Predictive model of Staphylococcus aureus growth on egg products

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Modeling naturally-occurring Vibrio parahaemolyticus in post-harvest raw shrimps

There is little known about the growth and survival of naturally-occurring Vibrio parahaemolyticus in harvested raw shrimps. In this study, the fate of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps was investigated from 4℃ to 30℃ using real-time PCR combined with propidium monoazide (PMA-qPCR). The Baranyi-model was used to fit the growth and survival data. A square root model and non-linear Arrhenius model was then used to quantify the parameters derived from the Baranyi-model. The results showed that naturally-occurring V. parahaemolyticus were slowly inactivated at 4℃ and 7℃ with deactivation rates of 0.019 Log CFU/g/h and 0.025 Log CFU/g/h. Conversely, at 15, 20, 25, and 30 °C, the average maximum growth rates (μmax) of naturally-occurring V. parahaemolyticus were determined to be 0.044, 0.105, 0.179 and 0.336 Log CFU/g/h, accompanied by the average lag phases (λ) of 15.5 h, 7.3 h, 4.4 h and 3.7 h. The validation metrics, Af and Bf, for both the square root model and non-linear, indicating that the model had a good ability to predict the growth behavior of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps. Furthermore, a comparative exploration between the growth of artificially contaminated V. parahaemolyticus in cooked shrimps and naturally-occurring V. parahaemolyticus in post-harvest raw shrimps revealed intriguing insights. While no substantial distinction in deactivation rates emerged at 4 °C and 7 °C (P > 0.05), a discernible disparity in growth rates was observable at 15 °C, 20 °C, 25 °C, and 30 °C, with the former surpassing the latter. Which indicated the risk of V. parahaemolyticus using models derived from cooked shrimps may be biased. Our study also unveiled a discernible seasonal effect. The μmax and λ of V. parahaemolyticus in shrimps harvested in summer were similar to those harvested in autumn, while the initial and maximum bacterial concentration harvested in summer were higher than those harvested in autumn. This predictive microbiology model of naturally-occurring V. parahaemolyticus in raw shrimps provides relevance to modelling growth in situ.

Levilactobacillus brevis KU15151 Inhibits Staphylococcus aureus Lipoteichoic Acid-Induced Inflammation in RAW 264.7 Macrophages

Inflammation is a host defense response to harmful agents, such as pathogenic invasion, and is necessary for health. Excessive inflammation may result in the development of inflammatory disorders. Levilactobacillus brevis KU15151 has been reported to exhibit probiotic characteristics and antioxidant activities, but the effect of this strain on inflammatory responses has not been determined. The present study aimed to investigate the anti-inflammatory potential of L. brevis KU15151 in Staphylococcus aureus lipoteichoic acid (aLTA)-induced RAW264.7 macrophages. Treatment with L. brevis KU15151 reduced the production of nitric oxide and prostaglandin E₂ by suppressing the expression of inducible nitric oxide synthase and cyclooxygenase-2. Additionally, the production of proinflammatory cytokines including tumor necrosis factor-α, interleukin (IL)-6, and IL-1β, decreased after treatment with L. brevis KU15151 in aLTA-stimulated RAW 264.7 cells. Furthermore, this strain alleviated the activation of nuclear factor-κB and mitogen-activated protein kinase signaling pathways. Moreover, the generation of reactive oxygen species was downregulated by treatment with L. brevis KU15151. These results demonstrate that L. brevis KU15151 possesses an inhibitory effect against aLTA-mediated inflammation and may be employed as a functional probiotic for preventing inflammatory disorders.

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.

Predictive model of growth kinetics for Staphylococcus aureus in raw beef under various packaging systems

This study describes a model of the growth kinetics for S. aureus in raw beef under wrapped packaging (WP), modified atmosphere packaging (MAP), vacuum packaging (VP), and vacuum skin packaging (VSP). Beef samples were inoculated with S. aureus and stored at 10, 15, 20, and 25 °C. VP and VSP showed lower maximum bacteria counts and higher lag time than WP and MAP at all temperatures. At 10 °C, S. aureus in VP and VSP decreased to about 2.5 Log CFU/g. Two primary models (modified Gompertz model and reparameterized Gompertz survival model) were used in the study. The secondary models were described using a polynomial equation and the Davey model. The bias factor (Bf), accuracy factor (Af), and root mean square error (RMSE) of the secondary models were 0.91-1.09, 1.00-1.13, and 0.00-0.68, respectively. The predictive models for kinetics of S. aureus in various packaged raw beef could help to predict the fate of S. aureus more accurately.