Dynamic Temperature Conditions Research Articles

Mathematical Modeling of Salmonella Inactivation During Apple Drying and Pre-Drying Heating in Closed Environments

Drying is one of the most effective preservation methods for extending the shelf-life of perishable foods. The microbial safety of low-moisture food products had not been recognized as a concern until outbreaks reported over the past decade in products contaminated with bacterial pathogens, in particular Salmonella. There is now an urgent need to understand the influence of process conditions on the thermal inactivation of pathogens in various drying operations. This study aimed to develop a predictive model for Salmonella inactivation in diced apples during hot air drying and in high-humidity heating in closed environments. Fresh-cut apple cubes (6 mm) inoculated with a cocktail of Salmonella enterica strains (Enteritidis PT30, Montevideo 488275, and Agona 447967) were placed in a customized box inside an oven for three different treatments: (1) open-box drying at oven temperature 90 °C (Drying-90); (2) close-box pre-drying heating at 90 °C (PD heating-90); and (3) close-box pre-drying heating at 70 °C (PD heating-70). Air temperature, relative humidity (RH), and sample temperatures were monitored, and Salmonella survival was measured at multiple time intervals. After 10 min, the air RH reached 66% in PD heating-90 and 74% in PD heating-70, versus 30% in Drying-90. A 5-log reduction in Salmonella was achieved in 8.5 min in PD heating-90, and 14 min in PD heating-70, compared to 28.7 min in Drying-90. A mathematical model using sample surface RH and sample temperature profiles accurately predicted Salmonella inactivation across all treatments (RMSE = 0.92 log CFU/g, R2 = 0.86), with thermal death parameters comparable to isothermal studies. This study underscores the role of humidity in enhancing microbial reduction during drying and proposes high-humidity pre-drying heating as an effective control step. The developed model shows promise for real-time prediction of microbial inactivation in complex drying environments with dynamic temperature and humidity conditions.

Dual-mode AT-cut quartz resonant pressure sensor for wide pressure and temperature measurement ranges

In this paper, a dual-harmonic-mode excited resonant pressure sensor with an AT-cut all-quartz sensing element was developed to synergistically achieve a wide measurement range for both temperature and pressure, along with a fast transient response. In this device, the excited third overtone mode indicated the applied pressure, while the beat frequency generated by the overtone and fundamental modes was used for self-temperature compensation. At the same time, the sandwiched all-quartz sensitive structure design effectively addressed the limitations associated with narrow pressure and temperature ranges. Especially, the dual-mode self-temperature sensing mechanism has been validated in the subzero temperature range for the first time. Quartz crystal processing techniques were employed to manufacture the sensor, and key performance metrics were characterized. The experimental results demonstrated that the sensor achieved an accuracy of ± 0.015 % full scale (FS) under a pressure range of 0–120 MPa and a temperature range of −40 to 150 °C. The pressure sensitivity of the overtone frequency was quantified as 365 Hz/MPa, and the beat frequency provided a temperature accuracy of 0.4 °C. Additionally, the developed sensor exhibited fast transient response under dynamic pressure and temperature conditions attributed to its self-temperature compensation capability. Thus, this study provides a practical solution for high-pressure measurements across a wide temperature range, combined with excellent transient performance.

Assessment and Validation of Predictive Growth Models for Locally Isolated Salmonella enterica and Listeria monocytogenes in Alfalfa Sprouts at Various Temperatures

ABSTRACTSprouts are popular due to their high nutritional content, including vitamins, minerals, antioxidants, and enzymes. However, the conditions favorable for sprouting, such as warm and humid environments, are also ideal for the growth of bacteria, including food‐borne pathogens. Here, we analyzed the growth and developed predictive models of locally isolated and commercial strains of Salmonella enterica and Listeria monocytogenes in alfalfa sprouts under various constant temperatures, ranging from 5°C to 25°C. Our findings indicated that these pathogens could grow at 5°C in sprouts, albeit with a low growth rate. A rapid increase in concentration occurred at temperatures of 10°C and above. The fitted models demonstrated high performance, with R2 values ranging from 0.964 to 0.997 and RMSE values ranging from 0.15 to 0.51, respectively. Based on the fitted values, bias factor (Af) values varied between 1.01 and 1.06, with all accuracy factor (Bf) values at 1.00. Acceptable prediction zone (APZ) values ranged from 81.8% to 100%. Validation of the models under dynamic temperature conditions for specific strains showed acceptable performance. This study enhances our understanding of S. enterica and L. monocytogenes growth in alfalfa sprouts. The findings of this study could be used to improve the risk assessment of these pathogens in alfalfa sprouts.

