Development of a general model to describe Salmonella spp. growth in chicken meat subjected to different temperature profiles

Effect of Rheum ribes L. pulp enriched with eugenol or thymol on survival of foodborne pathogens and quality parameters of chicken breast fillets

The aim of this study was to characterize the pulp of Rheum ribes L. and to determine the effect of the pulp enriched with eugenol (1 %) or thymol (1 %) on the microbiological and physico-chemical quality of chicken breast fillets. Chicken breast fillets, inoculated with Listeria monocytogenes, Salmonella enterica subsp. enterica serovar Typhimurium, and Escherichia coli O157:H7 (~6.0 log10), were marinated for 24 h in a mixture prepared from a combination of Rheum ribes L. pulp with eugenol or thymol. The quality parameters were analyzed for 15 days at +4 °C. The Rheum ribes L. pulp was found to have high antioxidant activity, high total phenolic content and contained 22 different phenolic substances, among which rutin ranked first. The pulp contained high levels of p-xylene and o-xylene as volatile substances and citric acid as an organic acid. The combination of Pulp + Eugenol + Thymol (PET) reduced the number of pathogens in chicken breast fillets by 2.03 to 3.50 log10 on day 0 and by 2.25 to 4.21 log10 on day 15, compared to the control group (P < 0.05). The marinating treatment significantly lowered the pH values of fillet samples on the first day of the study, compared to the control group (P < 0.05). During storage, TVB-N levels showed slower increase in the treatment groups compared to the control group (P < 0.05). In addition, the marinating process led to significant changes in physicochemical parameters such as water holding capacity, color, texture, cooking loss, and drip loss compared to the control group (P < 0.05). In conclusion, the results of this study showed that the pulp of Rheum ribes L., which has a high antioxidant capacity and contains various bioactive compounds. Furthermore, S. Typhimurium, E. coli O157:H7 and L. monocytogenes were inhibited considerably by marinating Rheum ribes L. pulp with a combination of eugenol and thymol.

Modeling nonlinear inactivation of hygiene indicator bacteria in pig carcasses during scalding at different pHs

This study aimed to investigate the influence of adding an alkalizing agent to the scalding water of a slaughterhouse in Brazil to inactivate hygiene indicator bacteria in pig carcasses. Scalding is critical during carcass processing because slaughterhouses' scalding water is constantly renewed; therefore, it is usually contaminated with organic matter, such as faeces and dirt from the previous carcasses. The treatments evaluated consisted of counting Enterobacteriaceae and mesophilic bacteria in pork jowls at 62 °C, 65 °C, 68 C, and 72 °C after 0.0, 1.5, 3.0, 4.5, 6.0, and 7.5 min of simulated scalding at the pHs of 7.0 (control) and 11.0 (after addition of alkalizing agent). Decimal reduction times of hygiene indicator bacteria for all treatments were estimated with different nonlinear bacterial inactivation models. As a result, adding the alkalizing agent did not significantly inactivate most of the bacteria in the studied samples. However, it contributed to the inactivation of some bacteria, mostly belonging to the mesophilic group, at some specific temperatures. The results obtained in the current study can provide useful insights into dealing with pig carcass contamination in a real-world scenario and applying the obtained information in the industrial environment.

Prevalence, serovars, and risk factors associated with the presence of Salmonella in pork sold in public markets in Quito, Ecuador

Background

Salmonella enterica are bacteria that include more than 2,500 serovars. Most of these serovars have been linked to human foodborne illnesses, mainly related to poultry and pigs. Thus, these animals are considered the reservoirs of many Salmonella serovars and strains related to antibiotic resistance. This study aimed to determine the prevalence, serovars, β-lactam resistance genes, and the risk factors associated with Salmonella enterica in pork commercialized in open markets of Quito city.

Methods

For this, 165 pork meat samples were taken from municipal markets in three areas in the city. These samples were microbiologically processed following the ISO 6579-2014 standardized method. The polymerase chain reaction (PCR) test was used to identify Salmonella serotyping and resistance genes. Strains not identified by PCR were typed by the Kauffman White Le Minor scheme. A multivariate analysis was performed to identify risk factors associated with the presence of the microorganism.

Results

Salmonella prevalence in pork was 9.1%. Identified serovars were 4, [5], 12: i:- (53.3%), Infantis (33.3%), and Derby (13.4%). Furthermore, the β-lactam resistance genes bla CTX-M-65 could be identified in three S. infantis isolates. Multivariate analysis showed that temperature (above 8°C) and cutting surfaces (wood) presented significant association values.

Conclusions

In conclusion, pork in traditional markets of Quito is contaminated with Salmonella enterica, whose main serovars pose a public health concern, and shows beta-lactam resistance.

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.

Modeling the Inactivation, Survival, and Growth of Salmonella enterica under Osmotic Stress Considering Inoculum Phase and Serotype

AIMS

This study evaluated the behaviour of the Salmonella enterica serotypes in osmotically-stressful BHI broth (0.940 ≤ a_w ≤ 0.960), assessing inoculum from two stages of the bacterial life cycle (exponential and stationary) and two temperatures (25 and 35 °C).

METHODS AND RESULTS

Four S. enterica serotypes (Typhimurium, Enteritidis, Heidelberg, and Minnesota) were grown in stressful BHI at 25 °C. A mathematical model was proposed for describing the total microbial count as the sum of two subpopulations, inactivating and surviving-then-growing. When submitted to a_w of 0.950 and 0.960, all strains showed a decreased count, followed by a period of unchanged count and then exponential growth (Phoenix Phenomenon). Strains inoculated at a_w = 0.940 and 0.945 showed inactivation kinetics only. Cells cultivated at 25 °C and inoculated from the exponential phase were the most reactive to the osmotic stress, showing a higher initial population reduction and shorter adaptation period. The proposed model described the inactivation data and the Phoenix Phenomenon accurately.

CONCLUSIONS

The results quantified the complex response of S. enterica to the osmotic environment in detail, depending on the inoculum characteristic and serotype evaluated.

SIGNIFICANCE AND IMPACT OF STUDY

Quantifying these differences is truly relevant to food safety and improves risk analysis.

Modeling Salmonella spp. inactivation in chicken meat subjected to isothermal and non-isothermal temperature profiles

Salmonella genus has foodborne pathogen species commonly involved in many outbreaks related to the consumption of chicken meat. Many studies have aimed to model bacterial inactivation as a function of the temperature. Due to the large heterogeneity of the results, a unified description of Salmonella spp. inactivation behavior is hard to establish. In the current study, by evaluating the root mean square errors, mean absolute deviation, and Akaike and Bayesian information criteria, the double Weibull model was considered the most accurate primary model to fit 61 datasets of Salmonella inactivation in chicken meat. Results can be interpreted as if the bacterial population is divided into two subpopulations consisting of one more resistant (2.3% of the total population) and one more sensitive to thermal stress (97.7% of the total population). The thermal sensitivity of the bacteria depends on the fat content of the chicken meat. From an adapted version of the Bigelow secondary model including both temperature and fat content, 90% of the Salmonella population can be inactivated after heating at 60 °C of chicken breast, thigh muscles, wings, and skin during approximately 2.5, 5.0, 9.5, and 57.4 min, respectively. The resulting model was applied to four different non-isothermal temperature profiles regarding Salmonella growth in chicken meat. Model performance for the non-isothermal profiles was evaluated by the acceptable prediction zone concept. Results showed that >80% of the predictions fell in the acceptable prediction zone when the temperature changes smoothly at temperature rates lower than 20 °C/min. Results obtained can be used in risk assessment models regarding contamination with Salmonella spp. in chicken parts with different fat contents.