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

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.

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

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. For this purpose, the bacterial growth data of Pseudomonas spp. in oyster mushroom (Pleurotus ostreatus) subjected to isothermal and non-isothermal storage temperatures were collected from previously published growth curves. Temperature-dependent kinetic growth parameters (maximum specific growth rate 'µmax' and lag phase duration 'λ') were described as a function of storage temperature using the direct two-step, direct one-step and inverse dynamic modelling approach based on Baranyi and Huang models. The fitting capability of the modelling approaches was separately compared, and the one-step modelling approach for the direct methods provided better goodness of fit results regardless of used primary models, which leads the Huang model with being RMSE = 0.226 and R2adj = 0.949 became best for direct methods. Like seen in direct methods, the Huang model gave better goodness of fit results than Baranyi model for inverse method. Results revealed there was no significant difference (p > 0.05) between the growth kinetic parameters obtained from direct one-step modelling approach and inverse modelling approaches based on the Huang model. Satisfactorily statistical indexes show that the inverse dynamic modelling approach can be reliably used as an alternative way of describing the growth behaviour of Pseudomonas spp. in oyster mushroom in a fast and minimum labour effort.

Bacterial microbiota profiling of oyster mushrooms (Pleurotus ostreatus) based on cultivation methods and distribution channels using high-throughput sequencing

The annual consumption and production of oyster mushrooms (Pleurotus ostreatus) have continued to rise due to its nutritive and health-promoting benefits. Cultivated mushrooms are mostly grown in small to medium-scaled scale production plants that present hygienic challenges which could, in turn, increase associated foodborne pathogenic outbreaks. The present study aimed to investigate the shift in microbial ecologies of oyster mushrooms from pre-distribution (cultivation in bottles or on shelves) to post-distribution at supermarkets and open-air markets. Aerobic plate counts and coliforms were quantified using traditional microbiological techniques, and the microbiome associated with oyster mushrooms (n = 70) was analyzed using 16S rRNA amplicon sequencing for an enhanced level of bacterial microbiota profiling. Overall, coliforms recovered from pre-distribution bottle-cultivated mushrooms were 1.9 log CFU/g higher (p < 0.05) than that of shelf-cultivated mushrooms. The mean aerobic plate counts of oyster mushrooms distributed to open-air markets was 1.2 log CFU/g higher (p < 0.05) than packaged mushrooms from supermarkets while there were no significant differences in coliform counts. The pattern of bacterial composition differed by post-distribution channels, with oyster mushrooms collected from the open-air markets demonstrating the richest microbiome diversity. An increase in the relative abundance of Enterobacteriaceae (55-68 %) and Pseudomonadaceae (27-35 %) was observed in pre- and post-distribution mushrooms, respectively. However, no distinct bacterial microbiota differences were observed for the different cultivation methods or different geographical locations for each market type. The current findings add to our understanding of the effects of cultivation methods and commercial distribution channels regarding the microbiome of oyster mushrooms and may inform potential intervention strategies for future production and distribution processes. Furthermore, the tandem analyses of culture-dependent and culture-independent methods can provide more comprehensive information than that obtained when using each approach independently.

Allicin Promoted Reducing Effect of Garlic Powder through Acrylamide Formation Stage

Background: Acrylamide is formed during food heating and is neurotoxic to animals and potentially carcinogenic to humans. It is important to reduce acrylamide content during food processing. Researchers have suggested that garlic powder could reduce acrylamide content, but the key substance and acrylamide reduction pathway of garlic powder was unclear. Methods: The inhibitory effect of garlic powder on acrylamide in asparagine/glucose solution and a fried potato model system were firstly evaluated. Furthermore, the effect of allicin on the amount of produced acrylamide in the asparagine/glucose solution model system and fried potatoes was studied with kinetic analysis. Results: The freeze-dried garlic powder had a higher inhibition rate (41.0%) than oven-dried garlic powder (maximum inhibition rate was 37.3%), and allicin had a 71.3% attribution to the reduction of acrylamide content. Moreover, the inhibition rate of allicin had a nonlinear relationship with the addition level increase. The kinetic analysis indicated that garlic powder and allicin could reduce acrylamide content through the AA formation stage, but not the decomposition stage. Conclusions: Allicin was the key component of garlic powder in reducing acrylamide content during acrylamide formation stage. This research could provide a new method to reduce acrylamide content during food processing and expand the application area of garlic.

