•Pizza is more frequently out of temperature control than many other foods.•pH and water activity of pizza indicate time temperature control for safety needed.•Total plate counts on pizza do not ...correlate with sample pick up temperature.•Predictive model shows that Staphylococcus aureus poses the greatest risk.•Risk manifests for pizza held at 30 °C or less for more than 8 hours.
Pizza is a popular food consumed around the world every day. Hot food temperatures were obtained from 19,754 nonpizza samples and 1,336 pizza temperatures were taken from dining facilities operated by Rutgers University between 2001 and 2020. These data showed that pizza was more frequently out of temperature control than many other foods. A total of 57 pizza samples that were out of temperature control were collected for further study. Pizza was tested for total aerobic plate count (TPC), Staphylococcus aureus, Bacillus cereus, Lactic acid bacteria, coliforms, and Escherichia coli. Water activity of pizza and surface pH of each individual pizza component (topping, cheese, bread) were measured. Predictions for the growth of four relevant pathogens were made for select pH and water activity values using ComBase. Rutgers University dining hall data show only about 60% of all foods that are pizza are held at the appropriate temperature. When pizza contained detectable microorganisms (∼70% of samples), average TPC ranged from 2.72 log CFU/g to 3.34 log CFU/g. Two pizza samples contained detectable S. aureus (∼50 CFU/g). Two other samples contained B. cereus (∼50 and 100 CFU/g). Five pizza samples contained coliforms (4–9 MPN/g), and no E. coli were detected. Correlation coefficients (R2 values) for TPC and pickup temperature are quite low (<0.06). Based on the pH and water activity measurements, most (but not all) of the pizza samples would be considered to potentially require time temperature control for safety. The modeling analysis shows that the organism most likely to pose a risk would be S. aureus, and the largest magnitude increase predicted is 0.89 log CFU at 30°C, pH 5.52, and water activity 0.963. The overall conclusion from this study is that while pizza represents a theoretical risk, the actual risk would likely only manifest for pizza samples that are held out of temperature control for time periods of more than eight hours.
Factors that control pathogen survival in low water activity foods are not well understood and vary greatly from food to food. A literature search was performed to locate data on the survival of ...foodborne pathogens in low-water activity (<0.70) foods held at temperatures <37 °C. Data were extracted from 67 publications and simple linear regression models were fit to each data set to estimate log linear rates of change. Multiple linear stepwise regression models for factors influencing survival rate were developed. Subset regression modeling gave relatively low adjusted R2 values of 0.33, 0.37, and 0.48 for Salmonella, E. coli and L. monocytogenes respectively, but all subset models were highly significant (p < 1.0e-9). Subset regression models showed that Salmonella survival was significantly (p < 0.05) influenced by temperature, serovar and strain type, water activity, inoculum preparation method, and inoculation method. E. coli survival was significantly influenced by temperature, water activity, and inoculum preparation. L. monocytogenes survival was significantly influenced by temperature, serovar and strain type, and inoculum preparation method. While many factors were highly significant (p < 0.001), the high degrees of variability show that there is still much to learn about the factors which govern pathogen survival in low water activity foods.
•Sixty-seven articles on pathogen survival in low aw foods were identified.•Adjusted R2 values for models were low (<0.50) suggesting unexplained variation.•All models had highly significant p-values (<1.0e-9) so parameters are meaningful.•Temperature and inoculum preparation significantly (p < 0.05) affected all pathogens.•Inoculation method significantly (p < 1.0e-06) affected Salmonella survival.
•Washing removed more (p < 0.05) curli− cells from leaf surfaces vs curli+ cells.•Washing also removed more (p < 0.05) curli− cells from leaf edges.•Curli+ cells always showed more (p < 0.05) ...transfer to noninoculated leaves.•When inoculated leaves were washed, most contamination ended up in the wash water.•Less than 1% of the contamination on inoculated leaves was transferred to other leaves.
