Modification of a Predictive Model To Include the Influence of Fat Content on Salmonella Inactivation in Low-Water-Activity Foods

2020 ◽  
Vol 83 (5) ◽  
pp. 801-815
Author(s):  
LISA M. TRIMBLE ◽  
JOSEPH F. FRANK ◽  
DONALD W. SCHAFFNER
Keyword(s):  
Water Activity ◽  
Survival Data ◽  
Peanut Butter ◽  
Weibull Model ◽  
Fat Content ◽  
Mitigation Strategies ◽  
Model Parameters ◽  
Least Squares Regression ◽  
Salmonella Spp ◽  
Model Predictions

ABSTRACT Low-water-activity (aw) foods (including those containing fat) are often implicated in outbreaks of Salmonella spp. The influence of fat content on survival in foods such as peanut butter remains unclear. Certain Salmonella serovars can survive for long periods in harsh temperatures and low moisture conditions. The objective of this study was to determine the influence of fat content on the survival of Salmonella in low-aw foods and expand an existing secondary inactivation model previously validated for lower-fat foods. Whey protein powder supplemented with peanut oil was equilibrated to five target aw values (aw < 0.60), inoculated with a dried four-strain cocktail of Salmonella, vacuum sealed, and stored at 22, 37, 50, 60, 70, and 80°C for 48 h, 28 days, or 168 days. Survival data were fitted to Weibull, Biphasic-linear, Double Weibull, and Geeraerd-tail models. The Weibull model was chosen for secondary modeling due to its ability to satisfactorily describe the data over most of the conditions under study. The influence of temperature, fat content, and aw on the Weibull model parameters was evaluated using nonlinear least squares regression, and a revised secondary model was developed based on parameter significance. Peanut butter, chia seed powder, toasted oat cereal, and animal crackers within the aw range of the model were used to validate the modified model within its temperature range. Fat content influenced survival in samples held at temperatures ≥50°C, whereas aw influenced survival at 37 and 70°C. The model predictions demonstrated improved % bias and % discrepancy compared with the previous model. Weibull model predictions were accurate and fail-safe in 38 and 58%, respectively, of the food and environmental conditions under study. Predictions were less reliable for peanut butter held at 80°C. This study provides data and a model that can aid in the development of risk mitigation strategies for low-aw foods containing fat. HIGHLIGHTS

Temperature Resistance of Salmonella in Low–Water Activity Whey Protein Powder as Influenced by Salt Content

2014 ◽  
Vol 77 (4) ◽  
pp. 631-634 ◽  
Author(s):  
S. M. SANTILLANA FARAKOS ◽  
J. W. HICKS ◽  
J. F. FRANK
Keyword(s):  
Whey Protein ◽  
Water Activity ◽  
Survival Data ◽  
Thermal Inactivation ◽  
Salt Content ◽  
Weibull Model ◽  
Inactivation Kinetics ◽  
Model Parameters ◽  
Conflicting Information ◽  
Kinetics Of

Salmonella can survive in low–water activity (aw) foods for long periods of time. Water activity and the presence of solutes may affect its survival during heating. Low-aw products that contain sodium levels above 0.1% (wt/wt) and that have been involved in major Salmonella outbreaks include peanut products and salty snacks. Reduced aw protects against thermal inactivation. There is conflicting information regarding the role of salt. The aim of this study was to determine whether NaCl influences the survival of Salmonella in low-aw whey protein powder independent of aw at 70 and 80°C. Whey protein powders of differing NaCl concentrations (0, 8, and 17% [wt/wt]) were equilibrated to target aw levels 0.23, 0.33, and 0.58. Powders were inoculated with Salmonella, vacuum sealed, and stored at 70 and 80°C for 48 h. Cells were recovered on nonselective differential media. Survival data were fit with the Weibull model, and first decimal reduction times (δ) (measured in minutes) and shape factor values (β) were estimated. The influence of temperature, aw, and salinity on Weibull model parameters (δ and β) was analyzed using multiple linear regression. Results showed that aw significantly influenced the survival of Salmonella at both temperatures, increasing resistance at decreasing aw. Sodium chloride did not provide additional protection or inactivation of Salmonella at any temperature beyond that attributed to aw. The Weibull model described the survival kinetics of Salmonella well, with R2 adj and root mean square error values ranging from 0.59 to 0.97 and 0.27 to 1.07, respectively. Temperature and aw influenced δ values (P < 0.05), whereas no significant differences were found between 70 and 80°C among the different salt concentrations (P > 0.05). b values were not significantly influenced by temperature, aw, or % NaCl (P > 0.05). This study indicates that information on salt content in food may not help improve predictions on the inactivation kinetics of Salmonella in low-aw protein systems within the aw levels and temperatures studied.

