Development and validation of a molecular predictive model to describe the growth of Listeria monocytogenes in vacuum-packaged chilled pork

Propidium monoazide (PMA) qPCR assay compared to the plate count method for quantifying the growth of Salmonella enterica serotypes in vacuum-packaged turkey breast combined with a mathematical modeling approach

This study compares the plate count (PC) and the Propidium Monoazide-quantitative Polymerase Chain Reaction (PMA-qPCR) methods to assess the growth of a cocktail of three serotypes of Salmonella enterica (Heidelberg, Typhimurium, and Enteritidis) in cooked, sliced, and vacuum-packaged turkey breast (STB) under isothermal storage temperatures (8 °C-20 °C), using predictive models. Standard curves were developed for PMA-qPCR, demonstrating high efficiency (101%) and sensitivity, with quantification limits ranging from 1 to 2 log10 CFU/g for all temperatures studied. Comparative analysis revealed a significant correlation (R2 = 0.99; 95% CI) between the PC and PMA-qPCR methods; however, the agreement analysis indicated a mean difference (Bias) of -0.11 log10 CFU/g (p < 0.05), suggesting underestimation by the PC method. This indicates the presence of stressed or viable but nonculturable (VBNC) cells, detectable by PMA-qPCR but not by PC. The Baranyi and Roberts model showed a good ability to describe the behavior of S. enterica cocktail in STB for PC and PMA-qPCR data under all isothermal conditions. The exponential secondary model more accurately represented the temperature dependence of the maximum specific growth rate compared to the Ratkowsky square root model, with R2 values ≥ 0.984 and RMSE values ≤ 0.011 for both methods. These results suggest that combining PMA-qPCR with predictive modeling allows for a more accurate prediction of S. enterica growth, compared to PC method. In the event of cold chain disruptions of meat products, the use of PMA-qPCR method allow the quantification of VBNC cells, that can still pose a health risk to consumers, especially in ready-to-eat products.

Investigation of Salmonella enteritidis Growth under Varying Temperature Conditions in Liquid Whole Egg: Proposals for Smart Management Technology for Safe Refrigerated Storage

This study investigates the growth characteristics of Salmonella enteritidis (S. enteritidis) in liquid whole egg under both isothermal and non-isothermal storage conditions to understand the risks associated with inadequate temperature management in the egg industry. Using controlled laboratory simulations, liquid whole egg samples inoculated with S. enteritidis were stored under various isothermal (5, 15, 25, 35, and 45 °C) and non-isothermal conditions (5-10, 15-20, 25-30, 35-40, and 45-50 °C). The growth behavior of the S. enteritidis was analyzed using a two-step predictive modeling approach. First, growth kinetic parameters were estimated using a primary model, and then the effects of temperature on the estimated specific growth rate and lag time were described using a secondary model. Independent growth data under both isothermal and non-isothermal conditions were used to evaluate the models. The results showed that S. enteritidis exhibits different growth characteristics depending on temperature conditions, emphasizing the need for strict temperature control to prevent foodborne illnesses. To address this, a predictive growth model tailored for non-isothermal conditions was developed and validated using experimental data, demonstrating its reliability in predicting S. enteritidis behavior under dynamic temperature scenarios. Additionally, temperature management technologies were proposed and tested to improve food safety during refrigerated storage. This study provides a scientific basis for improving food safety protocols in the egg industry, thereby protecting public health and maintaining consumer confidence amid temperature fluctuations.

