http://hdl.handle.net/123456789/1092 2026-04-14T23:26:46Z http://hdl.handle.net/123456789/12184 Development of Gluten-Free Noodles Based on Cassavabambara Groundnut Composite Flour Bassey, Akwetey, Ebenezer Noodles are now a common food in the World, eaten by many from different cultural backgrounds and races. However, noodles made from wheat are often seen as less healthy. As people become more health-conscious, researchers are experimenting with different ingredients to make noodles that are healthier, gluten-free, and meet changing food preferences. The study was centered on the development of gluten-free noodles from Casava-Bambara groundnut composite flour and the determination of the functional, textural, colour, sensory and nutritional composition of the resulting noodles. A simplex centroid mixture design in Minitab software was used for the formulations with the two mixture variables: Cassava flour [X1 (% w/w)] and Bambara groundnut flour [X2 (% w/w)] equaling 100 %. The ash, protein and fibre contents significantly increased (from 0.84 % to 1.52 %, 4.16 % to 7.27 % and 3.01 % to 4.48 % respectively) whereas the moisture and carbohydrate contents decreased (from 8.35 % to 7.35 and 87.23 % to 82.84 % respectively) as the percentage of Bambara Groundnut Flour increased. In addition, zinc, calcium, magnesium, iron, and potassium for Bambara Groundnut Flour incorporated noodles increased. Cooking losses were also minimal for formulations containing Bambara groundnut flour compared to the noodles prepared from 100 % Cassava Flour. Noodles with 30 % Bambara Groundnut Flour was the most preferred after the sensory evaluation. The results are relevant for utilizing Bambara groundnut as an ingredient in gluten-free noodle production as its addition improved the functional properties, nutritional composition and sensory attributes. xii, 85p:, ill. 2024-08-01T00:00:00Z http://hdl.handle.net/123456789/12175 Assessment of the Physicochemical and Microbial Quality of Groundnut Pastes from Major Markets in the Central Region of Ghana and Prediction of Groundnut Paste Adulteration Using Portable Handheld Nir Spectroscopy with a Mobile Phone Welbeck, Joel Groundnut paste safety and quality is of great concern to consumers due to potential contamination and adulteration which poses serious health risk. This study investigated the safety and quality of groundnut paste using wet chemistry standard method as well as develop a novel application of hand-held NIR spectrometry coupled with chemometrics for the examination of groundnut paste authenticity and quality in real time. Samples were collected within the major markets in the Central region (Mankessim, Kotokuraba, Twifo Praso, Swedru and Kasoa). The authenticity of groundnut paste was evaluated through a physicochemical analysis and fungi count were also determined. A handheld near-infrared spectrometer was used to predict the presence of cassava flour and roasted maize flour at different percentage purity. Among the pre-processing methods used to ensure the quality and accurately of the final analysis, standard normal variant (SNV) was found to be superior. Principal component analysis (PCA) was used to extract relevant information from the spectral data set and the results showed that groundnut paste samples of different categories could be clustered. The performance of the Support Vector Machine (SVM) model shows strong predictive capabilities, with R² values of 0.9751 for cassava flour and 0.9753 for roasted maize flour in the training phase, indicating that it explains a substantial portion of the variance in the data. Most of the groundnut paste samples examined showed low contamination of fungi ranging from 1.60 – 2.48 log10CFU/g. The current study showed that NIR spectroscopy can classify and determine groundnut paste adulterated with cassava flour and roasted maize flour. xiv, 132p:, ill. 2025-02-01T00:00:00Z http://hdl.handle.net/123456789/12131 Development and Evaluation of Crop Residue Shredder Osei, Seth The study aimed at developing and evaluating a crop residue shredder. The components of the machine include a frame, hopper, shafts, electric motor, blades, sieve, spur gear, and outlet, to convert crop residues into a smaller form. The performance of the developed machine was evaluated using maize, millet and sorghum stalk, as feeding materials, with varying speeds of 55 rpm, 110 rpm and 220 rpm. The machine achieved a maximum shredding efficiency of 82% for maize stalk residues passing through a 20 mm diameter sieve, and the shredding speed was 220 rpm. The maximum throughput capacity attained was 14.6 kg/h of maize stalk at 220 rpm speed, while the minimum throughput was 5.14 kg/h of sorghum stalk at a speed of 55 rpm. The optimization of shredding speeds shows significant energy savings, as demonstrated by reduced power consumption at higher speeds from 0.20 kWh to 0.10 kWh for maize and 0.25 kWh to 0.10 kWh for millet when speeds increase from lower to higher rpm. The particle size distribution of crop residues depends on shredding speed, with lower speeds (55 rpm) producing larger particles, medium speeds (110 rpm) creating balanced distributions ideal for composting, and higher speeds (220 rpm) generating finer particles for rapid decomposition. The machine is user-friendly to small scale farmers and could be useful for carrying around relevant residue size reduction operations in agriculture in Ghana and other Sub-Saharan Africa countries. Keywords: Crop residues, shredder, shredding efficiency, Agricultural machinery. xx, 134p:, ill. 2024-12-01T00:00:00Z http://hdl.handle.net/123456789/12095 Assessing the Influence of Tillage on Maize Performance Using Unmanned Aerial Vehicle Imagery Hans, Murangaza Fumba Maize is a staple food in Sub-Saharan Africa, and tillage is widely used to boost its yield, though it affects soil and the environment both positively and negatively. To support farmers and policymakers, a data-driven approach using UAV technology was introduced. This study was conducted for two seasons in a randomized complete block design with four treatments (Harrowing only, Ploughing only, Ploughing and Harrowing, and No-tillage). The results showed that No-tillage had the lowest growth parameters, while Ploughing and Harrowing recorded the highest in terms of LAI (1.50–1.75), stem diameter (20–22.5 mm), plant height (165–175 cm), and yield (7.20–10.93 t/ha biomass, 4.619–5.67 t/ha grain yield). Despite its lower yields, No-tillage showed the highest yield improvement (+1.11 t/ha). UAVs imagery with Yolov8-small achieved high germination rate detection (mAP50: 0.89–0.95) and accurate plant height estimation (RMSE < 7 cm, R²: 0.98–0.99). For LAI estimation, UAV technology coupled with Huber regression model achieved R² scores of 0.80– 0.94 and RMSE as low as 0.14, and coupled with Gradient Boosting Machines reached R² of 0.87 and RMSE of 0.281 t/ha at the vegetative stage for Yield prediction. Ploughing and Harrowing is recommended for short-term tillage, while No-tillage is better for the long term. UAV imagery with machine learning reliably monitors maize and predicts yield. Future research should explore the long-term effects of No-tillage, UAV-based stem girth estimation, and the cost-benefit of UAV adoption in small-scale farming. Keywords: Tillage, UAV technology, maize, yield prediction, maize. xiv 142p:, ill 2024-12-01T00:00:00Z