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This Project identified the relationships between storage conditions, dry matter losses (DMLs) caused by Fusarium species in cereal grains and mycotoxin contamination and assessed novel control strategies for post-harvest grain management including chemical control and ozone. F. graminearum, F. verticillioides and F. langsethiae were inoculated on wheat, maize and oats and stored under environmental conditions where marginal to optimum spoilage and mycotoxin contamination can occur. DMLs were calculated from the CO2 produced and were significantly correlated with deoxynivalenol (DON), zearalenone (ZEA), fumonisins (FUMs) and T-2 and HT-2 toxins respectively. Mycotoxin levels in wheat and maize exceeded the EU legislative limits with 0.9-1% DMLs. Therefore, CO2 monitoring during storage can indicate the level of contamination in a stored batch. Using CO2 production data at different water activity (aw) and temperature conditions, the environmental regimes at which F. langsethiae can grow and contaminate oats with T-2 and HT-2 toxins were identified for the first time. Five acids were examined in vitro and little effect was observed on Fusarium growth, in the aqueous form, while the effect on mycotoxin production varied. Dissolved in ethanol, adipic, fumaric and ferulic acids inhibited fungal growth and controlled DON and FUMs, but T-2 toxin was stimulated by the ethanol. Two garlic essential oils, propyl-propylthiosulfinate (PTS) and propyl propylthiosulfonate (PTSO) were studied for the first time. In vitro, 200 ppm reduced fungal growth (50-100%) and mycotoxin production by >90%. The efficacy was species-dependent. In naturally contaminated oats of 0.93 aw stored for 20 days, 16 ppm PTSO reduced T-2 and HT-2 toxins by 66% and ochratoxin A (OTA) by 88%, while 200 ppm PTS reduced OTA by 95%. In wheat, 100 ppm PTS reduced DON and ZEA and 300 ppm PTS reduced fumonisins by 40-80%. PTSO:PTS (1:1) at 400 and 600 ppm was very effective against DON and ZEA in wheat of 0.92 aw. Ozone (O3) exposure at 200 ppm for 30 min delayed Fusarium spore germination on media of 0.98 aw and inhibited germination at 0.94 aw. O3 was more effective against fungal spores than mycelium and little effect was observed on growing cultures. In vitro, mycotoxin production after exposure depended on the stage of life of the fungi. O3 reduced fungal populations in grains. Mycotoxin production in wet grains treated with 100-200 ppm O3 for 60 min and stored for up to 30 days was reduced or completely inhibited, depending on the species and the exposure system. Simultaneous drying of the grain due to the O3 passage was observed.

The selection of the best subset of variables, which will have a strong effect on an outcome of interest, is fundamental when avoiding overfitting in statistical modelling. However, when there are many variables, it is computationally difficult to find this best subset. The difficulties of variable selection would be more complex when designs are with restricted randomisation. This work aims to fill the gap of variable selection and model estimation for data from experiments subject to restricted randomisation by developing new methods for variable selection and model estimation using frequentist analysis and Bayesian analysis for experiments subject to restricted randomisation. Frequentist and Bayesian analysis methods are used to carry out a comparative study with respect to their performance in variable selection and model estimation. As a representative of frequentist analysis, the Penalised Generalised Least Square (PGLS) estimator is used in which a single shrinkage parameter is applied to all regression effects. Furthermore, as two different strata in split-plot design are existed, the PGLS approach is extended to perform variable selection and model estimation simultaneously in the context of split-plot design. The Penalised Generalised Least Squares for Split-Plot Design estimator (PGLS-SPD) is utilized, in which two shrinkage parameters are applied, one for the subplot effects and the other for the whole-plot effects. As a representative of Bayesian analysis, the Stochastic Search Variable Selection (SSVS) technique is used. This performs variable selection and model estimation simultaneously where the variance of all active factors will be sampled from one posterior distribution. As two different strata in split-plot design are existed, the SSVS approach to perform Bayesian variable selection is extended for the analysis of data from restricted randomised experiments by introducing the Stochastic Search Variable Selection for Split-Plot Design (SSVS-SPD) in which the variances of the active subplot and whole-plot factors are sampled from two different posterior distributions. The usefulness of frequentist and Bayesian approaches