Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions

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.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.

Antimicrobial and Cytotoxicity Activities of Medicinal Plants against Salmonella gallinarum Isolated from Chickens

Medicinal plants have been the good source of treatment for different ailments of humans as well as animals for centuries. However, in Tanzania, few plants were investigated for their efficacy against various diseases of chickens. In the present study, four medicinal plants were investigated against Salmonella gallinarum isolated from chickens. The minimum inhibitory concentration (MIC) using the broth microdilution methods and minimum bactericidal concentration (MBCs) were used to evaluate the activities of plants against chicken salmonellosis. For the safety of chickens against the toxicity of plants, the cytotoxicity assay was determined using a brine shrimp lethality test. Aloe secundiflora leaf ethyl acetate (ALEA), Aloe rabaiensis leaf methanolic (ArM), Aloe rabaiensis leaf ethyl acetate (ArLEA), and Punica granatum leaf ethyl acetate (PGLEA) extracts exhibited the highest MIC (0.3906 mg/mL) and MBC (3.125 mg/mL), respectively. The Dolichos kilimandscharicus tuber ethyl acetate (DTEA) and Dolichos kilimandscharicus tuber pet ether (DTPE) extracts displayed MIC of 1.563 mg/mL and 12.50 mg/mL and MBC of 12.50 mg/mL and 25.50 mg/mL, respectively. The highest LC₅₀ values exhibited in Dolichos kilimandscharicus ranged from 7.937 × 10⁻⁴ mg/mL to 7.242 × 10⁻² mg/mL for pet ether and methanolic extracts, respectively, while ALEA extract exhibited LC₅₀ of 7.645 × 10⁻³ mg/mL. Generally, the extracts with MIC 0.3906 mg/mL and MBC 3.125 mg/mL demonstrated the highest antibacterial activity with low toxicity efficient to manage chicken salmonellosis. Dolichos kilimandscharicus, which exhibited higher toxicity, warrants further investigation on insecticidal and anticancer agents.

The Potential Effect of Microbiota in Predicting The Freshness of Chilled Chicken

1. The goals of this study were to analyse the changes in microbiota composition of chilled chicken during storage and identify microbial biomarkers related to meat freshness.2. The study used 16S rDNA sequencing to track the microbiota shift in chilled chicken during storage. Associations between microbiota composition and storage time were analysed and microbial biomarkers were identified.3. The results showed that microbial diversity of chilled chicken decreased with the storage time. A total of 27 and 24 microbial biomarkers were identified by using orthogonal partial least squares (OPLS) and the random forest regression approach, respectively. The receiver operating characteristic (ROC) curve analysis indicated that the OPLS regression approach had better performance in identifying freshness-related biomarkers. The multiple stepwise regression analysis identified four key microbial biomarkers, including Streptococcus, Carnobacterium, Serratia and Photobacterium genera and constructed a predictive model.4. The study provided microbial biomarkers and a model related to the freshness of chilled chicken. These findings provide a basis for developing detection methods of the freshness of chilled chicken.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm². Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm², respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

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.

Growth Kinetics of Bacillus pasteurii in Solid-Free Drilling Fluids

A broken stratum is a complex stratum often encountered during drilling. Under erosion of the drilling fluid and disturbance of the drill pipe string, the rock in the well wall of the broken stratum is prone to collapsing and falling off, causing the well wall to lose its stability. Improving the cementing force between the broken blocks and forming a complete well wall are essential for overcoming this instability. The present study combined microbially induced calcite precipitation technology with solid-free drilling fluid technology for the first time to formulate a drilling fluid to overcome the instability of the well wall of a broken stratum. However, first and foremost, the growth of microorganisms in drilling fluids must be elucidated. To this end, experimental and theoretical analyses were performed to examine Bacillus pasteurii growth in drilling fluids composed of a single agent or combinations of various materials, such as a zwitterionic coating agent (FA367), a biopolymer (XC), a polyacrylate polymer (PAC-LV), and potassium polyacrylate (K-PAM). Experimental B. pasteurii growth data were then fitted using a modified Gompertz model. The mean square error indicated that the generated model had a reasonable degree of fit, and the bias and accuracy factors showed that the model could predict B. pasteurii growth. Among the different drilling fluid combinations used, suitable fluids for B. pasteurii growth were XC alone, XC, and PAC-LV in the two-material-based fluid and FA367, XC, and K-PAM in the three-material-based fluid. These results provide a solid foundation for the development of microbial drilling fluids to solve instability problems in broken geological formations.