Development of a New Modelling Approach and Performance Evaluation of Meta-heuristic Optimization Algorithms for the Prediction of Kinetic Growth Parameters for Pseudomonas spp. in Fish

The main aim of the current work was to build up a new mathematical modelling approach in predictive food microbiology field for the prediction of growth kinetics of microorganisms. For this purpose, the bacterial growth data of Pseudomonas spp. in whole fish (gilt-head seabream) subjected to isothermal and non-isothermal storage temperatures were collected from previously published growth curves. Maximum specific growth rate (1/h) and lag phase duration (h) were described as a function of storage temperature using the direct two-step, direct one-step and inverse dynamic modelling approaches based on various meta-heuristic optimization algorithms. The fitting capability of the modelling approaches and employed optimization algorithms was separately compared, and the one-step modelling approach for the direct methods and the Bayesian optimization method for the used algorithms provided the best goodness of fit results. These two were then further processed in validation step. The inverse dynamic modelling approach based on the Bayesian optimization algorithm yielded satisfactorily statistical indexes (1.02 > Bias factor > 1.09 and 1.07 > Accuracy factor > 1.13), which indicates it can be reliably used as an alternative way of describing the growth behaviour of Pseudomonas spp. in fish in a fast and efficient manner with minimum labour effort.

Dimensional Analysis Model Predicting the Number of Food Microorganisms

Predicting the number of microorganisms has excellent application in the food industry. It helps in predicting and managing the storage time and food safety. This study aimed to establish a new, simple, and effective model for predicting the number of microorganisms. The dimensional analysis model (DAM) was established based on dimensionless analysis and the Pi theorem. It was then applied to predict the number of Pseudomonas in Niuganba (NGB), a traditional Chinese fermented dry-cured beef, which was prepared and stored at 278 K, 283 K, and 288 K. Finally, the internal and external validation of the DAM was performed using six parameters including R ², R ² _adj , root mean square error (RMSE), standard error of prediction (%SEP), A _f , and B _f . High R ² and R ² _adj and low RMSE and %SEP values indicated that the DAM had high accuracy in predicting the number of microorganisms and the storage time of NGB samples. Both A _f and B _f values were close to 1. The correlation between the observed and predicted numbers of Pseudomonas was high. The study showed that the DAM was a simple, unified and effective model to predict the number of microorganisms and storage time.

Development and validation of a one-step modelling approach for the determination of chicken meat shelf-life based on the growth kinetics of Pseudomonas 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 Pseudomonas spp. were collected from published studies conducted in aerobically stored chicken meat product. Temperature-dependent kinetic parameters (maximum specific growth rate 'µ_max' and lag phase duration 'λ') were described as a function of storage temperature through the Ratkowsky model based on the different primary models. Then, the fitting capability of both modelling approaches was compared taking into account root mean square error, adjusted coefficient of determination (adjusted-R²) and corrected Akaike information criterion. The one-step modelling approach showed considerably improved fitting capability regardless of the used primary model. Finally, models developed from the one-step modelling approach were validated for the maximum growth rate data extracted from independent published literature using the statistical indexes Bias (B_f) and Accuracy (A_f) factors. The best prediction capability was obtained for the Baranyi model with B_f and A_f being very close to 1. The shelf-life of chicken meat as a function of storage temperature was predicted using both modelling approaches for the Baranyi model.

Dependence of bacterial growth rate on dynamic temperature changes

Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature.

Dissimilarity in sensory attributes, shelf life and spoilage bacterial and fungal microbiota of industrial-scale wet starch noodles induced by different preservatives and temperature

Shelf life, storage stability and microbial growth of wet starch noodles during storage were investigated, and spoilage microbiota was also analyzed to further reveal the decisive factor shaping the microbial community. Sensory analysis and microbiological results indicated that starch noodles treated with sodium dehydroacetate and stored at 4 °C could effectively delay the moldy decay and extend the shelf-life to 50 days, as compared to control and other treatments. In wet starch noodles, molds were found to have a higher spoilage potential than bacteria and yeasts. 16S rDNA sequencing revealed that preservatives, rather than temperature, could cause the significant difference (PERMANOVA p = 0.001) of spoilage bacterial community among samples and sodium dehydroacetate could markedly reduce the bacterial diversity. ITS rDNA sequencing results demonstrated that temperature was the decisive factor in shaping fungal spoilage microbiota (Mantel test r = 0.413, p = 0.002). Besides, Spearman correlation analysis illustrated that the abundance of some microorganisms such as Pseudomonas, Aspergillus and Penicillium were found to be significantly correlated with pH or temperature. These findings provide guiding information in the selection of preservatives and environmental condition for this high-moisture starch noodles.

