INDIVIDUAL-BASED MODELLING OF MICROBIAL COLONY DYNAMICS ON FOOD SURFACES IN A PARALLEL SIMULATOR

International audienceThroughout the whole food processing and distribution chain, an accurate assessment and control of microbiological food safety is indispensable to avoid large outbreaks of foodborne diseases. For this reason, mathematical models are developed in predictive microbiology to describe the growth and survival of food spoiling and pathogenic microorganisms as a function of the environmental conditions during food processing and distribution. Traditionally, these models are representative for the planktonic growth of axenic microbial cultures in perfectly mixed liquid media. However, most food products are characterised by a semi-solid structure, where the contaminating cells grow out as colonies. Diffusion limitations emerge in these colonies due to the high local cell density. Hence, it is most appropriate to simulate microbial colonies at a microscopic level, considering the cell as basic modelling unit in an individual-based modelling approach. Within this respect, the MICRODIMS model has been developed at the BioTeC+ research group. However, over the last years, it became clear that the implementation of this individual-based model in the standard Repast Simphony toolkit is rather slow for the simulation of mature colonies containing a large number of cells. For this reason, MICRODIMS has been ported to the TransProg library, which uses modern general-purpose multicore and multiprocessor computers to their fullest potential. This transfer enables the simulation of mature colony dynamics in three dimensions. In this paper, the branched morphology of colonies growing on the surface of a food substrate is investigated. It is demonstrated that the emergence of this pattern is dependent on the thickness of the food substrate and structural heterogeneities at the food surface

Short communication: Determination of lactoferrin in Feta cheese whey with reversed-phase high-performance liquid chromatography

Abstract In the current paper, a method is introduced to determine lactoferrin in sweet whey using reversed-phase HPLC without any pretreatment of the samples or use of a separation technique. As a starting point, the most common HPLC protocols for acid whey, which included pretreatment of the whey along with a sodium dodecyl sulfate-PAGE step, were tested. By skipping the pretreatment and the separation steps while altering the gradient profile, different chromatographs were obtained that proved to be equally efficient to determine lactoferrin. For this novel 1-step reversed-phase HPLC method, repeatability was very high over a wide range of concentrations (1.88% intraday to 5.89% interday). The limit of detection was 35.46μg/mL [signal:noise ratio (S/N)=3], whereas the limit of quantification was 50.86μg/mL (S/N=10). Omitting the pretreatment step caused a degradation of the column's lifetime (to approximately 2,000 samples). As a result, the lactoferrin elution time changed, but neither the accuracy nor the separation ability of the method was significantly influenced. We observed that this degradation could be easily avoided or detained by centrifuging the samples to remove fat or by extensive cleaning of the column after every 5 samples

Optimal Heating Strategies for a Convection Oven

In this study classical control theory is applied to a heat conduction model with convective boundary conditions. Optimal heating strategies are obtained through solution of an associated algebraic Riccati equation for a finite horizon linear quadratic regulator (LQR). The large dimensional system models, obtained after a Galerkin approximation of the original heat-conduction equations, describe the dynamics of the nodal temperatures driven by a forced convection boundary condition. The models are reduced using optimal Hankel minimum degree (OHMD) reduction. Optimal control histories are obtained for the reduced model and applied to the `full-scale' model. Performance of the regulator for various weighting matrices are compared and evaluated in two case studies, namely the heating of a cylindrically shaped container of mashed potato, and a container of ready-made lasagna. The approach taken here is geometry independent and closed loop meaning that the input is driven by temperature through a feedback mechanism which includes an optimal feedback gain matrix, which is calculated `off-line' through the backwards solution of an associated algebraic Riccati equation. The results indicate a T type heating profile, including a final oscillating behaviour that fine-regulates the temperature to an almost uniform temperature of 100°C