Soft Robotic Textiles for Adaptive Personal Thermal Management.

Thermal protective textiles are crucial for safeguarding individuals, particularly firefighters and steelworkers, against extreme heat, and for preventing burn injuries. However, traditional firefighting gear suffers from statically fixed thermal insulation properties, potentially resulting in overheating and discomfort in moderate conditions, and insufficient protection in extreme fire events. Herein, an innovative soft robotic textile is developed for dynamically adaptive thermal management, providing superior personal protection and thermal comfort across a spectrum of environmental temperatures. This unique textile features a thermoplastic polyurethane (TPU)-sealed actuation system, embedded with a low boiling point fluid for reversible phase transition, resembling an endoskeleton that triggers an expansion within the textile matrix for enhanced air gap and thermal insulation. The thermal resistance improves automatically from 0.23 to 0.48Km2W-1 by self-actuating under intense heat, exceeding conventional textiles by maintaining over 10°C cooler temperatures. Additionally, the knitted substrate incorporated into the soft actuators can substantially mitigate convective heat transfer, as evidenced by the thermal resistance tests and the temperature mapping derived from numerical simulations. Moreover, it boasts significantly increased moisture permeability. The thermoadaptation and breathability of this durable all-fabric system signify considerable progress in the development of protective clothing with high comfort for dynamic and extreme temperature conditions.

Outdoor cultivation and metabolomics exploration of Chlamydomonas engineered for bisabolene production

Demonstrating outdoor cultivation of engineered microalgae at considerable scales is essential for their prospective large-scale deployment. Hence, this study focuses on the outdoor cultivation of an engineered Chlamydomonas reinhardtii strain, 3XAgBs-SQs, for bisabolene production under natural dynamic conditions of light and temperature. Our preliminary outdoor experiments showed improved growth, but frequent culture collapses in conventional Tris-acetate-phosphate medium. In contrast, modified high-salt medium (HSM) supported prolonged cell survival, outdoor. However, their subsequent outdoor scale-up from 250 mL to 5 L in HSM was effective with 10 g/L bicarbonate supplementation. Pulse amplitude modulation fluorometry and metabolomic analysis further validated their improved photosynthesis and uncompromised metabolic fluxes towards the biomass and the products (natural carotenoids and engineered bisabolene). These strains could produce 906 mg/L bisabolene and 54 mg/L carotenoids, demonstrating the first successful outdoor photoautotrophic cultivation of engineeredC. reinhardtii,establishing it as a one-cell two-wells biorefinery.

Growth kinetics of Bacillus cytotoxicus in liquid Egg yolk during treatment with phospholipase A2 – A one-step global dynamic analysis

During commercial production of liquid egg yolk (LEY), phospholipase A2 (PLA2) is used to improve its emulsification capacity and thermal stability. The enzymatic treatment may occur at elevated temperatures such as 50 °C, potentially allowing foodborne pathogens, such as Bacillus cereus, to grow. Little knowledge is available concerning growth of B. cereus in LEY during PLA2 treatment. Therefore, the objective of this study was to investigate the growth kinetics of B. cereus during PLA2 treatment using pathogenic B. cytotoxicus NVH391-98, the most thermotolerant member in the B. cereus group, as a surrogate.Inoculated LEY samples were placed in precision programmable incubators to observe the growth of B. cytotoxicus NVH391-98 under multiple isothermal and dynamic temperature conditions between 20 and 53 °C. The bacterial growth was described using the differential Baranyi model coupled with two different secondary models. The kinetic parameters were determined using one-step dynamic inverse analysis of multiple growth curves. The least square method was used in combination with the 4th order Runge-Kutta method to solve the differential Baranyi model using multiple growth curves to determine the cardinal kinetic parameters.The results showed that B. cytotoxicus NVH391-98 can grow prolifically at 50 °C. The estimated minimum, optimum and maximum temperatures were 16.7 or 18.5, 47.8 or 48.1, and 52.1 or 52.4 °C, respectively, depending on the secondary models, with an optimum growth rate of 2.1 log colony-forming-unit (CFU)/g per hour. The dynamic model is validated using isothermal curves with reasonable accuracy. B. cytotoxicus died off slowly at 15 °C. At 55 °C, thermal inactivation was observed, with a D value of approximately 2.7 h. Holding at 55 °C or below 15 °C can effectively prevent the growth of B. cytotoxicus in egg yolk.

