Prediction of problematic wine fermentations using artificial neural networks

Bioprocess and Biosystems Engineering

Volumn 34, Issue 9, 2011, Pages 1057-1065

  Román, R César ^a   Hernández, O Gonzalo ^a,b   Urtubia, U Alejandra ^c,d  

^a UNIVERSIDAD DE VALPARAÍSO   (Chile)

^b UNIVERSITY OF CHILE   (Chile)

^c DEPARTAMENTO DE FÍSICA   (Chile)

^d Centro Regional de Estudios en Alimentos Saludables Valparaiso   (Chile)

Author keywords

Artificial neural networks; Fermentation; Pattern recognition; Wine

Indexed keywords

BIOPROCESSES; CABERNET-SAUVIGNON; INDUSTRIAL FERMENTATION; NONLINEAR PATTERN; PREDICTOR VARIABLES; WINE FERMENTATION; WINE PRODUCTION;

BREWERIES; FERMENTATION; FORECASTING; GLYCEROL; NITROGEN COMPOUNDS; ORGANIC ACIDS; PATTERN RECOGNITION; SUGARS; WINE;

NEURAL NETWORKS;

ACETIC ACID; ALANINE; ALCOHOL; AMMONIA; AMMONIUM CHLORIDE; ARGININE; ASPARTIC ACID; CARBOXYLIC ACID; CITRIC ACID; CYSTEINE; GLUTAMIC ACID; GLUTAMINE; GLYCEROL; GLYCINE; HISTIDINE; LACTIC ACID; LYSINE; MALIC ACID; METHIONINE; NITROGEN DERIVATIVE; PROLINE; SERINE; SUCCINIC ACID; SUGAR; TARTARIC ACID; THREONINE; TRYPTOPHAN; TYROSINE; VALINE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CABERNET SAUVIGNON; CHEMICAL COMPOSITION; COMPUTER MODEL; COMPUTER PREDICTION; CONTROLLED STUDY; FERMENTATION TECHNIQUE; GRAPE; MATHEMATICAL COMPUTING; PREDICTOR VARIABLE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROCESS TECHNOLOGY; REACTION TIME; WINE;

ALCOHOLS; ALGORITHMS; AMINO ACIDS; FERMENTATION; FOOD INDUSTRY; GLYCEROL; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NITROGEN; ORGANIC CHEMICALS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SOFTWARE; TIME FACTORS; WINE;

EID: 81755172025     PISSN: 16157591     EISSN: 16157605     Source Type: Journal    
DOI: 10.1007/s00449-011-0557-4     Document Type: Article

References (30)