Modeling naturally-occurring Vibrio parahaemolyticus in post-harvest raw shrimps

There is little known about the growth and survival of naturally-occurring Vibrio parahaemolyticus in harvested raw shrimps. In this study, the fate of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps was investigated from 4℃ to 30℃ using real-time PCR combined with propidium monoazide (PMA-qPCR). The Baranyi-model was used to fit the growth and survival data. A square root model and non-linear Arrhenius model was then used to quantify the parameters derived from the Baranyi-model. The results showed that naturally-occurring V. parahaemolyticus were slowly inactivated at 4℃ and 7℃ with deactivation rates of 0.019 Log CFU/g/h and 0.025 Log CFU/g/h. Conversely, at 15, 20, 25, and 30 °C, the average maximum growth rates (μmax) of naturally-occurring V. parahaemolyticus were determined to be 0.044, 0.105, 0.179 and 0.336 Log CFU/g/h, accompanied by the average lag phases (λ) of 15.5 h, 7.3 h, 4.4 h and 3.7 h. The validation metrics, Af and Bf, for both the square root model and non-linear, indicating that the model had a good ability to predict the growth behavior of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps. Furthermore, a comparative exploration between the growth of artificially contaminated V. parahaemolyticus in cooked shrimps and naturally-occurring V. parahaemolyticus in post-harvest raw shrimps revealed intriguing insights. While no substantial distinction in deactivation rates emerged at 4 °C and 7 °C (P > 0.05), a discernible disparity in growth rates was observable at 15 °C, 20 °C, 25 °C, and 30 °C, with the former surpassing the latter. Which indicated the risk of V. parahaemolyticus using models derived from cooked shrimps may be biased. Our study also unveiled a discernible seasonal effect. The μmax and λ of V. parahaemolyticus in shrimps harvested in summer were similar to those harvested in autumn, while the initial and maximum bacterial concentration harvested in summer were higher than those harvested in autumn. This predictive microbiology model of naturally-occurring V. parahaemolyticus in raw shrimps provides relevance to modelling growth in situ.

Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue

The growth models of total bacterial count in freshly squeezed strawberry juice were established by gas and taste sensors in this paper. By selecting the optimal sensors and fusing the response values, the Modified Gompertz, Logistic, Huang and Baranyi models were used to predict and simulate the growth of bacteria. The results showed that the R² values for fitting the growth model of total bacterial count of the sensor S7 (an electronic nose sensor), of sweetness and of the principal components scores were 0.890-0.944, 0.861-0.885 and 0.954-0.964, respectively. The correlation coefficients, or R-values, between models fitted by the response values and total bacterial count ranged from 0.815 to 0.999. A single system of electronic nose (E-nose) or electronic tongue (E-tongue) sensors could be used to predict the total bacterial count in freshly squeezed strawberry juice during cold storage, while the higher rate was gained by the combination of these two systems. The fusion of E-nose and E-tongue had the best fitting-precision in predicting the total bacterial count in freshly squeezed strawberry juice during cold storage. This study proved that it was feasible to predict the growth of bacteria in freshly squeezed strawberry juice using E-nose and E-tongue sensors.

Understanding the effects of metal pre-exposure on the sensitivity of zebrafish larvae to metal toxicity: A toxicokinetics-toxicodynamics approach

Organisms are increasingly tolerant to metal toxicity in the natural ecosystems, which did not match the results of the environmental risk assessment (ERA) of metals based on toxicity data from organisms in the laboratory. Studies have described the effects of pre-exposure to metals on metal toxicity tolerance in terms of the toxicokinetic (TK) process; however, the toxicodynamic (TD) process may be more susceptible to metal pre-exposure. Therefore, to determine whether pre-exposure to low concentrations of silver (Ag) or cadmium (Cd) affects the metal TK and TD processes of zebrafish (Danio rerio) larvae, we investigated four TK-TD model parameters that control tolerance and sensitivity to metal toxicity on the survival. Our results showed that the killing rate (k_s) of larvae exposed to high Cd concentrations was significantly lower following pre-exposure to 10 μg/L Cd than that of larvaenot pre-exposed. However, the k_s for high Ag concentrations was significantly higher in zebrafish larvae following pre-exposure to 2 μg/L Ag than in larvae not pre-exposed. In other words, a one-day pre-exposure to 2 µg/L Ag rendered the larvae more sensitive to Ag during a subsequent 4-day exposure to higher Ag concentrations, whereas a one-day pre-exposure to 10 µg/L Cd rendered the larvae more tolerance to Cd during a subsequent 4-day exposure to higher Cd concentrations. Our results further the current understanding of toxic metal tolerance mechanisms, both in TK and TD processes, and they will guide future laboratory studies to assess actual pre-exposure scenarios that occur in natural environments. Thus, our study can help reduce uncertainty in testing and improve ecological management concerning metal risk assessments.

