Faculty of Science, Engineering and Technology (FSET)

Permanent URI for this communityhttps://repository.chuka.ac.ke/handle/123456789/156

Browse

Now showing 1 - 20 of 54

A hybrid deep learning model for intrusion detection in cloud-based implantable medical devices
(Chuka University, 2015) Kirimi James
The rapidly evolving technologies in the healthcare sector, such as implantable medical devices (IMDs), require advanced security solutions that leverage the intelligence capabilities of these technologies while ensuring optimal safety and reliability. The IMD technology redefines healthcare service delivery by offering timely interventions, minimally invasive treatment options, and continuous patient condition monitoring to improve quality of life. Despite these achievements, IMDs face unauthorised access, data manipulation, and denial-of-service attacks, which conventional security solutions are limited in handling due to resource constraints within IMD ecosystems. As a result, different machine learning and deep learning frameworks have been proposed for real‐time threat detection. However, they still suffer from overfitting, slow inference, and excessive resource demands, hindering their effective integration into the IMD ecosystem. The study's primary goal was to design and develop a hybrid of deep autoencoders, convolutional neural networks, and long short-term memory (LSTM) strategies to provide a comprehensive detection model that reduces inference time for deployed models while enhancing performance. Autoencoders provide the fundamental architecture of the detection model, while convolutional neural networks are used in the encoder and decoder for simplicity and to capture nonlinear data effectively. The Long Short-Term Memory captures temporal dependencies in the model, enhancing overall detection capabilities. The study adopted an experimental approach, developing a hybrid deep autoencoder model to test its performance against convolutional neural networks, long short-term memory, and other conventional machine learning techniques. The results demonstrate that the hybrid model outperformed standalone models, achieving high accuracy scores across the datasets. The best model in the ICU dataset achieved 100% accuracy, precision, recall, and F1 score, and a false positive rate of 0.00%. The WUSTL had an accuracy of 79.32%, a recall of 79.92%, a precision of 79.41%, a specificity of 79.24%, and a false positive rate of 20.59%. The Edge IIoT dataset had a recall, F1, and accuracy of 96.87%, a precision of 96.94%, a specificity of 96.88%, and a false-positive rate of 3.12%. The model’s inference time was substantially reduced compared to the standard deep autoencoder model across the datasets, providing a lightweight detection environment for the intrusion detection system.
A hybrid of deep auto-encoder and feature embedding model for an e-commerce recommender system
(Chuka University, 2024) Ireri Justin Murithi
Recommender systems aim to predict user interests and suggest products that are likely to be of interest. These systems are widely used across various platforms, including online shopping, streaming services, and music stores, to provide personalized suggestions. Traditional machine learning-based models, such as collaborative filtering and content-based algorithms, often face challenges like low accuracy, data sparsity, and the cold start problem. The cold start problem occurs when a system lacks sufficient data to make accurate recommendations for new users or items. This study specifically focuses on addressing the visitor cold start problem, where the system does not have prior information about the new user’s preferences or behavior, making personalized recommendations difficult. To address this issue, a model was developed using deep auto-encoders integrated with feature embedding (DAE-FE), designed to improve item prediction accuracy for new users in an e-commerce recommender system. The model introduces an embedding layer after the dropout layer in the deep neural network, which automatically captures user data points such as time and location. These data points help in constructing a user profile necessary for prediction. This feature not only improves the accuracy of item predictions but also speeds up the process by filling in missing data for new users, allowing the system to proceed directly to prediction. An experimental research design was employed to compare the performance of the developed model with previous models that relied solely on provided datasets. In the experiment, user location and time of login were used as independent variables, while model accuracy served as the dependent variable. The model was trained and tested using the MovieLens 100k dataset, which was adapted to meet the requirements of the DAE-FE model. The hybrid model achieved a mean squared error of 0.0241 and a root mean squared error of 0.1443, indicating minimal deviation from the actual values. As a result, the model attained approximately 96% accuracy in predicting recommendations for cold start users. Overall, the model demonstrated strong performance and appears to be a promising solution for the cold start problem in ecommerce systems. The research found that incorporating more side information from users and items on the dataset during the model's training will yield more accuracy in item prediction.
