Extraction and microencapsulation of polyphenol-rich antioxidant from Clinacanthus nutans for controlled release formulation

Clinacanthus nutans (Burm.f.) Lindau is a herbal medicine traditionally consumed in Malaysia. Since most of herbal active compounds are complex and may cause interferences in human body, a delivery system is needed to control the delivery of the active compounds. Thus, this study aims to develop controlled release formulated C. nutans polyphenolic-rich antioxidant extracts. Firstly, standardize C. nutans extracts were prepared by extracting the leaves and stem by different extraction methods and type of solvents. The extracts were characterized based on the content of polyphenol (TPC), total flavonoid (TFC), four composition of C-glycoside flavones (vitexin, isovitexin, orientin, isoorientin), total antioxidant capacity (TAC) and other chemical antioxidant compounds using gas chromatography–mass spectrometry. Then, C. nutans leaves extracts were encapsulated in carrier agent (κC/CMC) and spray dried to obtain extract in powder form. The feed flow rate, inlet drying temperature and coating agent concentration were studied and the physicochemical properties and release profile for uncoated and coated spray-dried C. nutans were characterized. The antioxidant capacity of spray-dried C. nutans was then predicted using artificial neural network (ANN) model. From the results, a standardized extraction was obtained from C. nutans leaves extract using decoction method with high extraction efficiency of polyphenolic compounds and antioxidant properties with TPC of 44.76 mg gallic acid equivalent (GAE)/g, TFC of 7.39 mg quercetin equivalent (QE)/g, TACDPPH : 16.29 mg trolox equivalent antioxidant capacity (TEAC)/g; TACFRAP : 29.58 mg TEAC/g of dried extracts. The C. nutans leaves extracts enriched with flavonoid glycosides : isoorientin (811.0 µg/mL) as major compound, followed by isovitexin (204.9 µg/mL), orientin (138.5 µg/mL) and vitexin (135.6 µg/mL). The C. nutans extracts was successfully encapsulated in κC/CMC by spray drying process at 700 mL/h of feed flow rate, 130 °C of inlet drying temperature and 0.5 % (w/v) coating agent concentration with high encapsulation yield (25.5 %) and high antioxidant capacities (TPC: 41.9 mg GAE/g, TFC: 11.6 mg QE/g, TACDPPH: 1.3 mg TEAC/g of spray-dried powders). ANN modeling was able provide satisfactory prediction for antioxidant capacities of spray-dried C. nutans with high correlation coefficient (R2) determination for TPC (R2: 0.8697), TFC (R2: 0.6562), vitexin (R2: 0.9543), isovitexin (R2: 0.9445), orientin (R2: 0.9586), isoorientin (R2: 0.8396), and total antioxidant activity (R2 = 0.8599). The spray-dried encapsulated C. nutans had smooth surface and spherical shape with small particle size range between 1.72 to 3.35 µm and good stability with surface charge of -42.6 and polydispersity index (PDI) of 0.45. The uncoated spray-dried C. nutans (control) exhibited wrinkle surface and irregular morphology with small particle size of 1.24 µm but poor stability with zeta potential value of -10.2 and PDI value of 0.86 which indicate that coagulation and flocculation will occur. The antioxidant release studies of coated spray-dried C. nutans in simulated gastric and intestinal fluids showed burst release of antioxidants in first 5 to 15 min and controlled release up to 240 min. In conclusion, the spray-dried C. nutans using κC/NaCMC microspheres is highly suitable for the formulation of herbal product

Optimization of sequential purification of beta-glucosidase from tricoderma reesei in aqueous two-phase system

A novel sequential technique was developed for the purification of a valuable enzyme, beta-glucosidase, from microorganism Tricoderma reesei. The fungus T. reesei produces cellulose degrading enzymes, called cellulases: beta-glucosidase, endo-glucanase and exo-glucanase and low molecular weight proteins. For specific applications, the enzyme must be separated from other contaminants. The sequential technique, that included affinity precipitation with chitosan followed by separation with an aqueous two-phase system (ATPS), was implemented for the purification of beta-glucosidase from the culture filtrate of T. reesei. The cultivation medium (nutrient) was optimized for the production of betaglucosidase from T. reesei cell culture. Treatment of the crude extract of T. reesei with chitosan resulted in the precipitation of endo and exo-glucanases. During this separation step, beta-glucosidase activity was completely recovered in the supematant. The enzyme was further purified from other proteins by partitioning in aqueous two-phase systems. Preliminary investigation with pure beta-glucosidase showed that the ATPS composed of PEG 4000, Potassium Phosphate salt and water is the best system for extracting the enzyme. The influences of system conditions, such as system pH and temperature, on the partition coefficients of beta-glucosidase and total proteins were evaluated in order to determine the most favorable condition for the purification of the enzyme from the culture filtrate. For the range of pH (6.0-7.5) and temperature (25-55 0C) studied, a positive correlation was obtained between these two variables and the partition coefficients. The development of reliable tools, that can predict equilibrium phase compositions and the partitioning behavior of the system components, is critical for protein purification in ATPS. Artificial Neural-Network models (ANN) offered a remarkable performance to predict equilibrium phase compositions and beta-glucosidase partition coefficients. In addition, the pilot plant study with the culture filtrate was carried out in a continuous two-stage counter-current aqueous two-phase extractor system. The pilot plant experiments demonstrated the feasibility of the continuous counter current extraction process of ATPS for large-scale purification of beta-glucosidase

