Chemistry And Biotransformation Of Uapaca kirkiana Pulp In Development Of A Functional Food Using A Probiotic

ThesisModern advances in understanding the relationship between nutrition and health have resulted in the development of the concept of functional foods, which is a new practical approach earmarked to promote optimal health status. Underutilised wild fruits have great potential to improve overall human nutrition and help to mitigate malnutrition related problems faced by most communities in Sub-Saharan Africa. This research focused on the chemistry and biotransformation of Uapaca kirkiana pulp in the development of a functional food using a probiotic, Lactobacillus rhamnosus yoba. Lactobacillus rhamnosus yoba is a Gram negative, lactic acid bacterium, and generic probiotic of L. rhamnosus GG. The specific objectives of the study were the following: to determine the bioactive compounds, physiochemical properties, and functional potential of a highly nutritious, but underutilized U. kirkiana Muell. Arg (wild loquat) fruit; to produce a probiotic jam; to determine the functional properties of the jam and the bioaccessibility of iron and zinc, and its sensorial qualities. Ripe fruits were obtained from the Bikita, Gokwe, and Kazangarare areas in Zimbabwe and the bioactive phytochemical constituents, physicochemical properties and functional characteristics of the fruit pulp were analysed. The total soluble sugars, individual sugars and mineral contents in the fruit pulp were determined. Ascorbic acid was determined using the 2,6-Dichlorophenolindophenol (DCPIP) titration test. The total phenolic, tannin, and flavonoid contents were analysed using the Folin-Ciocalteu test, tannin binding test, and vanillin test, respectively. A composite pulp sample was obtained and its physicochemical properties (vitamin C, total titratable acid (TTA), pH, total soluble solids (TSS), antioxidant activity (AOA), moisture, and % pectin) were analysed before jam making. A probiotic jam was developed using the formulation- 55 % (wt/vol) pulp, 46 % (wt/vol) sugar, 1.5 % (wt/vol) pectin, and 0.5 % (wt/vol) citric acid. After preparation of the jam, the probiotic, L. rhamnosus yoba was inoculated into the jam, and the control jam sample was inoculated with distilled water. The viability of L. rhamnosus yoba in the jam was determined before consumption. Functional properties (vitamin C, total titratable acid (TTA), pH, total soluble solids (TSS), antioxidant activity (AOA), and moisture) of the jam inoculated with L. rhamnosus yoba were determined. Iron and zinc bioaccessibility in the probiotic jam were analysed using the in vitro simulated digestion protocol. The sensory evaluation of the jam was conducted by trained (n = 20) and untrained (n = 130) panellists. Sensory attributes, including taste, appearance, aroma, spreadability, mouthfeel, and texture were scored using a 9 point hedonic scale. A triangle test and preference test for overall acceptance were conducted. Pulp yield ranged from 12.15 ± 0.16 g/100 g to 15.09 ± 0.27 g/100 g and was significantly different (F = 158.71, p < 0.0001) in all fruits from the three study areas, and accounted for 96 % of the variation in the fruit. The TTA (0.3–0.48 g/kg) and pH (4.3–4.6) values of the pulp were significantly different (F = 12.58; P<0.0001 and F = 15.66, P< 0.0001, respectively) in fruits obtained from the three sampling areas. Fruit properties varied amongst the three study site and this was contributed by pH (74 %) level and TTA (69 %) content. The TSS (sugar content) was significantly different (F = 4.66, P < 0.0071) and accounted for 45 % of the fruit variation. There was a strong relationship between TTA and pH (r2 = 0.79); TTA and antioxidant (r2 = 0.72); and pH and phosphorus (r2 =0.81). The iron content ranged between 11.25 ± 0.52 mg/100 g to 12.16 ± 0.54 mg/100 g. Phosphorus, sodium and iron accounted for approximately 73 %, 50 %, and 43 % of the variation, respectively. The vitamin C content accounted for 27 % of the variation. Fructose was the dominant sugar. Tannins, flavonoids, and gallotannins were present. The fruit pulp had a total phenolic content of 67.0–82.5 μg GAE/g. Principal components 1 and 2 which represented physiochemical and functional properties of the pulp had eigenvalues of 5.59 and 2.13, and a variability of 37.31 % and 14.17 %, respectively. The jam inoculated with L. rhamnosus yoba had a vitamin, TTA, brix, and moisture content of 0.34 ±0.02 mg/100 g, 2.2 ± 0.11, 68.5 ± 0.2, and 34.8 ± 1.2, respectively. The fruit pulp had an antioxidant activity of 35 ± 1.02 %. Immediately after production, the jam inoculated with L. rhamnosus yoba had an iron and zinc content of 4.13 ± 0.52 mg/100 g and 0.36 ± 0.02 mg/100 g, respectively. The jam inoculated with L. rhamnosus yoba exhibited high fructose and sucrose content of 12.84 ± 0.21 g/100 g and 24.61 ± 0.12 g/100 g, respectively. Further, the jam inoculated with L. rhamnosus yoba had a TTA content of 2.2 at d 0 (after production), 2.37 ± 0.01 at d 4, and 2.48 ± 0.02 at d 7 of storage (25 °C). The jam inoculated with L. rhamnosus yoba had an iron bioaccessibility of 6.55 ± 0.36 % and a zinc bioaccessibility of 16.1 ± 0.50 %. The use of L. rhamnosus yoba in the jam showed a 4 % and 2 % increase in the iron and zinc bioaccessibility, respectively. L. rhamnosus yoba jam had mean scores of 7.5, 7.0, 6.0, and 6.5 for spreadability, taste, appearance, and mouthfeel, respectively. The jam inoculated with L. rhamnosus yoba had an overall acceptance score of 7.5 (n = 120). The good chemical and functional properties of the fruit pulp resulted in the utilisation of the fruit pulp in producing a probiotic jam through the biotransformation of nutrients. The fruit jam was able to deliver 6.2 ± 0.2 log CFU/mL live L. rhamnosus yoba cells, which make it a good probiotic food with possible functional benefits

Novel methods of object recognition and fault detection applied to non-destructive testing of rail’s surface during production

A series of rail image inspection algorithms have been developed for Tata Steels Scunthorpe rail production line. The following thesis describes the contributions made by the author in the design and application of these algorithms. A fully automated rail inspection system that has never been implemented before in any such company or setup has been developed. An industrial computer vision system (JLI) already exists for the image acquisition of rails during production at a rail manufacturing plant in Scunthorpe. An automated inspection system using the same JLI vision system has been developed for the detection of rail‟s surface defects during manufacturing process. This is to complement the human factor by developing a fully automated image processing based system to recognize the faults with an improved efficiency and to allow an exhaustive detection on the entire rail in production. A set of bespoke algorithms has been developed from a plethora of available image processing techniques to extract and identify components in an image of rail in order to detect abnormalities. This has been achieved through offline processing of the rail images using the blended use of different object recognition and image processing techniques, in particular, variation of standard image processing techniques. Several edge detection methods as well as adapted well known Artificial Neural Network and Principal Component Analysis techniques for fault detection on rail have been developed. A combination of customised existing image algorithms and newly developed algorithms have been put together to perform the efficient defect detection. The developed system is fast, reliable and efficient for detection of unique artefacts occurring on the rail surface during production followed by fault classification on the rail imaging system. Extensive testing shows that the defect detection techniques developed for automated rail inspection is capable of detecting more than 90% of the defects present in the available data set of rail images, which has more than 100,000 images under investigation. This demonstrates the efficiency and accuracy of the algorithms developed in this work