AB INITIO STUDY OF STRUCTURAL AND PIEZOELECTRIC PROPERTIES OF HAFNIUM DOPED BISMUTH SODIUM POTASSIUM TITANATE
(Chuka University, 2023-10) MWANZIA BONFACE MUTUKU
Piezoelectric materials have gained increased attention in the recent times due to their significant technological applications. These materials are widely used to make ultrasound transducers, sensors, actuators and others are used for energy harvesting. Due to its brilliant piezoelectric properties, Lead Zirconate Titanate (PZT) is mostly used with a piezoelectric constant of 𝑑33 = 374 𝑝𝐶/𝑁 from experimental reports and 306 − 314 𝑝𝐶/𝑁 from theoretical studies. However, due to the toxic nature of lead oxide which is formed when PZT is being manufactured, there is increased effort in development of lead-free materials. Several classes of materials have recently been studied and are now being considered as potential alternatives to PZT. Lead free perovskite systems such as Bismuth Sodium Potassium Titanate (BNKT) have been developed, with a piezoelectric constant 𝑑33 = 157 𝑝𝐶/𝑁 . However, the main drawback of this system is that it is highly corrosive and has a low piezoelectric constant compared to PZT. In the quest to provide suitable alternatives, dopants such as zirconium have been used, which improved the piezoelectric constant of BNKT up to203 𝑝𝐶/𝑁. Hf which possesses similar physico-chemical properties as zirconium has led to an improvement in the piezo electric constant of other piezoelectric systems such as in hafnium doped Barium Titanate (BT). It has an added advantage of being extremely resistant to corrosion, which is expected to mitigate the corrosive nature of BNKT. In this study, hafnium has been incorporated in BNKT so as to engineer an alternative material suitable for piezoelectric applications. Density Functional Theory (DFT) method was used to predict the structural and piezoelectric properties of hafnium doped BNKT, starting with those of Bismuth Sodium Titanate (BNT) and BNKT. The exchange and correlation was taken as the Generalized Gradient Approximation (GGA). The optimal lattice parameters for BNT were found to be 𝑎 = 5.57 Å and 𝑐/𝑎 ratio of 2.50 for the conventional cell, having space group R3c space group number 161. Piezoelectric constant for this system was found to be 97.67 pC/N. This structure was adopted for doping and further calculations. Potassium doped bismuth sodium titanate was modelled using VESTA software and its optimized lattice parameter was found to be 𝑎 = 5.60 Å. Piezoelectric constant for this system was found to be 147.42 pC/N. Hafnium doped BNKT had an improved piezoelectric constant of 205.52 pC/N for 3% hafnium doping, which decreased to 163.22 pC/N at the level of 6% doping. The results shows that small amounts of hafnium improved the piezoelectric constant of BNKT from 147.42 pC/N to 205.52 pC/N. Elastic and elastic compliance full tensors for these systems was also generated with elastic constants of C33 = 286.48 Gpa, 282.13 Gpa, 257.193 Gpa and 276.43 Gpa for BNT, BNKT, 3% Hf doped BNKT and 6% Hf doped BNKT respectively. This study concludes that doping BNKT with hafnium indeed improves the piezoelectric properties of BNKT. This makes this material more useful in energy generation since high piezoelectric constant leads to efficient mechanical – electrical energy conversion in the piezoelectric materials.
An efficient detection model of zero-day web application attacks based on convolution neural networks and deep auto encoders
(Chuka University, 2024) Tuei Kevin Kirui
The need for secure and trustworthy information systems has taken center stage and proven critical in supporting teleworking, online teaching, and research services. Artificial Intelligence (AI) is the primary driver of the 6th generation of computing, and innovations with applications of AI in computer vision, gaming, robotics, and security. Zero-day web application attacks take advantage of web application software weakness for as long as the developer is unaware and has not developed a mechanism to eliminate the weakness. Zero-day attacks leave vulnerable users grappling with data loss and have the propensity to push an organization out of business. Current zero-day attack detection methods built on signature-based or anomaly-based methods are inefficient in combating these attacks since they rely on previously detected weaknesses for signatures and a deviation from normal behavior for anomaly detection. These methods result in detection rates below 80%, meaning the propensity of Zero-day attacks going undetected is 20% or lower. The application of machine learning techniques has proven to be efficient because these techniques can continuously learn from the code as well as its execution to detect security breaches and trigger an alarm. With the need to improve these techniques, a novel classification model needs to be developed to increase the detection rate further and reduce the false alarm rate. This study applied a hybrid of two machine learning methods, Convolution Neural Networks and deep autoencoders, to develop a classification model that significantly increases the detection rate of zero-day attacks. The KDD'99 Dataset is a comprehensive repository of fully labeled intrusion detection records that was used to develop, test and validate the model. This dataset simulated real-world scenarios and assessed the model's performance under different intrusion scenarios. The Average Detection Rate, Accuracy and F1 score metrics were used to evaluate the model. The hybrid CNN-Deep Autoencoder model had a detection rate of 0.895 against 0.887 of the Fully Connected Network (FCN) with sampling and 0.885 of the pure CNN model. The accuracy and F1-score of the hybrid CNN-Deep Autoencoder were 0.973 and 0.971 respectively. The Hybrid Model of CNN and Deep Autoencoder is efficient in detecting Zero-Day Attacks making it possible for Software Developers to patch their systems sooner resulting in minimal dwell time.
