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ItemSorghum grain phenolic compounds, bioactive functions, and its application of its bran in food systemsXiong, Yun ( 2021)Sorghum is one of the world's most important but underutilised cereal crops. Sorghum grain, in addition to being a source of nutrition and energy, is replete with bioactive phytochemicals with many potential health benefits. In particular, sorghum grain has a distinct phenolic profile that is more abundant and diverse than other major cereal grains. Furthermore, the phenolic compounds in sorghum grain are concentrated in the bran, making the bran a potentially valuable functional ingredient or source of natural bioactive compounds for food and pharmaceutical applications. Therefore, understanding the phenolic profile and bioactive properties of sorghum grain/bran is important for its development and applications. The aim of this PhD project was to gain a comprehensive understanding of the phenolic profile of sorghum grain/bran, its bioactive functions, and its potential in food application. Five representative Australian sorghum grain varieties were selected for this project: white Liberty, red Mr-Buster, red Nuseed Cracka, brown IS131C, and black Shawaya Short Black 1 sorghum grains. In relation to sorghum phenolic profile, a total of 110 phenolic compounds (38 phenolic acids, 59 flavonoids and 13 other phenolic compounds) were identified and characterised in the five sorghum grains using HPLC-DAD-ESI-QTOF-MS/MS analysis; 56 of them were reported for the first time in sorghum. The distribution pattern of individual and subclass phenolic compounds among the sorghum grain varieties, location (bran or kernel fraction), and form (free or bound form) were directly visualised by the multiple factor analysis and heatmap. In general, the phenolic compounds were concentrated in the bran in terms of both diversity and quantity, with brown sorghum grain/bran having the most diverse and abundant phenolic compounds. In terms of bioactive properties, sorghum demonstrated significant in vitro inhibitory activity against alpha-glucosidase alpha-amylase digestive enzymes, and potent in vitro and cellular antioxidant activities. The enzyme inhibition and antioxidant activity of sorghum grains were related to their phenolic concentration and composition; the bioactivities varied between individual assays but were generally in the following order: brown and black > red > white coloured sorghum; bran > kernel; free > bound. Brown sorghum-bran-free and black sorghum-bran-free phenolic extracts were found to be the most effective in both enzyme inhibition and antioxidant activity studies. The active phenolic compounds that may be responsible for these bioactive properties were also identified. 3-Deoxyanthocyanidins were discovered to be a key group of active phenolic compounds that confer high antioxidant activity on sorghum. The structure-activity relationship study revealed that the B-ring catechol functional group was essential for 3-deoxyanthocyanidins to have high antioxidant activity. Regarding food application, the effects of brown and black sorghum bran incorporation on the physicochemical and microbiological properties of beef sausage were investigated. Unexpectedly, the incorporation of sorghum bran in sausages promoted discolouration, oxidation, and pH fluctuation. Nevertheless, sorghum bran demonstrated some antimicrobial activity. This thesis addresses a number of research gaps in sorghum grain research and expands our knowledge on sorghum grain phenolic profile, bioactive functions, and food applications, which could promote further research and development of sorghum in the food industry.
ItemThe use of computer vision techniques as noninvasive tools to monitor parameters related to the well-being and productive performance of cattle and pigsJorquera Chavez, Maria Fernanda ( 2020)Among a wide range of factors that can affect animals’ wellbeing, stress levels and health status have been identified as relevant factors in production animals. The increasing awareness about animal welfare and the impact that it has on farm productivity has been promoting scientific research and the development of novel and less invasive methods to monitor animals and obtain measurements that can be used as indicators to assess animal wellbeing, including stress and health status. Research presented in this thesis deals with red-green-blue (RGB) and thermal infrared (TIR) imagery, and computer vision techniques, as non-invasive tools to measure physiological changes in cattle and pigs to assist in the assessment of their stress and health. One study was conducted to evaluate the performance of the proposed methods, which were used to analyse RGB and TIR imagery to measure eye-temperature, heart rate (HR) and respiration rate (RR) in cattle. The study was performed in a robotic dairy farm, where TIR and RGB cameras recorded ten dairy cows during six handling procedures, across two consecutive days. Simultaneously, core body temperature, HR and RR were measured using standard methods for comparison with the data obtained from the recorded images using the developed algorithms. A feature tracking algorithm was developed to facilitate the processing of RGB videos, which showed an accuracy between 92% and 95% depending on the area analysed. From the physiological parameters analysed, the highest correlations were observed between eye-temperature and intravaginal temperature (r = 0.8; P<0.01), and between remote RR and the RR obtained from visual observations (r = 0.87; P<0.01). A further two studies were carried out to implement the proposed computer-based methods to remotely measure eye-temperature, heart rate and respiration rate of cattle, and to investigate whether these measures could be used to evaluate the physiological response of cattle to stressful situations and whether they could be used as predictors of beef quality. For these two studies, 215 beef cattle were recorded with RGB and TIR cameras on the farm and at the abattoir to obtain eye-temperature, HR, and RR measurements. Cuts of the respective beef were evaluated by consumers, and the ultimate pH (pHu) and meat colour were obtained from the respective carcasses. It was observed that the physiological variables of cattle were higher at the abattoir compared to the farm. Moreover, eye-temperature obtained on farm and at the abattoir were highly correlated. However, the results of these studies indicated that these measurements had low contribution when predicting beef quality. Finally, two studies were performed to investigate the use of the proposed computer vision methods with RGB and TIR imagery to monitor pigs and detect changes in eye-temperature, ear-base temperature, HR, and RR. The objective was to identify whether these physiological changes could assist in the early detection of pigs affected by respiratory disease. The first of these studies was performed under experimental conditions, where pigs were challenged with Actinobacillus pleuropneumoniae (APP). Images (TIR and RGB) were recorded after this challenge, and the data obtained was then compared between the group of pigs that presented clinical signs of respiratory disease and the group of pigs that were considered healthy. Clear changes in temperature and HR were observed six or more hours before the clinical observations identified sick animals. Conversely, changes in RR were detected in the last period of observations, around the time when clinical signs started to be present. The second of these experiments used different cameras and included improvements to the proposed methods to monitor pigs constantly and in a commercial setting. A total of 48 pigs were monitored between 9 and 20 weeks of age. Eye-temperature, HR and RR measurements were compared between the pigs that were identified as sick and those that were considered healthy. Similarly to what was observed in the previous study, changes in these parameters were identified before the clinical observations indicated signs of illness (up to 2 days before), where the earliest changes were observed in eye-temperature and HR, and the latest changes were observed in RR. This thesis provides evidence that computer vision techniques may be suitable as a non-invasive method for monitoring farm animals. Therefore, it prompts further investigation via controlled studies to continue the development and automatization of these techniques, leading to the improvement of science-based industry-relevant monitoring systems.