Biosystems and Food Engineering Theses
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This collection is made up of doctoral and master theses by research, which have been received in accordance with university regulations.
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Browsing Biosystems and Food Engineering Theses by Title
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- PublicationThe application of spectroscopic techniques for the prediction of phosphorus dynamics in agricultural soils(University College Dublin. School of Biosystems and Food Engineering, 2021)
; 0000-0002-8019-2991Sustainable management of soil phosphorus (P) is important, because fertiliser supply is limited to the mining of finite phosphate rock and over-application of P causes harm to the environment. Currently in Ireland, only one parameter (Morgan's P (mg l-1)) is used to describe the supply of available P for crop uptake. Knowledge about P dynamics would allow an advance on current agronomic advice that relies on a quantification of current state to define what action a farmer should take. Dynamic P parameters (e.g. Langmuir sorption maximum, binding energy and maximum buffer capacity) are usually derived from sorption isotherms that are too time-consuming for routine analysis. Infrared (IR) spectroscopy offers a rapid analysis technique that can potentially replace some extractive and digestive techniques. The aim of this study was to develop a framework for incorporating soil P dynamics, quantified using rapid, low cost methods, into Irish agronomic advice to optimise productivity while supporting water quality policy. This was addressed by considering the accuracy of IR spectroscopic predictions of parameters that describe P sorption in soil, the accuracy of pedotransfer functions to predict P isotherm parameters, by identifying different mechanisms driving P isotherm properties and by defining a conceptual framework of categorisation for soils based on their P sorption parameters coupled with STP data. First horizon subsamples (n = 225) were taken from the archive of the Soil Information System (SIS) Ireland (Creamer et al., 2016) at Johnstown Castle, Wexford. Phosphorus sorption Index (PSI, R2c = 0.63, R2v = 0.65), phosphorus sorption capacity remaining (PSCr, R2c = 0.77, R2v = 0.67), phosphorus sorption capacity total (PSCt, R2c = 0.75, R2v = 0.60), Langmuir isotherm parameters in the 0 – 25 mg P l-1 range of added P (Smax25, R2c = 0.70, R2v = 0.60; k25, R2c = 0.66, R2v = 0.62; MBC25, R2c = 0.66, R2v = 0.60) and Langmuir isotherm parameters in the 0 – 50 mg P l-1 range of added P (Smax50, R2c = 0.75, R2v = 0.67; k50, R2c = 0.79, R2v = 0.47; MBC50, R2c = 0.74, R2v = 0.53) were predicted using MIR to a standard suitable for rough screening of agricultural soils. Langmuir sorption maximum, Smax50, was reliably predicted using multiple linear regression (MLR) with S50, OM and Mehlich-3 Fe (R2c = 0.91 and R2v = 0.95). The chemical data were also used to better understand the different isotherm shapes identified in the Irish soil population, where 64 % had Gile's non-strict L shape isotherm mechanism and 27 % were classified as having C shape isotherms. A conceptual framework based on P sorption parameters that incorporated STP was defined for agricultural soils. It was recommended that current Index classes be re- evaluated to consider having a target value (as Index 3) and to split those requiring reduction, or drawn-down, into a class near the target and a class that is far from the target. This split would contribute to a tool for critical source area (CSA) identification for P management. It was found that soils could be reliably classified into the new framework using values predicted from both MIR spectroscopy and pedotransfer functions. Optimisation of the framework for P sorption specific management of agricultural soils will require two additional steps: (1) further development of the MIR methodology for field use and (2) field validation of the thresholds and interpretation proposed.18 - PublicationAtmospheric Cold Plasma Interaction with Allergens in Food Processing(University College Dublin. School of Biosystems and Food Engineering, 2022)
