Healthy FoodsPhoto: Manuel Gutjahr
Gentle processes for fresh products from harvest to consumption - to ensure healthy nutrition for both humans and animals
The program area 'Healthy Foods' addresses complex scientific issues in the spectrum between primary production and processing of food and feed.
'Healthy foods', which are produced fairly taking into account environmental and animal welfare, include hitherto underutilized plant species such as legumes and pseudocereals, as well as alternative bioresources, e.g. land-cultivated macroalgae and insects.
Our research aims to minimize post-harvest losses, improve product quality and simultaneously increase resource and process efficiency embedded in the context of a circular bioeconomy. To this end, we develop innovative interdisciplinary approaches along the entire value chain and integrate side streams into bioeconomic process chains.
Following the concept of 'Quality by Design' in the optimization and development of sensors and control systems, we first analyze the respective system requirements and design the process based on this. The development of tailored physical, physicochemical and/or biological processes considers important ingredients and product properties as well as desirable or avoidable microbial systems. We also analyze environmental impacts in production and post-harvest, especially with regard to water, wastewater, energy, by-products and residues.
For our research, we have access to laboratory areas with high-end specialized equipment, including the freshness lab and microbiology laboratory.
Product properties and in-situ sensing
For the in-situ monitoring of the quality in perishable food products along the value chain, we develop sensor-based methods that allow us to analyze product properties non-destructively and to record process variables. In the field and post-harvest, we use methods including hyperspectral imaging, chlorophyll fluorescence spectroscopy, laser-induced multispectral backscatter analysis and Raman spectroscopy. The product information obtained is used in agronomic models as well as for the development of digital twins.
Dynamics of microbial communities
We develop methods for early and specific detection of microbial contamination in postharvest fresh produce. In this regard, we pursue both cultivation-dependent and cultivation-independent approaches based on the analysis of microbial DNA sequences. The characterization of microbial communities and their dynamics along the process chains from substrate to product will contribute to the application of methods for the targeted inactivation of pathogens or also for the promotion of desirable microorganisms.
Thermal and non-thermal processes for preservation and hygienization
The focus is on efficient, product-friendly solutions for the preservation of food and feed - from their fundamentals to their industrial implementation. We are investigating product-process interactions in drying as a fundamental thermal process in the preparation of bulk materials such as grain. Using apples as an example, we further develop the combination of process models with measured ingredient-specific reaction kinetics.
In the area of non-thermal treatment processes for sanitization, such as hydrostatic high pressure and low-temperature plasma, we are further expanding our expertise through upscaling and transfer to industrial applications. A newly established laboratory platform enables basic, large-scale experiments with pathogens in model processing chains.
Packaging and storage systems
We investigate the interactions between environmental conditions and product quality during cooling, storage and transportation of fresh food. We develop technologies for a tailored packaging or storage regime along the supply chain to reduce post-harvest food losses. In particular, our goal is to better understand the mechanisms of water vapor and condensation dynamics in packaged fresh produce and, on this basis, to develop optimized packaging in a moisture-regulating design, increasingly from bio-based materials such as cellulose or polylactic acid (PLA).
