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Non-linear multivariate calibration methods are increasingly used to extract information from different types of sensors. As the complexity of the data increases, new methods are required to estimate parameters and to provide realistic estimates of uncertainty. Particularly, the quantification of uncertainty for the measurands, or for a sensor’s figures of merit (e.g. limit of detection (LOD), the lowest non-zero concentration of an analyte that can be reliably distinguished from a blank), is not well understood nor is it common practice. This thesis is largely focused on the study of establishing a meaningful limit of detection for sensors and sensor arrays with non-linear response, as well as developing advanced calibration techniques and estimators. Throughout, we use ion selective electrodes (ISEs) as a model system of specific interest. However, the techniques we employ could easily be adopted for other non-linear sensors governed by a physico-chemical model.

Although The International Union of Pure and Applied Chemistry (IUPAC) has recommended a probabilistic approach to determining LOD, the current LOD definition for ISEs conflicts with that recommendation. Here, a new LOD definition for ISEs and a Bayesian approach for LOD estimation for non-linear sensors is demonstrated. The method also provides estimates of LOD uncertainty that is currently missing from other LOD estimation procedures. The study shows that the proposed method has substantially less bias than the current official definition for ISEs.

Next, a Bayesian algorithm was developed to construct the LOD distribution for a non-linear sensor array. This algorithm accommodates the multivariate signal that arises when multiple individual sensors are incorporated into a sensor array. By combining sensors into an array, the overall LOD is reduced compared to LOD from individual sensors. In some cases, this means that low-quality sensors that may not individually meet the needs of a challenging application may provide sufficient quality estimates when combined into an array.

A comprehensive study of modularisation in sensors and sensor arrays is conducted. This is motivated by practical limitations of the “cut” function in Bayesian graphical models as implemented by BUGS (Bayesian inference Using Gibbs Sampling) statistical language, where the desired posterior distribution may not be returned. Alternative algorithms, based on Approximate Bayesian computation (ABC), for sensors and sensor arrays are proposed and evaluated.

Finally, my collaborators and I introduce an experimental protocol and a Bayesian model for the collection and analysis of complex data produced by potentiometer sensors, motivated by data produced by ‘electronic tongues’. This approach leads to better characterisation of the instrument and better estimates of experimental samples compared to common approaches currently in use.

Ammonia (NH₃) is a nitrogenous environmental pollutant that is associated with eutrophication and contamination of terrestrial ecosystems and can have detrimental impacts on human and livestock health. Livestock production is a significant contributor to global NH₃ emissions largely as a result of nitrogen losses from the breakdown of manure. Intensively housed livestock situations, such as exporting live animals, lead to increased concentration of manure and development of the manure pad through accumulation of faeces and urine. This increase in higher concentrations of NH₃ may also lead to health and welfare concerns of both humans and animals. Due to the complex interactions of factors contributing to the volatilsation of NH₃ from manure, NH₃ production in livestock operations is difficult to accurately predict with the various methods and modelling approaches currently utilised. Live export is an example of an intensively housed livestock industry where NH₃ production has the potential to impact health and welfare of humans and animals on board. Quantification of NH₃ using current methods is difficult in this industry due to highly variable conditions and limited understanding of how voyage conditions may affect NH₃ production. Methods to quantify NH₃ emissions from manure have some limitations in adoption and understanding of manure pad variables introduced directly by animals, such as disturbance. Use of in vitro methods rather than large scale animal experiments allows understanding of ammonia and testing of dose rates of ameliorates and also aligns with the animal ethics replace, reduce and refine.

The primary objectives of the current thesis were to develop an inexpensive, high throughput method of quantifying NH₃ in solution, and an in vitro method of quantifying NH₃ production from manure that would (1) evaluate the optimal microchamber design to quantify NH₃ production rates from manure; (2) quantify the effect of disturbance of manure on NH₃ production rates; (3) succeed as an alternative method of testing NH₃ production to reduce the need for, or better inform, large-scale animal experiments (4) successfully evaluate the effectiveness of mitigation techniques.

This thesis includes a review of the available literature and three experimental chapters addressing the development and use of the in vitro method and assessment of an available NH₃ mitigation technique. Findings and implications of this thesis include:

Development of a novel high-throughput plate-based analysis with a high degree of accuracy adapting the Berthelot method of quantifying NH₃-N in solution. Boric acid solution was shown to be an effective eluent for gas trap sampling of NH₃;

Successful development of a novel microchamber system. The microchamber design was evaluated and a standard size chosen for the following experiments. It is hypothesised interactions with pad surface areas and depth were dependent on variables facilitating mass transfer such as air flow and headspace. Microchamber results did not meet hypotheses, as (i) increasing microchamber manure surface area did not result in constant rates of NH₃/m² production, and (ii) increasing manure depth resulted in lower rates of NH₃/m² as opposed to a consistent production relative to surface area. The standard size chosen for the experiment on clinoptilolite (zeolite) was a surface area of 90mm diameter (SA₉₀) and manure depth of 30mm (D₃₀), based on less variation in results and ease of use;

Simulated animal movement through four repeated disturbances of cattle manure in micro-chambers every 90 minutes, resulted in increasing rates of NH₃ production (slope coefficients) with each disturbance over 480 min for all treatments. This suggests cattle movement may generate continual fluxes of NH₃ production from the manure over time;

The application of zeolite was successful in reducing NH₃ production from cattle manure. All treatments achieved an immediate and sustained reduction in NH₃ production over 21 hours. The minimum (1%) and maximum (10%) in-pad application rates resulted in a 32% to 70% reduction in NH₃ emissions over 21 hours, respectively.

