Technical University of Munich, Freising, Germany
Drying of pasta is a process with high cost of energy. In Europe, it is allowed to have a residual moisture content of 13% in dry pasta. Due to the inhomogeneous drying process, the industry dries the pasta down to 8% residual moisture. The inhomogeneous drying is visible all over the conveyor belt; the wet spots have a diameter of 10 cm.
The idea is to detect these spots with NIR measurement instrumentation to dry with local adaptive nozzles. First, it has to be known, which maximum air speed is possible, without flying pasta in the dryer. With these results, numerical simulations were done to investigate the main airflow through the pasta layer. Based on the first simulations, simulations were done to design the local adaptive nozzles.
To validate the numerical simulations a bench-scaled pasta dryer was build up. This dryer has near infrared (NIR) sensors and the designed nozzles. The main airflow, the humidity and the airflow through the nozzles can be controlled.
The experimental results confirm the numerical results; the airflow above the nozzles in the area of the pasta is 0.5 m/s higher than the main airflow. In this area, the drying is faster than in the rest of the dryer and parts with higher humidity can dry to the level of the pasta on the conveyor belt.
Fewer and fewer trained and experienced specialists are available for the production of baked goods. However, the determination of the kneading optimum of dough during kneading and the optimal state of ripeness of a dough piece during proofing is usually carried out by visual and tactile control by those very specialists. During baking, the oven drive and crust colour are visually checked. Due to the absence of comprehensive sensory control in large-scale production plants, kneading, proofing and baking are executed according to predefined programs. Process errors or variation of raw materials can lead to the production of bakery products that do not meet the expectations of neither the manufacturer nor the consumer.
Dynamic Laser Speckle Imaging (DLSI) is a method to tackle the challenges of industrial baking production. In DLSI, a laser beam is directed onto the surface of a sample. The light is scattered and forms a characteristic – in biological samples dynamic – pattern, the so-called laser speckle. A camera mounted next to the laser enables the recording of the laser speckle and the sample. During proofing, the activity of light scattering centres is increased by the development of gas, during baking, the activity decreases due to the formation of the crust. By recording the activity of light scattering centres, evaluating the speckle patterns and correlating them to the product parameters of the intermediate products (e.g. dough pieces), DLSI was utilized to determine the current process development status. In addition, the temporal autocorrelation of the laser speckle pattern and the generalized Stokes-Einstein-relation were used to calculate parameters that allow conclusions about the surface rheology of kneaded dough.
Therefore, the developed sensor allows the determination of process-relevant product properties and offers a very promising approach to monitor and control processes inline during industrial bakery production.
Stefan Steinhauser studied "Food Technology and Biotechnology" at the TUM School of Life Sciences Weihenstephan from 2010 to 2016. Since 2016, he has been working as a PhD student in the working group BioPAT and Digitization at the Chair of Brewing and Beverage Technology at the TU Munich. His main focus is on the development of optical sensors, in particular, laser techniques for the characterization of food surfaces.