Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

Cooling Uncovered Foods at a Depth of ∼5.1 cm (2 in.) or Less Poses Little Risk of Pathogen Growth

The U.S. Food and Drug Administration has guidelines for cooling cooked foods in retail operations. Data on foodborne illness risk factors in restaurants indicate that cooling is often out of compliance with these guidelines. We sought to identify factors under the control of the operator that had a significant effect on the cooling rates of cooked foods. Minneapolis Minnesota Health Inspectors were trained in standardized operating procedures for cooling data collection. Data loggers set to appropriate time intervals and calibrated to ±0.5 °C (∼1°F) were used in data collection. Analysis was performed using the R statistical computing language version 4.2.2. Preexisting pathogen models were used to predict Log CFU increases of Clostridium perfringens or Bacillus cereus. Data from 224 recipes were recorded by inspectors between October 2018 and October 2019. Food depth had a highly significant effect (p = 8.90E-08) on cooling rate. The use of an ice bath or ice wand was also significant (p < 0.005). There was a significant correlation between container material (metal or plastic) and food depth because foods with a greater depth are often being cooled in plastic containers. Foods at a depth greater than 5.1 cm (2 in.) that cooled faster than 0.23 log(°C)/h were often wholly or partially cooled in blast chillers or freezers, cooled using an ice bath or ice wand (or both), or were composed of protein pieces (e.g., chicken wings) that facilitated more rapid cooling due to air gaps in the food. Foods in shallow containers at a food depth of less than or equal to ∼5.1 cm (2 in.) that cooled more slowly than 0.23 log(°C)/h were being cooled at temperatures greater than 5 °C (41°F) or were partially or wholly covered. These foods also showed little evidence of pathogen growth from predictive models. Our analysis shows that cooling foods in shallow containers at a food depth of less than or equal to ∼5.1 cm (2 in.) poses little risk of significant pathogen growth.

Kinetic study of the thermal inactivation of Weizmannia coagulans during food thermal processing

Weizmannia coagulans has attracted attention due to its remarkable health benefits for human, but the dynamic changes of its viable bacteria during thermal processing have been less reported. In this study, a predictive model for the survival of Weissmanella coagulans during thermal processing of food was developed and validated during the processing of coffee, tea, instant noodles, calcium milk biscuits, muffin cake and steamed buns. The kinetics of heat inactivation activities of Weizmannia coagulans VHProbi C08 and Weizmannia coagulans GBI-30, 6086 at 85, 95, 105, 110 and 115 °C were investigated, and their coefficients of determination were greater than 0.91 and 0.87, and the root-mean-square errors were less than 0.64 and 0.43, respectively. The z-values of VHProbi C08 and GBI-30, 6086 were obtained by Bigelow model fitting as 36.1 °C and 36.9 °C, respectively. The developed prediction model was applied to the thermal processing of six food products and the measured values were all within ±0.5 Log10 (CFU/mL) of the predicted values, indicating high prediction accuracy. The model predicts the survival of Weissmanella coagulans simply by obtaining the initial number of viable bacteria and the change in temperature. These suggested that the model can be used as an effective tool to evaluate the stability of Weizmannia coagulans in food thermal processing.

Modelling growth of Bacillus cereus in paneer by one-step parameter estimation

Bacillus cereus phylogenetic group III and IV strains are commonly associated with food products and cause toxin mediated foodborne diseases. These pathogenic strains have been identified from milk and dairy products, such as reconstituted infant formula and several cheeses. Paneer is a fresh, soft cheese originating from India that is prone to foodborne pathogen contamination, such as by Bacillus cereus. However, there are no reported studies of B. cereus toxin formation in paneer or predictive models quantifying growth of the pathogen in paneer under different environmental conditions. This study assessed enterotoxin-producing potential of B. cereus group III and IV strains, isolated from dairy farm environments, in fresh paneer. Growth of a four-strain cocktail of toxin-producing B. cereus strains was measured in freshly prepared paneer incubated at 5-55 °C and modelled using a one-step parameter estimation combined with bootstrap re-sampling to generate confidence intervals for model parameters. The pathogen grew in paneer between 10 and 50 °C and the developed model fit the observed data well (R² = 0.972, RMSE = 0.321 log₁₀ CFU/g). The cardinal parameters for B. cereus growth in paneer along with the 95% confidence intervals were: μ_opt 0.812 log₁₀ CFU/g/h (0.742, 0.917); T_opt is 44.177 °C (43.16, 45.49); T_min is 4.405 °C (3.973, 4.829); T_max is 50.676 °C (50.367, 51.144). The model developed can be used in food safety management plans and risk assessments to improve safety of paneer while also adding to limited information on B. cereus growth kinetics in dairy products.

Quantitative risk assessment of Bacillus cereus in wet rice noodles from raw material to marketing phase

From 2018, several foodborne diseases caused by the consumption of wet rice noodles contaminated with microorganisms have attracted the attention of consumers and surveillance departments. We explored the crucial risk factors for the contamination of Bacillus cereus during the various steps of the wet rice noodles production chain (from raw material to marketing phase). A total of 273 samples were collected in each corresponding production phase. The contamination level was quantitatively detected in the samples, and the corresponding temperature and time were recorded and analyzed using @Risk software. The quantitative detection results of raw material were determined as the initial contamination level in the model to predict the final contamination level and assess the key risk factors for B. cereus contamination in wet rice noodles. The model predicted that the final contamination level of B. cereus was in the range of -3.55 to 4.34 log CFU/g in 95% wet rice noodles at the marketing phase. The highest predicted contamination level was 6.28 log CFU/g, and the risk of exceeding the threshold was 0.80%. The model was verified to be valid for R² > 0.96, and the predicted results could be used for reference. Moreover, the sensitivity analysis revealed that in addition to raw material, the key control factors were buffering temperature in the packaging delivery phase, transporting temperature and time from factory to marketing phase; their correlation coefficients (r) were 0.18, 0.16, and 0.15, respectively. Therefore, manufacturers need to adjust the current predelivery buffering and transporting mode. It is recommended to reduce the predelivery buffering temperature, and refrigerated trucks are preferred to control the proliferation of B. cereus in transported food, thus reducing the occurrence of foodborne diseases and improving the safety of food.