Microbiota of Chicken Breast and Thigh Fillets Stored under Different Refrigeration Temperatures Assessed by Next-Generation Sequencing

Chicken is one of the most widely consumed meats worldwide. The exploration of the bacterial diversity of chicken meat may provide new insights into the chicken-associated microbiome that will lead to moderation of food spoilage or safety. This study was undertaken to explore the bacterial communities of chicken breast and thigh fillets stored at refrigeration (0 °C and 5 °C) and slightly abuse (10 °C) temperatures for 5 days through conventional cultural methods along with next-generation sequencing (NGS) analysis. Total viable counts (TVC), Brochothrix thermosphacta, Pseudomonas spp., and lactic acid bacteria (LAB) were enumerated, while the bacterial communities were mapped through 16S rRNA gene amplicon sequencing. Chicken breast and thigh fillets possessed a complex bacterial structure that incorporated a total of >200 Operational Taxonomic Units (OTUs) at the genus level. The core microbiota of fresh samples consisted of Acinetobacter, Brochothrix, Flavobacterium, Pseudomonas, Psychrobacter, and Vibrionaceae (family). These genera persisted until the end of storage in >80% of samples, except Psychrobacter and Flavobacterium, while Photobacterium was also identified. Hierarchical clustering showed a distinction of samples based on storage time and chicken part. Conventional plate counting with growth media commonly used in spoilage studies did not always correspond to the microbial community profiles derived from NGS analysis, especially in Pseudomonas, Acinetobacter, Photobacterium, and Vibrionaceae. Results of the present study highlight Photobacterium and Vibrionaceae, in general, as potent chicken meat spoilers and suggest the necessity to combine classical microbiological methods along with NGS technologies to characterize chicken meat spoilage microbiota.

Air versus Water Chilling of Chicken: a Pilot Study of Quality, Shelf-Life, Microbial Ecology, and Economics

The United States' large-scale poultry meat industry is energy and water intensive, and opportunities may exist to improve sustainability during the broiler chilling process. By USDA regulation, after harvest the internal temperature of the chicken must be reduced to 40°F or less within 16 h to inhibit bacterial growth that would otherwise compromise the safety of the product. This step is accomplished most commonly by water immersion chilling in the United States, while air chilling methods dominate other global markets. A comprehensive understanding of the differences between these chilling methods is lacking. Therefore, we assessed the meat quality, shelf-life, microbial ecology, and techno-economic impacts of chilling methods on chicken broilers in a university meat laboratory setting. We discovered that air chilling methods resulted in superior chicken odor and shelf-life, especially prior to 14 days of dark storage. Moreover, we demonstrated that air chilling resulted in a more diverse microbiome that we hypothesize may delay the dominance of the spoilage organism Pseudomonas Finally, a techno-economic analysis highlighted potential economic advantages to air chilling compared to water chilling in facility locations where water costs are a more significant factor than energy costs.IMPORTANCE As the poultry industry works to become more sustainable and to reduce the volume of food waste, it is critical to consider points in the processing system that can be altered to make the process more efficient. In this study, we demonstrate that the method used during chilling (air versus water chilling) influences the final product microbial community, quality, and physiochemistry. Notably, the use of air chilling appears to delay the bloom of Pseudomonas spp. that are the primary spoilers in packaged meat products. By using air chilling to reduce carcass temperatures instead of water chilling, producers may extend the time until spoilage of the products and, depending on the cost of water in the area, may have economic and sustainability advantages. As a next step, a similar experiment should be done in an industrial setting to confirm these results generated in a small-scale university lab facility.

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.