This study aimed to evaluate the biochemical composition and biological activity of propolis samples from different regions of Türkiye to characterize and classify 24 Anatolian propolis samples according to their geographical origin. Chemometric techniques, namely, principal component analysis (PCA) and a hierarchical clustering algorithm (HCA), were applied for the first time to all data, including antioxidant capacity, individual phenolic constituents, and the antimicrobial activity of propolis to reveal the possible clustering of Anatolian propolis samples according to their geographical origin. As a result, the total phenolic content (TPC) of the propolis samples varied from 16.73 to 125.83 mg gallic acid equivalent per gram (GAE/g) sample, while the number of total flavonoids varied from 57.98 to 327.38 mg quercetin equivalent per gram (QE/g) sample. The identified constituents of propolis were phenolic/aromatic acids (chlorogenic acid, caffeic acid, p-coumaric acid, ferulic acid, and trans-cinnamic acid), phenolic aldehyde (vanillin), and flavonoids (pinocembrin, kaempferol, pinobanksin, and apigenin). This study has shown that the application of the PCA chemometric method to the biochemical composition and biological activity of propolis allows for the successful clustering of Anatolian propolis samples from different regions of Türkiye, except for samples from the Black Sea region.

Existing tools for bifurcation detection from signals of dynamical systems typically are either limited to a special class of systems or they require carefully chosen input parameters and a significant expertise to interpret the results. Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER plots. Betti numbers are topological invariants of topological spaces, while the CROCKER plot is a coarsened but easy to visualize data representation of a one-parameter varying family of persistence barcodes. The specific bifurcations we investigate are transitions from periodic to chaotic behavior or vice versa in a one-parameter collection of differential equations. We validate our methods using numerical experiments on ten dynamical systems and contrast the results with existing tools that use the maximum Lyapunov exponent. We further prove the relationship between the Wasserstein distance to the empty diagram and the norm of the Betti vector, which shows that an even more simplified version of the information has the potential to provide insight into the bifurcation parameter. The results show that our approach reveals more information about the shape of the periodic attractor than standard tools, and it has more favorable computational time in comparison with the Rösenstein algorithm for computing the maximum Lyapunov exponent.

Introduction: This study aimed to determine the bacterial diversity of chicken carcasses and their surrounding environment at various stages along a poultry slaughter line. Material and Methods: Amplicon sequencing of the 16S rRNA gene was employed to assess the shifts in bacterial community diversity at both phylum and genus levels. Samples were collected from September to November 2021, targeting carcass surfaces at various operational stages (post-defeathering, post-evisceration, post-water chilling, and post-cooling), as well as from the internal environments and air of these units. The study took place in a vertically integrated poultry slaughterhouse in Konya, Turkey. Results: Microbial diversity increased after the chilling and storage stages as a result of redistribution of the microorganisms after the physical effect of the slaughtering stages. The final product sample taken after storage had the highest bacterial abundance. The abundance at this stage was found to be strongly correlated with that at other slaughtering stages, as well as with the abundance in chilling water and on the personnel’s hands. The common genera in chicken carcasses during slaughter stages were Macrococcus, Acinetobacter, Enterococcus, Escherichia-Shigella, Psychrobacter, Streptococcus, Lactococcus and Ligilactobacillus. Microbiome data in environmental samples indicated that the genera in highest relative abundance were Bacillus, Anoxybacillus, Acinetobacter and Psychrobacter. In air samples, the storage room had the highest diversity and in this place Bacillus spp. and Staphylococcus spp. were in the majority. Conclusion: This study may provide some useful information to pinpoint the critical contamination sources in the poultry slaughtering process.