Identification of Listeria innocua surrogates for Listeria monocytogenes in hamburger patties

ABSTRACT:  Listeria innocua M1 has been used by many researchers as a nonpathogenic thermal processing surrogate for Listeria monocytogenes . However, L. innocua M1 has been criticized because its thermal survivability characteristics are not as closely parallel to L. monocytogenes as some would like in a variety of foods and processing conditions. The present study was conducted to compare multiple L. innocua and L. monocytogenes strains to validate L. innocua M1 as the ideal surrogate under high-temperature thermal processing conditions for L. monocytogenes . The D - and z -values of L. innocua M1, L. innocua strain SLCC 5639 serotype (6a), SLCC 5640 (6b), SLCC 2745 (4ab), and L. monocytogenes F4243 (4b) were calculated for raw hamburger patties. Hamburger patties were inoculated with 10^7–8 CFU/g of L. monocytogenes or L. innocua . Samples were heat treated at 4 temperatures (62.5 to 70 °C). At each temperature, the decimal reduction time ( D -value) was obtained by linear regression of survival curves. The D - and z -values were determined for each bacterium. The D -values of L. innocua and L. monocytogenes serotypes ranged from 3.17 to 0.13 min at 62.5 to 70 °C, and the z -values of L. innocua and L. monocytogenes were 7.44 to 7.73 °C. Two of the 4 L. innocua serotypes used in this experiment have the potential for use as surrogates in hamburger meat with varying margins of safety. L. innocua M1 should serve as the primary nonpathogenic surrogate with the greatest margin of safety in verifying a new thermal process to destroy L. monocytogenes .     

A chemical mechanism for low to high temperature oxidation of n-dodecane as a component of transportation fuel surrogates

Using surrogate fuels in lieu of real fuels is an appealing concept for combustion studies. A major limitation however, is the capability to design compact and reliable kinetic models that capture all the specificities of the simpler, but still multi-component surrogates. This task is further complicated by the fairly large nature of the hydrocarbons commonly considered as potential surrogate components, since they typically result in large detailed reaction schemes. Towards addressing this challenge, the present work proposes a single, compact, and reliable chemical mechanism, that can accurately describe the oxidation of a wide range of fuels, which are important components of surrogate fuels. A well-characterized mechanism appropriate for the oxidation of smaller hydrocarbon species [G. Blanquart, P. Pepiot-Desjardins, H. Pitsch, Chemical mechanism for high temperature combustion of engine relevant fuels with emphasis on soot precursors, Combust. Flame 156 (2009) 588–607], and several substituted aromatic species [K. Narayanaswamy, G. Blanquart, H. Pitsch, A consistent chemical mechanism for the oxidation of substituted aromatic species, Combust. Flame 157 (10) (2010) 1879–1898], ideally suited as a base to model surrogates, has now been extended to describe the oxidation of n-dodecane, a representative of the paraffin class, which is often used in diesel and jet fuel surrogates. To ensure compactness of the kinetic scheme, a short mechanism for the low to high temperature oxidation of n-dodecane is extracted from the detailed scheme of Sarathy et al. [S. M. Sarathy, C. K.Westbrook, M. Mehl, W. J. Pitz, C. Togbe, P. Dagaut, H. Wang, M. A. Oehlschlaeger, U. Niemann, K. Seshadri, Comprehensive chemical kinetic modeling of the oxidation of 2-methylalkanes from C₇ to C₂₀, Combust. Flame 158 (12) (2011) 2338–2357] and integrated in a systematic way into the base model. Rate changes based on recent rate recommendations from literature are introduced to the resulting chemical mechanism in a consistent manner, which improve the model predictions. Extensive validation of the revised kinetic model is performed using a wide range of experimental conditions and data sets.     

Molten Salt Oxidation of Ion-exchange Resins Doped with Toxic Metals and Radioactive Metal Surrogates

Ion-exchange resins doped with toxic metals and radioactive metal surrogates were test-burned in a bench-scale molten salt oxidation (MSO) reactor system. The purposes of this study are to confirm the destruction performance of the two-stage MSO reactor system for the organic ion-exchange resin and to obtain an understanding of the behavior of the fixed toxic metals and the sulfur in the cationic exchange resins. The destruction of the organics is very efficient in the primary reactor. The primarily destroyed products such as carbon monoxide are completely oxidized in the secondary MSO reactor. The overall collection of the sulfur and metals in the two-stage MSO reactor system appeared to be very efficient. Over 99.5% of all the fixed toxic metals (lead and cadmium) and radioactive metal surrogates (cesium, cobalt, strontium) remained in the MSO reactor bottom. Thermodynamic equilibrium calculations and the XRD patterns of the spent salt samples revealed that the collected metals existed in the form of each of their carbonates or oxides, which are non-volatile species at the MSO system operating conditions.     

Heat Resistance of Salmonella spp., Listeria monocytogenes, Escherichia coli 0157:H7, and Listeria innocua M1, a Potential Surrogate for Listeria monocytogenes, in Meat and Poultry: A Review

ABSTRACT: The heat-resistance data in meat and poultry for various strains of Salmonella, Listeria monocytogenes , and Escherichia coli O157:H7 as well as Listeria innocua M1 are summarized. Heat resistance of these organisms is affected by many factors. Different strains of the same organism have different responses to heat. Heat resistance can also be influenced by the age of the culture, growth conditions, pH, and numerous other factors. Data from this review may prove useful to processors in validating their times and temperatures for thermal processing of meat and poultry. The obvious gaps in the data will provide researchers opportunities to fill those gaps. In addition, it will encourage the development of surrogates, whether biological or otherwise, that will be able to be used in an actual processing environment.     