1. L Blateyron JM Sablayrolles 2001 Stuck and slow fermentations in enology: statistical study of causes and effectiveness of combined additions of oxygen and diammonium phosphate J Biosci Bioeng 91 184 189 10.1263/jbb.91.184 1:CAS:528:DC%2BD3MXis1Kns7g%3D (Pubitemid 32226030)
2. L Bisson C Butzke 2000 Diagnosis and rectification of stuck and sluggish fermentations Am J Enol Viticult 51 168 177 1:CAS:528:DC%2BD3cXmtlSmsLc%3D (Pubitemid 30661151)
3. Pszczólkowski P, Carriles P, Cumsille M, Maklouf M (2001) 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 Agronomía, Pontificia Universidad Católica de Chile, Chile
4. J Huang H Nanami A Kanda H Shimizu S Shioya 2002 Classification of fermentation performance by multivariate analysis based on Mean Hypothesis Testing J Biosci Bioeng 94 251 257 1:CAS:528:DC%2BD38XptlaktrY%3D (Pubitemid 35192073)
5. JM Lee Ch Yoo IB Lee 2003 On-line batch process monitoring using a consecutively updated multiway principal component analysis model Comput Chem Eng 27 1903 1912 10.1016/S0098-1354(03)00151-0 1:CAS:528:DC%2BD3sXotlygsLk%3D (Pubitemid 37408621)
6. L Chiang R Leardi R Pell M Seasholtz 2006 Industrial experiences with multivariate statistical analysis of batch process data Chemom Intell Lab Syst 81 109 119 10.1016/j.chemolab.2005.10.006 1:CAS:528:DC%2BD28XivVCmtr4%3D
7. A Ferreira J Lopes J Menezes 2007 Study of the application of multiway multivariate techniques to model data from an industrial fermentation process Anal Chim Acta 595 120 127 10.1016/j.aca.2007.05.007 1:CAS:528: DC%2BD2sXntF2ku7o%3D (Pubitemid 46990313)
8. A Faggian P Facco F Doplicher F Bezzo M Barolo 2009 Multivariate statistical real-time monitoring of an industrial fed-batch process for the production of specialty chemicals Chem Eng Res Des 87 325 334 10.1016/j.cherd.2008.08.019 1:CAS:528:DC%2BD1MXjs1Wlsrk%3D
9. C Bevin R Dambergs A Fergusson D Cozzolino 2008 Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis Anal Chim Acta 621 19 23 10.1016/j.aca.2007.10.042 1:CAS:528: DC%2BD1cXns1artb0%3D (Pubitemid 351902193)
10. D Cozzolino M Parker R Dambergs M Herderich M Gishen 2006 Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale Biotechnol Bioeng 95 1101 1107 10.1002/bit.21067 1:CAS:528:DC%2BD28Xht1Grt7bE (Pubitemid 44901325)
11. A Urtubia J Pérez-Correa A Soto P Pszczólkowski 2007 Using data mining techniques to predict industrial wine problem fermentations Food Control 18 1512 1517 10.1016/j.foodcont.2006.09.010 1:CAS:528: DC%2BD2sXnsFKksb4%3D (Pubitemid 47022904)
12. A Urtubia M Emparan S Almonacid M Pinto M Valdenegro 2010 Application of MPCA and MPLS on industrial batch bioprocesses J Biotechnol 150 310 10.1016/j.jbiotec.2010.09.284
13. Urtubia A, Roger JM (2011). Predictive power of LDA to discriminate abnormal wine fermentations. J Chemom. http://onlinelibrary.wiley.com/doi/10. 1002/cem.1362/pdf
14. U Thissen W Melsen L Buydens 2001 Nonlinear process monitoring using bottle-neck neural networks Anal Chim Acta 446 369 381 10.1016/S0003-2670(01) 01266-1
15. F Marini R Bucci A Magri 2008 Artificial neural networks in chemometrics: history, examples and perspectives Microchem J 88 178 185 10.1016/j.microc. 2007.11.008 1:CAS:528:DC%2BD1cXitFaksb8%3D
16. J Lopes J Menezes 2004 Multivariate monitoring of fermentation processes with non-linear modeling methods Anal Chim Acta 515 101 108 10.1016/j.aca.2004. 01.060 1:CAS:528:DC%2BD2cXkvFyrtLY%3D (Pubitemid 38798211)
17. Jain A, Mao J, Mohiuddin K (1996) Artificial neural networks: a tutorial. Computer 9:31-44 (Pubitemid 126515068)
18. Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford
19. Ripley B (2008) Pattern recognition and neural networks. Cambridge University Press, Cambridge
20. K Hornik M Stinchcombe H White 1989 Multilayer feedforward networks are universal approximators Neural Netw 2 359 366 10.1016/0893-6080(89)90020-8 (Pubitemid 20609008)
21. K Hornik 1991 Approximation capabilities of multilayer feedforward networks Neural Netw 4 251 257 10.1016/0893-6080(91)90009-T
22. Rumelheart D, Durbin R, Golden R, Chauvin Y (1995) Backpropagation: the basic theory. In: Chauvin Y, Rumelheart D (eds) Backpropagation: theory architectures and applications. Lawrence Erlbaum, Hillsdale
23. N Beltran M Duarte-Mermoud M Bustos S Salah E Loyola A Pena-Neira J Jalocha 2006 Feature extraction and classification of Chilean wines J Food Eng 75 1 10 10.1016/j.jfoodeng.2005.03.045 1:CAS:528:DC%2BD28XnsFSlsb4%3D (Pubitemid 43190098)
24. M Penza G Cassano 2004 Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks Food Chem 86 283 296 10.1016/j.foodchem.2003.09.027 1:CAS:528:DC%2BD2cXhs1Cqsr0%3D (Pubitemid 38283584)
25. Perez-Magariño S, Ortega-Heras M, Gonzalez-San Jose M, Boger Z (2004) Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines. Talanta 62:983-990
26. D Kruzlicova J Mocak B Balla J Petka M Farkova J Havel 2009 Classification of Slovak white wines using artificial neural networks and discriminant techniques Food Chem 112 1046 1052 10.1016/j.foodchem.2008.06.047 1:CAS:528:DC%2BD1cXhtVGjsLvI
27. Urtubia A (2006) Monitoring of wine fermentations using MIR spectroscopy and data mining techniques. PhD Thesis of Engineering Science. Pontificia Universidad Católica de Chile, Chile
28. S Dreiseitl L Ohno-Machado 2002 Logistic regression and artificial neural network classification models: a methodology review J Biomed Inform 35 352 359 10.1016/S1532-0464(03)00034-0 (Pubitemid 36951935)
29. King S (2003) Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality. In: Proceedings of the 13th Central Hardwood Forest Conference, pp 349-358
30. P Palmer M Montaño 1999 ¿Qué son las Redes Neuronales Artificiales? Aplicaciones realizadas en el ámbito de las adicciones Adicciones 11 3 243 255 (Pubitemid 29503314)