102 research outputs found
A Reproducible Method for Growing Biofilms on Polystyrene Surfaces: Biomass and Bacterial Viability Evolution of Pseudomonas fluorescens and Staphylococcus epidermidis
No full textsponsorship: This research was funded by project Bioclean MSCA-ITN-722871 of the HORIZON 2020 EU Framework Programme for Research and Innovation, by project C24/18/046 of the KU Leuven Research Council and by project G.0863.18 of the Research Foundation-Flanders. Simen Akkermans was supported by the Research Foundation-Flanders under grant 1224620N. (project Bioclean of the HORIZON 2020 EU Framework Programme for Research and Innovation|MSCA-ITN-722871, Research Foundation-Flanders|G.0863.18, Research Foundation-Flanders|1224620N, KU Leuven Research Council|C24/18/046)status: Publishe
Demo Optimal Experimental Design
No full textThis MATLAB code serves as an example for the use of the Doptimal-function to calculate the D-optimal experimental conditions for the identification of a secondary model for the microbial growth rate as a function of the environmental conditions.
Authors: Simen Akkermans, Philippe Nimmegeers, Jan Van Impe
Affiliation: KU Leuven - BioTeC+
This code is available free of charge FOR NON-COMMERCIAL use on an as is basis. The authors cannot be held liable for any deficiency, fault or inconvenience resulting from its use
Design of a Low-Power Radio Frequency Unit and Its Application for Bacterial Inactivation under Laboratory Conditions
No full textsponsorship: This work was funded by the KU Leuven Research Fund through project C24/18/046, by the Research Foundation Flanders (FWO) through project G0B4121N, and by the EU H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 956126. Authors Davy Verheyen and Simen Akkermans were funded by the Research Foundation Flanders (FWO), grant numbers 1254421N and 1224620N, respectively. (KU Leuven Research Fund|C24/18/046, Research Foundation Flanders (FWO)|G0B4121N, Research Foundation Flanders (FWO)|1254421N, Research Foundation Flanders (FWO)|1224620N, EU|956126)status: Published onlin
Effects of Temperature and pH on Recombinant Thaumatin II Production by Pichia pastoris
No full textThe sweet protein thaumatin is emerging as a promising sugar replacer in the market today, especially in the food and beverage sector. Rising demand for its production necessitates the large-scale extraction of this protein from its natural plant source, which can be limited in terms of raw material availability and production costs. Using a recombinant production technique via a yeast platform, specifically, Pichia pastoris, is more promising to achieve the product economically while maintaining batch-to-batch consistency. However, the bioproduction of recombinant proteins requires the identification of optimal process variables, constituting the maximal yield of the product of interest. These variables have a direct effect on the growth of the host organism and the secretion levels of the recombinant protein. In this study, two important environmental factors, pH, and temperature were assessed by cultivating P. pastoris in shake flasks to understand their influence on growth and the production levels of thaumatin II protein. The results from the pH study indicate that P. pastoris attained a higher viable cell density and secretion of protein at pH 6.0 compared to 5.0 when grown at 30 °C. Furthermore, within the three levels of temperatures investigated when grown at pH 6.0, the protein levels were the highest at 30 °C compared to 20 and 25 °C, whereas 25 °C exhibited the highest viable cell density. Interestingly, the trend observed from the qualitative effects of temperature and pH occurred in all the media that was investigated. These results broaden our understanding of how pH and temperature adjustment during P. pastoris cultivation aid in enhancing the production yields of thaumatin II prior to optimising the fed batch bioreactor operation.sponsorship: This research was funded by the KU Leuven Research Fund through project C24/18/046 and by the Research Foundation Flanders (FWO) through project G0B4121N. Author Simen Akkermans was funded by the Research Foundation Flanders (FWO), grant number 1224620N. (KU Leuven Research Fund|C24/18/046, Research Foundation Flanders (FWO)|1224620N, Research Foundation Flanders (FWO)|G0B4121N)status: Publishe
Effect of microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury of Listeria monocytogenes in fish-based food model systems