False memories for end-of-life decisions.

Objective: To examine people's false memories for end-of-life decisions. Design: In Study 1, older adults decided which life-sustaining treatments they would want if they were seriously ill. They made these judgments twice, approximately 12 months apart. At Time 2, older adults and their self-selected surrogate decision makers tried to recall the older adults' Time 1 decisions. In Study 2, younger adults made treatment decisions twice, approximately 4 months apart. At Time 2, younger adults tried to recall their Time 1 decisions. Main Outcome Measures: Percentage of participants who falsely remembered that their original treatment decisions were the same as their current decisions. Results: In Study 1, older adults falsely remembered that 75% of their original decisions were the same as their current decisions; surrogates falsely thought that 86% of older adults' decisions were the same. In Study 2, younger adults falsely remembered that 69% of their original decisions were the same as their current decisions. Conclusion: Age alone cannot account for people's false memories of their end-of-life decisions; we discuss other mechanisms. The results have practical implications for policies that encourage people to make legal documents specifying their end-of-life treatment decisions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks

Deep Neural Network (DNN) is widely used in engineering applications for its ability to handle problems with almost any nonlinearities. However, it is generally difficult to obtain sufficient high-fidelity (HF) sample points for expensive optimization tasks, which may affect the generalization performance of DNN and result in inaccurate predictions. To solve this problem and improve the prediction accuracy of DNN, this paper proposes an on-line transfer learning based multi-fidelity data fusion (OTL-MFDF) method including two parts. In the first part, the ensemble of DNNs is established. Firstly, a large number of low-fidelity sample points and a few HF sample points are generated, which are used as the source dataset and target dataset, respectively. Then, the Bayesian Optimization (BO) is utilized to obtain several groups of hyperparameters, based on which DNNs are pre-trained using the source dataset. Next, these pre-trained DNNs are re-trained by fine-tuning on the target dataset, and the ensemble of DNNs is established by assigning different weights to each pre-trained DNN. In the second part, the on-line learning system is developed for adaptive updating of the ensemble of DNNs. To evaluate the uncertainty error of the predicted values of DNN and determine the location of the updated HF sample point, the query-by-committee strategy based on the ensemble of DNNs is developed. The Covariance Matrix Adaptation Evolutionary Strategies is employed as the optimizer to find out the location where the maximal disagreement is achieved by the ensemble of DNNs. The design space is partitioned by the Voronoi diagram method, and then the selected point is moved to its nearest Voronoi cell boundary to avoid clustering between the updated point and the existing sample points. Three different types of test problems and an engineering example are adopted to illustrate the effectiveness of the OTL-MFDF method. Results verify the outstanding efficiency, global prediction accuracy and applicability of the OTL-MFDF method.     

Multiobjective optimization using an adaptive weighting scheme

A new Pareto front approximation method is proposed for multiobjective optimization problems (MOPs) with bound constraints. The method employs a hybrid optimization approach using two derivative-free direct search techniques, and intends to solve black box simulation-based MOPs where the analytical form of the objectives is not known and/or the evaluation of the objective function(s) is very expensive. A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Another contribution of this paper is the generalization of the star discrepancy-based performance measure for problems with more than two objectives. The method is evaluated using five test problems from the literature, and a realistic engineering problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points for all six test problems.     

Diffusion coefficients of diesel fuel and surrogate compounds in supercritical carbon dioxide

Facilitating a new concept of clean diesel combustion using supercritical fluids requires a better understanding of thermophysical properties of the diesel fuel/diluent system. Mass diffusivity is one such property that is important to understand diesel fuel/diluent mixing and spray and combustion of supercritical fuel mixtures. In this work, diffusion coefficients of diesel fuel and surrogate compounds in supercritical carbon dioxide were experimentally determined by the Taylor dispersion method at temperatures from 313.15 to 373.15 K and pressures up to 30 MPa. Difficulties were encountered to measure diffusion coefficients using the Taylor dispersion method near the critical region of CO₂ which resulted in curve-fitting errors greater than 5%. Predictive correlations including Wilke-Chang, Scheibel, and He-Yu were examined. Diffusivity data were also fitted by D₁₂/T − η and correlations. Results showed that the He-Yu correlation has the best prediction performance while the D₁₂/T − η correlation best fits the data with AAD% < 8%.     

Hydrogen Bond Surrogate-Constrained Dynamic Antiparallel β-Sheets

Antiparallel β-sheets are important secondary structures within proteins that equilibrate with random-coil states; however, little is known about the exact dynamics of this process. Here, the first dynamic β-sheet models that mimic this equilibrium have been designed by using an H-bond surrogate that introduces constraint and torque into a tertiary amide bond. 2D NMR data sufficiently reveal the structure, kinetics, and thermodynamics of the folding process, thereby leading the way to similar analysis in isolated biologically relevant β-sheets.