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

Baranyi model was fitted to experimental growth data of Pseudomonas spp. on the button mushrooms (Agaricus bisporus) stored at different isothermal conditions (4, 12, 20 and 28 °C), and the kinetic growth parameters of Pseudomonas spp. on the button mushrooms were obtained. The goodness of fit of the Baranyi model was evaluated by considering the root mean squared error (RMSE) and the adjusted coefficient of determination (adjusted-R²). The Baranyi model gave RMSE values lower than 0.193 and adjusted-R² values higher than 0.975 for all isothermal storage temperatures. The maximum specific growth rate (µ_max) was described as a function of temperature using secondary models namely, Ratkowsky and Arrhenius models. The Ratkowsky model described the temperature dependence of µ_max better than the Arrhenius model. Therefore, the differential form of the Baranyi model was merged with the Ratkowsky model, and solved numerically using the fourth-order Runge-Kutta method to predict the concentration of Pseudomonas spp. populations on button mushrooms under non-isothermal conditions in which they are frequently subjected to during storage, delivery and retail marketing. The validation performance of the dynamic model used was assessed by considering bias (B_f) and accuracy (A_f) factors which were found to be 0.998 and 1.016, respectively. The dynamic model developed also exhibited quite small mean deviation (MD) and mean absolute deviation (MAD) values being -0.013 and 0.126 log CFU/g, respectively. The modelling approach used in this work could be an alternative to traditional enumeration techniques to determine the number of Pseudomonas spp. on mushrooms as a function of temperature and time.

A mathematical modeling approach to the quantification of lactic acid bacteria in vacuum-packaged samples of cooked meat: Combining the TaqMan-based quantitative PCR method with the plate-count method

The TaqMan-based quantitative Polymerase Chain Reaction (qPCR) method and the Plate Count (PC) method are both used in combination with primary and secondary mathematical modeling, to describe the growth curves of Leuconostoc mesenteroides and Weissella viridescens in vacuum-packaged meat products during storage under different isothermal conditions. Vacuum-Packaged Morcilla (VPM), a typical cooked blood sausage, is used as a representative meat product, with the aim of improving shelf-life prediction methods for those sorts of meat products. The standard curves constructed by qPCR showed good linearity between the cycle threshold (C_T) and log₁₀ CFU/g, demonstrating the high precision and the reproducible results of the qPCR method. The curves were used for the quantification of L. mesenteroides and W. viridescens in artificially inoculated VPM samples under isothermal storage (5, 8, 13 and 18 °C). Primally, both the qPCR and the PC methods were compared, and a linear regression analysis demonstrated a statistically significant linear correlation between the methods. Secondly, the Baranyi and Roberts model was fitted to the growth curve data to estimate the kinetic parameters of L. mesenteroides and W. viridescens under isothermal conditions, and secondary models were used to establish the dependence of the maximum specific growth rate on the temperature. The results proved that primary and secondary models were adequate for describing the growth curves of both methods in relation to both bacteria. In conclusion, the results of all the experiments proved that the qPCR method in combination with the PC method can be used to construct microbial growth kinetics and that primary and secondary mathematical modeling can be successfully applied to describe the growth of L. mesenteroides and W. viridescens in vacuum-packaged morcilla and, by extension, other cooked meat products with similar characteristics.

Underscoring interstrain variability and the impact of growth conditions on associated antimicrobial susceptibilities in preclinical testing of novel antimicrobial drugs

In the era of multidrug resistant (MDR) organisms, reliable efficacy testing of novel antimicrobials during developmental stages is of paramount concern prior to introduction in clinical trials. Unfortunately, interstrain variability is often underappreciated when appraising the efficacy of innovative antimicrobials as preclinical testing of a limited number of standardized strains in unvarying conditions does not account for the vastness and potential for hyperdiversity among and within microbial populations. In this review, the importance of accounting for interstrain variability's potential to impact breadth of novel drug efficacy evaluation in the early stages of drug development will be discussed. Additionally, testing under varying microenvironmental conditions that may influence drug efficacy will be discussed. Biofilm growth, the influence of polymicrobial growth, mechanisms of antimicrobial resistance, pH, anaerobic conditions, and other virulence factors are some of critical issues that require more attention and standardization during preclinical drug efficacy evaluation. Furthermore, potential solutions for addressing this issue in pre-clinical antimicrobial development are proposed via centralization of microbial characterization and drug target databases, testing of a large number of clinical strains, inclusion of mutator strains in testing and the use of growth parameter mathematical models for testing.