Abstract
Standardization and the assessment of the quality of the final product is fundamental in food industry. Coffee particle properties are monitored continuously during coffee beans grinding. Operators control the grinders in order to keep coffee particle granulometry within specific thresholds. In this work, a general regression neural network approach is used to learn to control two grinders used for coffee production at LAVAZZA factory, obtaining average control error of the order of a few μm. The results appear promising for the future development of an automatic decision support system.
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References
Leslie, R.B., Oliveira, J.C., Medin, A.G.: Food Forum: a Research Forum for an Innovative and Globally Competitive European Food Industry. Food Research International 33(3-4), 295–297 (2000)
Linko, S.: Expert Systems - what can they do for the food industry? Trends in Food Science & Technology 9(1), 3–12 (1998)
Ilyukhin, S.V., Haley, T.A., Singh, R.K.: A survey of automation practices in the food industry. Food Control 12(5), 285–296 (2001)
Banga, J.R., Bsa-Canto, E., Moles, C.J., Alonso, A.A.: Improving food processing using modern optimization methods. Trends in Food Science & Technology 14(4), 131–144 (2003)
Bhuvaneswari, N.S., Uma, G., Rangaswamy, T.R.: Adaptive and optimal control of a non-linear process using intelligent controllers. Applied Soft Computing 9(1), 182–190 (2009)
Torrecilla, J.S., Otero, L., Sanz, P.D.: Artificial neural networks: a promising tool to design and optimize high-pressure food processes. Journal of Food Engineering 69(3), 299–306 (2005)
Guyer, D., Yang, X.: Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Computers and Electronics in Agriculture 29(3), 179–194 (2000)
Specht, D.F.: A General Regression Neural Network. IEEE Transactions on Neural Networks 2(6), 568–576 (1991)
LAVAZZA S.p.A., Strada Settimo 410, Torino
Tominaga, O., Ito, F., Hanai, T., Honda, H., Kobayashi, T.: Sensory Modeling of Coffee with a Fuzzy Neural Network. Journal of Food Science 67(1), 363–368 (2001)
Hernandez, J.A., Heyd, B., Trystram, B.: Prediction of brightness and surface area kinetics during coffee roasting. Journal of Food Engineering 89(2), 156–163 (2008)
Yip, D.H.F., Yu, W.W.H.: Classification of Coffee using Artificial Neural Network. In: IEEE International Conference on Evolutionary Computation, pp. 655–658. IEEE Press, Nagoya (1996)
Singh, S., Hines, E.L., Gardner, J.W.: Fuzzy neural computing of coffee and tainted water data from an electronic nose. Sens. Actuators B 30(3), 190–195 (1996)
Sharma, A.: Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification. Journal of Hydrology 239(1-4), 232–239 (2000)
Mesin, L., Orione, F., Taormina, R., Pasero, E.: A feature selection method for air quality forecasting. In: 20th IEEE International Conference on Artificial Neural Networks, pp. 489–494. IEEE Press, Thessaloniki (2010)
May, R.J., Maier, H.R., Dandy, G.C., Gayani Fernando, T.M.K.: Non-linear variable selection for artificial neural networks using partial mutual information. Envir. Mod. and Soft. 23, 1312–1326 (2008)
Parzen, E.: On Estimation of a Probability Density Function and Mode. Annals of Math. Statistics 33, 1065–1076 (1962)
Costa, M., Moniaci, W., Pasero, E.: INFO: an artificial neural system to forecast ice formation on the road. In: IEEE International Symposium on Computational Intelligence for Measurement Systems and Applications, pp. 216–221 (2003)
MATLAB, The language of technical computing, http://www.mathworks.com
Mesin, L., Alberto, D., Pasero, E., Cabilli, A.: Control of coffee grinding with Artificial Neural Networks. In: 22nd IEEE International Conference on Artificial Neural Networks. IEEE Press, Brisbane (2012)
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Mesin, L., Alberto, D., Pasero, E. (2013). Control of Coffee Grinding with General Regression Neural Networks. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_15
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DOI: https://doi.org/10.1007/978-3-642-35467-0_15
Publisher Name: Springer, Berlin, Heidelberg
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