Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed

• Coffee color during roasting in a spouted bed was assessed by an ANFIS model.
• The input variables were color parameters obtained from digital images.
• For monitoring in the process line, we constructed an image-capture device.
• The model's goodness of fit was assessed by R2, RMSE and SRIC.
• The model is proper for monitoring in the process line.

An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L*), browning index (BI) and the distance to a defined standard (ΔE). An image-capture device was designed to monitor color variations in the L*a*b* space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500 °C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error < 0.002 and a modified Schwarz–Rissanen information criterion < 0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process.