Synergistic Strategies of Heat and Peroxyacetic Acid Disinfection Treatments for Salmonella Control.

The food industry has recognized a pressing need for highly effective disinfection protocols to decrease the risk of pathogen emergence and proliferation in food products. The integration of antimicrobial treatments in food production has occurred as a potential strategy to attain food items of superior quality with respect to microbiological safety and sensory attributes. This study aims to investigate the individual and synergistic effects of heat and peroxyacetic acid on the inactivation of bacterial cells, considering various contact times and environmental conditions. Four Salmonella serotypes, isolated from industrial meat production surfaces, were employed as model organisms. By systematically assessing the impacts of individual factors and synergistic outcomes, the effectiveness of bacterial cell inactivation and the efficiency of heat and peroxyacetic acid could be predicted. To better approximate real-world food processing conditions, this study also incorporated a bovine albumin-rich condition as a simulation of the presence of organic loads in processing steps. The findings revealed the essential need for a synergistic interplay of investigated parameters with the following optimized values: 1.5% concentration of peroxyacetic acid, temperature range of 60-65 °C, and contact time of 3 min for the complete effect regardless of the degree of contamination.

Formation of Predictive-Based Models for Monitoring the Microbiological Quality of Beef Meat Processed for Fast-Food Restaurants.

Consumption of raw or undercooked meat is responsible for 2.3 million foodborne illnesses yearly in Europe alone. The greater part of this illness is associated with beef meat, which is used in many traditional dishes across the world. Beneath the low microbiological quality of beef lies (pathogenic) bacterial contamination during primary production as well as inadequate hygiene operations along the farm-to-fork chain. Therefore, this study seeks to understand the microbiological quality pathway of minced beef processed for fast-food restaurants over three years using an artificial neural network (ANN) system. This simultaneous approach provided adequate precision for the prediction of a microbiological profile of minced beef meat as one of the easily spoiled products with a short shelf life. For the first time, an ANN model was developed to predict the microbiological profile of beef minced meat in fast-food restaurants according to meat and storage temperatures, butcher identification, and working shift. Predictive challenges were identified and included standardized microbiological analyses that are recommended for freshly processed meat. The obtained predictive models (an overall r2 of 0.867 during the training cycle) can serve as a source of data and help for the scientific community and food safety authorities to identify specific monitoring and research needs.

Best-performing Bacillus strains for microbiologically induced CaCO3 precipitation: Screening of relative influence of operational and environmental factors.

The microbiologically induced calcite precipitation (MICP) can be an emerging approach that could tap onto soil bacterial diversity and use as a bioremediation technique. Based on the concept that bacteria with biomineralization capacity could be effective CaCO3 inductance agents, this study aimed to evaluate the simultaneous influence of 11 operational and environmental factors on the MICP process, for the first time. Therefore, Bacillus muralis, B. lentus, B. simplex, B. firmus, and B. licheniformis, isolated from alkaline soils, were used in the selection of the best performing bacterium compared with a well-known MICP bioagent Sporosarcina pasteurii DSM 33. Plackett-Burman's experimental design was labouring to screen all independent variables for their significances on five outputs (pH value, number of viable cells and spores, amount of urea and CaCO3 precipitate). According to experimentally obtained data, an artificial neural network model based on the Broyden-Fletcher-Goldfarb-Shanno algorithm showed good prediction capabilities, while differences in the relative influences were observed at the bacterial strain level. B. licheniformis turn out to be the most potent bioagent, with a maximum amount of CaCO3 precipitate of 3.14 g/100 mL in the optimal conditions.

Comprehensive Profiling of Microbiologically Induced CaCO3 Precipitation by Ureolytic Bacillus Isolates from Alkaline Soils.

Microbiologically induced CaCO3 precipitation (MICP) is a well-known bio-based solution with application in environmental, geotechnical, and civil engineering. The significance of the MICP has increased explorations of process efficiency and specificity via natural bacterial isolates. In this study, comprehensive profiling of five soil ureolytic Bacillus strains was performed through a newly formed procedure that involved six steps from selection and identification, through kinetic study, to the characterization of the obtained precipitates, for the first time. To shorten the whole selection procedure of 43 bioagents with the MICP potential, Standard Score Analysis was performed and five selected bacteria were identified as Bacillus muralis, B. lentus, B. simplex, B. firmus, and B. licheniformis by the MALDI-TOF mass spectrometry. Despite following the targeted activity, kinetic studies were included important aspects of ureolysis and the MICP such as cell concentration, pH profiling, and reduction in calcium ion concentration. At the final step, characterization of the obtained precipitates was performed using FTIR, XRD, Raman, DTA/TGA, and SEM analysis. Although all tested strains showed significant potential in terms of precipitation of calcite or calcite and vaterite phase, the main differences in the MICP behavior can be observed at the bacterial strain level. B. licheniformis showed favorable behavior compared to the reference Sporosarcina pasteurii DSM 33.

Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO3 precipitation-A mathematical modeling approach.

The microbiologically induced calcite precipitation (MICP) has been extensively studied for geotechnical engineering through simultaneous action of natural phenomena and engineering processes. The focus of bacterial contribution to the MICP has been directed to calcium carbonate productivity, while the additional bacterial role as a crystal nucleation center was not explained especially from a mathematical prediction modeling point of view. Therefore, this study provides explanations and a mathematical modeling approach of bacterial influence on the MICP induced by newly-isolated ureolytic Bacillus strains and Sporosarcina pasteurii DSM 33. Using the obtained results of low-cost, rapid, and simple assays, artificial neural network modeling was applied for cell surface predispositions, pH changes as well as calcium-involved function in biofilm formation during the MICP, for the first time. Based on the obtained contribution of the alkalophilic/alkaloresistant bacteria, calcite precipitation can be significantly directed by the presence, of ureolytic bacterial cells as nucleation centers during CaCO3 precipitation as well as their morphology, surface characteristics, potential to form a biofilm, and/or generate pH changes.

Chemical composition and antimicrobial activity of Osage orange (Maclura pomifera) leaf extracts.

The main goal of this study was to establish the chemical profile of Osage orange (Maclura pomifera) leaf extracts, obtained by conventional maceration technique, and to examine its antimicrobial activity. The identification and quantification of the extract compounds were done using ultra-high-performance liquid chromatography, with a diode array detector coupled with triple-quadrupole mass spectrometer and gas chromatography-mass spectrometry techniques. Thirty-one polyphenolic compounds were detected and identified in the ethanolic extracts, whereby 5-O-caffeoylquinic acid was found to be the dominant compound. Among other compounds, pentacosane and palmitic acid were the most abundant compounds in the dichloromethane extract. The preliminary antimicrobial activity screening shows that Gram-positive bacteria tend to be more sensitive to the investigated extracts. The highest antimicrobial activity was determined against Enterococcus faecalis ATCC 19433 and Listeria monocytogenes ATCC 35152. From these results, Osage orange leaves can be considered as plant material with significant antimicrobial properties.