The potential of waste sorghum (sorghum bicolor) leaves for bioethanol process development using Saccharomyces cerevisiae BY4743.

Masters Degree, University of KwaZulu-Natal, Pietermaritzburg.The limitations of first generation biofuels have prompted the quest for alternative energy sources. Approximately 60 million tonnes of sorghum are generated each year, with 90% being lignocellulosic waste, which is an ideal feedstock for biofuel production. The recalcitrance of lignocellulose often demands harsh pre-treatment conditions and results in the generation of fermentation inhibitors, negatively impacting process yields and economics. In this study, an artificially intelligent model to predict the profile of reducing sugars and all major volatile compounds from microwave assisted chemical pre-treatment of waste sorghum leaves (SL) was developed and validated. The pre-treated substrate was assessed for bioethanol production using Saccharomyces cerevisiae. Monod and modified Gompertz models were generated and the kinetic coefficients were compared with previous studies on different substrates. To develop the Artificial Neural Network (ANN) model, a total of 58 pre-treatment process conditions with varying parameters were experimentally assessed for reducing sugar (RS) and volatile compound production. The pre-treatment input variables consisted of acid concentration, alkali concentration, microwave duration, microwave intensity and solid-to-liquid ratio (S:L). Response Surface Methodology (RSM) was used to optimise RS production from microwave assisted acid pre-treatment of sorghum leaves, giving a coefficient of determination (R2 ) of 0.76, resulting in an optimal yield of 2.74 g RS/g SL. A multilayer perceptron ANN model was used, with a topology of 5-13-13-21. The model was trained using the backpropagation algorithm to minimise the net error value on validation. The model was validated on experimental data and R2 values of up to 0.93 were obtained. The developed model was used to predict the profile of inhibitory compounds under various pre-treatment conditions. Some of these inhibitory compounds were: acetic acid (0-186.26 ng/g SL), furfural (0-240.80 ng/g SL), 5-hydroxy methyl furfural (HMF) (0-19.20 ng/g SL) and phenol (0-7.76 ng/g SL). The developed ANN model was further subjected to knowledge extraction. Findings revealed that furfural and phenol generation during substrate pre-treatment exhibited high sensitivity to acid- and alkali concentration and S:L ratio, while phenol production showed high sensitivity to microwave duration and intensity. Furfural generation during pre-treatment of waste SL was majorly dependent on acid concentration and fit a dosage-response relationship model with a 2.5% HCl threshold. VI The pre-treated sorghum leaves were enzymatically hydrolysed and subsequently assessed for yeast growth and bioethanol production using Saccharomyces cerevisiae BY4743. Kinetic modelling was carried out using the Monod and the modified Gompertz models. Fermentations were carried out with varied initial substrate concentrations (12.5-30.0 g/L). The Monod model fitted well to the experimental data, exhibiting an R2 of 0.95. The model coefficients of maximum specific growth rate (μmax) and Monod constant (Ks) were 0.176 h-1 and 10.11 g/L respectively. Bioethanol production data fitted the modified Gompertz model with an R2 of 0.98. A bioethanol production lag time of 6.31 hours, maximum ethanol production rate of 0.52 g/L/h and a maximum potential bioethanol concentration of 17.15 g/L were obtained. These findings demonstrated that waste SL, commonly considered as post-harvest waste, contain sufficient fermentable sugar which can be recovered from appropriate HCl-based pre-treatment, for use as a low cost energy source for biofuel production. The extracted knowledge from the developed ANN model revealed significant non-linearities between the pre-treatment input conditions and generation of volatile compounds from waste SL. This predictive tool reduces analytical costs in bioprocess development through virtual analytical instrumentation. Monod and modified Gompertz coefficients demonstrated the potential of utilising sorghum leaves for bioethanol production, by providing data for early stage knowledge of the production efficiency of bioethanol production from waste SL. The generated kinetic knowledge of S. cerevisiae growth on waste SL and bioethanol formation in this study is of high importance for process optimisation and scale up towards the commercialisation of this fuel.Only available in English

Effects of Drinking Water Treatment Processes on Removal of Algal Matter and Subsequent Water Quality