A biological characteristic extrapolation of compound toxicity for different developmental stage species with toxicokinetic-toxicodynamic model

Intraspecific difference in toxicity brings uncertainty to ecological risk assessment (ERA) and water quality criteria (WQC) of chemicals. Here, we compared intraspecies sensitivity to toxicants for Mesocyclops leuckarti of which toxicity data was obtained from published literatures, and zebrafish Danio rerio of which toxicity data was done in this study). Due to the internal concentration of chemicals not measured, simplified toxicokinetic-toxicodynamic (TK-TD) models were used, and we investigated whether TK-TD parameters estimated by Bayesian method might represent the differences in sensitivity between life-stages of 2 species. The results demonstrated that the difference in TK-TD parameters (background mortality m₀, no effect concentration NEC, the killing rate k_s, and the dominant rate k_d) could represent the toxicity difference between life-stages of individual species. The TK-TD model could predict toxicity in individual species (Cyprinus carpio L., Enchytraeus crypticus, Folsomia candida, Hyalella Azteca) exposed to different chemical concentrations and successfully extrapolate toxicity between different life stages of Mesocyclops leuckarti and Danio rerio by scaling several TK-TD parameters. The modified TK-TD model on the extrapolation toxicity of chemicals between life stages for species could be useful for the ERA and for deriving and revising WQC for chemicals.

Predictive Growth Modeling of Listeria monocytogenes in Rice Balls and Its Risk Assessment

This study aimed to investigate the growth of Listeria monocytogenes in rice balls and to conduct its microbial risk assessment based on the Korean dietary pattern. Each tuna or ham rice ball was mixed with mayonnaise, soy sauce, or gochujang, a Korean traditional fermented red peeper paste, which was artificially contaminated with L. monocytogenes and then stored at 7°C–25°C to assess bacterial growth. Growth data were analyzed using three primary models (the Huang, Baranyi, and Gompertz models), and the growth pattern was found to fit well to the Baranyi model based on the following five statistical criteria: root mean square error (0.38–0.56), Akaike’s information criterion (−51.55–−26.99), coefficient of determination (0.72–0.97), bias factor (0.97–1.01), and accuracy factor (1.06–1.18). The effects of temperature on bacterial growth rate and lag time were evaluated using the square root model. The minimum growth temperature for L. monocytogenes in tuna or ham rice balls was the lowest when they were mixed with mayonnaise (−9.44°C or −15.37°C, respectively). Risk assessment using FDA-iRISK showed that tuna or ham rice balls mixed with gochujang exhibited the highest microbial risk among all the rice balls tested, regardless of the storage temperature. Tuna or ham rice balls mixed with gochujang had the highest disability-adjusted life years per year (0.015) followed by ham rice balls mixed with soy sauce (0.011–0.015) or mayonnaise (0.006–0.015) and then tuna rice balls mixed with soy sauce (0.006–0.008) or mayonnaise (<0.001). In conclusion, our results, determined using predictive growth models, allow the assessment of potential risk ranking associated with the consumption of rice balls contaminated with L. monocytogenes based on the number of illnesses experienced per serving and the disease burden.

qPCR quantification of Carnobacterium maltaromaticum, Brochothrix thermosphacta, and Serratia liquefaciens growth kinetics in mixed culture