Optimal Heating Strategies for a Convection Oven

In this study classical control theory is applied to a heat conduction model with convective boundary conditions. Optimal heating strategies are obtained through solution of an associated algebraic Riccati equation for a finite horizon linear quadratic regulator (LQR). The large dimensional system models, obtained after a Galerkin approximation of the original heat-conduction equations, describe the dynamics of the nodal temperatures driven by a forced convection boundary condition. The models are reduced using optimal Hankel minimum degree (OHMD) reduction. Optimal control histories are obtained for the reduced model and applied to the `full-scale' model. Performance of the regulator for various weighting matrices are compared and evaluated in two case studies, namely the heating of a cylindrically shaped container of mashed potato, and a container of ready-made lasagna. The approach taken here is geometry independent and closed loop meaning that the input is driven by temperature through a feedback mechanism which includes an optimal feedback gain matrix, which is calculated `off-line' through the backwards solution of an associated algebraic Riccati equation. The results indicate a T type heating profile, including a final oscillating behaviour that fine-regulates the temperature to an almost uniform temperature of 100°C

Inactivation model equations and their associated parameter values obtained under static acid stress conditions cannot be used directly for predicting inactivation under dynamic conditions

Organic acids (e.g., lactic acid, acetic acid and citric acid) are popular preservatives. In this study, the Listeria innocua inactivation is investigated under dynamic conditions of pH and undissociated lactic acid ([LaH]). A combined primary (Weibull-type) and secondary model developed for the L. innocua inactivation under static conditions [Janssen, M., Geeraerd, A.H., Cappuyns, A., Garcia-Gonzalez, L., Schockaert, G., Van Houteghem, N., Vereecken, K.M., Debevere, J., Devlieghere, F., Van Impe, J.F., 2007. Individual and combined effects of pH and lactic acid concentration on L. innocua inactivation: development of a predictive model and assessment of experimental variability. Applied and Environmental Microbiology 73(5), 1601-1611] was applied to predict the microbial inactivation under dynamic conditions. Because of its non-autonomous character, two approaches were proposed for the application of the Weibull-type model to dynamic conditions. The results quantitatively indicated that the L. innocua cell population was able to develop an induced acid stress resistance under dynamic conditions of pH and [LaH]. From a modeling point of view, it needs to be stressed that (i) inactivation model equations and associated parameter values, derived under static conditions, may not be suitable for use as such under dynamic conditions, and (ii) non-autonomous dynamic models reveal additional technical intricacies in comparison with autonomous models.[**]status: publishe

A variance propagation algorithm for stochastic heat and mass transfer problems in food processes

A variance propagation algorithm for stochastic coupled heat and mass transfer problems subjected to first order autoregressive random process boundary conditions was developed. The algorithm is based on the finite element formulation of Luikov's coupled heat and mass transfer equations and involves the numerical solution of coupled Lyapunov and Sylvester matrix differential equations. It offers a cheap alternative to the Monte Carlo method for the computation of the mean value and variance of the temperature and moisture content field. The algorithm is generally applicable and can easily be inserted in any existing finite element code. Also, it can be extended to other types of random processes. The algorithm was applied to analyse the drying of a soybean kernel. Simulation results show that random fluctuations of the process conditions may cause considerable variability of the temperature and the moisture content within the drying soybean kernel. This is an important feature to take into account for the design of a drying process, and for thermal food processes in genera

Stochastic finite element analysis of coupled heat and mass transfer problems with random field parameters

A first-order perturbation algorithm for the computation of mean values and variances of transient temperature and moisture fields during coupled heat and mass transfer problems with random field parameters has been developed and implemented. The algorithm is based on the Galerkin finite-element discretization of Luikov's heat and mass transfer equations for capillary porous bodies and is computationally less demanding than the Monte Carlo method. The algorithm has been programmed in MATLAB and applied to a published test case of a drying process for soybean kernels. The simulations indicate that the stochastic fluctuations of the thermophysical properties and the process conditions may cause a considerable level of uncertainty in the predicted temperatures and moisture contents inside the product