An improved metaheuristic-based MPPT for centralized thermoelectric generation systems under dynamic temperature conditions

An improved metaheuristic-based MPPT for centralized thermoelectric generation systems under dynamic temperature conditions

A streamlined approach to characterize microalgae strains for biomass productivity under dynamic climate simulation conditions

Screening microalgae strains under static light and temperature flask conditions cannot directly quantify the biomass productivities of algae growing in the dynamically fluctuating light and temperature conditions of outdoor ponds. In this effort, we describe a testing pipeline that screens for productivity under climate simulated conditions. A validated, miniaturized Laboratory Environmental Algae Pond Simulator (mini-LEAPS) photobioreactor was used to determine optimal medium salinities and biomass productivities of cold-tolerant microalgae collected from Arctic, subarctic, Antarctic, and subalpine habitats. Strains characterized in this effort include: Chlorella antarctica UTEX1959, Chlorella sp. UTEXSNO69, Chloromonas rosae UTEXSNO11, Phaeodactylum tricornutum UTEX646, and Stichococcus minutus CCALA727. Observed productivities of the characterized strains ranged from 1.74 ± 0.25 to 8.18 ± 0.81 g m−2 day−1 (as ash-free dry weight). For each strain, a temperature tolerance profile was generated to identify the most appropriate season (s) for cultivation. The two most promising strains, Chlorella sp. UTEX SNO69 and P. tricornutum UTEX646, were tested alongside the DISCOVR winter benchmark strain Monoraphidium minutum 26B-AM in outdoor open ponds at the PNNL Algae Testbed (PAT) in Arizona. Under cold-season outdoor pond conditions, Chlorella sp. UTEXSNO69 achieved average areal biomass productivities of 8.21 ± 0.50 g m−2 day−1, not significantly different from that of the benchmark strain (8.52 ± 0.43 g m−2 day−1, p > 0.05). Under spring outdoor pond conditions, P. tricornutum UTEX646 achieved average areal biomass productivities 10.34 ± 0.27 g m−2 day−1, not significantly different from that of the benchmark (10.07 ± 0.50 g m−2 day−1) under the tested conditions (p > 0.05). The streamlined pipeline was thus demonstrated to successfully characterize productive microalgae strains for outdoor deployment by weeding out algae strains that were not productive under the tested dynamic light and temperature conditions.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion.

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares-Regression (PLS-R) and support vector machine-regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error-RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Kinetic evaluation of the crosslinking of a low-temperature cured biobased epoxy-diamine structure

Bis(oxiran-2-ylmethyl) adipate, synthesized by adipic acid, was investigated for its capability to polymerize at ambient temperature when blended with 1-(2-aminoethyl) piperazine. The object of the current work is associated to the kinetic analysis of the aforementioned epoxy-amine system, qualifying it as a potential candidate in the adhesion field of manufacturing industries. Polymerization kinetics were followed by Differential Scanning Calorimetry measurements, carried out both under dynamic and constant temperature conditions. The first gave rise to the total heat released through polymerization and the glass transition temperatures of the initial monomers (Tgo) and the crosslinked network (Tgoo), found to be 356 J/g, −69.7°C and 1.8 °C, respectively. The later revealed the effective crosslinking of the system at 25, 40 and 60 °C under reasonable periods of time. Cyclical dynamic-dynamic and isothermal-dynamic DSC experiments were further performed, giving results that follow the Di Benedetto equation, with Tgo and Tgoo values very close to the experimentally calculated. According to Vyazovkin isoconversional method applied on dynamic DSC measurements, the activation energy found to be independent of the conversion degree throughout the curing process and equal to 61.4 kJ/mol, indicating a single-step reaction. Various autocatalytic reaction models were selected to reproduce constant heating rate experiments, all displaying very accurate results, while fitting parameters from the aforementioned simulations, along with kinetic parameters derived from Vyazovkin method, were utilized for the estimation of isothermal curing predictions. Isothermal predictions were validated through comparison to both solely isothermal DSC measurements and data gathered in cyclical isothermal-dynamic experiments. Consequently, the nth order with an autocatalytic constant (Kcat) reaction model Cn was selected for the construction of a Time-Temperature-Transformation plot, revealing optimum curing conditions for the investigated temperature range. The superiority of the present epoxy thermoset system lies in the monomers' organically originated nature, along with its low-temperature curing cycle, enhancing environmental consciousness and saving energy for generations to come.

