This work demonstrates the application of an electronic nose (e-nose) for discrimination between authentic and adulterated honey. The developed e-nose is based on electrodes covered with ionogel (ionic liquid + gelatin + Fe₃O₄ nanoparticle) films. Authentic and adulterated honey samples were submitted to e-nose analysis, and the capacity of the sensors for discrimination between authentic and adulterated honey was evaluated using principal component analysis (PCA) based on average relative response data. From the PCA biplot, it was possible to note two well-defined clusters and no intersection was observed. To evaluate the relative response data as input for autonomous classification, different machine learning algorithms were evaluated, namely instance based (IBK), Kstar, Trees-J48 (J48), random forest (RF), multilayer perceptron (MLP), naive Bayes (NB), and sequential minimal optimization (SMO). Considering the average data, the highest accuracy was obtained for Kstar: 100% (k-fold = 3). Additionally, this algorithm was also compared regarding its sensitivity and specificity, both being 100% for both features. Thus, due to the rapidity, simplicity, and accuracy of the developed methodology, the technology based on e-noses has the potential to be applied to honey quality control.

The swine production chain can be a reservoir of antimicrobial-resistant Escherichia coli, which transfers resistance genes to other bacteria, serving as an important biomarker in the One Health approach. This study aimed to identify the frequency and antimicrobial resistance profile of E. coli in the swine production chain, assess the presence of extended-spectrum beta-lactamases (ESBL), and compare resistance profiles across different sample types. A total of 622 samples of swine carcasses from various points of the slaughter process (n = 400), swine feces (n = 100), commercial cuts (n = 45), environment (n = 67), and feces from employees (n = 10) of a pig slaughterhouse certified by the Federal Inspection Service, located in São Paulo state, Brazil, were collected. A total of 1260 E. coli isolates were obtained from the samples, with 73.6% of the samples testing positive. The agar disk diffusion test was performed with 10 different classes of antimicrobials. To confirm the production of ESBLs, the isolates were submitted to a double-disk synergism test using cefotaxime, ceftazidime, and amoxicillin with clavulanic acid. Of the total isolates, 80.71% were multidrug resistant. All ESBL-producing isolates were multidrug resistant and resistant to amoxicillin, tetracycline, and chloramphenicol. Isolates from human feces samples had less chance of being multidrug resistant than samples from other sources. The diversity of resistance profiles was verified in the samples, not clustering according to the sources, except for human feces isolates that clustered, evidencing lower antimicrobial resistance variability of these samples. Antimicrobial resistance is significantly present in the pork production chain, necessitating a comprehensive multidisciplinary approach to effectively mitigate risks within the One Health framework.

Kefir is a naturally carbonated and mildly acidic milk beverage fermented by grains that consist of bacteria and yeast living in a symbiotic relationship. Its unique flavor arises from a blend of lactic acid, ethanol, carbon dioxide, and other compounds produced by the microorganisms within the kefir grains. The microorganisms in kefir are a complex microbial population that varies by geographical region. Kefir is widely acknowledged for its probiotic content, with reported health benefits that include modulation of intestinal microflora, immune system stimulation, and antimicrobial, antineoplastic, hypocholesterolemic, antidiabetic, and antimutagenic effects. It also exhibits β-galactosidase activity and benefits lipid and blood pressure levels. This review focuses on the microbiological aspects, composition, and production of milk kefir, highlighting how its biological properties correlate with the health benefits associated with its consumption.

Evaluation concerning the presence of bacteria in meat products is mandatory for commercializing these goods. Although food bacteria detection is based on microbiological methods, these assays are usually laborious and time-consuming. In this paper, an electronic nose is used to differentiate Salmonella spp. (SA), Escherichia coli (EC), and Pseudomonas fluorescens (PF) inoculated in raw meat (beef, chicken, and pork) and incubated at 22 °C for 3 days. The obtained data were evaluated by principal component analysis (PCA) and different machine learning algorithms. From the graphical analysis of the PCA, on day 1, the clusters were close to each other for beef, chicken, and pork, while on days 2 and 3, more separated bacteria clusters were obtained regardless of the meat type, allowing for the discrimination of the samples for the latter days. To estimate the growth rates of the microorganisms, the distance between clusters was calculated and provided a pattern for the three bacteria, with the slowest-, moderate-, and fastest-growing being EC, SA, and PF, respectively. Concerning the machine learning algorithms, the accuracy varied from 93.8 to 100% for beef and chicken, while for pork, it varied from 75% to 100%. Thus, these results suggest that the proposed methodology based on electronic nose has the potential for the direct discrimination of bacteria in raw meat, with reduced analysis time, costs, and manipulating steps.