Engineering modeling frameworks for microbial food safety at various scales

Modeling the influence of propionic acid concentration and pH on the kinetics of Salmonella Typhimurium

Salmonella Typhimurium is a foodborne pathogen often found in the poultry production chain. Antibiotics have been used to reduce S. Typhimurium contamination in poultry aviaries and improve chicken growth. However, antibiotics were banned in several countries. Alternatively, organic acids, such as propionic acid (PA), can control pathogens. This study determined the PA minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and mathematically modeled S. Typhimurium growth/inactivation kinetics under the influence of PA at different pH values (4.5, 5.5, and 6.5) which are within the pH range of the chicken gastrointestinal tract. The PA MIC against S. Typhimurium was pH-dependent, resulting in 5.0, 3.5 and 9.0 mM undissociated PA at pH 4.5, 5.5, and 6.5, respectively. The Baranyi and Roberts and the Weibull model fit growth and inactivation data well, respectively. Secondary models were proposed. The validated model predicted 3-log reduction of S. Typhimurium in 3 h at 68.2 mM of undissociated PA and pH 4.5. The models presented a good capacity to describe the kinetics of S. Typhimurium subjected to PA, representing a useful tool to predict PA antibacterial action depending on the pH.

Microbes in porous environments: from active interactions to emergent feedback

Microbes thrive in diverse porous environments-from soil and riverbeds to human lungs and cancer tissues-spanning multiple scales and conditions. Short- to long-term fluctuations in local factors induce spatio-temporal heterogeneities, often leading to physiologically stressful settings. How microbes respond and adapt to such biophysical constraints is an active field of research where considerable insight has been gained over the last decades. With a focus on bacteria, here we review recent advances in self-organization and dispersal in inorganic and organic porous settings, highlighting the role of active interactions and feedback that mediates microbial survival and fitness. We discuss open questions and opportunities for using integrative approaches to advance our understanding of the biophysical strategies which microbes employ at various scales to make porous settings habitable.

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values. We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression.

Computer-aided food engineering

Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.

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.

pH-taxis drives aerobic bacteria in duodenum to migrate into the pancreas with tumors

As oral or intestinal bacteria have been found in pancreatic cystic fluid and tumors, understanding bacterial migration from the duodenum into the pancreas via hepato-pancreatic duct is critical. Mathematical models of migration of aerobic bacteria from the duodenum to the pancreas with tumors were developed. Additionally, the bacterial distributions under the pH gradient and those under flow were measured in double-layer flow based microfluidic device and T-shaped cylinders. Migration of aerobic bacteria from the duodenum into pancreas is counteracted by bile and pancreatic juice flow but facilitated by pH-taxis from acidic duodenum fluid toward more favorable slightly alkaline pH in pancreatic juice. Additionally, the reduced flow velocity in cancer patients, due to compressed pancreatic duct by solid tumor, facilitates migration. Moreover, measured distribution of GFP E. coli under the pH gradient in a microfluidic device validated pH-tactic behaviors. Furthermore, Pseudomonas fluorescens in hydrochloride solution, but not in bicarbonate solution, migrated upstream against bicarbonate flow of > 20 μm/s, with an advancement at approximately 50 μm/s.