Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures

Predicting the impact of temperature and relative humidity on Salmonella growth and survival in sliced chard, broccoli and red cabbage

This study assessed the fate of a Salmonella enterica cocktail (S. Typhimurium, S. Enteritidis, S. Newport, S. Agona and S. Anatum; initial counts 3.5 log CFU/g) in minimally processed sliced chard, broccoli and red cabbage at 16 conditions of different temperature (7, 14, 21 and 37 °C) and relative humidity (RH; 15, 35, 65 and 95%) over six days (144 h). Linear regression was used to estimate the rate change of Salmonella in cut vegetables as a function of temperature and relative humidity (RH). R2 value of 0.85, 0.87, and 0.78 were observed for the rates of change in chard, broccoli, and red cabbage, respectively. The interaction between temperature and RH was significant in all sliced vegetables. Higher temperatures and RH values favored Salmonella growth. As temperature or RH decreased, the rate of S. enterica change varied by vegetable. The models developed here can improve risk management of Salmonella in fresh cut vegetables.

Effects of chlorine and peroxyacetic acid wash treatments on growth kinetics of Salmonella in fresh-cut lettuce

Fresh-cut produces are often consumed uncooked, thus proper sanitation is essential for preventing cross contamination. The reduction and subsequent growth of Salmonella enterica sv Thompson were studied in pre-cut iceberg lettuce washed with simulated wash water (SWW), sodium hypochlorite (SH, free chlorine 25 mg/L), and peroxyacetic acid (PAA, 80 mg/L) and stored for 9 days under modified atmosphere at 9, 13, and 18 °C. Differences in reduction between SH and PAA were non-existent. Overall, visual quality, dehydration, leaf edge and superficial browning and aroma during storage at 9 °C were similar among treatments, but negative effects increased with temperature. These results demonstrated that PAA can be used as an effective alternative to chlorine for the disinfection of Salmonella spp. in fresh-cut lettuce. The growth of Salmonella enterica sv Thompson was successfully described with the Baranyi and Roberts growth model in the studied storage temperature range, and after treatment with SWW, chlorine, and PAA. Subsequently, predictive secondary models were used to describe the relationship between growth rates and temperature based on the models' family described by Bělehrádek. Interestingly, the exposure to disinfectants biased growth kinetics of Salmonella during storage. Below 12 °C, growth rates in lettuce treated with disinfectant (0.010-0.011 log CFU/h at 9 °C) were lower than those in lettuce washed with water (0.016 log CFU/h at 9 °C); whereas at higher temperatures, the effect was the opposite. Thus, in this case, the growth rate values registered at 18 °C for lettuce treated with disinfectant were 0.048-0.054 log CFU/h compared to a value of 0.038 log CFU/h for lettuce treated with only water. The data and models developed in this study will be crucial to describing the wash-related dynamics of Salmonella in a risk assessment framework applied to fresh-cut produce, providing more complete and accurate risk estimates.

An Outbreak Investigation of Salmonella Typhimurium Illnesses in the United States Linked to Packaged Leafy Greens Produced at a Controlled Environment Agriculture Indoor Hydroponic Operation - 2021

In 2021, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and state partners investigated a multistate outbreak of Salmonella Typhimurium illnesses linked to packaged leafy greens from a controlled environment agriculture (CEA) operation in Illinois. Thirty-one illnesses and four hospitalizations were reported in four states, with a significant epidemiologic signal for packaged leafy greens from Farm A. A traceback investigation for leafy greens included seven points of service (POS) with food exposure data from eight ill people. Each POS was supplied leafy greens by Farm A. FDA investigators observed operations at Farm A and noted that 1) the firm did not consider their indoor hydroponic pond water as agricultural water, 2) condensate dripping from the chiller water supply line inside the building, and 3) unprotected outdoor storage of packaged soilless growth media and pallets used for finished product. FDA collected 25 product, water, and environmental samples from Farm A. The outbreak strain was recovered from a water sample collected from a stormwater drainage basin located on the property adjacent to Farm A. In addition, an isolate of Salmonella Liverpool was recovered from two indoor growing ponds within the same growing house, but no illnesses were linked to the isolate. Farm A voluntarily recalled all implicated products and provided their root cause analysis (RCA) and return-to-market plan to FDA. While the source and route of the contamination were not determined by the RCA, epidemiologic and traceback evidence confirmed the packaged salads consumed by ill persons were produced by Farm A. This was the first investigation of a multistate foodborne illness outbreak associated with leafy greens grown in a CEA operation. This outbreak demonstrated the need for growers using hydroponic methods to review their practices for potential sources and routes of contamination and to reduce food safety risks when identified.

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.

Dimensional Analysis Model Predicting the Number of Food Microorganisms

Predicting the number of microorganisms has excellent application in the food industry. It helps in predicting and managing the storage time and food safety. This study aimed to establish a new, simple, and effective model for predicting the number of microorganisms. The dimensional analysis model (DAM) was established based on dimensionless analysis and the Pi theorem. It was then applied to predict the number of Pseudomonas in Niuganba (NGB), a traditional Chinese fermented dry-cured beef, which was prepared and stored at 278 K, 283 K, and 288 K. Finally, the internal and external validation of the DAM was performed using six parameters including R ², R ² _adj , root mean square error (RMSE), standard error of prediction (%SEP), A _f , and B _f . High R ² and R ² _adj and low RMSE and %SEP values indicated that the DAM had high accuracy in predicting the number of microorganisms and the storage time of NGB samples. Both A _f and B _f values were close to 1. The correlation between the observed and predicted numbers of Pseudomonas was high. The study showed that the DAM was a simple, unified and effective model to predict the number of microorganisms and storage time.

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10-20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.