Improving Microbiological Safety and Quality Characteristics of Wheat and Barley by High Voltage Atmospheric Cold Plasma Closed Processing

Contamination of cereal grains as a key global food resource with insects or microorganisms is a persistent concern for the grain industry due to irreversible damage to quality and safety characteristics and economic losses. Atmospheric cold plasma presents an alternative to conventional grain decontamination methods owing to the high antimicrobial potential of reactive species generated during the treatment, but effects against product specific microflora are required to understand how to optimally develop this approach for grains. This work investigated the influence of ACP processing parameters for both cereal grain decontamination and grain quality as important criteria for grain or seed use. A high voltage (HV) (80 kV) dielectric barrier discharge (DBD) closed system was used to assess the potential for control of native microflora and pathogenic bacterial and fungal challenge microorganisms, in tandem with effects on grain functional properties. Response surface modelling of experimental data probed the key factors in relation to microbial control and seed germination promotion. The maximal reductions of barley background microbiota were 2.4 and 2.1 log10 CFU/g and of wheat - 1.5 and 2.5 log10 CFU/g for bacteria and fungi, respectively, which required direct treatment for 20 min followed by a 24 h sealed post-treatment retention time. In the case of challenge organisms inoculated on barley grains, the highest resistance was observed for Bacillus atrophaeus endospores, which, regardless of retention time, were maximally reduced by 2.4 log10 CFU/g after 20 min of direct treatment. The efficacy of the plasma treatment against selected microorganisms decreased in the following order: E. coli \u3e P. verrucosum (spores) \u3e B. atrophaeus (vegetative cells) \u3e B. atrophaeus (endospores). The challenge microorganisms were more susceptible to ACP treatment than naturally present background microbiota. No major effect of short term plasma treatment on the retention of quality parameters was observed. Germination percentage measured after 7 days cultivation was similar for samples treated for up to 5 min, but this was decreased after 20 min of direct treatment. Overall, ACP proved effective for cereal grain decontamination, but it is noted that the diverse native micro-flora may pose greater resistance to the closed, surface decontamination approach than the individual fungal or bacterial challenges, which warrants investigation of grain microbiome responses to ACP

Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives

There is currently a worldwide trend to reduce sugar consumption. This trend is mostly met by the use of artificial non-nutritive sweeteners. However, these sweeteners have also been proven to have adverse health effects such as dizziness, headaches, gastrointestinal issues, and mood changes for aspartame. One of the solutions lies in the commercialization of sweet proteins, which are not associated with adverse health effects. Of these proteins, thaumatin is one of the most studied and most promising alternatives for sugars and artificial sweeteners. Since the natural production of these proteins is often too expensive, biochemical production methods are currently under investigation. With these methods, recombinant DNA technology is used for the production of sweet proteins in a host organism. The most promising host known today is the methylotrophic yeast, Pichia pastoris. This yeast has a tightly regulated methanol-induced promotor, allowing a good control over the recombinant protein production. Great efforts have been undertaken for improving the yields and purities of thaumatin productions, but a further optimization is still desired. This review focuses on (i) the motivation for using and producing sweet proteins, (ii) the properties and history of thaumatin, (iii) the production of recombinant sweet proteins, and (iv) future possibilities for process optimization based on a systems biology approach

Modelling the Maximum Specific Microbial Growth Rate: Data, Models and Predictions

In 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

Mechanistic modelling of the inhibitory effect of pH on microbial growth

Modelling 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.status: publishe

Comparing design of experiments and optimal experimental design techniques for modelling the microbial growth rate under static environmental conditions

Building 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.status: publishe

Parameter estimations in predictive microbiology: Statistically sound modelling of the microbial growth rate

When building models to describe the effect of environmental conditions on the microbial growth rate, parameter estimations can be performed either with a one-step method, i.e., directly on the cell density measurements, or in a two-step method, i.e., via the estimated growth rates. The two-step method is often preferred due to its simplicity. The current research demonstrates that the two-step method is, however, only valid if the correct data transformation is applied and a strict experimental protocol is followed for all experiments. Based on a simulation study and a mathematical derivation, it was demonstrated that the logarithm of the growth rate should be used as a variance stabilizing transformation. Moreover, the one-step method leads to a more accurate estimation of the model parameters and a better approximation of the confidence intervals on the estimated parameters. Therefore, the one-step method is preferred and the two-step method should be avoided.status: publishe

