Draft Genome Sequence for Klebsiella michiganensis B199A, Originally Identified as Enterobacter aerogenes.

Here, we report the draft genome sequence of a strain of Klebsiella michiganensis originally identified as Enterobacter aerogenes B199A. This strain has been used as a Salmonella surrogate to study the effectiveness of handwashing and measure cross-contamination to and from a wide variety of surfaces and foods.

Initial and Final Cell Concentrations Significantly Influence the Maximum Growth Rate of Listeria monocytogenes in Published Literature Data for Whole Intact Fresh Produce.

ABSTRACT: Listeria monocytogenes has shown the ability to grow on fresh uncut produce; however, the factors that control growth are not well understood. Peer-reviewed journal articles (n = 29) meeting the inclusion criteria and related to the growth of L. monocytogenes on fresh produce were found through university library databases and Google Scholar searches. Growth models were fit to each of the extracted 130 data sets to estimate log CFU per day rates of growth by using the DMFit tool. Multiple linear stepwise regression models for factors influencing growth rate were developed using R software. Factors included were temperature, nutrient level of inoculation buffer, initial cell concentration, final cell concentration, inoculation method, container permeability, and surface characteristics. The full model produced adjusted R2, Akaike information criterion, and root mean square error values of 0.41, 488, and 1.61, respectively. Stepwise regression resulted in a reduced model with parameters for incubation temperature, inoculation buffer type, initial and final cell concentrations, container characteristics, and produce surface characteristics. Model fit statistics improved slightly in the reduced model. A further reduced three-parameter model included storage temperature and initial and final cell concentrations, with interaction terms. This three-parameter model had adjusted R2, Akaike information criterion, and root mean square error values of 0.66, 417, and 1.24, respectively. Incubation temperature (P = 1.00E-09) initial cell concentration (P = 3.05E-12), and final cell concentration (P = 4.17E-09) all had highly significant effects on maximum growth rate. Our findings show the importance of inoculum concentration and produce microbial carrying capacity on the estimated growth rate and highlight the overall importance that temperature has on growth rate. Future experiments should consider initial inoculum concentration carefully when conducting growth studies for L. monocytogenes on whole produce.

Modeling inactivation kinetics for Enterococcus faecium on the surface of peanuts during convective dry roasting.

Dry roasting can reduce Salmonella contamination on peanuts. While previous studies evaluated impact of product temperature, process humidity, product moisture, and/or product water activity on Salmonella lethality, no published study has tested multiple primary and secondary models on data collected in a real-world processing environment. We tested multiple primary and secondary models to quantify Salmonella surrogate, Enterococcus faecium, inactivation on peanuts. Shelled runner-type peanuts inoculated with E. faecium were treated at various air temperatures (121, 149, and 177 °C) and air velocities (1.0 and 1.3 m/s) for treatment times from 1 to 63 min. Peanut surface temperature was measured during treatment. Water activity and moisture content were measured, and E. faecium were enumerated after treatment. Microbial inactivation was modeled as a function of time, product temperature, and product moisture. Parameters (Dref, zT, zaw, zMC, and/or n) were compared between model fits. The log-linear primary model combined with either the modified Bigelow-type secondary model accounting for aw or moisture content showed improved fit over the log-linear primary model combined with the traditional Bigelow-type secondary model. The Weibull primary model combined with the traditional Bigelow-type secondary model had the best fit. All parameter relative errors were less than 15%, and RMSE values ranged from 0.379 to 0.674 log CFU/g. Incorporating either aw or moisture content in the inactivation models did not make a practical difference within the range of conditions and model forms evaluated, and air velocity did not have a significant impact on inactivation. The models developed can aid processors in developing and validating pathogen reduction during peanut roasting.

Models for factors influencing pathogen survival in low water activity foods from literature data are highly significant but show large unexplained variance.

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.

Validation of a Simple Two-Point Method To Assess Restaurant Compliance with Food Code Cooling Rates.

ABSTRACT: Outbreaks from improperly cooled foods continue to occur despite clearly described Food Code cooling guidelines. It is difficult for regulators to enforce these guidelines because they are typically in an establishment for less than the 6 h needed to document proper cooling. Prior research proposed using a novel method to estimate cooling rates based on two time-temperature points, but this method has not yet been validated. Time-temperature profiles of 29 different foods were collected in 25 different restaurants during cooling. Cooling curves were divided into two categories: typical (21 foods) and atypical (eight foods) prior to further analysis. Analysis of the typical cooling curves used simple linear regression to calculate cooling rates. The atypical cooling profiles were studied using Monte Carlo simulations of the cooling rate. Almost all linearized typical cooling curves had high (>0.90) R2 values. Six foods with typical cooling profiles that did not pass Food Code cooling times were correctly identified by the two-point model as having slow cooling rates. Three foods that did not pass Food Code cooling times were identified by the two-point model as having marginal cooling rates. Ten of 12 foods identified by the two-point model as having acceptable cooling rates met Food Code cooling times. Most (six of eight) foods that were considered to have atypical cooling curves failed to meet the Food Code cooling times. The two-point model was also able to determine whether these foods would fail based on Food Code guidelines depending upon the simulation criteria used. Our data show that food depth has a strong influence on cooling rate. Containers with a food depth ≥7.6 cm (3 in.) were more likely to have cooling rates slower than the U.S. Food and Drug Administration Model Food Code cooling rate. This analysis shows that the two-point method can be a useful screening tool to identify potential cooling rate problems during a routine restaurant inspection visit.

Quantifying the Influence of Relative Humidity, Temperature, and Diluent on the Survival and Growth of Enterobacter aerogenes.

Survival of bacteria on surfaces plays an important role in the cross-contamination of food. Temperature, relative humidity (RH), surface type, and inoculum diluent can affect bacterial survival. This study was conducted to examine how temperature, RH, and diluent affect the survival of Enterobacter aerogenes on stainless steel, polyvinyl chloride, and ceramic tile. Although surface type had little effect on survival, temperature had a clear effect. E. aerogenes survival was highest at 7°C and 15 and 50% RH on all surfaces. Some diluents allowed growth under high RH conditions. Cell populations in distilled water inoculated onto each surface decreased initially compared with populations in 1% phosphate-buffered saline (PBS) and 0.1% peptone broth. At 15 and 50% RH, cell populations in 1% PBS declined more sharply after 120 h than did those 0.1% peptone, but populations in both diluents had similar declines up to 3 weeks. Cell populations in 0.1% peptone had the greatest growth and reached the highest population density (∼8 log CFU/mL). Cell populations in PBS and distilled water increased by ∼2 log CFU/mL. When cells in 0.1% peptone were inoculated onto stainless steel at 100% RH, populations increased to ∼7 log CFU per coupon, whereas cells in 1% PBS increased to ∼5 log CFU per coupon followed by a decline over 3 weeks. DMFit and GInaFiT software modeled inactivation on surfaces at all conditions other than 100% RH at 21°C. These findings have important implications for experiments in which microorganisms are inoculated onto foods or food contact surfaces because the growth observed may be affected more by the inoculum diluent at high or uncontrolled RH than by the type of inoculated surface.