wine fermentations applying the support vector machine method with three different kernels: linear, polynomial and radial basis function. For the training algorithm, we use the same database of 22 wine fermentation studied in [1, 2] that contains approximately 22,000 points, considering the main chemical variables measured in this kind of processes: total sugar, alcoholic degree and density. Our main result establishes that the SVM method with third degree polynomial and radial basis kernels predict correctly 88 and 85 % respectively. The fermentation behavior results have been obtained for a 80–20 % training/testing percentage configuration and a time cutoff of 48 h.
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References
Urtubia, A., Hernández, G., Román, C.: Prediction of problematic wine fermentations using artificial neural networks. Bioprocess Biosyst. Eng. 34, 1057–1065 (2011)
Urtubia, A., Hernández, G., Roger, J.M.: Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques. J. Biotechnol. 159, 336–341 (2012)
Executive Report Chilean Wine Production, Servicio Agrí cola y Ganadero de Chile, 2011–2014
Informe del Sector, La industria del vino en Chile, Elena Fabiano, 2009
Bisson, L., Butzke, C.: Diagnosis and rectification of stuck and sluggish fermentations. Am. J. Enol. Vitic. 51(2), 168–177 (2000)
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. J. Biosci. Bioeng. 91(2), 184–189 (2001)
Pszczólkowski, P., Carriles, P., Cumsille, M., Maklouf, M.: Reflexiones sobre la madurez de cosecha y las condiciones de vinificaci ón, con relación a la Problemática de fermentaciones alcohó licas lentas y/o paralizante en Chile. Pontificia Universidad Católica de Chile, Facultad de Agronomía (2001)
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. Int. J. Food Microbiol. 121, 169–177 (2008)
Varela, C., Pizarro, F., Agosin, E.: Biomass Content govern fermentation rate in nitrogen-deficient wine musts. Appl. Environ. Microbiol. 70(6), 3392–3400 (2004)
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. Int. J. Food Sci. Technol. 41, 1152–1157 (2006)
Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley, Chichester (2007)
Bishop, C.M.: Pattern Rcognition and Machine Learning. Springer, New York (2006)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
Abe, S.: Support Vector Machines for Pattern Classification, 2nd edn. Springer, London (2010)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2008)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)
Sánchez, D.: Advanced support vector machines and kernel methods. Neurocomputing 55, 5–20 (2003)
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 Chem. 113(1), 291–296 (2009)
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 Chem. 135(3), 898–903 (2012)
Fagerlund, S.: Bird species recognition using support vector machines. EURASIP J. Adv. Signal Process. 1, 1–8 (2007)
Pierna, Fernandez, Pierna, J.A., Baeten, V., Renier, A.M., 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. J. Chemom. 18, 341–349 (2004)
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Research supported by Grants: FONDECYT 1120679 and Basal Project FB 0821.
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Hernández, G., León, R. & Urtubia, A. Detection of abnormal processes of wine fermentation by support vector machines. Cluster Comput 19, 1219–1225 (2016). https://doi.org/10.1007/s10586-016-0594-5
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DOI: https://doi.org/10.1007/s10586-016-0594-5