; 0000-0002-2361-9557Approximately 5-15% of allergen recalls are associated with consumer reactions, which are largely attributed to cross contamination with allergens. Therefore, it is essential to mitigate against allergen cross-contamination during food processing, distribution and storage. Atmospheric Cold plasma (ACP) was found to effect functionality and modify proteins such as enzymes, owing to the chemical and bioactive radicals generated known collectively as reactive oxygen (ROS) and nitrogen species (RONS), together with physical stress such as electric field and UV irradiation. Allergens are comprised of proteins; thus, ACP was investigated as a promising tool to mitigate allergen cross-contamination in food processing sectors. The aim of this work is to understand and develop ACP based approaches for control and prevention of allergen cross-contamination within food processing. This program of research focused on developing a mechanistic understanding of how ACP interacts with food allergens and affects the antigenicity of food allergens. RSS systems, which comprised two modes of tunable plasma discharge, spark discharge (SD) and glow discharge (GD), were used as plasma source for the liquid application environment. To optimise the efficacy of application, diagnostics studies of the plasma devices and the chemical composition of plasma activated water (PAW) generated by RSS system were investigated. SDPAW predominantly contains H2O2 and NO3-. GDPAW predominantly contains peroxide, NO2- and NO3-. The concentration of ROS and RNS was dependent on applied voltage and frequency of the plasma. To understand the mechanism of plasma in gas-phase, the ignition and propagation of air discharges of SD and GD were examined using fast imaging diagnostic techniques in collaboration with the University of Liverpool. SD drives both anode-directed and cathode-directed streamers, while GD drives only anode directed streamers. SD had higher OH emission than GD at both positive and negative half-periods. The effect of SD and GD on the antigenicity reduction of milk-derived and wheat-derived allergens were then studied. Casein, a-lactalbumin and ß-lactoglobulin are the major allergens in bovine milk while gliadin is the major allergen in wheat gluten. These allergen solutions were treated directly by SD and GD. The antigenicity of caseins, a-lactalbumin and gliadin decreased while ß-lactoglobulin increased. The results showed the modification of chemical structure of plasma treated allergens. A concentration dependence also emerged across the different studies. For example, indirect PAW treatment reduced casein antigenicity but only at low concentrations of 0.01 mg/ml, when applied in combination with a mild heat treatment at 60 °C. The ROS generated in SDPAW is attributed to antigenicity reduction of casein, while RNS generated in either SDPAW or GDPAW was not involved. The mode of application was probed, where liquid mediated direct SD and GD RSS were compared with indirect PAW treatment, and further compared to direct gaseous dry plasma approaches of dielectric barrier discharge (DBD) in-package system and plasma brush (PB) system. The antigenicity reduction of milk-derived and wheat-derived allergens were studied in terms of sample concentrations and plasma process duration. The results showed the conformational and linear epitopes of plasma treated allergens were altered. The efficacy for antigenicity reduction is correlated with protein composition, sample concentration and plasma process duration. Overall, dry or liquid mediated plasma processes can be tailored to mitigate allergen residues in food processing. The food allergens from two important foods were investigated to reveal the universality of ACP. The effectiveness of antigenicity reduction was well explained by the modification of conformational and linear structures of allergens treated by ACP.8 - PublicationHuman Health Risk Assessment for Engineered Nanoparticles: from Ranking to Risk Characterization(University College Dublin. School of Biosystems and Food Engineering, 2022)
; 0000-0002-1840-6442The ubiquitous application and potential environmental release of engineered nanoparticles (ENPs) has caused human health concerns worldwide. Considering the potential exposure from the usage of ENP containing products through environmental exposure pathways, this thesis developed a systematic human health risk assessment for ENPs. As a novel pollutant, the background knowledge on ENPs was evaluated, and the knowledge gaps were identified for further investigation. Sources of ENPs which can result in potential human exposure were outlined as: the release in the use phase of ENP containing consumer products and potential release into surface water, air, and soil in the natural environment. This thesis conducted a risk ranking for ENPs of human health concern. Silver nanoparticles (AgNP) were identified as having the greatest human health concern as a result of their environmental release and were selected for further risk quantification. As an essential step in a complete risk assessment, a hazard characterization was conducted for AgNPs by evaluating existing animal toxicity studies. Representative dose response relationships were converted to human equivalent doses (HED), showing the oral intake could be the most effective exposure pathway to induce histopathological responses. Potential release of AgNPs into agricultural soil has caused concern as a potential pathway for AgNPs to enter the food chain and resulting in human oral