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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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
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