Quantifying growth kinetics of specific spoilage microorganisms in mixed culture is required to describe the evolution of food microbiomes. A qPCR method was developed to selectively amplify individual meat spoilage bacteria, Carnobacterium maltaromaticum, Brochothrix thermosphacta and Serratia liquefaciens, within a broth medium designed to simulate the composition of beef. An optimized method of DNA extraction was produced for standard curve construction. Method specificity was determined by individual single peaks in melt curves. Reaction efficiency for standard curves of C. maltaromaticum, B. thermosphacta and S. liquefaciens was high (R² = 0.98-0.99), and linear quantification was achieved over a 5 log CFU/ml range. Coefficient of variation was calculated considering both threshold cycle (C_t) and bacterial concentration; the value did not exceed 14% for inter- or intra-runs for either method. Comparison of growth kinetic parameters derived from plate count and qPCR showed no significant variation (P > .05) for growth rate (GR) and maximum population density (MPD); lag phase duration (LPD) was not included in this comparison due to high innate variability. Log quantification of each isolate was validated in a mixed-culture experiment for all three species with qPCR and plate count differing less than 0.3 log CFU/ml (average 0.10 log CFU/ml, R² = 0.98).

Kinetics of 5-hydroxymethylfurfural formation in the sugar-amino acid model of Maillard reaction

BACKGROUND

As a potential health hazard, 5-hydroxymethylfurfural (HMF) has been detected in thermally processed foods high in sugar and amino acids. In order to analyze HMF quantitatively and investigate the kinetics of its formation, high-performance liquid chromatography was employed to determine the content of HMF in six sugar-amino acid thermal reaction models.

RESULTS

In thermal reaction models, formation of HMF was significantly affected by sugar and amino acid composition, pH value and heating conditions. HMF formation increased with increasing sugar and amino acid (cysteine excepted) content, temperature and reaction time. A maximum amount of HMF of 1.50 g kg^-1 was detected in the sucrose-glutamic acid model at 110 °C and 6 h. Low pH value and added acidic amino acids promoted the formation of HMF, especially in the sucrose-containing system.

CONCLUSION

HMF formation followed first-order kinetics in four models, including the model of glucose-cysteine, glucose-glutamic acid, glucose-leucine and sucrose-leucine. In contrast, HMF formation followed zero-order kinetics in the model of sucrose-glutamic acid. The quantity of HMF increased as the quantity of sugar and amino acid increased (cysteine excepted) in six tested models. © 2018 Society of Chemical Industry.

Reduction of infection risk mediated by co-culturing Vibrio parahaemolyticus and Listeria monocytogenes in refrigerated cooked shrimp

BACKGROUND

Vibrio parahaemolyticus and Listeria monocytogenes are seafood pathogens of public health significance, and predictive models are effective tools for quantitative microbial risk assessment of these pathogens. However, most current predictive models are based on growth of single strains in broth cultures, and interactions of two or more bacteria in a food matrix can skew the outcomes of the predictions. Therefore, the impact of V. parahaemolyticus and L. monocytogenes when co-cultured and in monoculture on cooked shrimp in cold storage was investigated.

RESULTS

The results indicated that L. monocytogenes co-cultured with V. parahaemolyticus exhibited reduced growth and longer lag phase at 4 °C and 10 °C. V. parahaemolyticus exhibited similar behavior when co-cultured with L. monocytogenes at 4 °C (death rate K =  - 0.67 log₁₀ CFU g^-1 day . The death rate K at 10 °C when V. parahaemolyticus co-cultured with L. monocytogenes was -1.62 log₁₀ CFU g^-1 day^-1 . There was no significant reduction of growth in monoculture experiments.

CONCLUSION

This study has revealed that interaction of V. parahaemolyticus and L. monocytogenes should be considered when quantifying risks posed by these pathogens during consumption of seafood products. © 2018 Society of Chemical Industry.