are demonstrated using two practical examples, and their properties are studied in simulation studies. The result of the comparative study of frequentist analysis and Bayesian analysis supports the utilization of SSVS-SPD method for the statistical analysis of data from experiments subject to restricted randomisation.s computationally difficult to find this best subset. The difficulties of variable selection would be more complex when designs are with restricted randomisation. This work aims to fill the gap of variable selection and model estimation for data from experiments subject to restricted randomisation by developing new methods for variable selection and model estimation using frequentist analysis and Bayesian analysis for experiments subject to restricted randomisation. Frequentist and Bayesian analysis methods are used to carry out a comparative study with respect to their performance in variable selection and model estimation. As a representative of frequentist analysis, the Penalised Generalised Least Square (PGLS) estimator is used in which a single shrinkage parameter is applied to all regression effects. Furthermore, as two different strata in split-plot design are existed, the PGLS approach is extended to perform variable selection and model estimation simultaneously in the context of split-plot design. The Penalised Generalised Least Squares for Split-Plot Design estimator (PGLS-SPD) is utilized, in which two shrinkage parameters are applied, one for the subplot effects and the other for the whole-plot effects. As a representative of Bayesian analysis, the Stochastic Search Variable Selection (SSVS) technique is used. This performs variable selection and model estimation simultaneously where the variance of all active factors will be sampled from one posterior distribution. As two different strata in split-plot design are existed, the SSVS approach to perform Bayesian variable selection is extended for the analysis of data from restricted randomised experiments by introducing the Stochastic Search Variable Selection for Split-Plot Design (SSVS-SPD) in which the variances of the active subplot and whole-plot factors are sampled from two different posterior distributions. The usefulness of frequentist and Bayesian approaches are demonstrated using two practical examples, and their properties are studied in simulation studies. The result of the comparative study of frequentist analysis and Bayesian analysis supports the utilization of SSVS-SPD method for the statistical analysis of data from experiments subject to restricted randomisation.

Optimal design is useful in improving the efficiencies of experiments with respect to a specified optimality criterion, which is often related to one or more statistical models assumed. In particular, sometimes chemical and biological studies could involve multiple experimental factors. Often, it is convenient to fit a nonlinear model in these factors. This nonlinear model can be either mechanistic or empirical, as long as it describes the unknown mechanism behind the response surface. In this thesis, our main interest is in exact optimal design of experiments for nonlinear multifactor models. In order to search for optimal designs, we can use the conventional point or coordinate exchange approach, which however can incorporate a new continuous optimisation method. On the basis of this idea, we further develop and implement a multistage hybrid method to construct local and (pseudo-)Bayesian optimal designs. The recommended hybrid exchange algorithm overcomes the shortcomings of the modified Fedorov exchange algorithm and the coordinate exchange algorithm, contributing to improved properties of the experimental designs obtained. In addition, Bayesian optimal design with respect to an expected criterion function is based on the assumed parameter prior distributions for the nonlinear model. To limit the time for approximating expected criterion values in the algorithm, we use some efficient numerical integration methods (e.g. a Gauss-Hermite quadrature), which are much superior to the traditional pseudo-Monte Carlo method. We demonstrate the hybrid exchange algorithm by means of several examples relevant to Michaelis-Menten kinetics and other biochemical applications. Under some of these circumstances, we consider hybrid nonlinear models which can be adopted to be fitted to the data of new experiments, the tailor-made optimal designs of which are therefore found and compared with each other. In order to normalise the error structure of such a hybrid model, sometimes the Box-Cox transformation can be applied and the result would be a transform-both-sides (TBS) model. Optimal designs for either untransformed models or TBS models can be used for future experiments, as well as for comprehensive studies of complicated mechanisms.