News related to the program
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The requirements of the F-Gas Regulation strengthen the use of the alternative refrigerants CO2 (R744) with direct cooling and propane (R290) with indirect cooling. These requirements and the significantly increasing ene…
High-quality animal proteins are difficult to replace largely by plant proteins in the feeding of carnivorous fish and omnivorous monogastric animals. The use of insect protein derived from by-products and residual strea…
One of the most important challenges the modern world is facing is food insecurity, while little progress has been achieved at introducing non-destructive and reliable food quality assesment methods at both pre- and post…
Global radiation and temperature rise cause huge risks for the fruit production already affecting the fruit quality, storability, and increasingly results in food waste. The varying training systems of woody plants and e…
Publications of the program area
- Marzban, N.; Libra, J.; Rotter, V.; Ro, K.; Moloeznik Paniagua, D.; Filonenko, S. (2023): Changes in Selected Organic and Inorganic Compounds in the Hydrothermal Carbonization Process Liquid While in Storage. ACS Omega. (4): p. 4234-4243. Online: https://doi.org/10.1021/acsomega.2c07419 1.0
- Bettoni, M.; Maerker, M.; Sacchi, R.; Bosino, A.; Conedera, M.; Simoncelli, L.; Vogel, S. (2023): What makes soil landscape robust? Landscape sensitivity towards land use changes in a Swiss southern Alpine valley. Science of the Total Environment. (2): p. 159779. Online: https://doi.org/10.1016/j.scitotenv.2022.159779 1.0
- Tkachenko, V.; Marzban, N.; Vogl, S.; Filonenko, S.; Antonietti, M. (2023): Chemical Insight into the Base-Tuned Hydrothermal Treatment of Side Stream Biomasses. Sustainable Energy & Fuels. : p. 769-777. Online: https://doi.org/10.1039/D2SE01513G 1.0
- Karimi, H.; Navid, H.; Dammer, K. (2023): A Pixel-wise Segmentation Model to Identify Bur Chervil (Anthriscus caucalis M. Bieb.) Within Images from a Cereal Cropping Field. Gesunde Pflanzen. (1): p. 25-36. Online: https://doi.org/10.1007/s10343-022-00764-6 1.0
- Alipasandi, A.; Mahmoudi, A.; Sturm, B.; Behfar, H.; Zohrabi, S. (2023): Application of meta-heuristic feature selection method in low-cost portable device for watermelon classification using signal processing techniques. Computers and Electronics in Agriculture. (107578): p. 1-16. Online: https://doi.org/10.1016/j.compag.2022.107578 1.0
- Küchler, J.; ; Reiß, E.; Nuß, L.; Conrady, M.; Ramm, P.; Schimpf, U.; Reichl, U.; Szewzyk, U.; Benndorf, D. (2023): Degradation Kinetics of Lignocellulolytic Enzymes in a Biogas Reactor Using Quantitative Mass Spectrometry. Fermentation. (1): p. 67. Online: https://doi.org/10.3390/fermentation9010067 1.0
- Klongklaew, A.; Unban, K.; Kalaimurugan, D.; Kanpiengjai, A.; Azaizeh, H.; Schroedter, L.; Schneider, R.; Venus, J.; Khanongnuch, C. (2023): Bioconversion of Dilute Acid Pretreated Corn Stover to L-Lactic Acid Using Co-Culture of Furfural Tolerant Enterococcus mundtii WX1 and Lactobacillus rhamnosus SCJ9. Fermentation. (2): p. 112. Online: https://doi.org/10.3390/fermentation9020112 1.0
- Specka, X.; Martini, D.; Weiland, C.; Arend, D.; Asseng, S.; Boehm, F.; Feike, T.; Fluck, J.; Gackstetter, D.; Gonzales-Mellado, A.; Hartmann, T.; Haunert, J.; Hoedt, F.; Hoffmann, C.; König, P.; Lange, M.; Lesch, S.; Lindstädt, B.; Lischeid, G.; Möller, M.; Rascher, U.; Reif, J.; Schmalzl, M.; Senft, M.; Stahl, U.; Svoboda, N.; Usadel, B.; Webber, H.; Ewert, F. (2023): FAIRagro: ein Konsortium in der nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung. Informatik Spektrum. (Januar): p. 1-12. Online: https://doi.org/10.1007/s00287-022-01520-w 1.0
- Dammer, K. (2023): Arbeitstagung Sensorgestützte Erkennung von Schaderregern in Freilandkulturen am Leibniz-Institut für Agrartechnik und Bioökonomie Potsdam-Bornim (ATB), 11. und 12. Mai 2022. Gesunde Pflanzen. : p. 1-4. Online: https://doi.org/10.1007/s10343-022-00799-9 1.0
- Gautam, S.; Höhne, M.; Hansen, S.; Jenssen, R.; Kampffmeyer, M. (2023): This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognition. (April): p. 109172. Online: https://doi.org/10.1016/j.patcog.2022.109172 1.0