Detection and prediction of foam evolution during the bottling of noncarbonated beverages using artificial neural networks

[Display omitted] •Noninvasive and robust foam detection by means of convolutional neural network.•Foaming behavior for the bottling of various noncarbonated beverages is examined.•Material properties and processing parameters serve as prediction input.•Recurrent neural network is capable to predict time resolved foam development.•Transferable methods to precisely detect and predict foam in bottling processes. Foams are relevant in many food products and food production processes. During the bottling of beverages, foam can severely impair the process as overflowing foam causes underfilled bottles and poses a high contamination risk. Consequently, the filling speed is limited by the foaming properties of the beverage. Several filling experiments with different juices were executed to improve the understanding of the influence of flow and material properties on the foaming behavior. The foam evolution in time was evaluated using a convolutional neural network (CNN) to detect visible foam regions in video recordings of the experiments. The use of image data has the advantage that its acquisition is noninvasive and no supplementary sensor hardware with direct product contact has to be installed into the process. The CNN was able to detect image regions that contain foam with errors of only a few millimeters. The detection worked for different juice colors, regardless of the lighting and noise in the image. Based on the filling speed, viscosity, surface tension, and density, the foaming behavior during the filling process was modeled with a recurrent neural network (RNN). The RNN was able to predict the foam height evolution with average errors below five millimeters. Both models are easily transferable to new use cases by retraining the networks.