An interaction model for the combined effect of temperature, pH and water activity on the growth rate of E. coli K12

Previous research has indicated that more complex model structures than the commonly used gamma model are needed to obtain an accurate prediction of the effect of multiple environmental conditions on the microbial growth rate. Due to the complexity associated with the development of such model structures, it is recommended that the model structure is compatible with a modular model building method. In this research, a gamma-interaction model was built to describe the combined effect of temperature, pH and water activity on the microbial growth rate of E. coli K12 based on a dataset of 68 bioreactor experiments. This novel interaction model was compared with the standard gamma model. The model structures were tested separately for the combined effects of (i) temperature and pH, (ii) pH and water activity, (iii) temperature and water activity and (iv) temperature, pH and water activity. Based on the results of this research, it was concluded that models for the combined effect of environmental conditions need to allow for sufficient flexibility for the description of combined effects of environmental conditions to obtain accurate model predictions. In the current study, this flexibility was successfully introduced by using the gamma-interaction model. A cross-validation study also demonstrated that the predictions of the interaction model are more robust with respect to the specific data used than the gamma model. As such, the gamma-interaction model provides food producers and food safety authorities with a more accurate and reliable tool for the prediction of the microbial growth rate as a function of multiple environmental conditions.status: publishe

A tutorial on uncertainty propagation techniques for predictive microbiology models: A critical analysis of state-of-the-art techniques

Building mathematical models in predictive microbiology is a data driven science. As such, the experimental data (and its uncertainty) has an influence on the final predictions and even on the calculation of the model prediction uncertainty. Therefore, the current research studies the influence of both the parameter estimation and uncertainty propagation method on the calculation of the model prediction uncertainty. The study is intended as well as a tutorial to uncertainty propagation techniques for researchers in (predictive) microbiology. To this end, an in silico case study was applied in which the effect of temperature on the microbial growth rate was modelled and used to make simulations for a temperature profile that is characterised by variability. The comparison of the parameter estimation methods demonstrated that the one-step method yields more accurate and precise calculations of the model prediction uncertainty than the two-step method. Four uncertainty propagation methods were assessed. The current work assesses the applicability of these techniques by considering the effect of experimental uncertainty and model input uncertainty. The linear approximation was demonstrated not always to provide reliable results. The Monte Carlo method was computationally very intensive, compared to its competitors. Polynomial chaos expansion was computationally efficient and accurate but is relatively complex to implement. Finally, the sigma point method was preferred as it is (i) computationally efficient, (ii) robust with respect to experimental uncertainty and (iii) easily implemented.status: publishe

Introducing a novel interaction model structure for the combined effect of temperature and pH on the microbial growth rate

Efficient modelling of the microbial growth rate can be performed by combining the effects of individual conditions in a multiplicative way, known as the gamma concept. However, several studies have illustrated that interactions between different effects should be taken into account at stressing environmental conditions to achieve a more accurate description of the growth rate. In this research, a novel approach for modeling the interactions between the effects of environmental conditions on the microbial growth rate is introduced. As a case study, the effect of temperature and pH on the growth rate of Escherichia coli K12 is modeled, based on a set of computer controlled bioreactor experiments performed under static environmental conditions. The models compared in this case study are the gamma model, the model of Augustin and Carlier (2000), the model of Le Marc et al. (2002) and the novel multiplicative interaction model, developed in this paper. This novel model enables the separate identification of interactions between the effects of two (or more) environmental conditions. The comparison of these models focuses on the accuracy, interpretability and compatibility with efficient modeling approaches. Moreover, for the separate effects of temperature and pH, new cardinal parameter model structures are proposed.publisher: Elsevier articletitle: Introducing a novel interaction model structure for the combined effect of temperature and pH on the microbial growth rate journaltitle: International Journal of Food Microbiology articlelink: http://dx.doi.org/10.1016/j.ijfoodmicro.2016.06.011 content_type: article copyright: © 2016 Published by Elsevier B.V.status: publishe

Effects of Temperature and pH on Recombinant Thaumatin II Production by Pichia pastoris

The 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