A Comprehensive Research on Antibiotic Resistance Genes in Microbiota of Aquatic Animals

The occurrence of antibiotic resistance genes (ARGs) as emerging contaminants is of continued concern for human health. Antibiotics used in aquaculture have promoted the evolution and spread of ARGs. This study aimed to investigate the occurrence of 37 ARGs conferring resistance to six classes of antibiotics in 94 aquatic animals from five cities in southeast coast of China. The results showed that floR, sulII, sulI, strB, strA, aadA, and tetS were identified as the prominent ARGs with the high detection frequencies ranging from 30.9 to 51.1% in total samples. Then relative expression amount of seven prominent ARGs quantified by qPCR, ranging from 0.003 to 0.065. The tetS was the most abundant ARG among the seven ARGs. Though aadA was the second highest detection frequency of ARGs, it was the lowest expression amount ARG. The occurrences and abundances of ARGs in freshwater aquatic animals were greater than those in marine, reflecting the discrepancy of cultivation pattern between the freshwater and marine aquaculture. Shanghai was considered as the most prevalent site with 16 ARGs, and Ningbo merely contained 9 ARGs without of β-lactam ARGs and quinolone ARGs, showing variations of ARGs with geographical location. Eight kinds of sulfonamides and one chloramphenicol residues were further measured in samples from Shanghai. Interestingly, no target antibiotics were found, but sulfonamides resistance genes (sulI, sulII) and chloramphenicol resistance genes (floR) persisted at aquatic animals in the absence of selection pressure. Our research firstly shows comprehensive information on the ARGs in skin microbiota of aquatic animals, which could provide useful information and a new insight for better understanding on the ARGs dissemination in aquatic animals.

Mathematical modelling of growth of Listeria monocytogenes in raw chilled pork

The aim of this study was to analyse the growth kinetics of Listeria monocytogenes in naturally contaminated chilled pork. A cocktail of 26 meat-borne L. monocytogenes was inoculated to raw or sterile chilled pork to observe its growth at 4, 10, 16, 22 and 28°C respectively. The growth data were fitted by the Baranyi model and Ratkowsky square-root model. Results showed that the Baranyi model and Ratkowsky square-root model could describe the growth characteristics of L. monocytogenes at different temperatures reasonably well in raw chilled pork (1·0 ≤ Bf ≤ Af ≤ 1·1). Compared with the growth of L. monocytogenes in sterile chilled pork, the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The microbial predictive models developed in this study can be used to predict the growth of L. monocytogenes during natural spoilage, and construct quantitative risk assessments in chilled pork.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study simulated the actual growth of Listeria monocytogenes in chilled pork to the maximum extent, and described its growth characteristics of L. monocytogenes during natural spoilage. This study showed that the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The models developed in this study can be used to predict the growth of L. monocytogenes during refrigerated storage.

A Multiplex RT-PCR Assay for S. aureus, L. monocytogenes, and Salmonella spp. Detection in Raw Milk with Pre-enrichment

This study firstly developed a multiplex real-time PCR (RT-PCR) technique combined with a pre-enrichment step to simultaneously detect Staphylococcus aureus (S. aureus), Listeria monocytogenes (L. monocytogenes) and Salmonella spp. in raw milk and the dairy farm environment (feces, soil, feed, water) in one reaction. Brain heart infusion (BHI) broth was selected for the enrichment step to increase the density of the target bacteria by using an incubation of 4 h before multiplex RT-PCR. The results showed that the detection limit of the multiplex real-time assay was approximately 102 CFU/mL for pure cultures and artificially contaminated milk without enrichment, while 12, 14, and 10 CFU/25 mL, respectively, for S. aureus, L. monocytogenes, and Salmonella spp. after pre-enrichment. The newly developed multiplex RT-PCR assay was applied to 46 dairy farm environmental samples and raw milk samples covering a wide variety of sample types. The results demonstrated that the multiplex RT-PCR assay coupled with the BHI enrichment broth was suitable for the simultaneous screening of S. aureus, L. monocytogenes, and Salmonella spp. in the pasture environment and in raw milk. The multiplex RT-PCR assay clearly and successfully shortened the total detection time and reduced labor compared to conventional culture-based methods for testing natural samples.

