Enhancing the health-status of processed meats through ingredient manipulation and its effects on sensory and physiochemical product attributes

An important component of this Ph.D. thesis was to determine the European consumers’ views on processed meats and bioactive compounds. Thus a survey gathered information form over 500 respondents and explored their perceptions on the healthiness and purchase-ability for both traditional and functional processed meats. This study found that the consumer was distrustful towards processed meat, especially high salt and fat content. Consumers were found to be very pro-bioactive compounds in yogurt style products but unsure of their feelings on the idea of them in meat based products, which is likely due to the lack of familiarity to these products. The work in this thesis also centred on the applied acceptable reduction of salt and fat in terms of consumer sensory analysis. The products chosen ranged in the degree of comminution, from a coarse beef patty to a more fine emulsion style breakfast sausage and frankfurter. A full factorial design was implemented which saw the production of twenty beef patties with varying concentrations of fat (30%, 40%, 50%, 60% w/w) and salt (0.5%, 0.75%, 1.0%, 1.25%, 1.5% w/w). Twenty eight sausage were also produced with varying concentrations of fat (22.5%, 27.5%, 32.5%, 37.5% w/w) and salt (0.8%, 1%, 1.2%, 1.4%, 1.6%, 2%, 2.4% w/w). Finally, twenty different frankfurters formulations were produced with varying concentrations of fat (10%, 15%, 20%, 25% w/w) and salt (1%, 1.5%, 2%, 2.5%, 3% w/w). From these products it was found that the most consumer acceptable beef patty was that containing 40% fat with a salt level of 1%. This is a 20% decrease in fat and a 50% decrease in salt levels when compared to commercial patty available in Ireland and the UK. For sausages, salt reduced products were rated by the consumers as paler in colour, more tender and with greater meat flavour than higher salt containing products. The sausages containing 1.4 % and 1.0 % salt were significantly (P<0.01) found to be more acceptable to consumers than other salt levels. Frankfurter salt levels below 1.5% were shown to have a negative effect on consumer acceptability, with 2.5% salt concentration being the most accepted (P<0.001) by consumers. Samples containing less fat and salt were found to be tougher, less juicy and had greater cooking losses. Thus salt perception is very important for consumer acceptability, but fat levels can be potentially reduced without significantly affecting overall acceptability. Overall it can be summarised that the consumer acceptability of salt and fat reduced processed meats depends very much on the product and generalisations cannot be assumed. The study of bio-actives in processed meat products found that the reduced salt/fat patties fortified with CoQ10 were rated as more acceptable than commercially available products for beef patties. The reduced fat and salt, as well as the CoQ10 fortified, sausages were found to compare quite well to their commercial counterparts for overall acceptability, whereas commercial frankfurters were found to be the more favoured in comparison to reduced fat and CoQ10 fortified Frankfurters.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Early warning sign attributes for the safety of drinking water treatment works

A proactive risk management strategy seeks to prevent accidents from taking place and maintain the safety of a system. In this context, the task of identifying and disseminating early warning signs and signals is among the most important. The problem is that warning signs that are present before an accident takes place are often being overlooked and not picked up or identified as warning signs. If these warning signs were responded to, then an accident may be averted. Accidents occuring in the critical domain of a drinking water treatments works can have serious implications for the public health of consumers of the water supplied. Realising and comprehending early warning signs is a major challenge for the domain of systems safety and especially in the domain of a water treatment works. The approaches that are typically used to enhance the realisation, comprehension and dissemination of early warning signs in the water treatment domain in Ireland mainly involves the creation of accident scenarios, the use of monitoring data and procedures for the dissemination of warnings. While all of these approaches are all useful to inform the mental or process models of possible accident scenarios, nevertheless, accidents are still occurring in this domain. Therefore, a new approach to enhance the comprehension of and effective dissemination of early warning signs is required in order to improve safety and proactive risk management strategies. The contributions of this thesis is the provision of a set of attributes associated with the early warning sign concept that provides meaningful data on the early warning signs and allows recipients to better comprehend them. The values of these attributes were customised for application in the water treatment domain. This research proves that early warning signs at a water treatment works received with information on their attributes are comprehended and communicated more effectively and efficiently than the usual pragmatic approach and thereby improves the safety and proactive risk management strategies.