Predictive food microbiology for the meat industry: a review

Ultrasound-Assisted Nanoemulsion Loaded with Optimized Antibacterial Essential Oil Blend: A New Approach against Escherichia coli, Staphylococcus aureus, and Salmonella Enteritidis in Trout (Oncorhynchus mykiss) Fillets

This study aimed to obtain and characterize an oil-in-water nanoemulsion (NE) loaded with an in vitro optimized bactericidal essential oil blend of 50% oregano, 40% thyme, and 10% lemongrass and to evaluate its potential at three different concentrations (0.5%, 1%, and 2%) in the inactivation of Escherichia coli, Staphylococcus aureus, and Salmonella enterica serotype Enteritidis inoculated in rainbow trout fillets stored at 4 °C for 9 days. Regarding the NE, the nanometric size (<100 nm) with low polydispersion (0.17 ± 0.02) was successfully obtained through ultrasound at 2.09 W/cm2. Considering the three concentrations used, S. Enteritidis was the most susceptible. On the other hand, comparing the concentrations used, the NE at 2% showed better activity, reducing S. Enteritidis, E. coli, and S. aureus by 0.33, 0.20, and 0.73 log CFU/g, respectively, in the trout fillets. Thus, this data indicates that this is a promising eco-friendly alternative to produce safe fish for consumption and reduce public health risks.

Evaluation of the Microbiological Status of Cattle Carcasses in Mongolia: Considering the Hygienic Practices of Slaughter Establishments

The meat industry has received great attention in Mongolia, having over 70 million livestock, and is important to the nation's economy. Systematic microbiological testing of carcasses has not been mandatorily regulated in all abattoir premises, and the efficacy of the introduction of the Good Hygiene Practice and Hazard Analysis Critical Control Points (HACCP) to some plants has not yet been tested microbiologically in Mongolia. Therefore, samples were collected from two establishments: plant A with an HACCP certificate from a third party and plant B without an HACCP certificate. The rates and levels of the total bacterial count (TBC) as overall hygiene indicators, the Enterobacteriaceae count (EBC) as fecal contamination indicators, and the Staphylococcus spp. count (SC) as personal hygiene indicators were determined on different parts of beef carcasses. The contamination rates in most parts were lower in plant A than in plant B (e.g., TBC in the rump and flank: 103-105 and 105-107, in plant A vs. 104-106 and 105-108 in plant B, respectively). Plant A also had a lower EBC and SC (p < 0.001). Furthermore, 2 out of 100 beef carcasses (2%) were positive for enterohemorrhagic Escherichia coli as a foodborne pathogen indicator in plant A.

Potential of Essential Oils in the Control of Listeria monocytogenes

Listeria monocytogenes is a foodborne pathogen, the causative agent of listeriosis. Infections typically occur through consumption of foods, such as meats, fisheries, milk, vegetables, and fruits. Today, chemical preservatives are used in foods; however, due to their effects on human health, attention is increasingly turning to natural decontamination practices. One option is the application of essential oils (EOs) with antibacterial features, since EOs are considered by many authorities as being safe. In this review, we aimed to summarize the results of recent research focusing on EOs with antilisterial activity. We review different methods via which the antilisterial effect and the antimicrobial mode of action of EOs or their compounds can be investigated. In the second part of the review, results of those studies from the last 10 years are summarized, in which EOs with antilisterial effects were applied in and on different food matrices. This section only included those studies in which EOs or their pure compounds were tested alone, without combining them with any additional physical or chemical procedure or additive. Tests were performed at different temperatures and, in certain cases, by applying different coating materials. Although certain coatings can enhance the antilisterial effect of an EO, the most effective way is to mix the EO into the food matrix. In conclusion, the application of EOs is justified in the food industry as food preservatives and could help to eliminate this zoonotic bacterium from the food chain.