An enhanced convolutional neural network model for translating Kenyan sign language into text in english
(Chuka University, 2024) Muthui Nancy Njoki
Most people communicate effectively and socialize through verbal means, such as talking. However, mute and deaf people cannot interact with society through speech. So, they use the non-verbal modes of communication. Non-verbal communication is a sort of usual body movements, hand gestures, and facial expressions like sign language, and this needs translation according to the specific patterns that the gestures and facial expressions or positioning of the hands, fingers, and arms carry with them during sign language. While it bridges a gap between those who can hear and those who cannot, it is by no means universally comprehended, thus standing as a barrier that leads to frustration and social exclusion of deaf people. As such, a translation tool may help convert sign language into easily understandable written language that will facilitate smooth communication between hearing and hard-of-hearing persons. While lots of research is going on in the area, little attention has been given to translating Kenyan Sign Language into some of the commonly spoken languages in Kenya. Besides, most translation tools face several challenges due to changing environmental conditions and the movement of a person while performing sign language, leading to changes in background lighting. This work translates KSL into English text through the experimental approach using a deep learning CNN model, DenseNet121, preprocessed by Contrast-Limited Adaptive Histogram Equalization. This architecture has been developed, trained, and tested on the dataset provided by the Kenyan Sign Language Classification Hackathon with an accuracy of 91.5%. The proposed model will bridge communication gaps and help include people who are hard of hearing in educational, health, and employment opportunities.
ANALYSIS OF PRODUCTION, SOCIO-ECONOMIC AND INSTITUTIONAL FACTORS AFFECTING TECHNICAL EFFICIENCY OF SMALLHOLDER BANANA PRODUCERS IN KIRINYAGA CENTRAL SUB-COUNTY, KENYA
(Chuka University, 2023-10) MATIVA JACKSON MKENYE
Banana provides food, nutrition security and income for most households and is fourth most popular food crop in the world after wheat, maize and rice. Despite its significance, full potential of banana production in Kenya remains unexploited by smallholder producers. This is as a result of low technical efficiency especially in utilization of farming inputs and producer specific factors like production, socio-economic and institutional factors among others. In Kirinyaga County, the actual banana production is at 4-18 tonnes per acre against the potential of 30-40 tonnes. Due to the limited supply of resources for production, attainment of highest possible levels of technical efficiency is key to achieving sufficiency in banana farming. This study aimed at analyzing the effects of production, socio-economic and institutional factors on technical efficiency of smallholder banana producers in Kirinyaga Central Sub-County, Kenya. The study used a cross-sectional research design and targeted a population of 24,440 smallholder banana producers. Multistage sampling technique was employed where purposive sampling and simple random sampling methods were used in some stages to sample respondents in the study area. A sample of 402 smallholder banana producers were selected. Using a questionnaire, primary data on production, socio-economic and institutional factors affecting technical efficiency of banana production was collected. The data was then analyzed using Stata version 17 and SPSS version 25. Descriptive statistics were used to describe the production, socio-economic and institutional factors of the smallholder banana producers. A stochastic frontier analysis approach was used to model the technical efficiency level using the Cobb-Douglas function. The stochastic production function of the Cobb Douglas function was estimated using the maximum likelihood estimation technique. The study showed that the level of banana production technical efficiency among the smallholder producers varied between 0.9% to 95.5% and average technical efficiency of 83.1%. According to the model parameters calculated, land set aside for production of banana, banana suckers and agrochemicals were significant production factors in banana cultivation at 5% significance level. The study found that agrochemicals and planting materials had positive effects on technical efficiency whereas land size had a negative impact on technical efficiency. This implied that increasing the amount of land set aside for banana production by an acre reduces the amount of banana harvested by 0.438 kgs while increasing the amount of planting materials and agrochemicals used by one unit increases banana production by 1.315 and 0.155 kgs, respectively. The study found that decision makers’ age and size of the household had negative effects on banana production technical efficiency whereas education, experience, producer group membership and market access had positive effects. The study recommends people with high levels of education to venture into banana production. In addition, producer group formation and membership be encouraged for the benefit of increasing technical efficiencies. The inefficient producers are advised to increase their present output by enhancing technical efficiency as a result of the inefficient utilization of their resources throughout production.