Predictive models for the growth of Salmonella spp., Listeria spp., and Escherichia coli in lettuce harvested on Taiwanese farms.

This study aimed at developing predictive models for Salmonella, Listeria, and E. coli in lettuce iceberg (Lactuca sativa) locally grown in Taiwan. The models were developed under constant temperature levels (5, 10, 15, 20, and 25°C) and validated under dynamic temperature conditions (18°C for 4 h, 7°C for 48 h, 23°C for 4 h). The result showed that (1) all strains were unable to grow at 5°C except for standard strain of Listeria obtained from the BCRC and (2) the growth rate of locally isolated strains of Salmonella and Listeria was higher than the standard one at certain temperature levels and lower than the growth rates of E. coli. The findings in this study enhance our understanding about the growth variability between Salmonella, Listeria, and E. coli strains on vegetables locally grown in Taiwan and may be used to improve the management of proper storage temperature in the lettuce supply chain in this country. Considering the temperature recommendation for refrigerated food in Taiwan, the findings in this study therefore recommend that fresh vegetables (e.g., lettuce) should be stored at 5°C or lower to prevent the rapid growth of these microorganisms. Finally, the developed models can be used in the assessment of the microbiological risk of Salmonella, Listeria, and E. coli contamination in lettuce locally grown in Taiwan. PRACTICAL APPLICATION: This study developed predictive models describing the growth of Salmonella, Listeria, and E. coli in lettuce locally grown in Taiwan. The models developed in this study can be used in quantitative microbial risk assessment.

Development and validation of a mathematical model for pseudomonads growth as a basis for predicting spoilage of fresh poultry breast and thigh fillets

The growth of naturally contaminated pseudomonads on fresh breast and thigh poultry fillets during aerobic storage was studied and modeled as a function of temperature (0–30°C). A statistical comparison of the models for breast and thigh fillets showed that muscle type does not significantly affect the temperature dependence of pseudomonads growth kinetics. A unified model for breast and thigh was developed and validated against pseudomonads growth rate data under isothermal conditions extracted from literature and experimental data under dynamic temperature conditions. The validation results showed a satisfactory performance of the model with the bias and accuracy factors ranging from 0.85 to 1.09 and 1.02 to 1.21, respectively. The model was further used to predict the shelf life of fresh poultry as the time required by pseudomonads to reach the spoilage level for various scenarios of temperature, initial contamination level, and physiological state of pseudomonads demonstrating its application in a risk-based shelf-life assessment of fresh poultry products.

Optimization of the thermal conductivity test for building insulation materials under multifactor impact

The thermal conductivity of building materials is an important parameter in building thermal engineering and energy conservation. When building thermal insulation materials are placed in the real environment, they are affected by dynamic temperature and humidity conditions, and most materials have certain moisture content. However, moist materials have a different thermal conductivity than dry materials, and an inaccurate estimation of thermal conductivity will cause errors in the energy conservation calculation of the building envelope. To accurately measure the thermal conductivity under a controllable environment and reduce energy conservation calculation errors, the effects of the measuring apparatuses, drying temperature, specimen size, specimen moisture content, ambient temperature and humidity on the thermal conductivity of the building thermal insulation materials are studied. The sensitivity of thermal conductivity to various influencing factors is analyzed, and the thermal conductivity value of moist building thermal insulation materials is determined as the basis to select or design products for building envelopes. In addition, the test method of thermal conductivity for building thermal insulation materials is optimized.

