Modeling the Effects of Product Temperature, Product Moisture, and Process Humidity on Thermal Inactivation of Salmonella in Pistachios during Hot-Air Heating

2020 ◽  
Vol 84 (1) ◽  
pp. 47-57
Author(s):  
KAITLYN E. CASULLI ◽  
KIRK D. DOLAN ◽  
BRADLEY P. MARKS
Keyword(s):  
Moisture Content ◽  
Salmonella Enteritidis ◽  
Thermal Inactivation ◽  
Mean Squared Error ◽  
Dew Point ◽  
Moist Air ◽  
Product Temperature ◽  
Air Heating ◽  
Air Convection ◽  
Primary Model

ABSTRACT Prior efforts to model bacterial thermal inactivation in and on low-moisture foods generally have been based on isothermal and iso-moisture experiments and have rarely included dynamic product and process variables. Therefore, the objective of this study was to test appropriate secondary models to quantify the effect of product temperature, product moisture, and process humidity on thermal inactivation of Salmonella Enteritidis PT30 on pistachios subjected to dynamic dry- or moist-air heating. In-shell pistachios were inoculated with Salmonella Enteritidis PT30, equilibrated in controlled-humidity chambers (to target water activities [aw] of 0.45 or 0.65), and in some cases, subjected to a presoak treatment prior to heating in a laboratory-scale, moist-air convection oven at multiple combinations (in duplicate) of dry bulb (104.4 or 118.3°C) and dew point (∼23.8, 54.4, or 69.4°C) temperatures, with air speed of ∼1.3 m/s. Salmonella survivors, pistachio moisture content, and aw were quantified at six time points for each condition, targeting cumulative lethality of ∼3 to 5 log. The resulting data were used to estimate parameters for five candidate secondary models that included combinations of product temperature, product moisture, aw, and/or process dew point (coupled with a log-linear primary model). A model describing the D-value as a function of temperature and dew point fit the data well (root mean squared error [RMSE] = 0.86 log CFU/g); however, adding a term to account for dynamic product moisture improved the fit (RMSE = 0.83 log CFU/g). In addition, product moisture content yielded better model outcomes, as compared with aw, particularly in the case of the presoaked pistachios. When validated at the pilot scale, the model was conservative, always underpredicting the experimental log reductions. Both dynamic product moisture and process humidity were critical factors in modeling thermal inactivation of Salmonella in a low-moisture product heated in an air-convection system. HIGHLIGHTS

Thermal Inactivation Kinetics for Salmonella Enteritidis PT30 on Almonds Subjected to Moist-Air Convection Heating

2009 ◽  
Vol 72 (8) ◽  
pp. 1602-1609 ◽  
Author(s):  
SANGHYUP JEONG ◽  
BRADLEY P. MARKS ◽  
ALICIA ORTA-RAMIREZ
Keyword(s):  
Salmonella Enteritidis ◽  
Thermal Inactivation ◽  
Dew Point ◽  
Traditional Model ◽  
Moist Air ◽  
Validation Data ◽  
Dynamic Surface ◽  
Modified Model ◽  
Air Heating ◽  
Z Value

A traditional thermal inactivation kinetic model (D- and z-value) was modified to account for the effect of process humidity on thermal inactivation of Salmonella Enteritidis PT30 on the surface of almonds subjected to moist-air heating. Raw almonds were surface inoculated to ∼108 CFU/g and subjected to moist-air heating in a computer-controlled laboratory-scale convection oven. Time-temperature data were collected for 125 conditions (five dry bulb temperatures, 121 to 232°C; five process humidity levels, 5 to 90% moisture by volume; and five process durations). Moisture status at the surface of the almond, rather than the humidity of the bulk air, was a primary factor controlling the rate of inactivation; therefore, the D-value could not be a simple function of process temperature. Instead, the traditional D- and z-value model was modified to account for the dynamic water status at the surface of the product under humid heating conditions. The modified model needs only the dew point temperature of the processing air and dynamic surface temperature history of the almonds during moist-air heating. The modified model was more robust and accurate than the traditional model. The accuracy of the modified model was improved by 32 to 44% (in terms of the root mean squared error [RMSE] for the model fit) when compared with the traditional model in all moist-air heating conditions. Also, the prediction error of the modified model (RMSE = 1.33 log reductions) against an independent validation data set was approximately one-half that of the traditional model (RMSE = 2.56 log reduction) in the humidity range of 5 to 90% moisture by volume.

