Peptide fingerprinting and predictive modelling of fermented milk : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North Campus, New Zealand

Fermented milk products are valued by consumers and the food industry for their nutritional properties, pleasant taste, and texture. Consumer demands and expectations for such products are constantly changing. Understanding how consumers perceive the sensory characteristics of food and the relationship these characteristics have with the chemical components of food can provide insight that can enable food researchers and manufacturers to develop food products that are tailored to provide enhanced sensory qualities. Establishing techniques that allow for in-silico prediction or correlation of sensory qualities can enable a more rapid approach that would aim to enable researchers to meet the demands of consumers. This research firstly explored mass spectrometric techniques for the rapid fingerprinting of milk and fermented milk products, using Matrix-Assisted Laser Desorption Ionisation - Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and Rapid Evaporative Ionisation Mass Spectrometry (REIMS), two technologies that require minimal sample preparation and can rapidly generate a fingerprint of a food’s chemical components. Peptide fingerprints obtained by MALDI-TOF MS and analysed by principal component analysis were effective at discriminating the two fermented milk and milk samples. Supervised discrimination of low molecular weight fingerprints obtained via REIMS and MALDI-TOF MS proved less effective but demonstrated some potential and could be used alongside other analyses in future studies. These techniques were explored with a view to establishing a technique that could provide rapid insights into a food’s chemical composition, and which could also effectively discriminate the chemical components of the product. Such techniques could be used for rapid screening of products and can provide insight into the chemical components that are driving the variation in different products, which may be reflective of the differences in sensory characteristics. Next, peptide fingerprinting and predictive modelling were investigated in milk fermented with various bacterial combinations, including probiotic cultures. Fingerprinting was performed on samples collected at each hour of fermentation. Predictive modelling techniques, using both regression and classification approaches, were trialled in order to predict the change in signal intensity throughout fermentation. This aimed to understand if peptides could be predicted throughout fermentation, with a view to enable the targeted prediction of desirable peptides, or other relevant components, which may impart favourable sensory qualities in the final product. Regression techniques were somewhat effective for predicting the signal intensity of individual m/z ions throughout fermentation. Most of the ions did not follow a linear relationship, and, as such, a multiple linear regression model was unable to model most of the ions. Using a generalised additive model, a non-linear approach, improved the performance in most cases and could predict the signal intensity of individual ions throughout fermentation. However, the model was unable to correctly predict all cases. Classification techniques were effective for predicting the general direction of the signal intensity between start and end fermentation times. Five classification techniques were trialled, with each model providing accurate predictions for the increase or decrease of signal intensity between early and late fermentation times. Lastly, consumer panellists were recruited to evaluate the change in important sensory characteristics throughout the fermentation of milk prepared using two different starter cultures. This aimed to understand if consumer responses to such products could be correlated with instrumental analysis, in order to predict the consumer responses from instrumental data. Consumers perceived significant differences in bitterness and flavour intensity between fermented milk samples at different fermentation time points. There were significant correlations between peptide fingerprints and the consumer rankings for the sensory attributes in each fermented milk product. XGBoost regression could predict consumer responses with reasonable accuracy. This thesis explored the fermentation of milk using specific bacteria and fermentation processes. To validate this work, further products could be explored, in addition to different processing parameters. Furthermore, a more in-depth analysis of the chemical components of the products could be investigated and analysed with additional sensory evaluation to further explore and confirm the findings

Prediction of Caesarean Delivery

For expectant parents, a first birth is notable for its unpredictability, and the path to safe labour and delivery is commonly complicated by a requirement for unplanned caesarean delivery. The ability to anticipate an uncomplicated vaginal birth, or to predict the requirement for unplanned caesarean delivery, carries the potential to facilitate optimal birth choices. For example, elective caesarean delivery confers substantially less risk than unplanned caesarean delivery performed during the course of labour. Pre-delivery knowledge of a high predictive risk of requiring intrapartum caesarean delivery could lead to women opting to deliver by elective caesarean delivery, thereby lowering associated risks. Equally, pre-labour knowledge of a high prospect of achieving a successful and uncomplicated vaginal birth could result in enhanced motivation for women to deliver in a less medicalised environment. Predictive risk models have been utilised to good effect in other areas of medicine. The incorporation of a risk predictive tool for intrapartum caesarean delivery would enable women and their caregivers to choose the most appropriate management plan for each woman