Establishment and Validation of RNA-Based Predictive Models for Understanding Survival of Vibrio parahaemolyticus in Oysters Stored at Low Temperatures

This study developed RNA-based predictive models describing the survival of Vibrio parahaemolyticus in Eastern oysters (Crassostrea virginica) during storage at 0, 4, and 10°C. Postharvested oysters were inoculated with a cocktail of five V. parahaemolyticus strains and were then stored at 0, 4, and 10°C for 21 or 11 days. A real-time reverse transcription-PCR (RT-PCR) assay targeting expression of the tlh gene was used to evaluate the number of surviving V. parahaemolyticus cells, which was then used to establish primary molecular models (MMs). Before construction of the MMs, consistent expression levels of the tlh gene at 0, 4, and 10°C were confirmed, and this gene was used to monitor the survival of the total V. parahaemolyticus cells. In addition, the tdh and trh genes were used for monitoring the survival of virulent V. parahaemolyticus Traditional models (TMs) were built based on data collected using a plate counting method. From the MMs, V. parahaemolyticus populations had decreased 0.493, 0.362, and 0.238 log10 CFU/g by the end of storage at 0, 4, and 10°C, respectively. Rates of reduction of V. parahaemolyticus shown in the TMs were 2.109, 1.579, and 0.894 log10 CFU/g for storage at 0, 4, and 10°C, respectively. Bacterial inactivation rates (IRs) estimated with the TMs (-0.245, -0.152, and -0.121 log10 CFU/day, respectively) were higher than those estimated with the MMs (-0.134, -0.0887, and -0.0732 log10 CFU/day, respectively) for storage at 0, 4, and 10°C. Higher viable V. parahaemolyticus numbers were predicted using the MMs than using the TMs. On the basis of this study, RNA-based predictive MMs are the more accurate and reliable models and can prevent false-negative results compared to TMs.IMPORTANCE One important method for validating postharvest techniques and for monitoring the behavior of V. parahaemolyticus is to establish predictive models. Unfortunately, previous predictive models established based on plate counting methods or on DNA-based PCR can underestimate or overestimate the number of surviving cells. This study developed and validated RNA-based molecular predictive models to describe the survival of V. parahaemolyticus in oysters during low-temperature storage (0, 4, and 10°C). The RNA-based predictive models show the advantage of being able to count all of the culturable, nonculturable, and stressed cells. By using primers targeting the tlh gene and pathogenesis-associated genes (tdh and trh), real-time RT-PCR can evaluate the total surviving V. parahaemolyticus population as well as differentiate the pathogenic ones from the total population. Reliable and accurate predictive models are very important for conducting risk assessment and management of pathogens in food.

Time-varying effects of aromatic oil constituents on the survival of aquatic species: Deviations between model estimates and observations

There is a need to study the time course of toxic chemical effects on organisms because there might be a time lag between the onset of chemical exposure and the corresponding adverse effects. For aquatic organisms, crude oil and oil constituents originating from either natural seeps or human activities can be relevant case studies. In the present study the authors tested a generic toxicokinetic model to quantify the time-varying effects of various oil constituents on the survival of aquatic organisms. The model is based on key parameters applicable to an array of species and compounds with baseline toxicity reflected by a generic, internal toxicity threshold or critical body burden (CBB). They compared model estimates with experimental data on the effects of 8 aromatic oil constituents on the survival of aquatic species including crustaceans and fish. The average model uncertainty, expressed as the root mean square error, was 0.25 (minimum-maximum, 0.04-0.67) on a scale between 0 and 1. The estimated survival was generally lower than the measured survival right after the onset of oil constituent exposure. In contrast, the model underestimated the maximum mortality for crustaceans and fish observed in the laboratory. Thus, the model based on the CBB concept failed to adequately predict the lethal effects of the oil constituents on crustaceans and fish. Possible explanations for the deviations between model estimates and observations may include incorrect assumptions regarding a constant lethal body burden, the absence of biotransformation products, and the steady state of aromatic hydrocarbon concentrations in organisms. Clearly, a more complex model approach than the generic model used in the present study is needed to predict toxicity dynamics of narcotic chemicals. Environ Toxicol Chem 2017;36:128-136. © 2016 SETAC.

