Growth of Clostridium perfringens in cooked chicken during cooling: One-step dynamic inverse analysis, sensitivity analysis, and Markov Chain Monte Carlo simulation

Effect of allyl isothiocyanate on the growth and virulence of Clostridium perfringens and its application on cooked pork

The objective of this study is to explore the antibacterial action modes and virulence-inhibitory effects of allyl isothiocyanate (AITC) against Clostridium perfringens (C. perfringens). The minimum inhibitory concentration (MIC) of AITC against vegetative cells of Cp 13124 was 0.1 μL/mL, and the time-kill kinetics analysis revealed that AITC could significantly suppress the growth of Cp 13124. According to the results from scanning electron microscopy (SEM), fluorescence microscopy, and UV absorbance substance detection, the cell membrane of Cp 13124 was damaged upon AITC treatment, causing a loss of integrity and the release of intracellular substances. Meanwhile, the fluorescence quenching experiment indicated the interaction of AIT-C with membrane proteins, which caused changes in the conformation of membrane proteins. Measurement of reactive oxygen species (ROS) and flow cytometry analysis demonstrated that AITC could induce apoptosis through oxidative stress. The formation of Cp 13124 biofilms was inhibited by AITC using the crystalline violet method, which was possibly related to the inhibition of sliding motility. Finally, low concentrations of AITC could be used as an antibacterial agent to inhibit the outgrowth of Cp 13124 in cooked pork, suggesting that AITC is a promising candidate for novel preservatives in the meat business.

Application of interaction models in predicting the simultaneous growth of Staphylococcus aureus and different concentrations of background microbiota in Chinese-style braised beef

This study aimed to investigate the growth kinetics of S. aureus and different concentrations of background microbiota in Chinese-style braised beef (CBB). A one-step analysis method was applied to develop predictive model to describe the simultaneous growth and interaction of S. aureus with different concentrations of background microbiota in CBB. The results show that a one-step method successfully models the growth of S. aureus and background microbiota in CBB and the competing interactions between the two. In sterile CBB, the estimated minimum growth temperatures (T_min,S) and the maximum growth concentrations (Y_max,S) were 8.76 °C and 9.58 log CFU/g for S. aureus. Under competition, the growth of background microbiota was not affected by S. aureus, the estimated T_min,B and Y_max,B was 4.46 °C and 9.94 log CFU/g. The background microbiota in CBB did not affect the growth rate of S. aureus (α₁ = 1.04), but had an inhibitory effect on the number of S. aureus (α₂ = 0.69) at the later growth stage. The Root Mean Square Error (RMSE) of the modeling data was 0.34 log CFU/g, with 85.5% of the residual errors within ±0.5 log CFU/g of experimental observations. The one-step analysis and dynamic temperatures (8 °C-32 °C) verification indicated that the RMSE of prediction was <0.5 log CFU/g for both S. aureus and background microbiota. This study demonstrates that microbial interaction models are a useful and promising tool for predicting and evaluating the spatiotemporal population dynamics of S. aureus and background microbiota in CBB products.

Effect of Alternative Preservation Steps and Storage on Vitamin C Stability in Fruit and Vegetable Products: Critical Review and Kinetic Modelling Approaches

Vitamin C, a water-soluble compound, is a natural antioxidant in many plant-based products, possessing important nutritional benefits for human health. During fruit and vegetable processing, this bioactive compound is prone to various modes of degradation, with temperature and oxygen being recognised as the main factors responsible for this nutritional loss. Consequently, Vitamin C is frequently used as an index of the overall quality deterioration of such products during processing and post-processing storage and handling. Traditional preservation methods, such as thermal processing, drying and freezing, are often linked to a substantial Vitamin C loss. As an alternative, novel techniques or a combination of various preservation steps ("hurdles") have been extensively investigated in the recent literature aiming at maximising Vitamin C retention throughout the whole product lifecycle, from farm to fork. In such an integrated approach, it is important to separately study the effect of each preservation step and mathematically describe the impact of the prevailing factors on Vitamin C stability, so as to be able to optimise the processing/storage phase. In this context, alternative mathematical approaches have been applied, including more sophisticated ones that incorporate parameter uncertainties, with the ultimate goal of providing more realistic predictions.

Implementing a new dose-response model for estimating infection probability of Campylobacter jejuni based on the key events dose-response framework

Understanding the dose-response relationship between ingested pathogenic bacteria and infection probability is a key factor for appropriate risk assessment of foodborne pathogens. The objectives of this study were to develop and validate a novel mechanistic dose-response model for Campylobacter jejuni and simulate the underlying mechanism of foodborne illness during digestion. Bacterial behavior in the human gastrointestinal environment, including survival at low pH in the gastric environment after meals, transition to intestines, and invasion to intestinal tissues, was described using a Bayesian statistical model based on the reported experimental results of each process while considering physical food types (liquid or solid) and host age (young adult or elderly). Combining the models in each process, the relationship between pathogen intake and the infection probability of C. jejuni was estimated and compared with reported epidemiological dose-response relationships. Taking food types and host age into account, the prediction range of the infection probability of C. jejuni successfully covered the reported dose-response relationships from actual C. jejuni outbreaks. According to sensitivity analysis of predicted infection probabilities, the host age factor and the food type factor have relatively higher relevance than other factors. Thus, the developed Key Events Dose Response Framework can derive novel information for quantitative microbiological risk assessment in addition of dose-response relationship. The developed framework is potentially applicable to other pathogens to quantify the dose-response relationship from experimental data obtained from digestion. Importance Based on the mechanistic approach called Key Events Dose Response Framework alternative to previous non-mechanistic approach, the dose-response models for infection probability of C. jejuni were developed considering with age of people who take pathogen and food type. The developed predictive framework illustrated highly accurate prediction of dose (minimum difference 0.21 log CFU) for a certain infection probability compared with the previously reported dose-response relationship. In addition, the developed prediction procedure revealed that the dose-response relationship strongly depends on food type as well as host age. The implementation of Key Event Dose Response Framework will mechanistically and logically reveal the dose-response relationship and provide useful information with quantitative microbiological risk assessment of C. jejuni on foods.

On optimum dynamic temperature profiles for thermal inactivation kinetics determination

Determination of inactivation kinetics, associated with thermal processing of foods and obtained from dynamic temperature experiments, requires carefully designed experiments, the primary element being the selection of the appropriate temperature profile along with a carefully planned sampling schedule. In the present work, a number of different dynamic temperature profiles were investigated in terms of their ability to generate accurate kinetic parameters with low confidence intervals (CIs). Although alternative models have been also tested, our work was concentrated on thermal inactivation kinetics that could be described by the classical D-z values. A pair of D and z values was assumed, and for each temperature profile tested, concentration data at different processing times were generated through the appropriate models. Next, an error (up to ±2.5% or ±5%) was introduced on these theoretical values to generate pseudo-experimental data, and the back-calculation of the assumed kinetic parameters by non-linear regression was performed. The accuracy and the 95% CIs of the estimated kinetic parameters were evaluated; joint confidence regions were also constructed to investigate parameters correlation. The effect of temperature profile pattern, level of error, number of experimental points, and reference temperature was assessed. A stepwise increasing and a single triangle-pattern temperature profile were the best profiles among those tested. As a general observation, based on different kinetic models investigated, temperature profiles and sampling intervals that result in concentration versus time diagrams having shapes as suggested by the primary model used when isothermally applied are not considered appropriate for parameter estimation.

Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions

Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models’ parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.