Quantifying the Performance of Pediococcus sp. (NRRL B-2354: Enterococcus faecium) as a Nonpathogenic Surrogate for Salmonella Enteritidis PT30 during Moist-Air Convection Heating of Almonds

2011 ◽  
Vol 74 (4) ◽  
pp. 603-609 ◽  
Author(s):  
SANGHYUP JEONG ◽  
BRADLEY P. MARKS ◽  
ELLIOT T. RYSER
Keyword(s):  
Salmonella Enteritidis ◽  
Phage Type ◽  
Moist Air ◽  
Salmonella Enterica Serovar Enteritidis ◽  
Convection Oven ◽  
Air Heating ◽  
Air Convection ◽  
Convection Heating ◽  
The Mean ◽  
Computer Controlled

Pediococcus sp. NRRL B-2354 was investigated as a potential nonpathogenic surrogate for Salmonella enterica serovar Enteritidis phage type 30 (SE PT30) on the surface of almonds subjected to moist-air heating. Both microorganisms were subjected to various time, temperature, and humidity regimens on almonds processed in a computer-controlled, laboratory-scale, moist-air convection oven. Overall, the mean log reductions for Pediococcus sp. were 0.6 log and 1.4 log lower than those for SE PT30 (P < 0.05) at predicted reductions of 3 and 5 log, respectively. Also, the Dref-values for Pediococcus sp., calculated using a modified inactivation model (accounting for moisture) for SE PT30 on the surface of almonds subjected to moist-air heating (30 to 90% moisture by volume) were ~30% larger than those for SE PT30. Based on these findings, Pediococcus sp. NRRL B-2354 can be used as a conservative surrogate for SE PT30 during moist-air heating, and this organism is also likely to be an acceptable surrogate for steam heating.

scholarly journals Thermal Inactivation of Salmonella Enteritidis PT 30 in Almond Kernels as Influenced by Water Activity

2013 ◽  
Vol 76 (1) ◽  
pp. 26-32 ◽  
Author(s):  
ROSSANA VILLA-ROJAS ◽  
JUMING TANG ◽  
SHAOJIN WANG ◽  
MENGXIANG GAO ◽  
DONG-HYUN KANG ◽  
...  
Keyword(s):  
Moisture Content ◽  
Water Activity ◽  
Salmonella Enteritidis ◽  
Thermal Inactivation ◽  
Thermal Control ◽  
Heat Inactivation ◽  
Inactivation Kinetics ◽  
Salmonella Enterica Serovar Enteritidis ◽  
Time Required ◽  
Almond Cultivar

Salmonellosis outbreaks related to consumption of raw almonds have encouraged the scientific community to study the inactivation kinetics of pathogens in this dry commodity. However, the low moisture content of the product presents a challenge for thermal control, because the time required to achieve the desired thermal inactivation of microorganisms increases sharply with reduced moisture content and water activity. In this study, we explored and modeled the heat inactivation of Salmonella enterica serovar Enteritidis PT 30 in almond cultivar ‘Nonpareil’ kernel flour at four water activity (aw) values (0.601, 0.720, 0.888, and 0.946) using four temperatures for each aw. The results showed that the inactivation was well fitted by both Weibull distribution (R2 = 0.93 to 1.00) and first-order kinetics (R2 = 0.82 to 0.96). At higher aw values, the rate of inactivation increased and less time was needed to achieve the required population reduction. These results suggest that, to avoid deterioration of product quality, shorter process times at lower temperatures may be used to achieve desired inactivation levels of Salmonella Enteritidis PT 30 by simply increasing the moisture content of almonds. These goals could be achieved with the use of existing procedures already practiced by the food industry, such as washing or prewetting scalding before heat inactivation.

Impact of Process Temperature, Humidity, and Initial Product Moisture on Thermal Inactivation of Salmonella Enteritidis PT 30 on Pistachios during Hot-Air Heating

2018 ◽  
Vol 81 (8) ◽  
pp. 1351-1356 ◽  
Author(s):  
KAITLYN E. CASULLI ◽  
FRANCISCO J. GARCES-VEGA ◽  
KIRK D. DOLAN ◽  
ELLIOT T. RYSER ◽  
LINDA J. HARRIS ◽  
...  
Keyword(s):  
Salmonella Enteritidis ◽  
Thermal Inactivation ◽  
Pure Water ◽  
Dew Point ◽  
Process Temperature ◽  
Thermal Processes ◽  
Initial Product ◽  
Log Reduction ◽  
Product And Process ◽  
The Impact

