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

Abstract

A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat‐shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R2 values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single‐rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (−1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. Practical Application Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.

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

“…The optimum growth temperature for B. cereus has been reported as 38 °C [36], and can be attributed to the reduced growth rate at low-temperature due to inhibition of B. cereus in rice cake. A similar observation has been reported with the inhibition of B. cereus growth at low temperatures [21,38,43].…”

“…However, at a few experimental points, higher prediction was observed especially at higher storage temperatures, which yielded lower R 2 and RMSE values. Similar results were reported for growth models of B. cereus in rice cake [38], which were attributed to an altered physiological state of the microorganism due to cold shock-inducing a lag phase, especially close to growth levels near the optimum growth points [38]. Furthermore, the results of prediction error in the range of −1.0 to 0.5, suggesting the acceptability of the models predicting bacterial growth [38], shows that the predictive model validates the effect of storage temperature in our study under all temperatures tested.…”

“…where n represents sample size, and e i denotes model prediction error at the i th term calculated as the predicted value minus the observed value. Furthermore, by following the method described by Juneja et al [38] the prediction error (PE) for each observation with log 10 CFU/g units carrying a value of 0 was considered a perfect prediction. Prediction error values ranging between −1.0 and 0.5 were considered "acceptable".…”

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

“…This methodology was occasionally already applied in the 1980s and 1990s, but has been widely used in recent literature. An extensive list of example studies exploiting this approach is provided in Table 1 , both for microbial growth [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] and thermal inactivation [ 55 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], with a focus on early and recent examples. Interestingly, however, this predictive microbiology approach bears some similarities to the traditional challenge testing approach, in which microbial growth/inactivation experiments were also conducted directly in/on the food product of interest [ 5 ].…”

Predictive modeling for physicochemical and microbial quality assessment of vacuum‐packed yak milk paneer under various storage temperatures

“…(2017) determined the combined effect of vacuum packaging with different ozone doses on shelf‐life of fresh chicken legs and also conducted the linear regression modeling analysis for microbial growth during storage. Several predictive microbial models have been developed to estimate the growth of microbes (Juneja et al., 2018, 2019; Kim et al., 2018; Tarlak et al., 2018; Zhi et al., 2018). In recent past, such predictive mathematical models were used to assess the shelf‐life of different dairy products like yogurt (Zhi et al., 2018), spreadable processed Gouda cheese (Weiss et al., 2018), milk powder (Cheng et al., 2017), pearl millet‐based kheer mix (Bunkar et al., 2014), instant kheer mix powder (Jha & Patel, 2014), burfi (Goyal & Goyal, 2013), and yogurt with fruits (Mataragas et al., 2011).…”