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R² of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R² ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ_max (all R² ≥ 0.97) or Lag (all R² ≥ 0.98). Growth models were validated by the actual test values, with B_f and A_f close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10⁵ CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

Development and validation of a predictive model for pathogenic Escherichia coli in fresh-cut produce

This study was performed to develop and validate a predictive growth model of pathogenic Escherichia coli to ensure the safety of fresh-cut produce. Samples were inoculated with a cocktail of seven E. coli strains of five pathotypes (EHEC, Enterohemorrhagic E. coli; ETEC, Enterotoxigenic E. coli; EPEC, Enteropathogenic E. coli; EIEC, Enteroinvasive E. coli, and EAEC, Enteroaggregative E. coli) and stored at 4, 10, 12, 15, 25, 30, and 37°C. Growth of pathogenic E. coli was observed above 12°C. The primary growth model for pathogenic E. coli in fresh-cut produce was developed based on the Baranyi model. The secondary model was developed as a function of temperature for lag phase duration (LPD) and maximum specific growth rate (μmax) based on the polynomial second-order model. The primary and secondary models for pathogenic E. coli were fitted with a high degree of goodness of fit (R2 ≥ 0.99). The bias factor (Bf), accuracy factor (Af), and root mean square error (RMSE) were 0.995, 1.011, and 0.084, respectively. The growth model we developed can provide useful data for assessing the quantitative microbial risk of pathogenic E. coli in fresh-cut produce intended for human consumption. In addition, it is thought to be widely available in industries that produce, process, distribute, and sell fresh-cut produce.

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.

Evaluation of Simulated Shelf-Life Conditions for Food Service Applications on Chicken Tenderloins

The objective of this study was to validate the shelf-life of marinated and frozen chicken tenderloins. Treatments were randomly assigned to the age of the tenderloins post-harvest, days aged (DA): DA4, DA5, DA6, DA7, and DA8. Microbial analyses were used to analyze the growth of aerobic, psychotropic, and lactobacilli bacteria to assess the shelf-life of bulk-packaged chicken tenderloins. Tenderloins were sampled fresh, then vacuum tumbled in a marinade. After marination, the tenderloins were sampled with the remaining tenderloins packaged and frozen (-25 °C). After freezing the chicken tenderloins were slacked in a refrigerated cooler (2.2 °C) for up to 132 h (h) and sampled at 36 h, then every 24 h following. After marination, each treatment significantly (p < 0.05) decreased in aerobic and psychotropic counts except DA4. During slacking, no treatment crossed the threshold of 10⁶ CFU/mL (Log 6) set for this study. Though none crossed the threshold, treatments DA4, DA5, and DA6 had significant (p < 0.05) increases in aerobic bacteria after 7 days of age. The psychotropic bacteria continuously grew at each sampling period, with DA4 and DA5 surpassing the other treatments (p < 0.05) at 108 h and 132 h reaching 105 CFU/mL. Every treatment remained below the spoilage threshold, suggesting that this method of storage is suitable for chicken tenderloin shelf-life.

Kinetics of Staphylococcus aureus growth and Enterotoxin A production in milk under shaking and static conditions

Numerous studies on bacterial growth or survival predictive models have been conducted since the establishment of predictive microbiology. However, limited research focused on the prediction of bacteria-producing enterotoxins, which are often the causative agents of food-borne diseases. This study aimed to determine an appropriate kinetic model of staphylococcal enterotoxin A (SEA) production in milk after contamination with Staphylococcus aureus. An S. aureus strain producing SEA was inoculated into milk with an initial inoculum concentration of approximately 3.0 log CFU/mL. All samples were incubated for 30-48 h at 20 °C ± 1 °C, 28 °C ± 1 °C, and 36 °C ± 1 °C separately under shaking or static conditions. Duplicate samples were carried out at appropriate intervals to count the number of S. aureus colonies and detect the concentration of SEA. Experimental results showed that the SEA concentration curves under all experimental conditions were sigmoidal and consisted of three phases: lag, exponential, and stationary. Thus, the modified Gompertz model was used to describe the profile of SEA concentration in milk during the incubation. A good fitting accuracy (R² > 0.97) indicated that the modified Gompertz model was appropriate. In addition to temperature, shaking during incubation also affected the maximal production rate of SEA and the maximal SEA concentrations, and shortened the lag phase at lower incubation temperatures, suggesting that the mechanical movements (e.g., stirring, pumping, and flowing) during the milk processing would increase the risk of SEA occurrence. Besides, the time to detection (TTD) of SEA was found to range from 3 to 24.5 h at temperatures of 36 °C ± 1 °C-20 °C ± 1 °C, at which time the concentrations of S. aureus ranging from 5.0 log CFU/mL-6.9 log CFU/mL at the TTD. This study contributed to understanding the kinetics of SEA production and the possible factors affecting the synthesis of SEA during the manufacturing of liquid foods, such as milk.