Modeling the effects of ecosystem changes on seagrass wrack valorization: Merging system dynamics with life cycle assessment

Seagrass meadows, while recognized as essential ecosystem service providers, are degrading worldwide. This has a profound impact on the environment but also on socioeconomic systems which hope to utilize beach-cast seagrass (wrack) as a bioresource. This study integrates system dynamics (SD) thinking with life cycle assessment (LCA) and life cycle costing (LCC) to understand how a degraded ecosystem feedbacks into the circular bioeconomy. An SD model was created to assess the impacts of seagrass meadow changes on wrack production and on ecosystem services accounting, considering an Italian case study of wrack deposited on a beach. Environmental and economic impacts of wrack valorization through anaerobic digestion (AD) were then determined through LCA and LCC. Finally, an extended LCC combined the results of the SD model, LCA, and LCC to demonstrate the cost of seagrass meadow degradation and the value of restoration. The results confirmed complexities in stakeholder perspective within the waste-to-resource framework. For the AD operator, meadow restoration would increase the profits from wrack valorization (23.10 €/ton), while for the municipality, meadow degradation would reduce the high costs associated with management (104.29–140.00 €/ton). When also considering the impacts on the environment and local community, valuation of ecosystem services and cost of restoration were influential. Meadow restoration with wrack valorization was the most favorable option if the natural capital of the seagrass meadows was valued appropriately (>0.065 €/m2) and direct costs of restoration could be kept relatively low (<1179 €/ha). Overall, the model resulted in a total net present cost of −3.161,462.40 € for the baseline scenario, −1,488,277.28 € for the scenario of wrack valorization, and −1,231,325.12 € for the scenario of wrack valorization and meadow restoration

Spontaneous cognition in dysphoria: reduced positive bias in imagining the future.

Anomalies in future-oriented cognition are implicated in the maintenance of emotional disturbance within cognitive models of depression. Thinking about the future can involve mental imagery or verbal-linguistic mental representations. Research suggests that future thinking involving imagery representations may disproportionately impact on-going emotional experience in daily life relative to future thinking not involving imagery (verbal-linguistic representation only). However, while higher depression symptoms (dysphoria) are associated with impaired ability to deliberately generate positive relatively to negative imagery representations of the future (when instructed to do so), it is unclear whether dysphoria is associated with impairments in the tendency to do so spontaneously (when not instructed to deliberately generate task unrelated cognition of any kind). The present study investigated dysphoria-linked individual differences in the tendency to experience spontaneous future-oriented cognition as a function of emotional valence and representational format. Individuals varying in dysphoria level reported the occurrence of task unrelated thoughts (TUTs) in real time while completing a sustained attention go/no-go task, during exposure to auditory cues. Results indicate higher levels of dysphoria were associated with lower levels of positive bias in the number of imagery-based future TUTs reported, reflecting higher negative imagery-based future TUT generation (medium to large effect size), and lower positive imagery-based TUT generation (small to medium effect size). Further, this dysphoria-linked bias appeared to be specific in temporal orientation (future, not past) and representational format (imagery, not non-imagery). Reduced tendency to engage in positive relative to negative imagery-based future thinking appears to be implicated in dysphoria