intake. This thesis adopted regional variables in Europe and Ireland to develop a comparative risk assessment for human health risk from AgNPs through crop consumption, including through leafy vegetables, wheat based products, and root vegetables. Results show the negligible risk under the current situation and in future scenarios in both the EU and Irish spatial scopes (the highest mean hazard quotient (HQ) value is 4.47E-04). Irrigation using surface water is a major contributor to the final risk. The irrigation methods are critical to the level of AgNPs internalization in crop edible parts. As the AgNPs released in the surface water were determined to be an important source of risk, the aquatic behavior of AgNPs was identified as a critical knowledge gap and was investigated in this thesis using an experimental approach. The removal efficiency was parameterized by key influential environmental parameters including dissolved organic matters concentration, water hardness, water temperature, and incubation time. Particular high persistence can be observed with DOM mediated secondary formation of AgNPs surrounding primary AgNPs in the HDD of 77 ± 1.2 nm group. The interactive effect between 1.1 mM Ca2+ concentration and 99 mg/L DOM concentration was highlighted which can induce the highest AgNP aquatic persistence in both size groups. Having considered the persistence of AgNPs in the aquatic environment, this allows greater potential for more accurately estimating exposure routes through water sources. Uncertainty exists regarding potential human oral exposure to AgNPs through surface water via drinking water consumption. This thesis thus developed a quantitative risk assessment using a probabilistic approach to model the human health risk for adults and children resulting from drinking water consumption. The model assesses AgNPs fate from nine different source water scenarios considering river, lake, and transitional water combined with different levels of water hardness as source water for a DWTP. Traditional processes in the DWTP exhibited effective removal of AgNPs from the inflow, and resulted in effective control of the final human health risk. The final risk shows negligible hazardous impacts resulting from AgNPs ingestion through drinking water consumption for both age groups (the highest mean HQ value is 4.04E-02). The results on sources of human health risks resulting from AgNPs can help future monitoring and regulation development targeting the safe application of AgNPs.7 - PublicationMonitoring and control of Cronobacter sakazakii in Irish dairy powder processing facilitiesCronobacter sakazakii is a pathogen widely associated with powdered infant formula (PIF). However, there is a broadening concern in the dairy powder industry relating to Cronobacter sakazakii, particularly if the dairy ingredients are subsequently used in PIF products. The first objective of this thesis was to use whole genome sequencing in an extensive monitoring programme for Cronobacter sakazakii in an Irish dairy process facility. A yearlong surveillance programme of a large-scale Irish milk protein concentrate processing facility detected a significant amount of Cronobacter sakazakii. Subsequent sequencing and species identification confirmed that all 88 positive isolates were Cronobacter sakazakii strains. Phylogenetic analysis demonstrated frequent genetically similar isolates among the strains recovered indicating that they most likely came from the same individual initial contamination event. In all nine separate genetically similar groups were identified pointing to potentially multiple contamination events in the process facility before / during the monitoring period. Phylogenetic analysis coupled with temporal and spatial analysis of the isolates also indicated a significant amount of persistence of strains in the process facility. The second major objective was to identify the occurrence of thermal tolerant regions in the genome of Cronobacter sakazakii strains recovered from Irish dairy process facilities and to undertake laboratory thermal inactivation studies to confirm that presence of the thermal tolerant region confers enhanced thermal tolerance to the strain. Of 114 isolates examined, 56 isolates contained the shorter thermal tolerant gene island as present in Cronobacter sakazakii, strain SP291. Thermal inactivation studies confirmed that in general, the short thermal tolerant region found in Cronobacter sakazakii strains recovered from Irish dairy process facilities confers enhanced thermal tolerance to strains containing the genomic region. Overall, the thesis demonstrated the utility of using whole genome sequencing as a ‘fingerprinting’ tool in pathogen monitoring programmes in food process facilities and demonstrated that increasingly, genomic data can be used to predict phenotypic behaviour such as thermal tolerance.