Predictive modeling of the early stages of semi-solid food ripening: Spatio-temporal dynamics in semi-solid casein matrices

A mechanistic, spatio-temporal model to predict early stage semi-solid food ripening, exemplary for semi-solid casein matrices, was created using software based on the finite element method (FEM). The model was refined and validated by experimental data obtained during 8 wk of ripening of a casein matrix that was inoculated by one single central injection of starter culture. The resulting spatio-temporal distributions of lactococci strains, lactose, lactic acid/lactate and pH allowed us to optimize a number of parameters of the predictive model. Using the optimized model, the agreement between simulation and experiment was found to be satisfactory, with the pH matching best. The predictive model unveiled that effective diffusion of substrate and metabolites were crucial for an eventual homogeneous distribution of the measured substances. Hence, while using the optimized parameters from the single injection model, an injection technology for starter culture to inoculate and ferment casein matrices homogeneously was developed by means of solving another optimization problem with respect to injection positions. The casein matrix inoculated by the proposed injection pattern (21 injections, distance = 19 mm) showed sufficient homogeneity (bacterial activity and pH distribution) after the early stages of ripening, demonstrating the potential of application of the injection technology for fermentation of casein-based foods e.g. cheese.

Bacterial community dynamics during different stages of processing of smoked bacon using the 16S rRNA gene amplicon analysis

To identify the microbial community and origin of the spoilage flora of bacon, the changes in microbial population numbers and community structure were followed along the processing line, using culture-independent and culture-dependent methods. 16S rRNA gene amplicon sequencing (16S-seq) analysis showed that community complexity and structure significantly differed at different processing stages. Some 428 bacterial groups were ascertained at genus level, and Acinetobacter, Pseudomonas, Psychrobacter, and Brochothrix were the predominant bacteria on raw meats. After curing specimens dominated by Psychrobacter, Weissella, Vibrio, Leuconostoc, Myroides, Acinetobacter, and Lactobacillus, a total of 33 species were identified by traditional microbiological analyses and direct sequence determination methods. Our results indicated that curing should be considered one of the primary factors during various processing steps, presumably contaminating the products directly or indirectly.

Dust accumulated fungi in air-conditioning system: Findings based on field and laboratory experiments

This study analyzes the growth and reproduction of dust accumulated fungi (DAF) in an air-conditioning system based on field measurement and molecular biology, laboratory experiment and prediction modelling. The field measurement was conducted to collect dust in filter screen, surface cooler and air supply duct of two air handling units (AHUs). The results indicate that dust volume and fungal number in two AHUs generally met the hygienic specification of public buildings, but the cleansing did not fulfil requirements. High-throughput sequencing was conducted, revealing that the dominant fungal species were Alternaria_betae-kenyensis, Cladosporium_delicatulum, Aspergillus_sydowii, Verticillium_dahliae. Laboratory experiment was conducted to analyze the impact of several factors (e.g. growth time, temperature, relative humidity, duct material) and their combination on the DAF growth. The results indicate that fungal growth increased with time, peaking at 4 days or 5 days. Higher relative humidity or temperature was conducive to fungal growth. The orthogonal experiment revealed that the condition of "antibacterial composite, 22 ± 1 °C and 45%-55% RH" had the strongest inhibiting impact on fungal growth. Logistic model, Gompertz model and square-root model were further developed to predict the fungal growth under different conditions. The results show that the Logistic model had high feasibility and accuracy, the Gompertz model was feasible with lower accuracy and the square-root model was feasible with high accuracy. Overall, this study facilitates the understanding of the DAF growth in air-conditioning ducts, which is important for real-time prediction and timely control of the fungal contamination.