Role of water in unexpectedly large changes in emission flux of volatile organic compounds in soils under dynamic temperature conditions

Understanding the diffusive transport behavior of volatile organic compounds (VOCs) in near-surface soils is important because soil VOC emissions affect atmospheric conditions and climate. Previous studies have suggested that temperature changes affect the transport behavior; however, the effect of these changes are poorly understood. Indeed, under dynamic temperature conditions, the change in VOC flux is much larger than that expected from the temperature dependency of the diffusion coefficient of VOCs in air. However, the mechanism is not well understood, although water in soil has been considered to play an important role. Here, we present the results of experiments for the upward vertical vapor-phase diffusive transport of two VOCs (benzene and tetrachloroethylene) in sandy soil under sinusoidal temperature variations of 20–30 °C, as well as its numerical representation. The results clarify that the unexpectedly large changes in emission flux can occur as a result of changes in the VOC concentration gradient due to VOC release (volatilization) from/trapping (dissolution) into water, and that such flux changes may occur in various environments. This study suggests the importance of a global evaluation of soil VOC emissions by continuous measurements in various soil environments and/or predictions through numerical simulations with thorough consideration of the role of water in dynamic soil environments.

Predictive model for growth of Clostridium botulinum from spores at temperatures applicable to cooling of cooked ground pork

Cooling deviations and temperature abuse are two main reasons leading to the risk of Clostridium botulinum outgrowth in cooked pork. The aim of this research was to create a model that could be used to estimate C. botulinum growth from spores in cooked pork at temperatures similar to those used to chill cooked pork in processing facilities and food establishments. A cocktail of proteolytic C. botulinum types A and B consisting of five strains per type were used to inoculate pork to a final spore concentration of approximately 2 log CFU/g and cooked to 71 °C to heat shock the spores and kill vegetative microbes. The growth of C. botulinum was established at constant storage temperatures from 10 to 46 °C. C. botulinum growth was also studied under dynamic temperature conditions with cooling set to start at 54.4 °C and end at 4.4 °C or 7.2 °C in monophasic or biphasic cooling profiles, respectively. Growth parameters were estimated using the Baranyi model as a primary model and growth rates were fitted using the modified Ratkowsky secondary model with respect to temperature. The R2 values ranged from 0.7653 to 0.9995 indicating that the Baranyi primary model was well suited to the growth data. The modified Ratkowsky secondary model's R2 was 0.9653 and its root mean square error (RMSE) was 0.0687. All 11 prediction error values computed were within the limit of acceptable prediction zone (−1.0 to 0.5) suggesting a good fit of the model. The predictive model can provide information for the safety of cooked pork exposed to longer chilling times or for customized process schedule development as cooling of larger diameter products presents a processing challenge in the meat process operations.

Remaining Useful Life Estimation and Prognostication of SAC305 Printed Circuit Boards for Dynamic Conditions of Temperature and Vibration Loads

Abstract This study focuses on the feature vector identification and remaining useful life (RUL) estimation of SAC305 solder alloy printed circuit boards (PCBs) of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The first type of analysis emphasizes the vibration characteristic for varying acceleration conditions while holding the temperature levels constant, and the second studies the shift in characteristics for various ambient temperatures by maintaining constant acceleration levels. The above analyses attempt to imitate an electronic board's actual working conditions in an automobile subjected to varying temperature and vibration environments. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels is investigated. The principal component analysis (PCA) is used as the data reduction and pattern recognition technique to the different operating conditions of vibration environments. The residual of the autocorrelation function (ACF) of frequency and instantaneous frequency (IF) matrices shows behavior that can predict the packages' failure on the PCB irrespective of varying conditions of temperature and acceleration levels. This feature vector's effectiveness is verified on two different configurations of PCB and for the two separate analyses discussed prior in the study. The RUL of the packages was estimated using a deep learning approach based on long short-term memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The ACF's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions.

