Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions

Changes to Pork Bacterial Counts and Composition After Dielectric Barrier Discharge Plasma Treatment and Storage in Modified-Atmosphere Packaging

The aim of this study was to compare the succession of natural microbiota in pork held under refrigerated storage for up to 10 days after dielectric barrier discharge (DBD) plasma treatment. Two methods were used to assess the impact of DBD on microorganisms. Firstly, traditional selective media (SM) were employed to detect the bactericidal effects of DBD on Pseudomonas spp., Enterobacteriaceae, Lactic acid bacteria (LAB), and Brochothrix thermosphacta. Secondly, the thin agar layer (TAL) method was used to further evaluate the bactericidal effects of DBD. In addition, the Baranyi and Roberts model was applied to explore the kinetic parameters of Pseudomonas spp., Enterobacteriaceae, LAB, and B. thermosphacta during storage. Finally, the modified Lotka-Volterra model was used to describe the interactions between each microorganism. The study found that when using traditional selective media (SM), 85 kV DBD had a significant bactericidal effect on Pseudomonas spp., Enterobacteriaceae, LAB, and Brochothrix thermosphacta. However, when using the thin agar layer (TAL) method, the results suggested that DBD had no significant bactericidal effect, suggesting that DBD caused sublethal damage to the natural microorganisms on pork. Analysis with the Baranyi and Roberts model showed that DBD treatment significantly extended the lag phase of these four types of microorganisms and significantly reduced the μmax of all microorganisms except LAB. The analysis results of the modified Lotka-Volterra model showed that LAB had a greater impact on Pseudomonas spp., Enterobacteriaceae, and B. thermosphacta (a21 > a12). In conclusion, DBD treatment was shown to have a significant sublethal bactericidal effect that impacted both the count and composition of natural microorganisms found on pork.

Contribution of mono- and co-culture of Pseudomonas paralactis, Acinetobacter MN21 and Stenotrophomonas maltophilia to the spoilage of chill-stored lamb

Exploring the contribution of common microorganisms to spoilage is of great significance in inhibiting spoilage in lamb. This work investigated the extent of protein degradation and profile changes of free amino acids (FAAs), free fatty acids (FFAs) and volatile organic compounds (VOCs) in lamb caused by single- and co-culture of the common aerobic spoilage bacteria, P. paralactis, Ac. MN21 and S. maltophilia. Meanwhile, some key VOCs produced by the three bacteria during lamb spoilage were also screened by orthogonal partial least square discriminant analysis and difference value in VOCs content between inoculated groups and sterile group. Lamb inoculated with P. paralactis had the higher total viable counts, pH, total volatile base nitrogen and TCA-soluble peptides than those with the other two bacteria. Some FAAs and FFAs could be uniquely degraded by P. paralactis but not Ac. MN21 and S. maltophilia, such as Arg, Glu, C15:0, C18:0 and C18:1n9t. Co-culture of the three bacteria significantly promoted the overall spoilage, including bacterial growth, proteolysis and lipolysis. Key VOCs produced by P. paralactis were 2, 3-octanedione, those by Ac. MN21 were 1-octanol, octanal, hexanoic acid, 1-pentanol and hexanoic acid methyl ester, and that by S. maltophilia were hexanoic acid. The production of extensive key-VOCs was significantly and negatively correlated with C20:0, C23:0 and C18:ln9t degradation. This study can provide a basis for inhibiting common spoilage bacteria and promoting high-quality processing of fresh lamb.

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 Growth of Six Listeria monocytogenes Strains in Smoked Salmon Pâté

In this study, the growth of six L. monocytogenes strains isolated from different fish products was quantified and modeled in smoked salmon pâté at a temperature ranging from 2 to 20 °C. The experimental data obtained for each strain was fitted to the primary growth model of Baranyi and Roberts to estimate the following kinetic parameters: lag phase (λ), maximum specific growth rate (μ_max), and maximum cell density (N_max). Then, the effect of storage temperature on the obtained μ_max values was modeled by the Ratkowsky secondary model. In general, the six L. monocytogenes strains showed rapid growth in salmon pâté at all storage temperatures, with a relatively short lag phase λ, even at 2 °C. The growth behavior among the tested strains was similar at the same storage temperature, although significant differences were found for the parameters λ and μ_max. Besides, the growth variations among the strains did not follow a regular pattern. The estimated secondary model parameter T_min ranged from -4.25 to -3.19 °C. This study provides accurate predictive models for the growth of L. monocytogenes in fish pâtés that can be used in shelf life and microbial risk assessment studies. In addition, the models generated in this work can be implemented in predictive modeling tools and repositories that can be reliably and easily used by the fish industry and end-users to establish measures aimed at controlling the growth of L. monocytogenes in fish-based pâtés.

Optimization of the Effects of Different Temperatures and Compositions of Filmogenic Solution on Lactobacillus Salivarius Using Predictive Mathematical Models

It is well known that intake of probiotic brings health benefits. Lactic bacteria with probiotic potential have aroused the interest of the industry in developing food products that incorporate such benefits. However, incorporating probiotic bacteria into food is a challenge for the industry, given the sensitivity of probiotic cultures to process conditions. Therefore, the objective of this study is to evaluate gelatin- and inulin-based filmogenic solutions as a potential vehicle for incorporating probiotics into food products and to model the fermentation kinetics. L. salivarius (Lactobacillus salivarius) growth in filmogenic solutions was analyzed under the influence of a variety gelatin concentrations (1.0–3.0%) and inulin concentrations (4.0–6.0%) and fermented under the effect of different temperatures (25–45 °C). A full 2³ factorial plan with three replicates at the central point was used to optimize the process. The impacts of process conditions on cell development are fundamental to optimize the process and make it applicable by the industry. The present study showed that the optimal conditions for the development of probiotic cells in filmogenic solutions are a combination of 1.0% gelatin with 4.0% inulin and fermentation temperature of 45 °C. It was observed that the maximum cell growth occurred in an estimated time of about 4 h of fermentation. L. salivarius cell production and substrate consumption during the fermentation of the filmogenic solution were well simulated by a model proposed in this article, with coefficients of determination of 0.981 (cell growth) and 0.991 (substrate consumption).