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Different conveyor belt materials used by the meat and other food industries were compared, regarding their cleanability as bacterial reduction rates in relation to their surface topography. Eleven thermoplastic polymers, four stainless steels, and five aluminized nanostructured surfaces were investigated under laboratory conditions. Cleanings were conducted with water only, and with an alkaline foam detergent. Overall, scanning electron microscopy revealed remarkable differences in the surface topography of the tested surfaces. Water cleaning results showed that nanostructured aluminized surfaces achieved significantly higher cleanability rates compared to the eight thermoplastic surfaces, as well as the glass-bead blasted rough stainless steel. Thermoplastic surfaces showed overall low cleanability rates when cleaned with alkaline detergent, while stainless steel and nanoporous aluminum showed high variations. Overall, nanoporous aluminum showed promising results as it can be used to coat conveyor belts. However, compatibility with cleaning detergent and sensitivity to scratches must be further investigated. Overall, it can be concluded that cleanability is not only influenced by surface roughness, but also by the overall surface finish, scratches, and defects., Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05778-0., Competing Interests: Conflict of interestThe authors declare no conflicts of interests., (© The Author(s) 2023.)

Modified atmosphere (MA) packaging plays an important role in improving food quality and safety. By using different gas mixtures and packaging materials the shelf life of fresh produce can significantly be increased. A Gram-negative-staining, rod-shaped, orange-pigmented strain DH-B6 T , has been isolated from MA packed raw pork sausage (20% CO 2 , 80% O 2 ). The strain produced biofilms and showed growth at high CO 2 levels of up to 40%. Complete 16S rRNA gene and whole-genome sequences revealed that strain DH-B6 T belongs to the genus Chryseobacterium , being closely related to strain Chryseobacterium indologenes DSM 16777 T (98.4%), followed by Chryseobacterium gleum NCTC11432 T (98.3%) and Chryseobacterium lactis KC1864 T (98.2%). Average nucleotide identity value between DH-B6 T and C. indologenes DSM 16777 T was 81.1% and digital DNA-DNA hybridisation was 24.9%, respectively. The DNA G+C content was 35.51 mol%. Chemotaxonomical analysis revealed the presence of the rare glycine lipid cytolipin, the serine-glycine lipid flavolipin and the sulfonolipid sulfobacin A, as well as phosphatidylethanolamine, monohexosyldiacylglycerol and ornithine lipid, including the hydroxylated forms. Major fatty acids were iC 15 : 0 (50.7%) and iC 17 : 1 cis 9 (28.7%), followed by iC 15 : 0 2-OH (7.0%) and iC 17 : 0 3-OH (6.2%). The isolated strain contained MK-6 as the only respiratory quinone and flexirubin-like pigments were detected as the major pigments. Based on the phenotypic, chemotaxonomic and phylogenetic characteristics, the strain DH-B6 T (=DSM 110542 T =LMG 31915 T ) represents a novel species of the genus Chryseobacterium , for which the name Chryseobacterium capnotolerans sp. nov. is proposed. Emended descriptions of the genus Chryseobacterium and eight species of this genus based on polar lipid characterisation are also proposed.

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta , Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.