APPLICATION OF QUEUING THEORY FOR OPTIMAL CUSTOMER CENTRICITY TO THE BANKING SECTOR IN KENYA
(Chuka University, 2023-10) JUMA SAMWEL KISIANG’ANI
Long queues and waiting times are common in banks, resulting in customer dissatisfaction and low customer retention. The study applied a descriptive research design to investigate queuing dynamics in a banking hall at a commercial bank in Kenya. A single server system (M/M/1) queuing model was used to estimate the average waiting time, system intensity, service time, and optimal number of staff during peak and off-peak periods (July). The study used secondary data on daily waiting times, service times, the number of customers, and servers for May and July 2019, 2020, and 2021 during working hours between 8.30 a.m. and 4 p.m. on Monday to Friday and 8:30 a.m. and 12 p.m. on Saturdays. Data analysis was done using R and Excel. The research findings indicated that the peak periods (May) recorded an average waiting time (AWT) of 13 minutes, 35 seconds in 2019, 10 minutes, 14 seconds in 2020, and 8 minutes, 36 seconds in May 2021. In the off-peak periods (July), an AWT of 3 minutes, 46 seconds, was registered in 2019, 5 minutes, 12 seconds in 2020, and 7 minutes, 42 seconds in 2021. An average service time (AST) of 1 minute 52 seconds in May 2019, 2 minutes 34 seconds in May 2020, and 2 minutes 27 seconds in May 2021. In the off-peak periods (July), an AST of 3 11 seconds was registered in 2019, 3 4 seconds in July 2020, and 2 43 seconds in July 2021. Overall, the system intensities are low to moderate, with the COVID-19 pandemic severely impacting the peak period more than the off-peak. In the peak periods, the service rates averaged 33, 24, and 25 persons per hour in May 2019, May 2020, and May 2021. The respective system intensities were 0.534, 0.360, and 0.492. In the off-peak periods, the average service rates were 19, 20, and 23 persons per hour in July 2029, July 2020, and July 2021. The respective associated system intensities of 0.535, 0.461, and 0.487. From the pooled data for 2019 and 2021, the study recommends that banks operate with an AWT of 6 minutes, 24 seconds, and an AST of 3 minutes. Further, the study established that a bank could work with an optimal four servers with an AST of 2 minutes, 35 seconds (a service rate of 20 people per hour), and achieve a moderate average service intensity of 0.552.
