Color (gray values) estimation during roasting coffee
Special Symposium - Innovations in Food Technology (LMC Congress)
Innovations in Food Technology - Poster Session (LMC/Food - P1)
Keywords: roasting coffee, color (gray values) and neural networks
J. A. Hernández a, B. Heyd b, G. Trystram b
a Research Center of Engineering and Applied Sciences (CIICAp), Autonomous University of Morelos State (UAEM); Av. Universidad No. 1001 Col. Chamilpa, C.P. 62210, Cuernavaca, Mor., Mexico.
b Joint Research Unit Food Process Engineering (Cemagref, ENSIA, INAPG, INRA) ENSIA, 1 avenue des Olympiades, 91744 Massy Cedex France.
ABSTRACT
In order to optimize the quality of roasted coffee, it is important to measure and to control a large number of factors during the process. Image analysis allows on-line measurement of essential values such as the color and the swell of the beans. However this technical of analysis of image is difficult to employee in the industries of roasting coffee. By this raison, it is necessary to developer a technical to color estimate. Consequently, this work propose a mathematical model based in artificial neural network for estimate the color (gray values) during roasting coffee. The mathematical model consider as input variable the time and temperature of the beans. A feedforward networks with one hidden layer is used to predict the gray values. For the network, the Levenberg-Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer-function and the linear transfer-function were used. The best fitting training data set was obtained with three neurons in the hidden layer, which made it possible to predict gray values with accuracy at least as good as that of the experimental error, over the whole experimental range. On the validation data set, simulations and experimental data test were in good agreement (R2>0.987). The developed model can be used for a reliable on-line state estimation and control of roasting coffee.
See the full pdf manuscript of the abstract.
Presented Wednesday 19, 13:30 to 15:00, in session Innovations in Food Technology - Poster Session (LMC/Food - P1).