7 - PublicationMultiscale spectral imaging for food safety with relevance to dairy processing(University College Dublin. School of Biosystems and Food Engineering, 2022)
; 0000-0003-2298-0508Multi-modal spectral imaging (e.g., Raman scattering, reflectance, darkfield) in different wavelength ranges (such as Infrared, near Infrared, Visible) at different spatial scales (from microscopic to macroscopic), combined with chemometrics approaches were investigated for the quality control of dairy products and define the hygienic level of the surfaces related to the dairy industry. In order to achieve these goals, the thesis has been developed in three steps: Quality control: identification of different dairy products based on macronutrients and lactose quantification in whole milk. Food safety: detection of dairy contamination on metallic surfaces related to food processing Food safety: detection of bacteria and biofilm contamination on surfaces related to the dairy industry. In addition, the detection of biofilm growth in presence of milk simulating possible scenarios in dairy processing has been carried out.26 - PublicationThe non-invasive detection of soil compaction and the effect of traffic management on crop performanceThe increasing weight of contemporary agricultural vehicles causes risk of widespread soil compaction which is a threat both to productivity and sustainability of soil resources. A range of prevention measures are being introduced in agriculture, of which Controlled Traffic Farming (CTF) and low tyre pressure (LTP) systems are commercially adopted. A quick proximal method to detect the presence and severity of soil compaction would be beneficial as it would enable planning spatially targeted, less expensive and potentially more effective remediation operations. This thesis reports on a 3-year study, where the experimental design was based on precisely planned field traffic consisting of offset tractor passes, so the resulting sub-plot spatial structure consisted of a “stripey” pattern of elongated rectangles that received a known number of wheel passes annually (0-5 passes), in combination with tillage depth and tyre pressure. Supporting studies were conducted on a sandy loam site in West Midlands, UK, with a 10-year history of controlled traffic of similar design, and in the soil hall at Harper Adams University. Soil and crop response to the experimental factors were measured and analysed, testing the application of ground penetrating radar (GPR) for soil compaction detection. Agricultural traffic was found to have an adverse effect on soil physical properties by increasing penetrometer resistance and bulk density, and on crop performance, by decreasing emergence percentage and yield, typically by 20-30% compared to untrafficked areas, with higher losses recorded in winter barley in wet conditions. The application of low tyre pressures was found to increase emergence, NDVI and yield, compared to standard pressures. On the field sites, heavily trafficked tractor wheelways and relatively untrafficked plot centres were scanned with GPR. In the soil hall, three zones were scanned: untrafficked, trafficked, trafficked and covered with loose soil. Scanning was performed statically at 0.4 m height. Numeric signal attributes derived from GPR traces served as feature vectors for supervised classification by the following machine learning algorithms: 1) deep learning (Keras), 2) random forest (RF), 3) the model with the highest AUC supplied by the H2O AutoML function. Training and classification were done within each dataset separately. In the laboratory setting, with 3 predicted classes (traffic zones), overall accuracy was 59% with deep learning and 83% with random forest. In one-vs-one tests, accuracy was 70-84% with deep learning and 83-96% with random forest. In the detection of traffic zone on the field sites, the leader (highest AUC) models selected by the H2O AutoML function, all Stacked Ensemble type, achieved accuracy 67-87%, depending on dataset. A higher-than-random accuracy using Keras and RF was achieved in one dataset only (67% Keras, 71% RF). Accompanying results point to the possible role of traffic-related variability in soil moisture in detection.
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