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Open AccessArticle

Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning

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Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
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BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4266; https://doi.org/10.3390/app9204266 (registering DOI)
Received: 24 July 2019 / Revised: 30 September 2019 / Accepted: 8 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
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. View Full-Text
Keywords: fermentation monitoring; quality inspection; process automation; deep learning; superellipsoid model fitting; optical sensor fermentation monitoring; quality inspection; process automation; deep learning; superellipsoid model fitting; optical sensor
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Giefer, L.A.; Lütjen, M.; Rohde, A.-K.; Freitag, M. Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning. Appl. Sci. 2019, 9, 4266.

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