Modeling of starter cultures growth for improved Thai sausage fermentation and cost estimating for sausage preparation and transportation

The purpose of this study was to improve Thai fermented sausage flavor by adding starter cultures (i.e., Pediococcus pentosaceus, Pediococcus acidilactici, Weissella cibaria, Lactobacillus plantarum, Lactobacillus pentosus, and Lactobacillus sakei) as compared with naturally fermented sausage. The predictive mathematical models for growth of P. acidilactici and natural lactic acid bacteria (LAB) in Thai fermented sausage were developed to obtain specific prepared sausage quality. Furthermore, comparisons of sausage preparation and transportation cost between nonrefrigerated and refrigerated trucks were studied. The concentration of 3-methyl-butanoic acid synthesized from LAB inoculated sausage was higher than in the control sample which contributed to the flavor forming. Moreover, the proposed unstructured kinetic models of Thai fermented sausage substrates and products describing the consumption of total protein and glucose, and the production of nonprotein nitrogen responsible for flavor enhancer, lactic acid and formic acid concentration were successfully fitted with two selected experimental data sets of the in situ fermentation of Thai fermented sausage. Finally, the transportation of inoculated sausages in a nonrefrigerated truck by combining fermentation process and transportation was more cost efficient for delivering sausages in a long distance.

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Mathematical models are often used to predict microbial growth in food products. An important class of these models involves the adaptation of classical sigmoid functions, such as the Gompertz and logistic functions. This study aimed to validate the use of the modified Richards model in various situations, which have not previously been tested. The model was obtained through solving a system of two differential equations and could be applied to both isothermal and non-isothermal environments. To test and validate this model, we used published datasets containing data for the growth of Pseudomonas spp. in fish products. The results obtained after fitting the model showed that it could be effectively used to describe and predict the Pseudomonas growth curves under various temperature regimens. However, the influence of the shape parameter on the growth curve is an issue that needs further evaluation.

Salmonella Typhimurium and Staphylococcus aureus dynamics in/on variable (micro)structures of fish-based model systems at suboptimal temperatures

The limited knowledge concerning the influence of food (micro)structure on microbial dynamics decreases the accuracy of the developed predictive models, as most studies have mainly been based on experimental data obtained in liquid microbiological media or in/on real foods. The use of model systems has a great potential when studying this complex factor. Apart from the variability in (micro)structural properties, model systems vary in compositional aspects, as a consequence of their (micro)structural variation. In this study, different experimental food model systems, with compositional and physicochemical properties similar to fish patés, are developed to study the influence of food (micro)structure on microbial dynamics. The microbiological safety of fish products is of major importance given the numerous cases of salmonellosis and infections attributed to staphylococcus toxins. The model systems understudy represent food (micro)structures of liquids, aqueous gels, emulsions and gelled emulsions. The growth/inactivation dynamics and a modelling approach of combined growth and inactivation of Salmonella Typhimurium and Staphylococcus aureus, related to fish products, are investigated in/on these model systems at temperatures relevant to fish products' common storage (4°C) and to abuse storage temperatures (8 and 12°C). ComBase (http://www.combase.cc/) predictions compared with the maximum specific growth rate (μ_max) values estimated by the Baranyi and Roberts model in the current study indicated that the (micro)structure influences the microbial dynamics. Overall, ComBase overestimated microbial growth at the same pH, a_w and storage temperature. Finally, the storage temperature had also an influence on how much each model system affected the microbial dynamics.

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R²) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10⁻⁴ and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.

Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods

Predictive models are mathematical expressions that describe the growth, survival, inactivation, or biochemical processes of foodborne bacteria. During processing of contaminated raw materials and food preparation, bacteria are entrapped into the food residues, potentially transferred to the equipment surfaces (abiotic or inert surfaces) or cross-contaminate other foods (biotic surfaces). Growth of bacterial cells can either occur planktonically in liquid or immobilized as colonies. Colonies are on the surface or confined in the interior (submerged colonies) of structured foods. For low initial levels of bacterial population leading to large colonies, the immobilized growth differs from planktonic growth due to physical constrains and to diffusion limitations within the structured foods. Indeed, cells in colonies experience substrate starvation and/or stresses from the accumulation of toxic metabolites such as lactic acid. Furthermore, the micro-architecture of foods also influences the rate and extent of growth. The micro-architecture is determined by (i) the non-aqueous phase with the distribution and size of oil particles and the pore size of the network when proteins or gelling agent are solidified, and by (ii) the available aqueous phase within which bacteria may swarm or swim. As a consequence, the micro-environment of bacterial cells when they grow in colonies might greatly differs from that when they grow planktonically. The broth-based data used for modeling (lag time and generation time, the growth rate, and population level) are poorly transferable to solid foods. It may lead to an over-estimation or under-estimation of the predicted population compared to the observed population in food. If the growth prediction concerns pathogen bacteria, it is a major importance for the safety of foods to improve the knowledge on immobilized growth. In this review, the different types of models are presented taking into account the stochastic behavior of single cells in the growth of a bacterial population. Finally, the recent advances in the rules controlling different modes of growth, as well as the methodological approaches for monitoring and modeling such growth are detailed.

Dissolved carbon dioxide and oxygen concentrations in purge of vacuum-packaged pork chops and the relationship to shelf life and models for estimating microbial populations

The objectives of this study were to determine the dissolved CO2 and O2 concentrations in the purge of vacuum-packaged pork chops over a 60 day storage period, and to elucidate the relationship of dissolved CO2 and O2 to the microbial populations and shelf life. As the populations of spoilage bacteria increased, the dissolved CO2 increased and the dissolved O2 decreased in the purge. Lactic acid bacteria dominated the spoilage microflora, followed by Enterobacteriaceae and Brochothrix thermosphacta. The surface pH decreased to 5.4 due to carbonic acid and lactic acid production before rising to 5.7 due to ammonia production. A mathematical model was developed which estimated microbial populations based on dissolved CO2 concentrations. Scanning electron microscope images were also taken of the packaging film to observe the biofilm development. The SEM images revealed a two-layer biofilm on the packaging film that was the result of the tri-phase growth environment.

Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP) 2013

A predictive model was performed to describe the growth of Staphylococcus aureus in raw pork by using Integrated Pathogen Modeling Program 2013 and a polynomial model as a secondary predictive model. S. aureus requires approximately 180 h to reach 5-6 log CFU/g at 10 °C. At 15 °C and 25 °C, approximately 48 and 20 h, respectively, are required to cause food poisoning. Predicted data using the Gompertz model was the most accurate in this study. For lag time (LT) model, bias factor (Bf) and accuracy factor (Af) values were both 1.014, showing that the predictions were within a reliable range. For specific growth rate (SGR) model, Bf and Af were 1.188 and 1.190, respectively. Additionally, both Bf and Af values of the LT and SGR models were close to 1, indicating that IPMP Gompertz model is more adequate for predicting the growth of S. aureus on raw pork than other models.

Modeling and predicting the effect of temperature on the growth of Proteus mirabilis in chicken

A predictive model to study the effect of temperature on the growth of Proteus mirabilis was developed. The growth data were collected under several isothermal conditions (8, 12, 16, 20, 25, 30, 35, 40, and 45°C) and were fitted into three primary models, namely the logistic model, the modified Gompertz model, and the Baranyi model. The statistical characteristics to evaluate the models such as R(2), mean square error, and Sawa's Bayesian information criteria (BIC) were used. Results showed that the Baranyi model performed best, followed by the logistic model and the modified Gompertz model. R(2) values for the secondary model derived from logistic, modified Gompertz, and Baranyi models were 0.965, 0.974, and 0.971, respectively. Bias factor and accuracy factor indicated that both the modified Gompertz and Baranyi models fitted the growth data better. Therefore, the Baranyi model was proposed to be the best predictive model for the growth of P. mirabilis.