Abstract
Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.
Highlights
Fermentation MonitoringFew studies deal with the topic of monitoring the fermentation state of dough pieces
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By fitting superquadrics to the segmented data, the type of the object is detected based on the shape
Summary
Few studies deal with the topic of monitoring the fermentation state of dough pieces. By means of that method, the influence of different process parameters such as temperature and humidity can be observed Both of these proposed methods have the disadvantage of being restricted to only one dough sample at a time and being inappropriate for the control of a whole batch of fermenting bread without human supervision. Ivorra et al propose an optical method of continuous fermentation state monitoring using a 3D vision system composed of a line laser and a camera and installed inside a fermentation chamber [4] By means of this method, the height and transversal area of only one dough sample can be measured, and the fermentation state controlled. The monitoring of one sample and even of one layer representatively is not sufficient because parameters like temperature and humidity can vary at different areas within the fermentation environment, leading to a different fermentation mellowness
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