Browsing by Person "Mazzoni, Marco"

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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.
Genomic landscape of high‐altitude adaptation in East African mountain honey bees (Apis mellifera)
(2025) Mazzoni, Marco; Loidolt, Florian; Kersten, Sonja; Amulen, Deborah Ruth; Vudriko, Patrick; Meyer, Philipp; Scharnhorst, Victor Sebastian; Scheiner, Ricarda; Hasselmann, Martin
Understanding the evolutionary processes leading to differentiation within species is a central goal in population biology. A key process is local adaptation, for which organisms evolve traits enhancing the survival and reproduction in specific environments. Honey bees ( Apis mellifera ) in East Africa are well adapted to highland environments, showing different phenotypes, including behavior, compared to lowland bees. Despite these differences, highland and lowland honey bees show very low genetic differentiation, with the exception of two segments on chromosome 7 (r7) and chromosome 9 (r9), which were previously identified as chromosomal inversions. These inversions are rare in lowland populations, suggesting a key role in adaptation to high‐elevation habitats. In this study, we obtained 24 whole genomes from honey bees of Western Uganda and compared these with existing data from Kenya. We show that the chromosomal inversions play a pivotal role in local adaptation in both regions but with substantial differentiation. Genome‐wide analysis of polymorphism revealed additional genomic regions potentially involved in high‐altitude adaptation. The acquisition of transcriptome data from highland and lowland honey bees in Uganda has enabled the first insights into the differential expression of genes between these bees. Our findings elucidate the involvement of genes in behavioral and oxygen consumption processes. This paves the way to clarify the interplay of r7 and r9 with gene expression and to unravel the regulatory network underlying A. mellifera adaptation to high‐elevation habitats. Our study will contribute to a better understanding of the evolutionary processes in honey bee populations driven by environmental conditions.