Escherichia coli O157:H7 expresses extracellular proteins called curli that are essential for surface colonization. Transfer rates of E. coli O157:H7 0018+ (curli+), and 0018- (curli−) from inoculated to noninoculated lettuce pieces during washing were quantified in this study. Romaine lettuce pieces were inoculated with ∼6 log CFU on just the surface, just the cut edges, or both surface and cut edges. Samples were dried for 2 h in a biosafety cabinet and then washed with ten (10) noninoculated lettuce pieces in 500 mL of water for 30 s. The curli− strain was more readily removed (3 log reduction) compared to the curli+ (1 log reduction) when only the lettuce surface was inoculated (p > 0.05). The same was true when only the lettuce piece edge was inoculated (p > 0.05), although the magnitude of the reduction was less. There was no significant difference in reduction of curli+ strain between any of the surfaces. There was a significant difference (p < 0.05) in reduction of the curli− strains when comparing the leaf surface (more removal) to the cut leaf edge (less removal). The curli+ strain always showed significantly (p < 0.05) more transfer to noninoculated leaves regardless of the inoculation location. The curli+ strain transferred about −1 log percent (∼0.1%) to noninoculated pieces, while the curli− strain transferred about −2 log percent (∼0.01%) CFU to the noninoculated pieces. Mean log percent transfer was not significantly different within the curli+ or curli− experiments (p > 0.05). When the leaf surface was inoculated, there was about 2 log percent (i.e., close to 100% transfer) into the wash water for both the curli+ and curli− strains. When only the cut edges or surface and edge were inoculated, observed mean transfer rates were lower but not significantly different (p > 0.05). Further research is needed to more fully understand the factors that influence bacterial cross-contamination during the washing of fresh produce.
•Three berries heated for 60 s at 100% power yielded a 3.8 ± 0.2 log reduction in MS2.•Surveyed berry package microwaving directions are very diverse.•Virus inactivation could be modeled using a ...temperature path-dependent D-value.•The model fit the data well, with a RMSE of 0.5 PFU/g for a 6-log reduction.•This is a first step to model microwave virus inactivation on frozen strawberries.
Frozen berries have been repeatedly linked to acute gastroenteritis caused by norovirus, the most common cause of foodborne illness in the United States. Many guidelines recommend that frozen berries be microwaved for at least 2 min, but it is unclear if this thermal treatment is effective at inactivating norovirus. The objective of this study was to model the effect of microwave heating at varying power levels on the survival of bacteriophage MS2, a norovirus surrogate, when inoculated onto frozen strawberries. Bacteriophage MS2 was inoculated onto the surface of frozen strawberries with a starting concentration of approximately 10 log PFU/g. Samples (either 3 or 5 whole strawberries) were heated in a 1300-Watt domestic research microwave oven (frequency of 2450 MHz) at power levels of 30, 50, 70, and 100% (full power), for times ranging from 15 to 300 s to determine inactivation. Temperatures at berry surfaces were monitored during heating using fiberoptic thermometry. All experiments were conducted in triplicate. The primary model for thermal inactivation was a log-linear model of logN vs. time. The secondary model was for a D-value decreasing linearly with temperature and an added term that was path-dependent on the thermal history. Parameters in the model were estimated using dynamic temperature history at the surface of the berry, via nonlinear regression using all data simultaneously. The root mean square error was ∼0.5 PFU/g out of a total 6-log reduction. Log reductions of 1.1 ± 0.4, 1.5 ± 0.5, 3.1 ± 0.1, and 3.8 ± 0.2 log PFU/g were observed for 30, 50, 70, and 100% microwave power levels when three berries were heated for 60 s. D-values were 21.4 ± 1.95 s and 10.6 ± 1.1 s at 10 and 60°C, respectively. This work demonstrates an approach to estimate inactivation parameters for viruses from dynamic temperature data during microwave heating. These findings will be useful in predicting the safety effect of microwave heating of berries in the home or food service.
Eggs in the United States are typically washed using chemical sanitizers such as quaternary ammonia (QA) or chlorine. Such treatments generate wash water, which could be potentially hazardous to the ...environment. A novel, nonthermal sanitization technique for washing shell eggs using cold plasma-activated water (PAW) was investigated in this study. The inactivation efficacy of PAW on Klebsiella michiganensis and the impact of PAW on the cuticle of the eggshell and shell strength were tested in comparison to QA. Washing inoculated eggs with PAW and QA achieved a similar microbial reduction (>5.28 log CFU/egg). Colorimetric analysis showed that ∆E-value for PAW-treated eggs was significantly lower than QA-treated eggs, suggesting higher cuticle coverage in eggs treated with PAW. The texture analysis to test for shell egg strength indicated that washing eggs with PAW did not affect the structural integrity of the eggshell when compared to eggs washed with QA. According to this study, PAW has the potential as an alternative to commercial sanitizers like QA in the egg-washing industry. PAW does not detrimentally impact shell strength or cuticle coverage and provides similar microbial reduction efficacy.
Many factors have been shown to influence bacterial transfer between surfaces, including surface type, bacterial species, moisture level, pressure, and friction, but the effect of inoculum size on ...bacterial transfer has not yet been established. Bacterial cross contamination rates during performance of common food service tasks were previously determined in our laboratory using nalidixic acid-resistant Enterobacter aerogenes. Eight different transfer rates were determined, each involving a minimum of 30 volunteers. The influence of source inoculum level on the percentage of bacteria transferred (percent transfer rates) and log10 CFU per recipient surface was determined using statistical analysis. The effect of inoculum size on transfer rate was highly statistically significant (P < 0.0001) for all transfer rate data combined (352 observations) and for each individual cross contamination rate, except for data on contamination via transfer from chicken to hand through a glove barrier (P = 0.1643). Where inoculum size on the source was greater, transfer rates were lower, and where inoculum size on the source was less, transfer rates were higher. The negative linear trend was more obvious for activities that had a larger range of inoculum sizes on the source surface. This phenomenon has serious implications for research seeking to determine bacterial cross contamination rates, since the different transfer efficiencies that were previously shown to be associated with certain activities may actually be the result of differing initial inoculum levels. The initial inoculum size on the source and the amount of bacteria transferred must both be considered to accurately determine bacterial transfer rates.
•Health and hygiene was ranked of highest importance with industry community members.•PAC respondents ranked adjacent land use of highest importance.•Grower and technical assistance groups similarly ...ranked priorities.•Priority rankings differed by operation size.
A broad understanding of community member food safety priorities in the fresh produce supply chain does not currently exist. This information is essential to improve food safety knowledge and practices effectively and efficiently throughout the fresh produce industry; therefore, the goal of this study was to identify and rank community produce safety priorities in the United States. Survey questions were designed and approved by food safety experts for participants to rank 24 fresh produce safety priorities. The anonymous survey was distributed online via Qualtrics™ to fresh produce community members from November 2020 to May 2021. A score was calculated for each priority by summing weighted ranking scores across responses. Descriptive statistics and logistic regression were used to determine frequencies and distribution of response and identify factors (e.g., role in produce safety, size/location of organization/operation) that influenced rankings. A total of 281 respondents represented fourteen different roles in the fresh produce industry, with most identified as growers (39.5%). Produce operations were distributed across the U.S. and annual produce sales ranged from below $25,000 to over $5,000,000. Health and hygiene, training, postharvest sanitation, traceability, and harvest sanitation were ranked as the top five food safety priorities. These findings provide insight into community member priorities in fresh produce safety and can be used to inform intervention efforts, ranging from specialized training for produce growers and packers, industry-driven research projects, and gaps in risk communication strategies.
The growth parameters (growth rate, μ and lag time, λ) of three different strains each of Salmonella enterica and Listeria monocytogenes in minimally processed lettuce (MPL) and their changes as a ...function of temperature were modeled. MPL were packed under modified atmosphere (5% O2, 15% CO2 and 80% N2), stored at 7–30 °C and samples collected at different time intervals were enumerated for S. enterica and L. monocytogenes. Growth curves and equations describing the relationship between μ and λ as a function of temperature were constructed using the DMFit Excel add-in and through linear regression, respectively. The predicted growth parameters for the pathogens observed in this study were compared to ComBase, Pathogen modeling program (PMP) and data from the literature. High R2 values (0.97 and 0.93) were observed for average growth curves of different strains of pathogens grown on MPL. Secondary models of μ and λ for both pathogens followed a linear trend with high R2 values (>0.90). Root mean square error (RMSE) showed that the models obtained are accurate and suitable for modeling the growth of S. enterica and L. monocytogenes in MP lettuce. The current study provides growth models for these foodborne pathogens that can be used in microbial risk assessment.
► Modeling the growth of three different strains of Salmonella and L. monocytogenes. ► Primary and secondary modeling of growth rate and lag time in mix of lettuces. ► Salmonella grew faster than L. monocytogenes in the temperatures tested (7–30 °C). ► L. monocytogenes showed higher lag times at 7 and 10 °C than Salmonella enterica. ► Secondary modeling for both pathogens showed a linear trend with R2 values >0.90.
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