Abstract
The apparent simplicity of food processes often hides complex systems, where physical, chemical and living organisms’ processes co-exist and interact to create the final product. Data can be plagued by uncertainty; heterogeneity of available information is likely; qualitative and quantitative data may also coexist in the same process, from expert perception of food quality to nano-properties of ingredients. In order to obtain reliable models, it then becomes necessary to acquire additional information from external sources. Experts of a domain can provide invaluable insight in products and processes, but this precious knowledge is often available only in the form of intuition and implicit expertise. Including expert insight in a model can be tackled by having humans interacting with a machine learning process, through visualization or via specialists in encoding implicit domain knowledge. In this chapter, three selected case studies in food science portray different success stories of combining machine learning and expert interaction. We show that expert knowledge can be integrated at different stages of the modelling process, either online or offline, to initialize, enrich or guide this process.
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Notes
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eRosa European project, http://www.erosa.aginfra.eu/.
- 2.
COST Action CA15118 FoodMC, http://www.inra.fr/foodmc.
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References
Baudrit, C., Sicard, M., Wuillemin, P.H., Perrot, N.: Towards a global modelling of the Camembert-type cheese ripening process by coupling heterogeneous knowledge with dynamic Bayesian networks. J. Food Eng. 98(3), 283–293 (2010)
Baudrit, C., Perrot, N., Brousset, J.M., Abbal, P., Guillemin, H., Perret, B., Goulet, E., Guerin, L., Barbeau, G., Picque, D.: A probabilistic graphical model for describing the grape berry maturity. Comput. Electron. Agric. 118, 124–135 (2015)
Chabin, T., Barnabé, M., Boukhelifa, N., Fonseca, F., Tonda, A., Velly, H., Perrot, N., Lutton, E.: Interactive evolutionary modelling of living complex food systems: freeze-drying of lactic acid bacteria. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 267–268. ACM (2017)
Chabin, T., Barnabé, M., Boukhelifa, N., Tonda, A., Velly, H., Lemaitre, B., Perrot, N., Lutton, E.: LIDeOGraM: an interactive evolutionary modelling tool. In: Proceedings of the International Conference on Artificial Evolution (Evolution Artificielle) (2017)
Champagne, C., Gardner, N., Brochu, E., Beaulieu, Y.: Freeze-drying of lactic acid bacteria. a review. Can. Inst. Food Sci. Technol. J. (Journal de l’Institut canadien de science et technologie alimentaire) (1991)
Cheng, J., Bell, D.A., Liu, W.: An algorithm for Bayesian belief network construction from data. In: proceedings of AI & STAT97, pp. 83–90 (1997)
Coulon-Leroy, C., Charnomordic, B., Rioux, D., Thiollet-Scholtus, M., Guillaume, S.: Prediction of vine vigor and precocity using data and knowledge-based fuzzy inference systems. Journal International des Sciences de la Vigne et du Vin 46(3), 185–205 (2012)
Cros, M.J., Duru, M., Garcia, F., Martin-Clouaire, R.: A biophysical dairy farm model to evaluate rotational grazing management strategies. Agronomie 23(2), 105–122 (2003)
Dai, Z.W., Vivin, P., Génard, M.: Modelling the effects of leaf-to-fruit ratio on dry and fresh mass accumulation in ripening grape berries. In: VIII International Symposium on Modelling in Fruit Research and Orchard Management, vol. 803, pp. 283–292 (2007)
Dai, Z.W., Vivin, P., Robert, T., Milin, S., Li, S.H., Génard, M.: Model-based analysis of sugar accumulation in response to source-sink ratio and water supply in grape (vitis vinifera) berries. Funct. Plant Biol. 36(6), 527–540 (2009)
De Jong, K.A.: Evolutionary Computation: A Unified Approach. MIT Press, Cambridge (2006)
Druzdzel, M.J.: Smile: Structural modeling, inference, and learning engine and genie: a development environment for graphical decision-theoretic models. In: AAAI/IAAI, pp. 902–903 (1999)
Fadock, M., Brown, R.B., Reynolds, A.G.: Visible-near infrared reflectance spectroscopy for nondestructive analysis of red winegrapes. Am. J. Enology Vitic. (2015)
Ghoniem, M., Fekete, J.D., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: IEEE Symposium on Information Visualization. IEEE
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)
Krause, J., Perer, A., Bertini, E.: INFUSE: interactive feature selection for predictive modeling of high dimensional data. IEEE Trans. Vis. Comput. Graph. 20(12), 1614–1623 (2014)
Lutton, E., Tonda, A., Boukhelifa, N., Perrot, N.: Complex systems in food science: human factor issues. In: Van Impe, J. (ed.) FoodSIM. EUROSIS-ETI (2016)
Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley (2002)
Passot, S., Fonseca, F., Cenard, S., Douania, I., Trelea, I.C.: Quality degradation of lactic acid bacteria during the freeze drying process: experimental study and mathematical modelling (2011)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Burlington (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Michel, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Perrot, N., Baudrit, C., Brousset, J.M., Abbal, P., Guillemin, H., Perret, B., Goulet, E., Guerin, L., Barbeau, G., Picque, D.: A decision support system coupling fuzzy logic and probabilistic graphical approaches for the agri-food industry: prediction of grape berry maturity. PLOS ONE 10(7), e0134373 (2015)
Raynal, M., Debord, C., Guittard, S., Vergnes, M.: Epicure, a geographic information decision support system risk assessment of downy and powdery mildew epidemics in Bordeaux vineyards (2010). https://doi.org/10.1007/978-1-4302-3031-1
Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009)
Sicard, M., Baudrit, C., Leclerc-Perlat, M., Wuillemin, P., Perrot, N.: Expert knowledge integration to model complex food processes. Application on the camembert cheese ripening process. Expert Syst. Appl. 38(9), 11804–11812 (2011)
Steel, R.G.D., James, H.: Principles and Procedures of Statistics: With Special Reference to the Biological Sciences. McGraw-Hill, New York (1960)
Turkay, C., Slingsby, A., Lahtinen, K., Butt, S., Dykes, J.: Supporting theoretically-grounded model building in the social sciences through interactive visualisation. Neurocomputing (2017)
Velly, H., Fonseca, F., Passot, S., Delacroix-Buchet, A., Bouix, M.: Cell growth and resistance of Lactococcus lactis subsp. lactis TOMSC161 following freezing, drying and freeze-dried storage are differentially affected by fermentation conditions. J. Appl. Microbiol. 117(3), 729–740 (2014)
Velly, H., Bouix, M., Passot, S., Penicaud, C., Beinsteiner, H., Ghorbal, S., Lieben, P., Fonseca, F.: Cyclopropanation of unsaturated fatty acids and membrane rigidification improve the freeze-drying resistance of Lactococcus lactis subsp. lactis TOMSC161. Appl. Microbiol. Biotechnol. 99(2), 907–918 (2015)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
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Tonda, A. et al. (2018). Interactive Machine Learning for Applications in Food Science. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_22
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