Seasonal algal blooms in drinking water sources have increased significantly over the recent past as a result of increased temperature and nutrient loading in surface water due to agricultural and surface runoff. More than 95% of algal cells can be removed by coagulation and flocculation processes. However, algal organic matter (AOM) is not removed well during coagulation, thus causes several operational challenges in drinking water treatment. This research was conducted to investigate the effectiveness of coagulation, granular activated carbon adsorption, and filtration processes on AOM removal and to evaluate disinfection by-products formation potential with/without UV irradiation. Initially, coagulation performance for the treatment of algae-laden raw water was investigated systematically by central composite design using response surface methodology. The main mechanism of algae and AOM removal was charge neutralization at an optimum pH of around 6.0. Thereafter, the optimum coagulation conditions using alum for AOM of six different algal and cyanobacterial species were determined. The AOM removal by coagulation correlated well with the hydrophobicity of the AOM solution. The disinfection by-product formation potential of the AOM due to chlorination was determined after coagulation. The efficiency and mechanism of AOM removal by granular activated carbon (GAC) adsorption were determined by batch adsorption experiments. The adsorption equilibrium data followed both Langmuir and Freundlich models. The adsorption process followed a pseudo-second-order kinetic model, and the calculated thermodynamic parameters indicated that GAC adsorption for AOM removal was spontaneous and endothermic in nature. The fouling behavior of the microfiltration membranes after GAC adsorption pre-treatment was investigated and the filtration resistance and AOM removal efficiency were determined. The GAC adsorption increased the removal of AOM, decreased membrane fouling, and identified intermediate blocking as the major fouling mechanism of the membrane. The effects of combined low-pressure ultraviolet (LPUV) irradiation and chlorination on the disinfection byproducts (DBPs) formation from AOM was investigated for common algae existed in surface water, AOM degradation was likely promoted by photodegradation of aromatics, and chlorine oxidation/substitution. Insights obtained of this work will help in properly designing and operating the AOM removal and reducing DBPs formation during water treatment of algae-laden source water

Diagnostic strategy and risk assessment framework for complex chemical mixtures

Environmental contamination comprises a complex mixture of both organic and inorganic contaminants. Understanding their distribution, behaviour and chemical interactions provides the evidence necessary to make informed decision and implement robust remediation strategies. However most of the current risk assessment frameworks, used to manage land contamination, are based on the total contaminant concentration rather than the concentration likely to pose significant risk, the bioavailable concentration. Further to this, the exposure assessments embedded within the frameworks do not explicitly address the partitioning and bioavailability of chemical mixtures. This inability may contribute to an overestimation of both the eco-toxicological effects of the fractions and their mobility in air and water; leading to an overestimation of health and environmental effects. In turn, this may limit the efficacy of the risk assessment frameworks to inform targeted and proportionate remediation strategies. The aim of this PhD study was to address this gap by delivering an integrated risk assessment framework for sites contaminated with complex chemical mixtures. Specifically, this PhD study investigated the fate and behaviour of complex mixtures of petroleum hydrocarbons, metals and metalloids in soils and its implication for partitioning, bioavailability and risk assessment through a 12 month mesocosms study. Further to this, an integrated approach, where contaminants bioavailability and distribution changes along with a range of microbiological indicators and ecotoxicological bioassays, was used to provide multiple lines of evidence to support the risk characterisation and assess the remediation end-point over a 6 month study. From the empirical data obtained from the two mesocosm studies, two Machine Leaning (ML) approaches have been developed to provide a quick and reliable tool to assess multi-contaminated sites with Visible and Near-Infrared Spectroscopy (Vis-NIRS), and to predict bioavailability and toxicity changes occurring during bioremediation. Overall this PhD study shed light on the behaviour of bioavailability, and toxicity of complex chemical mixtures in soils genuinely contaminated. This was supported through a comprehensive and integrated analytical framework providing the necessary lines of evidence to evaluate the implications for risk assessment and identify the end point remediation. The developed framework can significantly help to identify optimal remediation strategies and contribute to change the over-conservative nature of the current risk assessments

Detecting and monitoring the development stages of wild flowers and plants using computer vision: approaches, challenges and opportunities

Wild flowers and plants play an important role in protecting biodiversity and providing various ecosystem services. However, some of them are endangered or threatened and are entitled to preservation and protection. This study represents a first step to develop a computer vision system and a supporting mobile app for detecting and monitoring the development stages of wild flowers and plants, aiming to contribute to their preservation. It first introduces the related concepts. Then, surveys related work and categorizes existing solutions presenting their key features, strengths, and limitations. The most promising solutions and techniques are identified. Insights on open issues and research directions in the topic are also provided. This paper paves the way to a wider adoption of recent results in computer vision techniques in this field and for the proposal of a mobile application that uses YOLO convolutional neural networks to detect the stages of development of wild flowers and plants