The incidence of frequent foodborne disease outbreaks due to Staphylococcus aureus contamination necessitates the urgent searching for effective methods to monitor S. aureus. This research aims to construct model with a dynamic survival curve and some static growth curves to predict the behavior of S. aureus in raw pork. Lack of research about S. aureus kinetics in pork under fluctuating temperature conditions across freezing and thawing necessitates this study. One‐step analysis was used to determine the model parameters, which was more efficient than conventional model analysis with two steps. The results of kinetic analysis showed that Tmin (minimum growth temperature) was 6.85°C, which is close to the estimated values in previous reports. Subsequently, validation results indicated the integrated model can accurately predict the behavior of S. aureus regardless of isothermal or nonisothermal conditions with the root‐mean‐square errors (RMSE < 0.44 log CFU/g, 73.9% of the errors of prediction falls within ±0.5 log CFU/g), accuracy factors Af and bias factors Bf were both close to 1. This work may offer an effective method for the assessment of microbial security related to S. aureus in pork.

Dynamic model to describe kinetic behavior of Listeria monocytogenes in smoked salmon

AbstractIn this study, the dynamic model was developed to describe cell counts of Listeria monocytogenes in smoked salmon under distribution conditions. A 9‐strain mixture of L. monocytogenes was inoculated in smoked salmon at 3–4 Log CFU/g. The inoculated samples were stored at 4°C, 10°C, and 20°C for up to 16 days, and the bacterial cell counts were enumerated on PALCAM agar. The Baranyi model was fitted to the L. monocytogenes cell counts to calculate the lag phase duration (LPD; hr) and maximum growth rate (μmax; Log CFU/g/hr). LPD was 74.22 hr at 4°C, and the value was rapidly decreased to 4.84 hr at 20°C as temperature increased. μmax ranged from 0.01 to 0.13 Log CFU/g/hr, depending on the storage temperature. The kinetic parameters were fitted with secondary models as a function of temperature. Cell counts predicted by the developed model were compared with the predicted cell counts obtained at 8°C and 15°C, and the differences between predicted and observed cell counts were indicated by root mean square error (RMSE). The dynamic model was then developed with the kinetic parameters derived from the secondary model and simulated the L. monocytogenes growth. RMSE (0.368) indicates that the model performance was appropriate. These results suggest that the developed kinetic models can be useful in predicting the fates of L. monocytogenes in smoked salmon under the dynamic conditions of storage temperature and time.

Growth of Listeria monocytogenes in Partially Cooked Battered Chicken Nuggets as a Function of Storage Temperature.

Battered poultry products may be wrongly regarded and treated by consumers as ready-to-eat and, as such, be implicated in foodborne disease outbreaks. This study aimed at the quantitative description of the growth behavior of Listeria monocytogenes in fresh, partially cooked (non-ready-to-eat) battered chicken nuggets as function of temperature. Commercially prepared chicken breast nuggets were inoculated with L. monocytogenes and stored at different isothermal conditions (4, 8, 12, and 16 °C). The pathogen’s growth behavior was characterized via a two-step predictive modelling approach: estimation of growth kinetic parameters using a primary model, and description of the effect of temperature on the estimated maximum specific growth rate (μmax) using a secondary model. Model evaluation was undertaken using independent growth data under both constant and dynamic temperature conditions. According to the findings of this study, L. monocytogenes may proliferate in battered chicken nuggets in the course of their shelf life to levels potentially hazardous for susceptible population groups, even under well-controlled refrigerated storage conditions. Model evaluation demonstrated a satisfactory performance, where the estimated bias factor (Bf) was 0.92 and 1.08 under constant and dynamic temperature conditions, respectively, while the accuracy factor (Af) value was 1.08, in both cases. The collected data should be useful in model development and quantitative microbiological risk assessment in battered poultry products.

Ultrasonic standing wave chamber for engineering microstructures of water- and lipid-based materials

Ability to control the structure of water- and lipid-based materials is important when tailoring their mechanical, thermal, and sensory properties in e.g. food, cosmetic, pharmaceutical, and chemical applications. We present an approach for organizing solid particle dispersions and lipid crystals during their formation using ultrasonic standing waves (US-SW). We designed and built a chamber to apply US-SW to water- and lipid-based samples, control their cooling rates, and observe the processes in real time using optical microscopy. As an example, we demonstrated that this process is applicable during the crystallization of saturated fat, the formation of oleogels, and sucrose crystals dispersions. The results indicate ability to control crystalline microstructures in static and dynamic temperature conditions.