Machine Learning-Based Software for Predicting spp. Growth Dynamics in Culture Media.

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR).

Imidazolium-based ionic liquids disrupt saccharomyces cerevisiae cell membrane integrity.

Ionic liquids (ILs) are interesting chemical compounds that have a wide range of industrial and scientific applications. They have extraordinary properties, such as the tunability of many of their physical properties and, accordingly, their activities; and the ease of synthesis methods. Hence, they became important building blocks in catalysis, extraction, electrochemistry, analytics, biotechnology, etc.

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.

Prediction of spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods.

Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations.

Development of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach.

The purpose of this study was to create a tool for predicting the growth of total mesophilic bacteria in spinach using machine learning-based regression models such as support vector regression, decision tree regression, and Gaussian process regression. The performance of these models was compared to traditionally used models (modified Gompertz, Baranyi, and Huang models) using statistical indices like the coefficient of determination (R) and root mean square error (RMSE). The results showed that the machine learning-based regression models provided more accurate predictions with an R of at least 0.

Modeling the Growth of Six Strains in Smoked Salmon Pâté.

In this study, the growth of six 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 (), maximum specific growth rate (), and maximum cell density (). Then, the effect of storage temperature on the obtained values was modeled by the Ratkowsky secondary model.

Comparison of modelling approaches for the prediction of kinetic growth parameters of spp. in oyster mushroom ().

In predictive microbiology, primary and secondary models can be used to predict microbial growth, usually in a two-step modelling approach. The inverse dynamic modelling approach is an alternative method to direct modelling methods, in which the primary and secondary models are fitted simultaneously from non-isothermal data, minimising experimental effort and costs. Thus, the main aim of the present study was to compare the prediction capabilities of the mathematical modelling approaches used for calculating growth kinetics of microorganisms in predictive food microbiology field.

Development and validation of a one-step modelling approach for the determination of chicken meat shelf-life based on the growth kinetics of spp.

The main objective of the present study was to investigate the effect of storage temperature on aerobically stored chicken meat spoilage using the two-step and one-step modelling approaches involving different primary models namely the modified Gompertz, logistic, Baranyi and Huang models. For this purpose, growth data points of spp. were collected from published studies conducted in aerobically stored chicken meat product.

A new expanded modelling approach for investigating the bioprotective capacity of Latilactobacillus sakei CTC494 against Listeria monocytogenes in ready-to-eat fish products.

Understanding the role of food-related factors on the efficacy of protective cultures is essential to attain optimal results for developing biopreservation-based strategies. The aim of this work was to assess and model growth of Latilactobacillus sakei CTC494 and Listeria monocytogenes CTC1034, and their interaction, in two different ready-to-eat fish products (i.e.

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

The aim of this study was to model the growth and survival behaviour of Reading and endogenous lactic acid bacteria on fresh pre-cut iceberg lettuce stored under modified atmosphere packaging for 10 days at different temperatures (4, 8 and 15 °C). The Baranyi and Weibull models were satisfactorily fitted to describe microbial growth and survival behaviour, respectively. Results indicated that lactic acid bacteria (LAB) could grow at all storage temperatures, while .

Mathematical modelling of temperature effect on growth kinetics of Pseudomonas spp. on sliced mushroom (Agaricus bisporus).

The growth data of Pseudomonas spp. on sliced mushrooms (Agaricus bisporus) stored between 4 and 28°C were obtained and fitted to three different primary models, known as the modified Gompertz, logistic and Baranyi models. The goodness of fit of these models was compared by considering the mean squared error (MSE) and the coefficient of determination for nonlinear regression (pseudo-R).

The role of flexibility and optimality in the prediction of intracellular fluxes of microbial central carbon metabolism.

Prediction of intracellular metabolic fluxes based on optimal biomass assumption is a well-known computational approach. While there has been a significant emphasis on the optimality, cellular flexibility, the co-occurrence of suboptimal flux distributions in a microbial population, has hardly been considered in the related computational methods. We have implemented a flexibility-incorporated optimization framework to calculate intracellular fluxes based on a few extracellular measurement constraints.