Application of zeolite suspended in the microchamber headspace reduced the presence of gaseous NH₃ contamination in the air by 37% over 21 hours, similar to 1% zeolite applied in-pad over 21 hours.

Overall, the high-throughput plate-based methodology and microchamber system provided valuable insight into increasing fluxes of NH₃ volatilsation with disturbance and optimal application rates of zeolite to reduce NH₃ production. The aim of the microchamber design was to standardise headspace and air exchange rates in order to quantify the effect of increasing manure surface area and depth. The results demonstrated air flow dynamics are important and challenging factors when comparing these variables, however a standardised microchamber was successfully deployed to compare and determine an optimal application rate of zeolite for NH₃ reduction.

Chicken anaemia virus (CAV) is a small circular virus that belonging to the genus Gyrovirus in the family Circoviridae, and is the causal agent of chicken infectious anaemia. The virus is distributed worldwide with little genetic and antigenic variability. It is a relatively new disease, first reported around four decades ago, although its economic significance has been documented more recently. Although clinical signs could be induced in young birds, it usually causes subclinical infection in older birds. The primary target cells are haemocytoblasts in the bone marrow and lymphoblasts in the thymic cortex and while mortality induced by CAV in chickens is low, serious complications due to immunosuppression and secondary infections may occur. These effects lead to significant economic loss in the global poultry industry. Surprisingly, a significant basic information about CAV is lacking in the literature, including, the shedding routes and rate of the virus excretion from the host, and routes of lateral infection of flockmates. Traditional diagnostic methods for the disease and/or infection are often not sensitive enough to detect the presence of the virus in the host, in the absence of specific clinical signs. In light of the above, the main objectives of this thesis are to determine the shedding rate of the virus and its lateral transmission routes and to develop a reliable and quick diagnostic and monitoring methods of CAV infection based on quantitative real-time PCR (qPCR). Defining the dynamics of the virus in host tissues is also an objective.

Four animal experiments were conducted to address the above objectives. The first two experiments were conducted to determine the tissue distribution (in four selected tissues) and shedding profiles of CAV in specific pathogen-free (SPF) and commercial broiler chickens respectively up to 28 days post infection (DPI). The third experiment was conducted to study the dynamics of the virus over a longer period of time (56 DPI) in nine tissues. CAV genome detection and qPCR quantification in dust, litter and faeces were attempted in all these three experiments with variable results. A fourth experiment was carried out to investigate the lateral transmission routes of CAV.

Investigations into the dynamics of CAV in nine tissue samples (thymus, bone marrow, bursa, spleen, liver, kidney, gonads, skin, and feather shaft) over time up to 56 DPI revealed that all tissue types were positive between 6 and 56 DPI. This result suggests that there is no specific tissue tropism of CAV and virus is present in lymphoid cells distributed in all tissues. The highest load of virus was detected in thymus and bone marrow which contain major aggregations of the target lymphocytes or their precursor cells; therefore, these two tissue samples are preferred samples for diagnosis and monitoring of CAV infection in chickens. Day of collection is not critical because the virus is detectable from days 6 to 56 post-infection in significant levels. The CAV genome was also detected and quantified in dust and faeces but making these non-invasive samples potentially be used for monitoring CAV in chicken flocks. However, detection in litter samplers was less successful. It was demonstrated that the virus shed in faeces are infective and can infect flockmates orally. Virus shed in faeces as early as 5 DPI are infective, but the highest level of faecal shedding occurs around 2-3 weeks post-infection. In the present study, we established that a minimum of 10⁴ genomes in faecal material are required to gain 100 % infection and 10^3.57 is required to orally infect 50 % of chickens at 16 days of age. Airborne transmission of the virus was also demonstrated, although the origin of the virus in air could not be determined; it could be from fragmented faeces, chicken dander or both. Nor could it be ascertained whether the airborne transmission involved primary infection via the ocular, respiratory or digestive epithelia given the anatomical linkages between the three. In the end of this thesis it has been determined the shedding rate of CAV in faeces and profile in dust, confirmed the faecal-oral route of CAV transmission, also demonstrated airborne transmission and developed diagnostic and monitoring technique for CAV infection either from tissue or environmental samples such as poultry dust. Further validation will be required for monitoring based on environmental samples before use in the industry.