No full textThe development of more accurate predictive models that describe the microbial kinetics of mild thermal treatments of foods requires knowledge concerning the influence of food microstructure and initial cell conditions on foodborne pathogens’ inactivation kinetics. The effect of food microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury (SI) of Listeria monocytogenes was investigated at 59, 64 and 69°C. Fish-based food model systems with different microstructures, possessing minimal compositional and physicochemical variations, were used. L. monocytogenes growth morphology had no significant influence on thermal inactivation kinetics. A gelled matrix resulted in a lower specific inactivation rate kmax and a higher residual cell population Nres, while the presence of fat droplets resulted in a higher kmax and did not influence Nres. SI was higher in viscous than in gelled systems and more prominent for cells that were grown inside the matrix. Hence, predictive thermal inactivation models could benefit from the inclusion of factors related to the nature of the food matrix and fat properties. Starting inactivation from cells that were grown inside the matrix, resulted in lower (i.e., fail-safe) kmax values and more uncertainty on Nres as compared to starting from cells grown at optimal conditions.sponsorship: This work was supported by the Norconserv Foundation, FWO Vlaanderen (grant G.0863.18) and the KU Leuven Research Fund (Center of Excellence OPTEC-Optimization in Engineering and project C24/18/046). Authors Maria Baka and Simen Akkermans were supported by the research council of KU Leuven for post-doctoral researchers (grants PDM/16/125 and PDM/18/136). (Norconserv Foundation, FWO Vlaanderen|G.0863.18, KU Leuven Research Fund (Center of Excellence OPTEC-Optimization in Engineering), research council of KU Leuven|PDM/16/125, research council of KU Leuven|PDM/18/136, KU Leuven Research Fund|C24/18/046)status: Published onlin
Towards Overset LES for Aeroacoustic Source Prediction
No full textIn this contribution an application of a computational aeroacoustics code (CAA) as a hybrid Zonal DNS tool is presented. The here used hybrid approach is based on a novel implementation of the Non-Linear Perturbation Equations (NLPE) extended with viscous terms, denoted as overset since a perturbation analysis is performed on top of a background flow. It is found that Direct Noise Computation results of a cylinder in uniform flow show the dipolar sound radiation characteristic as well as the expected decay of sound pressure level with distance. The extension to LES is illustrated with isotropic decaying turbulence, where the expected -5/3 slope of the reference DNS data is recovered with the LES employing the classical Smagorinsky model
Modellering van de maximale specifieke microbiële groeisnelheid: Data, modellen en voorspellingen
No full textIn predictive microbiology, mathematical models are built to describe microbial responses as a function of the intrinsic and extrinsic properties of food products with the aim of improving microbial food safety and quality. One of the most common types of models that are studied in this field are the so called secondary models for growth. These models are used to describe the effect of environmental conditions on the maximum specific microbial growth rate. However, several problems can be distinguished when building such models. The four problems dealt with in this thesis are (i) the high experimental workload, (ii) the effect of measurement uncertainty on the modelling results, (iii) the difficulty of correctly calculating the model prediction uncertainty and (iv) the lack of an adequate model structure for the combined effect of environmental conditions on the microbial growth rate.
The first part of this thesis presents a general introduction to the research and a detailed background on the modelling cycle for predictive microbiology. The second part focusses on improving the modelling cycle from experimental data collection to the calculation of the model prediction accuracy. The first research chapter deals with determining the most efficient experimental designs for identifying parameters of secondary models. The objective here was to achieve the most accurate overall model predictions. The inscribed central composite design was selected as the most efficient design of experiments technique, but it was overshadowed by the much more efficient D-optimal design. The two next chapters investigate how the uncertainty on the experimental measurements should be taken into account when building secondary models. The one-step parameter estimation method was found to be more suitable than the two-step method for correctly taking the variation of the dependent variable into account. Moreover, a weighted total least squares parameter estimation was proposed for taking errors on the measurement of the independent variables into account. The last chapter of this part studied the use of different uncertainty propagation methods for calculating the model prediction uncertainty. The sigma point method was found to deliver a robust calculation that entails a limited computational workload and is relatively easy to implement.