Survival of Salmonella enterica Serotype Tennessee during Simulated Gastric Passage Is Improved by Low Water Activity and High Fat Content

2013 ◽  
Vol 76 (2) ◽  
pp. 333-337 ◽  
Author(s):  
BRYAN AVILES ◽  
COURTNEY KLOTZ ◽  
TWYLA SMITH ◽  
ROBERT WILLIAMS ◽  
MONICA PONDER
Keyword(s):  
Water Activity ◽  
Salmonella Enterica ◽  
Peanut Butter ◽  
Gastric Fluid ◽  
Fat Content ◽  
Simulated Gastric Fluid ◽  
Food Matrix ◽  
High Fat ◽  
Low Fat ◽  
Infectious Dose

The low water activity (aw 0.3) of peanut butter prohibits the growth of Salmonella in a product; however, illnesses are reported from peanut butter contaminated with very small doses, suggesting the food matrix itself influences the infectious dose of Salmonella, potentially by improving Salmonella's survival in the gastrointestinal tract. The purpose of our study was to quantify the survival of a peanut butter outbreak–associated strain of Salmonella enterica serotype Tennessee when inoculated into peanut butters with different fat contents and aw (high fat, high aw; high fat, low aw; low fat, high aw; low fat, low aw) and then challenged with a simulated gastrointestinal system. Exposures to increased fat content and decreased aw both were associated with a protective effect on the survival of Salmonella Tennessee in the simulated gastric fluid compared with control cells. After a simulated intestinal phase, the populations of Salmonella Tennessee in the control and low-fat formulations were not significantly different; however, a 2-log CFU/g increase occurred in high-fat formulations. This study demonstrates that cross-protection from low-aw stress and the presence of high fat results in improved survival in the low pH of the stomach. The potential for interaction of food matrix and stress adaptations could influence the virulence of Salmonella and should be considered for risk analysis.

Heat Tolerance of Salmonella enterica Serovars Agona, Enteritidis, and Typhimurium in Peanut Butter

2006 ◽  
Vol 69 (11) ◽  
pp. 2687-2691 ◽  
Author(s):  
DINA SHACHAR ◽  
SIMA YARON
Keyword(s):  
Water Activity ◽  
Heat Tolerance ◽  
Salmonella Enterica ◽  
Thermal Inactivation ◽  
Colloidal Suspension ◽  
Peanut Butter ◽  
Peanut Meal ◽  
Cell Number ◽  
Fat Content ◽  
Lower Probability

Recent large foodborne outbreaks caused by Salmonella enterica serovars have been associated with consumption of foods with high fat content and reduced water activity, even though their ingredients usually undergo pasteurization. The present study was focused on the heat tolerance of Salmonella enterica serovars Agona, Enteritidis, and Typhimurium in peanut butter. The Salmonella serovars in the peanut butter were resistant to heat, and even at a temperature as high as 90°C only 3.2-log reduction in CFU was observed. The obtained thermal inactivation curves were upwardly concave, indicating rapid death at the beginning (10 min) followed by lower death rates and an asymptotic tail. The curves fitted the nonlinear Weibull model with β parameters <1, indicating that the remaining cells have a lower probability of dying. β at 70°C (0.40 ± 0.04) was significantly lower than β at 80°C (0.73 ± 0.19) and 90°C (0.69 ± 0.17). Very little decrease in the viable population (less than 2-log decrease) was noted in cultures that were exposed to a second thermal treatment. Peanut butter is a highly concentrated colloidal suspension of lipid and water in a peanut meal phase. We hypothesized that differences in the local environments of the bacteria, with respect to fat content or water activity, explained the observed distribution and high portion of surviving cells (0.1%, independent of the initial cell number). These results demonstrate that thermal treatments are inadequate to consistently destroy Salmonella in highly contaminated peanut butter and that the pasteurization process cannot be improved significantly by longer treatment or higher temperatures.

Moisture Transmission Through Peanut Oil Films

1974 ◽  
Vol 1 (2) ◽  
pp. 47-50 ◽  
Author(s):  
Peter J. Tiemstra ◽  
John P. Tiemstra
Keyword(s):  
Film Thickness ◽  
Water Activity ◽  
Moisture Transfer ◽  
Transmission Rate ◽  
Peanut Butter ◽  
Fat Content ◽  
Peanut Oil ◽  
Activity Phase ◽  
The Difference ◽  
Oil Films

Abstract The rate of moisture transfer through a peanut oil film was measured. It was found that the rate can be expressed by the equation: r = [k a1 (Δ a)]/t where r is the rate in g water/cm2/day, a1 is the higher water activity, Δ a is the difference in the water activity across the barrier, t is the film thickness and k is a constant. This principle was extended to fat-humectant systems where the lower water activity phase is intimately distributed through the fatty medium such as peanut butter and chocolate liquor. Doubling the fat content should increase the film thickness around each particle by 2 and halve the transmission rate as was found experimentally.

scholarly journals High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

Moisture migration in a cereal composite food at high water activity: Effects of initial porosity and fat content

2006 ◽  
Vol 43 (2) ◽  
pp. 144-151 ◽  
Author(s):  
Elisabeth Roca ◽  
Valérie Guillard ◽  
Stéphane Guilbert ◽  
Nathalie Gontard
Keyword(s):  
Water Activity ◽  
High Water ◽  
Fat Content ◽  
Initial Porosity ◽  
Moisture Migration

Experiences with computer simulation at two large wastewater treatment plants in northern Poland

2002 ◽  
Vol 45 (6) ◽  
pp. 209-218 ◽  
Author(s):  
J. Makinia ◽  
M. Swinarski ◽  
E. Dobiegala
Keyword(s):  
Computer Simulation ◽  
Wastewater Treatment ◽  
Nutrient Removal ◽  
Wastewater Treatment Plants ◽  
Biological Nutrient Removal ◽  
Model Parameters ◽  
Total N ◽  
Northern Poland ◽  
Model Predictions ◽  
Steady State Simulation

Mathematical modelling and computer simulation have became a useful tool in evaluating the operation of wastewater treatment plants (WWTPs) in terms of nutrient removal capability. In this study, steady-state simulation results for two large biological nutrient removal WWTPs are presented. The plants are located in two neighbouring cities Gdansk and Gdynia in northern Poland. Simulations were performed using a pre-compiled model and layouts (MUCT and Johannesburg processes) implemented in the GPS-X simulation package. The monthly average values of conventional parameters, such as COD, Total Suspended Solids, total N, N-NH4+, P-PO4− were used as input data. The measured effluent concentrations of COD, N-NH4+, N-NO3− and P-PO4− as well as reactor MLSS were compared with model predictions. During calibration, performed from the process engineering perspective, default values of only five model parameters were changed. The opportunities for further applications of such models in municipal WWTPs are discussed.

Effect of High Hydrostatic Pressure on Salmonella Inoculated into Creamy Peanut Butter with Modified Composition

2014 ◽  
Vol 77 (10) ◽  
pp. 1664-1668 ◽  
Author(s):  
TANYA D'SOUZA ◽  
MUKUND KARWE ◽  
DONALD W. SCHAFFNER
Keyword(s):  
High Pressure ◽  
Hydrostatic Pressure ◽  
Water Activity ◽  
High Hydrostatic Pressure ◽  
Peanut Butter ◽  
High Pressure Processing ◽  
Peanut Oil ◽  
Peanut Flour ◽  
Log Reduction ◽  
High Hydrostatic Pressure Processing

Peanut butter has been associated with several large foodborne salmonellosis outbreaks. This research investigates the potential of high hydrostatic pressure processing (HPP) for inactivation of Salmonella in peanut butter of modified composition, both by modifying its water activity as well by the addition of various amounts of nisin. A cocktail of six Salmonella strains associated with peanut butter and nut-related outbreaks was used for all experiments. Different volumes of sterile distilled water were added to peanut butter to increase water activity, and different volumes of peanut oil were added to decrease water activity. Inactivation in 12% fat, light roast, partially defatted peanut flour, and peanut oil was also quantified. Nisaplin was incorporated into peanut butter at four concentrations corresponding to 2.5, 5.0, 12.5, and 25.0 ppm of pure nisin. All samples were subjected to 600 MPa for 18 min. A steady and statistically significant increase in log reduction was seen as added moisture was increased from 50 to 90%. The color of all peanut butter samples containing added moisture contents darkened after high pressure processing. The addition of peanut oil to further lower the water activity of peanut butter further reduced the effectiveness of HPP. Just over a 1-log reduction was obtained in peanut flour, while inactivation to below detection limits (2 log CFU/g) was observed in peanut oil. Nisin alone without HPP had no effect. Recovery of Salmonella after a combined nisin and HPP treatment did show increased log reduction with longer storage times. The maximum log reduction of Salmonella achieved was 1.7 log CFU/g, which was comparable to that achieved by noncycling pressure treatment alone. High pressure processing alone or with other formulation modification, including added nisin, is not a suitable technology to manage the microbiological safety of Salmonella-contaminated peanut butter.

scholarly journals The Magnetized Disk-Halo Transition Region of M51

2018 ◽  
Vol 14 (A30) ◽  
pp. 319-322 ◽  
Author(s):  
M. Kierdorf ◽  
S. A. Mao ◽  
A. Fletcher ◽  
R. Beck ◽  
M. Haverkorn ◽  
...  
Keyword(s):  
Transition Region ◽  
Large Scale ◽  
Model Parameters ◽  
Very Large Array ◽  
X Band ◽  
Model Predictions ◽  
The Galaxy ◽  
Linearly Polarized ◽  
Grand Design ◽  
Different Depths

AbstractAn excellent laboratory for studying large scale magnetic fields is the grand design face-on spiral galaxy M51. Due to wavelength-dependent Faraday depolarization, linearly polarized synchrotron emission at different radio frequencies gives a picture of the galaxy at different depths: Observations at L-band (1 – 2 GHz) probe the halo region while at C- and X-band (4 – 8 GHz) the linearly polarized emission probe the disk region of M51. We present new observations of M51 using the Karl G. Jansky Very Large Array (VLA) at S-band (2 – 4 GHz), where previously no polarization observations existed, to shed new light on the transition region between the disk and the halo. We discuss a model of the depolarization of synchrotron radiation in a multilayer magneto-ionic medium and compare the model predictions to the multi-frequency polarization data of M51 between 1 – 8 GHz. The new S-band data are essential to distinguish between different models. Our study shows that the initial model parameters, i.e. the total regular and turbulent magnetic field strengths in the disk and halo of M51, need to be adjusted to successfully fit the models to the data.

scholarly journals Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

2021 ◽  
Author(s):  
Douglas E. Morrison ◽  
Roch Nianogo ◽  
Vladimir Manuel ◽  
Onyebuchi A. Arah ◽  
Nathaniel Anderson ◽  
...  
Keyword(s):  
School Attendance ◽  
Secondary Infection ◽  
Mitigation Strategies ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Safety Education ◽  
Vaccination Rates ◽  
Health Authorities ◽  
Infection Dynamics ◽  
Public Health Authorities

AbstractObjectiveTo support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsUnder default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model’s parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

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