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.

Modeling Bacillus cereus Growth and Cereulide Formation in Cereal-, Dairy-, Meat-, Vegetable-Based Food and Culture Medium

This study describes the simultaneous Bacillus cereus growth and cereulide formation, in culture medium and cereal-, dairy-, meat-, and vegetable-based food matrices. First, bacterial growth experiments were carried out under a wide range of temperatures (from 9 to 45°C), using the emetic reference strain F4810/72, in the above-mentioned matrices. Then, the generated data were put in a modeling framework where the response variable was a vector of two components: the concentration of B. cereus and that of its toxin, cereulide. Both were considered time-, temperature- and matrix-dependent. The modeling was carried out in a series of steps: the parameters fitted in one step became the response variable of the following step. Using the square root link function, the maximum specific growth rate of the organism and the time to the appearance of quantifiable cereulide were modeled against temperature by cardinal parameters models (CPM), for each matrix. Finally, a validation study was carried out on an independent data set obtained in the same matrices and using various Bacillus cereus strains. Results showed that both growth and toxin-formation depended on the food matrix and on the environment but not in the same way. Thus, the matrix (culture medium), where the highest growth rate of B. cereus was observed, was not the medium where the shortest time to quantifiable cereulide occurred. While the cereal-based matrix generated the smallest growth rates (0.41-times smaller than culture medium did), quantifiable cereulide appeared in it at earlier times compared to the other tested matrices. In fact, three groups of matrices could be distinguished based on their ability to support cereulide formation (1) the cereal-based matrix (highest), (2) the culture medium and the dairy-based matrix (intermediate), and (3) the meat- and vegetable-based matrices (lowest). This ranking between the matrices is quite different from that based on their suitability to the growth of the organism. Our models can be used in HACCP studies, to improve shelf-life predictions and, generally, microbiological food safety assessments of products for which B. cereus is the main concern.

Effects of Low-Temperature Drying with Intermittent Gaseous Chlorine Dioxide Treatment on Texture and Shelf-Life of Rice Cakes

We investigated the effect of chlorine dioxide (ClO2) under low temperature drying to suppress rice cake stickiness during the cutting process by initiating the onset of retrogradation until the stickiness is minimized for shelf-life extension. The intermittent ClO2 application at low-temperature drying was conducted at 10 °C for different drying periods (0, 6, 12, 18, and 24 h). Texture analysis showed significant differences with increasing values of hardness (901.39 ± 53.87 to 12,653 ± 1689.35 g) and reduced values of modified adhesiveness (3614.37 ±578.23 to 534.81 ± 89.37 g). The evaluation of rice cake stickiness during the cutting process revealed an optimum drying period of 18 h with no significant difference (p ≤ 0.05) compared to the 24 h drying process. Microbial contamination during the drying process increased, with microbial load from 6.39 ± 0.37 to 7.94 ± 0.29 CFU/g. Intermittent ClO2 application at 22 ppm successfully reduced the microbial load by 63% during drying process. The inhibitory property of ClO2 was further analyzed on a sample with high initial microbial load (3.01 ± 0.14 CFU/g) using primary and modified secondary growth models fitted to all experimental storage temperatures (5–25 °C) with R2 values > 0.99. The model demonstrated a strong inhibition by ClO2 with microbial growth not exceeding the accepted population threshold (106 CFU/g) for toxin production. The shelf-life of rice cake was increased by 86 h and 432 h at room temperature (25 °C) and 5 °C respectively. Microbial inactivation via ClO2 treatment is a novel method for improved food storage without additional thermal sterilization or the use of an additional processing unit.

Predictive Model of Listeria monocytogenes Growth in Queso Fresco

Listeria monocytogenes is a hardy psychrotrophic pathogen that has been linked to several cheese-related outbreaks in the United States, including a recent outbreak in which a fresh cheese (queso fresco) was implicated. The purpose of this study was to develop primary, secondary, and tertiary predictive models for the growth of L. monocytogenes in queso fresco and to validate these models using nonisothermal time and temperature profiles. A mixture of five strains of L. monocytogenes was used to inoculate pasteurized whole milk to prepare queso fresco. Ten grams of each fresh cheese sample was vacuum packaged and stored at 4, 10, 15, 20, 25, and 30°C. From samples at each storage temperature, subsamples were removed at various times and diluted in 0.1% peptone water, and bacteria were enumerated on Listeria selective agar. Growth data from each temperature were fitted using the Baranyi model as the primary model and the Ratkowsky model as the secondary model. Models were then validated using nonisothermal conditions. The Baranyi model was fitted to the isothermal growth data with acceptable goodness of fit statistics (R² = 0.928; root mean square error = 0.317). The Ratkowsky square root model was fitted to the specific growth rates at different temperatures (R² = 0.975). The tertiary model developed from these models was validated using the growth data with two nonisothermal time and temperature profiles (4 to 20°C for 19 days and 15 to 30°C for 11 days). Data for these two profiles were compared with the model prediction using an acceptable prediction zone analysis; >70% of the growth observations were within the acceptable prediction zone (between -1.0 and 0.5 log CFU/g). The model developed in this study will be useful for estimating the growth of L. monocytogenes in queso fresco. These predictions will help in estimation of the risk of listeriosis from queso fresco under extended storage and temperature abuse conditions.