ABSTRACT Some thermal processes, such as pistachio roasting, are not yet well characterized with respect to the impact of product and process variables on Salmonella lethality. This study aimed to quantify the effects of process temperature, humidity, and initial product water activity (aw), on Salmonella lethality for in-shell pistachios. In-shell pistachios were inoculated with Salmonella Enteritidis PT 30 (∼8.5 log CFU/g), equilibrated (0.45 or 0.65 aw), and heated without soaking (“dry”) or after a pure-water or 27% NaCl brining pretreatment (“presoaked”). Inoculated pistachio samples (15 g) were heated in a laboratory-scale, moist-air convection oven at 104.4 or 118.3°C, humidities of ∼3, 15, or 30%, v/v (∼24.4, 54.4, or 69.4°C dew point), and air speed of 1.3 m/s. Salmonella survivors were quantified at six times during each treatment, targeting total reductions of ∼3 to 5 log. Survivor data were analyzed using analysis of variance to identify main effects (time, temperature, humidity, and initial aw) and two-term interactions with time. As expected, lethality increased (P < 0.05) with temperature and humidity. For example, the time to achieve a 4-log reduction decreased 50 to 80% when humidity increased from ∼3 to 30%. When the dry and presoaked treatments were analyzed separately, initial product aw (0.45 versus 0.65 aw or 0.75 versus 0.95 aw) did not affect lethality (P > 0.05). However, when comparing dry against presoaked treatments, the time to achieve a 4-log reduction decreased 55 to 85% (P < 0.05) for presoaked pistachios subjected to the same temperature-humidity treatment. Salt had no effect (P > 0.05) on lethality outcomes. These results, relative to initial aw, process humidity, brining, and salt effects on process lethality, are critically important and must be considered in the design and validation of thermal processes for Salmonella reduction in pistachio processing.

scholarly journals Moisture content of bacterial cells determines thermal resistance of Salmonella Enteritidis PT 30

2020 ◽  
Author(s):  
Yucen Xie ◽  
Jie Xu ◽  
Ren Yang ◽  
Jaza Alshammari ◽  
Mei-Jun Zhu ◽  
...  
Keyword(s):  
Moisture Content ◽  
Thermal Resistance ◽  
Salmonella Enteritidis ◽  
Thermal Inactivation ◽  
Bacterial Pathogens ◽  
Intrinsic Factor ◽  
Inactivation Kinetics ◽  
Bacterial Cells ◽  
Freeze Dried ◽  
Low Moisture Foods

Salmonella spp. are resilient bacterial pathogens in low-moisture foods. There has been a general lack of understanding of critical factors contributing to the enhanced thermal tolerance of Salmonella spp, in dry environments. In this study, we hypothesized that the moisture content (XW) of bacterial cells is a critical intrinsic factor influencing the resistance of Salmonella spp. against thermal inactivation. We selected Salmonella Enteritidis PT 30 to test this hypothesis. We first produced viable freeze-dried S. Enteritidis PT 30, conditioned the bacterial cells to different XW (7.7, 9.2, 12.4 and 15.7 g water/100g dry solids), and determined thermal inactivation kinetics of those cells at 80 °C. The results show that D-value (time required to achieve one-log reduction) decreased exponentially with increasing XW. We further measured water activities (aw) of the freeze-dried S. Enteritidis PT 30 as influenced by temperature between 20 and 80 °C. By using those data, we estimated the XW of S. Enteritidis PT 30 from the published papers that related D-values of the same bacteria strain at 80 °C with aw of five different food and silicon dioxide matrices. We discovered that the logarithmic D-values of S. Enteritidis PT 30 in all those matrices also decreased linearly with increasing XW of the bacterial cells. The findings suggest that the amount of moisture in S. Enteritidis PT 30 is a determinant factor on their ability to resist thermal inactivation. Our results may help future research into fundamental mechanisms for thermal inactivation of bacterial pathogens in dry environments. IMPORTANCE This paper established a logarithmic relationship between the thermal death time (D-value) of S. Enteritidis PT 30 and the moisture content (XW) of the bacterial cells by conducting thermal inactivation tests on freeze-dried S. Enteritidis PT 30. We further verified this relationship using literature data for S. Enteritidis PT 30 in five low moisture matrices. The findings suggest that XW of S. Enteritidis PT 30, which is rapidly adjusted by microenvironmental aw, or relative humidity, during heat treatments, is the key intrinsic factor determining thermal resistance of the bacterium. The quantitative relationships reported in this study may help guide future designs of industrial thermal processes for control of S. Enteritidis PT 30 or other Salmonella stains in low-moisture foods. Our findings highlight a need for further fundamental investigation into the role of water in protein denaturation and accumulation of compatible solutes during thermal inactivation of bacterial pathogens in dry environments.