Modeling the Effect of Storage Temperatures on the Growth of Listeria monocytogenes on Ready-to-Eat Ham and Sausage

The aim of this study was to model the growth kinetics of Listeria monocytogenes on ready-to-eat ham and sausage at different temperatures (4 to 35°C). The observed data fitted well with four primary models (Baranyi, modified Gompertz, logistic, and Huang) with high coefficients of determination (R(2) > 0.98) at all measured temperatures. After the mean square error (0.009 to 0.051), bias factors (0.99 to1.06), and accuracy factors (1.01 to 1.09) were obtained in all models, the square root and the natural logarithm model were employed to describe the relation between temperature and specific growth rate (SGR) and lag time (LT) derived from the primary models. These models were validated against the independent data observed from additional experiments using the acceptable prediction zone method and the proportion of the standard error of prediction. All secondary models based on each of the four primary models were acceptable to describe the growth of the pathogen in the two samples. The validation results indicate that the optimal primary model for estimating the SGR was the Baranyi model, and the optimal primary model for estimating LT was the logistic model in ready-to-eat (RTE) ham. The Baranyi model was also the optimal model to estimate the SGR and LT in RTE sausage. These results could be used to standardize predictive models, which are commonly used to identify critical control points in hazard analysis and critical control point systems or for the quantitative microbial risk assessment to improve the food safety of RTE meat products.

Development of Predictive Models for the Growth Kinetics of Listeria monocytogenes on Fresh Pork under Different Storage Temperatures

This study was conducted to develop a predictive model to estimate the growth of Listeria monocytogenes on fresh pork during storage at constant temperatures (5, 10, 15, 20, 25, 30, and 35°C). The Baranyi model was fitted to growth data (log CFU per gram) to calculate the specific growth rate (SGR) and lag time (LT) with a high coefficient of determination (R(2) > 0.98). As expected, SGR increased with a decline in LT with rising temperatures in all samples. Secondary models were then developed to describe the variation of SGR and LT as a function of temperature. Subsequently, the developed models were validated with additional independent growth data collected at 7, 17, 27, and 37°C and from published reports using proportion of relative errors and proportion of standard error of prediction. The proportion of relative errors of the SGR and LT models developed herein were 0.79 and 0.18, respectively. In addition, the standard error of prediction values of the SGR and LT of L. monocytogenes ranged from 25.7 to 33.1% and from 44.92 to 58.44%, respectively. These results suggest that the model developed in this study was capable of predicting the growth of L. monocytogenes under various isothermal conditions.

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.

Simultaneous construction of PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps

The aim of this study was to simultaneously construct PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps at 4 and 10°C. Calibration curves were established to correlate peak density of DGGE bands with microbial counts. Microbial counts derived from PCR-DGGE and plate methods were fitted by Baranyi model to obtain molecular and traditional predictive models. For L. monocytogenes, growing at 4 and 10°C, molecular predictive models were constructed. It showed good evaluations of correlation coefficients (R(2) > 0.92), bias factors (Bf ) and accuracy factors (Af ) (1.0 ≤ Bf ≤ Af ≤ 1.1). Moreover, no significant difference was found between molecular and traditional predictive models when analysed on lag phase (λ), maximum growth rate (μmax ) and growth data (P > 0.05). But for V. parahaemolyticus, inactivated at 4 and 10°C, molecular models show significant difference when compared with traditional models. Taken together, these results suggest that PCR-DGGE based on DNA can be used to construct growth models, but it is inappropriate for inactivation models yet. This is the first report of developing PCR-DGGE to simultaneously construct multiple molecular models.

SIGNIFICANCE AND IMPACT OF THE STUDY

It has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment).