Modelling the effect of temperature, carbon dioxide, water activity and pH on growth and histamine formation by Morganella psychrotolerans

A mathematical model was developed to predict growth and histamine formation by Morganella psychrotolerans depending on temperature (0-20 degrees C), atmosphere (0-100% CO2), NaCl (0.0-6.0%) and pH (5.4-6.5). Data from experiments with both sterile tuna meat and Luria Bertani broth was used to develop the mathematical growth and histamine formation model. The expanded Logistic model with a growth dampening parameter (m) of 0.7 was used as primary growth model. A primary model for histamine formation during storage was obtained by combining the expanded Logistic growth model with a yield factor (YHis/CFU). 120 maximum specific growth rate (micromax)-values were generated for M. psychrotolerans and used to model the combined effect of the studied environmental parameters. A simple cardinal parameter type secondary model was used to model the effect of the four parameters on micro(max). The maximum population density (log Nmax) was correlated with log (YHis/CFU) and a simple constrained polynomial (quadratic) secondary model was developed for the effect of the environmental conditions on these model parameters. The developed model describes the effect of initial cell concentrations, storage conditions and product characteristics on histamine formation. This is a significant progress compared to previously available models for the effect of storage temperature only.     

Growth and histamine formation of Morganella morganii in determining the safety and quality of inoculated and uninoculated bluefish (Pomatomus saltatrix).

The objective of this study was to determine the effect of normal microflora and Morganella morganii on histamine formation and olfactory acceptability in raw bluefish under controlled storage conditions. Fillets inoculated with and without M. morganii were stored at 5, 10, and 15 degrees C for 7 days. Microbial isolates from surface swabs were identified and screened for histidine decarboxylase activity. Olfactory acceptance was performed by an informal sensory panel. Histamine levels were quantified using high-performance liquid chromatography and fluorescence detection. While olfactory acceptance decreased, histamine concentration and bacterial counts increased. Storage temperature had a significant effect on histamine levels, bacterial counts, and olfactory acceptance of the bluefish. Inoculation with M. morganii had a positive significant effect on histamine formation for bluefish held at 10 and 15 degrees C (P < 0.0001). The results of the study will serve in supporting U.S. Food and Drug Administration (FDA) regulations regarding guidance and hazard levels of histamine in fresh bluefish.     

Detection of Morganella morganii,a prolific histamine former,by the polymerase chain reaction assay with 16S rDNA-targeted primers

A polymerase chain reaction (PCR) assay for the rapid and sensitive detection of the most prolific histamine former, Morganella morganii, was developed. 16S rDNA targeted PCR primers were designed, and the primer specificity and sensitivity of the PCR assay were evaluated. The 16S rDNA sequence (1,503 bp) for M. morganii showed 95% identity to those for enteric bacteria, i.e., Enterobacter spp., Klebsiella spp., Citrobacter spp., Hafnia alvei, Proteus spp., and Providencia spp. The unique primers for M. morganii were designed on the basis of the variable regions in the 16S rDNA sequence. The primers showed positive reactions with all M. morganii strains tested. However, PCR amplification was not detected when the primers were tested with other enteric or marine bacteria. When the sensitivity of the assay was evaluated, M. morganii was detected at levels ranging from 10(6) to 10(8) CFU/ml in albacore homogenate after the PCR amplification. The sensitivity of the assay was greatly improved with the enrichment of samples, and 9 CFU of M. morganii per ml of albacore homogenate was detected after 6 h of enrichment at 37 degrees C.     

Application of the Bigelow (z-value) model and histamine detection to determine the time and temperature required to eliminate Morganella morganii from seafood

In New Zealand, the product most frequently implicated in cases of scombroid or histamine poisoning is the hot-smoked fish, kahawai (Arripis trutta). A properly controlled heating step in the production of hot-smoked seafood could eliminate bacteria able to convert the amino acid histidine to histamine. In this study, we determined the core temperatures and times required during hot smoking of kahawai to eliminate histamine-forming bacteria and to ensure a final product that will not produce histamine if subsequent temperature abuse occurs. Morganella morganii strains previously isolated from portions of hot-smoked kahawai with elevated histamine levels were inoculated onto product to be tested. A variation of the Bigelow or z-value model was used to generate a thermal death time graph, where the production of histamine, in a heat-treated and subsequently temperature-abused sample, was scored as a positive value (growth) and the absence of histamine was scored as a negative value (no growth). From a line fitted to the data, calculated times for the elimination of histamine-forming bacteria at test temperatures of 58, 59, 60, 61, and 62 degrees C were estimated to be 15.27, 8.81, 4.79, 2.68, and 1.46 min, respectively, giving a z value of 3.85 degrees C. This approach to thermal death determination, based on the presence or absence of a bacterial metabolite, proved to be an efficient way to determine the thermal regime required to eliminate bacteria capable of converting histidine to histamine on kahawai.     

Inhibitory effect of spices on in vitro histamine production and histidine decarboxylase activity ofMorganella morganii and on the biogenic amine formation in mackerel stored at 30°C

The inhibitory effects of clove, cinnamon, cardamom, turmeric and pepper on the histamine production and histidine decarboxylase activity ofMorganella morganii (a potent histamine-producing bacteria in fish) was examined at 30°C using HPLC. Cinnamon and clove exhibited a significant (P<0.01) inhibitory effect, whereas turmeric and cardamom had a moderate effect. These spices were applied to whole mackerel at a level of 3% and their inhibitory effect on biogenic amine formation at 30°C was also examined. As in the in vitro study, clove and cinnamon showed a significant (P< 0.05) inhibitory effect on histamine, putrescine and tyramine formation but not on that of cadaverine. Cardamom and turmeric exhibited a moderate effect and pepper was ineffective. Therefore, clove and cinnamon are more helpful than cardamom and turmeric in the minimization of the formation of toxic histamine in mackerel.     

Short communication: Development and evaluation of predictive models of body weight for crossbred Holstein-Zebu dairy heifers

Equations to predict body weight (BW) of crossbred Holstein-Zebu dairy heifers were developed and compared with current models (Heinrichs et al. for Holsteins, United States; Reis et al. for crossbred Holstein-Zebu, Brazil). The data set was constructed from 150 measurements of BW (320 ± 107 kg) and biometric measurements such as heart girth (HG, 161 ± 19.5 cm), withers height (WH, 126 ± 11.0 cm), and hip height (HH, 132 ± 11.3 cm) of heifers from 5 commercial dairy producers in the southern Amazon region in Brazil. The data were evaluated using mixed nonlinear models with herd as a random effect. Three nonlinear equations were fitted: BW (kg) = 0.00058·HG (cm)^2.6135; BW (kg) = 0.000618·HG (cm)^2.7362; and BW (kg) = 0.000196·HH ^2.8793. An independent database was constructed to evaluate the models from 38 treatment means of 4 feeding trials: BW 258 ± 54.3 kg, HG 142.5 ± 11.8 cm, WH 113.2 ± 6.0 cm, and HH 118.7 ± 9.1 cm (mean ± SD). The evaluations were based on the relationship between observed and predicted values of BW by linear regression, root mean square prediction error (RMSPE), and concordance correlation coefficient analysis. Only the proposed model using HG accurately predicted observed BW, with bias (observed – predicted) of 4.83 kg and RMSPE of 5.41% of observed BW (87.7% of random error). The models using WH and HH failed to accurately predict observed BW, with a bias of −3.06 and 72.02 kg, and RMSPE of 9.40% of observed BW (75.2% of random error and 23.1% of systematic error) and 30.81% of observed BW (81.2% of mean bias). Additionally, the models of Heinrichs and Reis used for comparison did not predict BW accurately, with a bias of 19.32 and 29.37 kg and RMSPE of 9.08% of observed BW (68.4% of mean bias and 31.4% of random error) and 12.58% of observed BW (81.9% of mean bias). The largest concordance correlation coefficient of the proposed HG-nonlinear model (0.930), compared with the models of Heinrichs and Reis of 0.845 and 0.708, confirmed the greater accuracy and precision of the new equation to predict BW in crossbred Holstein-Zebu dairy heifers.     

Mathematic predictive models for calculating copper, iron and zinc dialysability in infant formulas

Differences in the dialysability of a mineral element from infant formulas of the same type were detected in a previous study. As these may be due to the effects that different levels of the components of the formulas could have on dialysability, we attempted to establish mathematical models to predict the dialysability of Cu, Fe and Zn from infant formulas according to their ascorbic, citric, or selected amino acid contents. A simple linear regression was applied between the ascorbic acid, citric acid and amino acid contents and the Cu, Fe and Zn dialysability of 18 powdered infant formulas of different types. Significant correlations (P<0.05) were obtained between the dialysability of Fe and ascorbic acid, citric acid, cysteine (Cys), glycine (Gly) and lysine (Lys) contents; between Cu and Cys, arginine (Arg) and tyrosine (Tyr) contents; and between Zn and Cys, Gly, Lys and Arg contents. A multiple regression was applied in those cases in which a significant correlation (P<0.05) was obtained. A joint effect of ascorbic and either citric acid or Cys on Fe dialysability was observed. The effect of Arg on Zn dialysability and the joint effect of Tyr and Cys on Cu dialysability must also be mentioned.     

A predictive model to determine the effects of pH, milkfat, and temperature on thermal inactivation of Listeria monocytogenes.

Listeria monocytogenes is a foodborne pathogen of significance because of its comparatively high heat resistance, zero tolerance in ready-to-eat foods, and growth at refrigeration temperatures. A 3 x 3 x 3 factorial study was done to determine the effects of milkfat (0%, 2.5%, 5.0%), pH (5.0, 6.0, 7.0), and processing temperature (55 degrees C, 60 degrees C, 65 degrees C) on the thermal resistance of L. monocytogenes in a formulated and homogenized milk system. Data were fit to a modified Gompertz equation where parameter estimates characterized three regions of a survival curve: the shoulder, maximum slope, and tail. Statistical analysis was done for each of the 27 individual treatment sets to visualize individual effects on parameter estimates and to evaluate how well the Gompertz equation represented the data. A regression model for the Gompertz equation was generated to predict the logarithmic surviving fraction of L. monocytogenes based on all 27 treatments and their single and interactive effects. The shoulder region of the survival curve was affected by pH; however, the maximum slope was affected by temperature, milkfat, and the interaction of temperature x milkfat. Validation of the model suggests that the predictions are best suited for processing above 62 degrees C. Trends over time for a 4-log reduction in cells (4D values) were evaluated using results from the 27 individual treatment sets, the regression model for the Gompertz equation, and a linear equation. At lower temperatures, 4D values by the three methods varied by twofold. At higher temperatures, all methods gave similar 4D values, suggesting that death became more linear. Based on this study all three factors affect heat resistance for specific regions of a survival curve, and a predictive model was developed that can be used as a preliminary estimate for L. monocytogenes inactivation.     

Growth characteristics and development of a predictive model for Bacillus cereus in fresh wet noodles with added ethanol and thiamine

Response surface methodology was used to determine growth characteristics and to develop a predictive model to describe specific growth rates of Bacillus cereus in wet noodles containing a combination of ethanol (0 to 2% [vol/wt]) and vitamin B(1) (0 to 2 g/liter). B. cereus F4810/72, which produces an emetic toxin, was used in this study. The noodles containing B. cereus were incubated at 10°C. The growth curves were fitted to the modified Gompertz equation using nonlinear regression, and the growth rate values from the curves were used to establish the predictive model using a response surface methodology quadratic polynomial equation as a function of concentrations of ethanol and vitamin B(1). The model was shown to fit the data very well (r(2) = 0.9505 to 0.9991) and could be used to accurately predict growth rates. The quadratic polynomial model was validated, and the predicted growth rate values were in good agreement with the experimental values. The polynomial model was found to be an appropriate secondary model for growth rate (GR) and lag time (LT) based on the correlation of determination (r(2) = 0.9899 for GR, 0.9782 for LT), bias factor (B(f) = 1.006 for GR, 0.992 for LT), and accuracy factor (A(f) = 1.024 for GR, 1.011 for LT). Thus, this model holds great promise for use in predicting the growth of B. cereus in fresh wet noodles using only the bacterial concentration, an important contribution to the manufacturing of safe products.     

Growth of Clostridium perfringens from spore inocula in cooked cured beef: development of a predictive model

The objective of this study was to develop a model to predict the growth of C. perfringens from spores at temperatures applicable to the cooling of cooked cured meat products. C. perfringens growth from spores was not observed at a temperature of 12 °C for up to 3 weeks. The two parameters: germination, outgrowth, and lag (GOL) time and exponential growth rate, EGR, were determined using a function derived from mechanistic and stochastic considerations and the observed relative growths at specified times. A general model to predict the amount of relative growth for arbitrary temperature was determined by fitting the exponential growth rates to a square root Ratkowsky function, and assuming a constant ratio of GOL and generation times. The predicted relative growth is sensitive to the value of this ratio. A closed form equation was developed that can be used to estimate the relative growth for a general cooling scenario and determine a standard error of the estimate. The equation depends upon microbiological assumptions of the effect of history of the GOL times for gradual changes in temperature. Applying multivariate statistical procedures, a confidence interval was computed on the prediction of the amount of growth for a given temperature. The model predicts, for example, a relative growth of 3.17 with an upper 95% confidence limit of 8.50 when cooling the product from 51 to 11 °C in 8 h, assuming a log linear decline in temperature with time.     

Growth kinetics and predictive model of Aeromonas hydrophila in a broth-based system

This study developed a predictive model of Aermonas hydrophila in tryptic soy broth for any combination of temperatures (5 to 40°C), pH (6 to 8), and NaCl (0 to 5%) using a response surface model. A. hydrophila tended to grow within a pH range of 6.0 to 8.0 and could not tolerate NaCl up to 5.0%. The interaction of pH and NaCl did not affect the specific growth rates (SGR). The primary model to obtain the SGR showed a good fit (R²≥0.980). A secondary model was obtained by non-linear regression analysis and calculated as: SGR= 0.4577+0.0529X₁−0.1641X₂−0.1493X₃−0.0016X₁X₂−0.0001X₁X₃+0.0115X₂X₃0.0006X₁ ²+0.0114X₂ ²+0.0150X₃ ² (X₁=temperature, X₂=pH, X₃=NaCl). The appropriateness of the polynomial model was verified by the mean square error (0.0023), bias factor (0.922), accuracy factor (1.343), and coefficient of determination (0.937). The newly secondary model of SGR for A. hydrophila could be incorporated into the tertiary model to predict the growth of A. hydrophila.     

Genetic evaluation of mastitis in dairy cattle using linear models, threshold models, and survival analysis: a simulation study

The objective was to study, by simulation, whether survival analysis results in a more precise genetic evaluation for mastitis in dairy cattle than cross-sectional linear models and threshold models by using observation periods for mastitis of 2 lengths (the first 150 d of lactation, and the full lactation, respectively). True breeding values for mastitis liability on the underlying scale were simulated for daughters of 400 sires (average daughter group size, 60 or 150), and the possible event of a mastitis case within lactation for each cow was created. For the linear models and the threshold models, mastitis was defined as a binary trait within either the first 150 d of lactation or the full lactation. For the survival analysis, mastitis was defined as the number of days from calving to either the first case of mastitis (uncensored record) or to the day of censoring (i.e., day of culling, lactation d 150 or day of next calving; censored record). Cows could be culled early in lactation (within 10 d after calving) for calving-related reasons or later on because of infertility. The correlation between sire true breeding values for mastitis liability and sire predicted breeding values was greater when using the full lactation data (0.76) than when using data from the first 150 d (0.70) with an average of 150 daughters per sire. The corresponding results were 0.60 and 0.53, respectively, with an average of 60 daughters per sire. Under these simulated conditions, the method used had no effect on accuracy. The higher accuracy of sire breeding values can be translated into a greater genetic gain, unless counteracted by a longer generation interval.     

Development of predictive modelling approaches for surface temperature and associated microbiological inactivation during hot dry air decontamination

This research deals with the development of predictive modelling approaches in the field of heat transfer and microbial inactivation. Upon making some backstage microbiological considerations, surface temperature predictions during hot dry air decontaminations are incorporated in a microbial inactivation model, in order to describe inactivation kinetics under realistic (time-varying) temperature conditions.

In the present study, the following parts are presented. (i) First, a one-dimensional heat transfer model is developed taking into account exchanges by convection, radiation and evaporation. The model is subsequently validated on a laboratory setup and on a test rig, assuming no water activity changes. This test rig is developed for studying—at a later stage—surface pasteurisation treatment on food products with the use of hot dry air. (ii) Isothermal inactivation data of Escherichia coli K12 MG1655 have been collected and inactivation parameters are accurately estimated by using a primary and a secondary model in a global modelling approach. (iii) Microbiological considerations such as microbial growth effects during come-up times, initial temperature of inactivation, and heat resistance effects, based on experimental observations and on literature studies, are formulated in order to evaluate possible microbial effects arising under the dynamic temperature conditions modelled in step (i). (iv) Microbial inactivation simulations with the incorporation of surface temperature predictions are presented. (v) Finally, the level of the microbial decontamination in an example based on the design of an industrial installation is presented, outlining the importance of the combination of surface temperature and microbial inactivation modelling approaches.     

A predictive model to determine the effects of temperature,sodium pyrophosphate,and sodium chloride on thermal inactivation of starved Listeria monocytogenes in pork slurry

The effects and interactions of 27 combinations of heating temperature (57.5 to 62.5 degrees C), sodium pyrophosphate (SPP) level (0 to 0.5%, wt/vol), and salt (NaCl) level (0 to 6%, wt/vol) on the thermal inactivation of starved Listeria monocytogenes ATCC 19116 in pork slurry were investigated. A split-split plot experimental design was used to compare all 27 combinations. L. monocytogenes survivors were enumerated on tryptic soy agar supplemented with 0.6% yeast extract. The natural logarithm (loge) of the means of decimal reduction times (D-values) were modeled as a function of temperature, SPP level, and NaCl level. Increasing concentrations of SPP or NaCl protected starved L. monocytogenes from the destructive effect of heat. For example, D-values for the pathogen at 57.5 degrees C in pork slurry with 0, 3, and 6% NaCl were 2.79, 7.75, and 14.59 min, respectively. All three variables interacted to affect the thermal inactivation of L. monocytogenes. A mathematical model describing the combined effect of temperature, SPP level, and NaCl level on the thermal inactivation of starved L. monocytogenes was developed. There was strong correlation (R2 = 0.97) between loge D-values predicted by the model and those observed experimentally. The model can predict D-values for any combination of variables that falls within the range of those tested. This predictive model can be used to assist food processors in designing thermal processes that include an adequate margin of safety for the control of L. monocytogenes in processed meats.     

Growth, survival and inactivation of Pseudomonas aeruginosa and Staphylococcus aureus strains of various origin in the presence of ethanol

The influence of ethanol on the behaviour of Pseudomonas aeruginosa and Staphylococcus aureus strains was evaluated throughout this study. Strains of different origin were used: collection, clinical and industrial strains were selected. Concentrations of ethanol from 0 to 20% (v/v) were evaluated by automated optical density measurements and by enumeration. When growth conditions were observed, predictive microbiology models were used to assess quantitatively for the ethanol effect. Primary modelling of kinetics was performed to determine growth rate values; secondary modelling was performed on these growth rates as influenced by ethanol, and minimum inhibitory concentrations of ethanol were determined for each strain. Staphylococcus aureus strains were more resistant to ethanol than P. aeruginosa strains, in growth conditions as well as in inactivation conditions. Furthermore, clinical S. aureus strains were more resistant than the collection strain. The method was promising for management of microbiological safety in cosmetics.     

Prediction of traditionally utilised wheat dough technological quality parameters from Mixolab values: development and evaluation of regression models

The research was conducted with the aim to investigate the possibility of Farinograph, Extensograph and Amylograph values prediction with linear and/or multiple Mixolab regression models. In total, 163 flour samples were divided based on Machalanobis distances into three sets: prediction, validation and external sample set. Determination coefficients ranged from 0.145 to 0.640 for linear regression models and from 0.279 to 0.739 for multiple regression models. Internal and external validation of developed regression models was conducted. Testing of developed models resulted in a high share of samples for which predicted values were out of the ranges of method official reproducibility and a high share of samples for which predicted values were out of the quality level range obtained by analytical measurement. It was concluded that it is impossible to develop applicable regression models for prediction of Farinograph, Extensograph and Amylograph parameters on the basis of Mixolab values from standard protocol.     

Comparison of predictive models for growth of parent and green fluorescent protein-producing strains of Salmonella

The green fluorescent protein (GFP) from the jellyfish Aequorea victoria can be expressed in, and used to follow the fate of, Salmonella in microbiologically complex ecosystems such as food. As a first step in the evaluation of GFP as a tool for the development of predictive models for naturally contaminated food, the present study was undertaken to compare the growth kinetics of parent and GFP-producing strains of Salmonella. A previously established sterile chicken burger model system was used to compare the growth kinetics of stationary-phase cells of parent and GFP strains of Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Dublin. Growth curves for constant temperatures from 10 to 48 degrees C were fit to a two- or three-phase linear model to determine lag time, specific growth rate, and maximum population density. Secondary models for the growth parameters as a function of temperature were generated and compared between the parent and GFP strain pairs. The effects of GFP on the three growth parameters were significant and were affected by serotype and incubation temperature. The expression of GFP reduced specific growth rate and maximum population density while having only a small effect on the lag times of the three serotypes. The results of this study indicate that the growth kinetics of the GFP strains tested were different from those of the parent strains and thus would not be good marker strains for the development of predictive models for naturally contaminated food.     

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.     

Analysis of a novel class of predictive microbial growth models and application to coculture growth

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277–294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.