Institut für Nutztierwissenschaften

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/20

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Changes of microorganism composition in fresh and stored bee pollen from Southern Germany
(2021) Friedle, Carolin; D’Alvise, Paul; Schweikert, Karsten; Wallner, Klaus; Hasselmann, Martin
Analysis of plant pollen can provide valuable insights into the existing spectrum of microorganisms in the environment. When harvesting bee-collected pollen as a dietary supplement for human consumption, timely preservation of the freshly collected pollen is fundamental for product quality. Environmental microorganisms contained in freshly collected pollen can lead to spoilage by degradation of pollen components. In this study, freshly collected bee pollen was sampled at different locations and stored under various storage conditions to examine the hypothesis that storage conditions may have an effect on the composition of microorganisms in pollen samples. The samples were analyzed using 16S and 18S amplicon sequencing and characterized by palynological analysis. Interestingly, the bacterial communities between pollen samples from different locations varied only slightly, whereas for fungal community compositions, this effect was substantially increased. Further, we noticed that fungal communities in pollen are particularly sensitive to storage conditions. The fungal genera proportion Cladosporium and Mycosphaerella decreased, while Zygosaccharomyces and Aspergillus increased during storage. Aspergillus and Zygosaccharomyces fractions increased during storage at 30 °C, which could negatively impact the pollen quality if it is used as a dietary supplement.
Electronic nose for the rapid detection of deoxynivalenol in wheat using classification and regression trees
(2022) Camardo Leggieri, Marco; Mazzoni, Marco; Bertuzzi, Terenzio; Moschini, Maurizio; Prandini, Aldo; Battilani, Paola
Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014–2015 and 2017–2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.
Food fermentation: an essential unit operation towards secure, sustainable, safe, and sustaining food systems
(2025) Gänzle, Michael G.; Seifert, Jana; Weiss, Jochen; Zijlstra, Ruurd T.

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