Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Vision and Imaging · Glycosylation and Glycoproteins Research · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany
Funding
Bundesministerium für Wirtschaft und Energie
References (34)
Show source-provided references
1. Zentralverband des Deutschen Bäckerhandwerks e. V (2018, December 13). Entwicklung Beschäftigte & Betriebe: Beschäftigtenzahlen im Bäckerhandwerk. Available online: https://www.baeckerhandwerk.de/baeckerhandwerk/zahlen-fakten/entwicklung-beschaeftigte-betriebe/.
2. HuffPost Deutschland (2019, March 15). Konkurrenz für McDonald’s und Burger King: Das ist der überraschende Spitzenreiter bei Schnell-Restaurants. Available online: https://www.huffingtonpost.de/2015/05/04/schnell-restaurant-spitzenreiter_n_7205306.html.
3. Wing, D., and Scott, A. (1999). The Bread Builders: Hearth Loaves and Masonry Ovens, Chelsea Green Publishing.
4. Continuous monitoring of bread dough fermentation using a 3D vision Structured Light technique10.1016/j.jfoodeng.2013.12.031 ↗
5. Benz, F. (1986). Backwarenherstellung. Fachkunde für Bäcker, 6, Dr Schroedel.
6. A Survey on Computer Vision Technology for Food Quality Evaluation
7. Lösche, K., and Reichenbach, A. (2015). Aerosole & Bio-Additive in der Bäckerei: Innovative Wege zur Verbesserung von Qualität, Haltbarkeit und Energieeffizienz. Bäckereitechnologie: Forschung und Innovationen, f2m Food multimedia Gmbh.
8. BIBA—Bremer Institut für Produktion und Logistik GmbH, ttz Bremerhaven (Vorrichtung und Verfahren zur Prozessüberwachung mehrerer Teiglinge in einer Prozesskammer (Pending), 2018). Vorrichtung und Verfahren zur Prozessüberwachung mehrerer Teiglinge in einer Prozesskammer (Pending), 102018124378.2.
9. Monitoring Dough Fermentation Using Acoustic Waves10.1205/096030803322437983 ↗
10. A new acoustic technique to monitor bread dough during the fermentation phase10.1016/j.jfoodeng.2009.02.005 ↗
11. Continuous monitoring of dough fermentation and bread baking by magnetic resonance microscopy10.1016/j.mri.2010.10.010 ↗
12. Monitoring the dynamic density of dough during fermentation using digital imaging method10.1016/j.jfoodeng.2011.06.010 ↗
13. Scholz-Reiter, B., and Thamer, H. (2011). Multi-View Sensor Fusion of Synthetic ToF Images for Object Recognition of Universal Logistic Goods, GfaI.
14. Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21–26). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.
15. He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22–29). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision, ICCV, Venice, Italy.10.1109/ICCV.2017.322 ↗
16. Fang, X.S. (1994, January 21–23). The extruded generalized cylinder: A deformable model for object recovery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.
17. Recognition-by-components: A theory of human image understanding10.1037/0033-295X.94.2.115 ↗
18. Superquadrics and angle-preserving transformations10.1109/MCG.1981.1673799 ↗
19. Recovery of parametric models from range images: The case for superquadrics with global deformations10.1109/34.44401 ↗
20. 3D-Objekterkennung von heterogenen Stückgütern—Flexible Automatisierung basierend auf 3D-Bildverarbeitung
21. West, J., Ventura, D., and Warnick, S. (2007). Spring Research Presentation: A Theoretical Foundation for Inductive Transfer, Brigham Young University, College of Physical and Mathematical Sciences.
22. Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Dollár, P. (2019, October 11). Microsoft COCO: Common Objects in Context. Available online: http://arxiv.org/pdf/1405.0312v3.
23. Quaternion and its application in rotation using sets of regions
24. Kak, A.C., Tsuji, S., and Eklundh, J.-O. (1990, January 4–7). From uncertainty to visual exploration. Proceedings of the Third International Conference on Computer Vision, Osaka, Japan.
25. Bardinet, E., Ayache, N., and Cohen, L.D. (1994, January 24–25). Fitting of iso-surfaces using superquadrics and free-form deformations. Proceedings of the IEEE Workshop on Biomedical Image Analysis, Seattle, WA, USA.
26. Duncan, K., Sarkar, S., Alqasemi, R., and Dubey, R. (2013, January 6–10). Multi-scale superquadric fitting for efficient shape and pose recovery of unknown objects. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.10.1109/ICRA.2013.6631176 ↗
27. Biegelbauer, G., and Vincze, M. (2007, January 10–14). Efficient 3D Object Detection by Fitting Superquadrics to Range Image Data for Robot’s Object Manipulation. Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Rome, Italy.10.1109/ROBOT.2007.363129 ↗
28. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming10.1007/s10107-004-0559-y ↗
29. Jaklič, A., Leonardis, A., and Solina, F. (2000). Superquadrics and their geometric properties. Segmentation and Recovery of Superquadrics, Springer.10.1007/978-94-015-9456-1 ↗
30. Sinha, N. (2007). Handbook of Food Products Manufacturing, 2 Volume Set, John Wiley & Sons.
31. Kerker, M. (2013). The Scattering of Light and other Electromagnetic Radiation: Physical Chemistry: A Series of Monographs, Academic Press.
32. Viscosity modelling of dough in extrusion10.1111/j.1365-2621.1986.tb00438.x ↗
33. Koc, A. (2007). Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biology and Technology, Elsevier.10.1016/j.postharvbio.2007.03.010 ↗
34. Determination of kiwifruit volume using ellipsoid approximation and image-processing methods