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

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## Abstract

**:**

## 1. Introduction

## 2. State of the Art

#### 2.1. Fermentation Monitoring

#### 2.2. Robust Object Recognition

## 3. Materials and Methods

#### 3.1. Dough Volume Monitoring

#### 3.2. Data Processing

#### 3.3. Preparation of the Dough Pieces and Fermentation Process

## 4. Results

^{3}for the three dough pieces during the fermentation period of 90 min, which resembles our measurement of the static test objects. Furthermore, the relative volume difference with regards to the dough volume decreased with increasing fermentation time, which resulted in more accurate volume estimation when the process approaches the optimal state. The method for estimating the volume of watermelons by means of image processing proposed in [33] achieves a deviation of approximately 7.7 % on average. Approximately the same deviation (7.8 %) is stated by the authors in [34] while estimating the volume of kiwi fruits using image processing techniques. Compared to those, our method performs slightly weaker at the beginning with a small object volume, but with increasing fermentation time we obtain more accurate volume estimations because the relative volume error decreases.

## 5. Conclusions and Future Work

## 6. Patents

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Volume development during fermentation (own illustration based on [5]).

**Figure 3.**Schematical illustration of the measurement system (

**left**) and real measurement module (

**right**).

**Figure 6.**Architecture of Mask R-CNN (own illustration based on [15]).

**Figure 8.**Superimposition of dough surface with (pink) and without induced aerosol (purple) of three different dough objects (

**a**–

**c**).

**Figure 10.**Processing steps: (

**a**) Measured topography point cloud of the fermentation chamber inside; (

**b**) Segmented dough pieces; (

**c**) Model-fitted dough pieces.

**Figure 11.**Course of width (

**upper left**) and height (upper right) of three dough objects, the quotient W/h (

**lower left**) and calculated volume (

**lower right**).

Test dough Piece | Shape | Real Dough Volume | Calculated Dough Volume | Deviation [%] |
---|---|---|---|---|

1 | Round | 70 | 78 | 11.4 |

2 | Round | 70 | 76 | 8.6 |

3 | Long | 140 | 119 | 15.0 |

4 | Long | 140 | 122 | 12.9 |

5 | Long | 90 | 79 | 12.2 |

6 | Round | 70 | 73 | 4.3 |

7 | Long | 90 | 81 | 10.0 |

8 | Long | 70 | 62 | 11.4 |

9 | Round | 140 | 143 | 2.1 |

10 | Round | 70 | 76 | 8.6 |

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**MDPI and ACS Style**

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.
https://doi.org/10.3390/app9204266

**AMA Style**

Giefer LA, 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. *Applied Sciences*. 2019; 9(20):4266.
https://doi.org/10.3390/app9204266

**Chicago/Turabian Style**

Giefer, Lino Antoni, Michael Lütjen, Ann-Kathrin Rohde, and Michael Freitag.
2019. "Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning" *Applied Sciences* 9, no. 20: 4266.
https://doi.org/10.3390/app9204266