Disgust Enhances the Recollection of Negative Emotional Images

Memory is typically better for emotional relative to neutral images, an effect generally considered to be mediated by arousal. However, this explanation cannot explain the full pattern of findings in the literature. Two experiments are reported that investigate the differential effects of categorical affective states upon emotional memory and the contributions of stimulus dimensions other than pleasantness and arousal to any memory advantage. In Experiment 1, disgusting images were better remembered than equally unpleasant frightening ones, despite the disgusting images being less arousing. In Experiment 2, regression analyses identified affective impact – a factor shown previously to influence the allocation of visual attention and amygdala response to negative emotional images – as the strongest predictor of remembering. These findings raise significant issues that the arousal account of emotional memory cannot readily address. The term impact refers to an undifferentiated emotional response to a stimulus, without requiring detailed consideration of specific dimensions of image content. We argue that ratings of impact relate to how the self is affected. The present data call for further consideration of the theoretical specifications of the mechanisms that lead to enhanced memory for emotional stimuli and their neural substrates

Life Cycle Assessment Tool for Food Supply Chain Environmental Evaluation

Food is at the centre of efforts to combat climate change, reduce water stress, pollution, and conserve the world’s wildlife. Assessing the environmental performance of food companies is essential to provide a comprehensive view of the production processes and gain insight into improvement options, but such a tool is currently non-existent in the literature. This study proposed a tool based on the life cycle assessment methodology focused on six stages of the food chain, raw materials acquisition, supplier, manufacturing, distribution, retail and wastes. The user can also evaluate the implementation of Internet of Things (IoT) technologies to reduce food waste applied in the real-world problems. The tool was validated through a case study of a food manufacturing company that prepares frozen meals via vending machines. The LCA results provided by the tool showed that food raw materials production is the main hotspot of nine impact categories. The IoT technologies’ contribution increased the company’s impact by around 0.4%. However, it is expected that employing these monitoring technologies would prevent food waste generation and the associated environmental impacts. Therefore, the results of this paper provide evidence that the proposed tool is suitable for determining environmental impacts and savings of food supply chain companies

Oxytocin bolus versus oxytocin bolus and infusion for control of blood loss at elective caesarean section: double blind, placebo controlled, randomised trial

Objectives To determine the effects of adding an oxytocin infusion to bolus oxytocin on blood loss at elective caesarean section

Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection

Prevalence and predictive value of ICD-11 post-traumatic stress disorder and Complex PTSD diagnoses in children and adolescents exposed to a single-event trauma.

BACKGROUND: The 11th edition of the International Classification of Diseases (ICD-11) made a number of significant changes to the diagnostic criteria for post-traumatic stress disorder (PTSD). We sought to determine the prevalence and 3-month predictive values of the new ICD-11 PTSD criteria relative to ICD-10 PTSD, in children and adolescents following a single traumatic event. ICD-11 also introduced a diagnosis of Complex PTSD (CPTSD), proposed to typically result from prolonged, chronic exposure to traumatic experiences, although the CPTSD diagnostic criteria do not require a repeated experience of trauma. We therefore explored whether children and adolescents demonstrate ICD-11 CPTSD features following exposure to a single-incident trauma. METHOD: Data were analysed from a prospective cohort study of youth aged 8-17 years who had attended an emergency department following a single trauma. Assessments of PTSD, CPTSD, depressive and anxiety symptoms were performed at two to four weeks (n = 226) and nine weeks (n = 208) post-trauma, allowing us to calculate and compare the prevalence and predictive value of ICD-10 and ICD-11 PTSD criteria, along with CPTSD. Predictive abilities of different diagnostic thresholds were undertaken using positive/negative predictive values, sensitivity/specificity statistics and logistic regressions. RESULTS: At Week 9, 15 participants (7%) were identified as experiencing ICD-11 PTSD, compared to 23 (11%) experiencing ICD-10 PTSD. There was no significant difference in comorbidity rates between ICD-10 and ICD-11 PTSD diagnoses. Ninety per cent of participants with ICD-11 PTSD also met criteria for at least one CPTSD feature. Five participants met full CPTSD criteria. CONCLUSIONS: Reduced prevalence of PTSD associated with the use of ICD-11 criteria is likely to reduce identification of PTSD relative to using ICD-10 criteria but not relative to DSM-4 and DSM-5 criteria. Diagnosis of CPTSD is likely to be infrequent following single-incident trauma