Abstract
Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (color, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of french Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modeling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aldarf, M., Fourcade, F., Amrane, A., Prigent, Y.: Substrate and metabolite diffusion within model medium for soft cheese in relation to growth of Penicillium camembertii. J. Ind. Microbiol. Biotechnol. 33, 685–692 (2006)
Bongard, J., Lipson, H.: Active Coevolutionary Learning of Deterministic Finite Automata. Journal of Machine Learning Research 6, 1651–1678 (2005)
Barile, D., Coisson, J.D., Arlorio, M., Rinaldi, M.: Identification of production area of Ossolano Italian cheese with chemometric complex aproach. Food Control 17(3), 197–206 (2006)
Baudrit, C., Wuillemin, P.-H., Sicard, M., Perrot, N.: A Dynamic Bayesian Metwork to represent a ripening process of a solf mould cheese (submitted)
Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar IFS + Parisian Genetic Programming = Efficient IFS Inverse Problem Solving. Genetic Programming and Evolvable Machines Journal 1(4), 339–361 (2000)
Davis, L.: Adapting Operators Probabilities in Genetic Algorithms. In: 3rd ICGA conference, pp. 61–69. Morgan Kaufmann, San Francisco (1989)
Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the third Conference on Genetic Algorithms, pp. 42–50 (1989)
Ellis, D.I., Broadhurst, D., Goodacre, R.: Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning. Analytica Chimica Acta 514(2), 193–201 (2004)
Gripon, A.: Mould-ripened cheeses. In: Fox, P.F. (ed.) Cheese: Chemistry, Physics and Microbiology, vol. 2, pp. 111–136. Chapman & Hall, London (1993)
Silva, S.: GPLAB A Genetic Programming Toolbox for MATLAB, http://gplab.sourceforge.net/
Ioannou, I., Mauris, G., Trystram, G., Perrot, N.: Back-propagation of imprecision in a cheese ripening fuzzy model based on human sensory evaluations. Fuzzy Sets And Systems 157, 1179–1187 (2006)
Ioannou, I., Perrot, N., Curt, C., Mauris, G., Trystram, G.: Development of a control system using the fuzzy set theory applied to a browning process - a fuzzy symbolic approach for the measurement of product browning: development of a diagnosis model - part I. Journal Of Food Engineering 64, 497–506 (2004)
Ioannou, I., Perrot, N., Mauris, G., Trystram, G.: Development of a control system using the fuzzy set theory applied to a browning process - towards a control system of the browning process combining a diagnosis model and a decision model - part II. J.Food Eng. (64), 507–514 (2004)
Jimenez-Marquez, S.A., Thibault, J., Lacroix, C.: Prediction of moisture in cheese of commercial production using neural networks. Int. Dairy J. 15, 1156–1174 (2005)
Leclercq-Perlat, M.N., Picque, D., Riahi, H., Corrieu, G.: Microbiological and Biochemical Aspects of Camembert-type Cheeses Depend on Atmospheric Composition in the Ripening Chamber. J. Dairy Sci. (89), 3260–3273 (2006)
Leclercq-Perlat, M.N., Buono, F., Lambert, D., Latrille, E., Spinnler, H.E., Corrieu, G.: Controlled production of Camembert-type cheeses. J. Dairy Res. (71), 346–354 (2004)
Ni, H.X., Gunasekaran, S.: Food quality prediction with neural networks. Food Technology 52, 60–65 (1998)
Ochoa, G., Lutton, E., Burke, E.: Cooperative Royal Road Functions. In: Evolution Artificielle, Tours, France, October 29-31 (2007)
Pinaud, B., Baudrit, C., Sicard, M., Wuillemin, P.-H., Perrot, N.: Validation et enrichissement interactifs d’un apprentissage automatique des paramètres d’un réseau bayésien dynamique appliqué aux procédés alimentaires, Journées Francophone sur les Réseaux Bayésiens, Lyon, France (2008)
Riahi, M.H., Trelea, I.C., Leclercq-Perlat, M.N., Picque, D., Corrieu, G.: Model for changes in weight and dry matter during the ripening of a smear soft cheese under controlled temperature and relative humidity. International Dairy Journal 17, 946–953 (2007)
Tarantilis, C.D., Kiranoudis, C.T.: Operational research and food logistics. Journal of Food Engineering 70(3), 253–255 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barrière, O., Lutton, E., Baudrit, C., Sicard, M., Pinaud, B., Perrot, N. (2008). Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_85
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)