The third part of this thesis discusses a novel model structure that was developed for modelling the combined effect of environmental conditions on the microbial growth rate. This gamma-interaction model is proposed as an alternative to the commonly used gamma model in the first chapter of this part. It is also compared with two additional model structures with a higher complexity than the gamma model. This comparison was based on an experimental case study on the effect of temperature and pH on the growth rate of Escherichia coli. The gamma-interaction model was preferred on the basis that it was the most accurate, compatible with an efficient modelling method and easy to implement and interpret. The second chapter of this part continues the comparison between the gamma and gamma-interaction model by including the effect of water activity in the model for temperature and pH. One of the main findings of this chapter is that a cross-validation study demonstrates that the gamma-interaction model delivered more accurate predictions due to the additional model complexity and parameters. The final part of this thesis summarises the conclusions and provides an outlook to future research.status: Publishe
Comparing design of experiments and optimal experimental design techniques for modelling the microbial growth rate under static environmental conditions
No full textBuilding secondary models that describe the growth rate as a function of multiple environmental conditions is often very labour intensive and costly. As such, the current research aims to assist in decreasing the required experimental effort by studying the efficacy of both design of experiments (DOE) and optimal experimental designs (OED) techniques. This is the first research in predictive microbiology (i) to make a comparison of these techniques based on the (relative) model prediction uncertainty of the obtained models and (ii) to compare OED criteria for the design of experiments with static (instead of dynamic) environmental conditions.
A comparison of the DOE techniques demonstrated that the inscribed central composite design and full factorial design were most suitable. Five conventional and two tailor made OED criteria were tested. The commonly used D-criterion performed best out of the conventional designs and almost equally well as the best of the dedicated criteria. Moreover, the modelling results of the D-criterion were less dependent on the experimental variability and differences in the microbial response than the two selected DOE techniques. Finally, it was proven that solving the optimisation of the D-criterion can be made more efficient by considering the sensitivities of the growth rate relative to its value as Jacobian matrix instead of the sensitivities of the cell density measurements.sponsorship: This work was supported by project PFV/10/002 (Center of Excellence OPTEC-Optimization in Engineering) of the KU Leuven Research Fund, projects G.0930.13 and KAN.15189.13 of the Fund for Scientific Research-Flanders, and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian Federal Science Policy Office (IAP Phase VII/19 DYSCO). ((Center of Excellence OPTEC-Optimization in Engineering) of the KU Leuven Research Fund|PFV/10/002, Fund for Scientific Research-Flanders|G.0930.13, Fund for Scientific Research-Flanders|KAN.15189.13, Belgian Program on Interuniversity Poles of Attraction, Belgian Federal Science Policy Office (IAP Phase VII/19 DYSCO))status: Publishe
Mechanistic modelling of the inhibitory effect of pH on microbial growth
No full textModelling and simulation of microbial dynamics as a function of processing, transportation and storage conditions is a useful tool to improve microbial food safety and quality. The goal of this research is to improve an existing methodology for building mechanistic predictive models based on the environmental conditions. The effect of environmental conditions on microbial dynamics is often described by combining the separate effects in a multiplicative way (gamma concept). This idea was extended further in this work by including the effects of the lag and stationary growth phases on microbial growth rate as independent gamma factors. A mechanistic description of the stationary phase as a function of pH was included, based on a novel class of models that consider product inhibition. Experimental results on Escherichia coli growth dynamics indicated that also the parameters of the product inhibition equations can be modelled with the gamma approach. This work has extended a modelling methodology, resulting in predictive models that are (i) mechanistically inspired, (ii) easily identifiable with a limited work load and (iii) easily extended to additional environmental conditions.sponsorship: This work was supported by project PFV/10/002 (Center of Excellence OPTEC-Optimization in Engineering) of the KU Leuven Research Council, projects G093013N of the Fund for Scientific Research-Flanders, and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian Federal Science Policy Office. (KU Leuven Research Council|PFV/10/002, Fund for Scientific Research-Flanders|G093013N, Belgian Program on Interuniversity Poles of Attraction)status: Publishe
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