scholarly journals Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO<sub>2</sub> budget in South America

2013 ◽  
Vol 6 (1) ◽  
pp. 453-494 ◽  
Author(s):  
D. S. Moreira ◽  
S. R. Freitas ◽  
J. P. Bonatti ◽  
L. M. Mercado ◽  
N. M. É. Rosário ◽  
...  
Keyword(s):  
South America ◽  
Atmospheric Chemistry ◽  
Land Surface ◽  
Amazon Basin ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Dew Point ◽  
Surface Model ◽  
Co2 Concentrations ◽  
Wide Range

Abstract. This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

Modeling the Effect of Prior Sublethal Thermal History on the Thermal Inactivation Rate of Salmonella in Ground Turkey

2008 ◽  
Vol 71 (2) ◽  
pp. 279-285 ◽  
Author(s):  
M. J. STASIEWICZ ◽  
B. P. MARKS ◽  
A. ORTA-RAMIREZ ◽  
D. M. SMITH
Keyword(s):  
Thermal History ◽  
Thermal Inactivation ◽  
Mean Squared Error ◽  
Inactivation Rate ◽  
Prediction Errors ◽  
Independent Data ◽  
Validation Data ◽  
Data Set ◽  
Dependent Model ◽  
Path Dependent

Traditional models for predicting the thermal inactivation rate of bacteria are state dependent, considering only the current state of the product. In this study, the potential for previous sublethal thermal history to increase the thermotolerance of Salmonella in ground turkey was determined, a path-dependent model for thermal inactivation was developed, and the path-dependent predictions were tested against independent data. Weibull-Arrhenius parameters for Salmonella inactivation in ground turkey thigh were determined via isothermal tests at 55, 58, 61, and 63°C. Two sets of nonisothermal heating tests also were conducted. The first included five linear heating rates (0.4, 0.9, 1.7, 3.5, and 7.0 K/min) and three holding temperatures (55, 58, and 61°C); the second also included sublethal holding periods at 40, 45, and 50°C. When the standard Weibull-Arrhenius model was applied to the nonisothermal validation data sets, the root mean squared error of prediction was 2.5 log CFU/g, with fail-dangerous residuals as large as 4.7 log CFU/g when applied to the complete nonisothermal data set. However, by using a modified path-dependent model for inactivation, the prediction errors for independent data were reduced by 56%. Under actual thermal processing conditions, use of the path-dependant model would reduce error in thermal lethality predictions for slowly cooked products.

scholarly journals Field Calibration of TDR to Assess the Soil Moisture of Drained Peatland Surface Layers

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1842 ◽  
Author(s):  
Tomasz Gnatowski ◽  
Jan Szatyłowicz ◽  
Bogumiła Pawluśkiewicz ◽  
Ryszard Oleszczuk ◽  
Maria Janicka ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Mean Squared Error ◽  
Time Domain Reflectometry ◽  
Calibration Model ◽  
Surface Layers ◽  
Calibration Equation ◽  
North East

The proper monitoring of soil moisture content is important to understand water-related processes in peatland ecosystems. Time domain reflectometry (TDR) is a popular method used for soil moisture content measurements, the applicability of which is still challenging in field studies due to requirements regarding the calibration curve which converts the dielectric constant into the soil moisture content. The main objective of this study was to develop a general calibration equation for the TDR method based on simultaneous field measurements of the dielectric constant and gravimetric water content in the surface layers of degraded peatlands. Data were collected during field campaigns conducted temporarily between the years 2006 and 2016 at the drained peatland Kuwasy located in the north-east area of Poland. Based on the data analysis, a two-slopes linear calibration equation was developed as a general broken-line model (GBLM). A site-specific calibration model (SSM-D) for the TDR method was obtained in the form of a two-slopes equation describing the relationship between the soil moisture content and the dielectric constant and introducing the bioindices as covariates relating to plant species biodiversity and the state of the habitats. The root mean squared error for the GBLM and SSM-D models were equal, respectively, at 0.04 and 0.035 cm3 cm−3.

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