ASSESSMENT OF QUALITY OF SHALLOW WELLS WATER IN CHUKA SUB-COUNTY, KENYA
(Chuka University, 2020-12) Rugendo, Edith Mwende
Groundwater from shallow wells is an important source of water for domestic and agricultural uses in Chuka Sub-County. However, most shallow wells in Chuka Sub-County are undocumented because approval is not required to sink a shallow well. Groundwater from these shallow wells is therefore susceptible to contamination by both geogenic and anthropogenic sources. This study was conducted to determine the quality of shallow wells water in Chuka Sub-County. Samples were collected from twenty shallow wells in Chuka Sub-County during the dry and wet seasons. The temperature, pH and conductivity were determined in situ using a conductivity meter. The concentration of cations in the water samples was determined using an Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) system. The concentration of chloride and nitrate ions in the water samples were determined using the argentometric and the spectrophotometric method, respectively. The concentration of sulphate ions was determined using a turbidimetric method while the concentration of phosphate ions was determined using a colorimetric method. The data obtained were subjected to ANOVA to test the significance differences using R-3.5.2. Mean comparison was achieved through LSD. The temperatures of the waters were significantly higher during the dry season. The pH of water in most shallow wells during the wet season was within the limits set by KEBS and WHO. However, shallow well water at Karandini (T15) was highly acidic (pH of 3.95) during the wet season. During the dry season, the shallow well waters were slightly acidic (4.42 - 6.44) except for the shallow wells at Ndagani market (T11), whose water was alkaline (pH = 8.75). The shallow wells water at site T11 had remarkably higher conductivities than the other shallow wells in the study area during both the wet (1150 μs/cm) and the dry (1208 μs/cm) seasons. The concentrations of macro-cations (Ca2+ and Mg2+) were higher during the dry season. The concentrations of the trace and toxic cations differed significantly across sites and seasons but were within the limits set by KEBS and WHO. The concentrations of anions (NO3-, SO42-, Cl- and PO43-) were within the limits set by KEBS and WHO for portable water. Groundwater from most shallow wells in Chuka Sub-County is generally safe for domestic uses but regular monitoring for quality is recommended because, the concentration of nitrate in several wells during the dry season was within the range that causes chronic health effects including colorectal, ovarian, thyroid, kidney and bladder cancers.
CHARACTERIZATION AND SCREENING OF ANTIBACTERIAL PROPERTIES OF Actinomycetes FROM RIVER TANA AND LAKE ELEMENTAITA, KENYA
(Chuka University, 2023-04) SHIKUKU BONFACE OLOO
The increased prevalence of bacterial infections has been a major challenge to human with devastating high mortality and morbidity rates. This situation has been worsened by increasing antibiotic resistant strains of pathogenic bacteria, reduced effectiveness of antibiotics in the market, and the emergence of new bacterial infections. This study aimed at identification of antibacterial Actinomycetes species using biochemical and molecular methods, screening for their antibacterial secondary metabolite and determination of effect of pH, fructose, sucrose, urea and sodium nitrate on their antibacterial activities. The experiments for this study was laid out in Complete Randomized Design and replicated thrice to determine the difference between the inhibition zones (mm) of isolates against the tests organisms and effects of different levels of pH, sucrose and fructose on antbacterial properties of isolates. The resultant data ( zones of inhibition in millmetres) was analysed using One Way Analysis of Variance and Kruskal Wallis test in SAS version 9.4. A total of six antibiotic producing Actinomycetes species were isolated from river Tana and lake Elementaita and identified through morphological, biochemical and molecular methods.There was a significant (p<0.05) different antibacterial activity of Actinomycetes isolates against Staphylococcus aureus, Salmonella typhi and Escherichia coli. The thin layer chromatography profiling for secondary metabolites in extracts revealed a total of 13 different spots with each having a unique retardation factor. The GC-MS analysis of the extracts revealed 140 different metabolites which have been documented to have antibacterial properties from the six Actinomycetes isolates. There was a significant (p<0.05) effects of different levels of pH and concentration of fructose, urea and sodium nitrate on the antibacterial activity of Actinomycetes isolates against Escherichia coli. The study has revealed different secondary metabolites in unique combinations across the six Actinomycetes isolates with antibacterial activities against Staphylococcus aureus, Salmonella typhi and Escherichia coli. The findings of this study can help in developing new or alternative antibiotics that can be used for treatment of pathogenic and resistant bacteria.
Characterization of clay samples from Murang’a, Nyeri, Embu and Tharaka Nithi Counties for adsorption of cadmium for water purification
(Chuka University, 2025) Thuo Maryrose Wandia
Heavy metal pollution, particularly cadmium (Cd² ), remains a critical environmental challenge threatening water quality, aquatic ecosystems, and human health. Cadmium is widely introduced into aquatic systems through industrial effluents, agricultural runoff, and urban discharges. This study investigated the adsorption potential of locally available clay minerals as cost-effective and sustainable remediation materials. Clay samples were collected from Gakoigo (S), Mukurwe-ini (2A) Gakindu K1, Karurina (K2) and Mbogoni (M). The clay samples were characterized using Atomic Absorption Spectroscopy (AAS), X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). The results from elemental analysis and ANOVA revealed difference in concentrations of iron, magnesium, sodium, and aluminum across the counties, explaining the calculated F-statistics 2.83 (p = 0.048), 3.01 (p = 0.035), 4.51 (p = 0.020), and 5.22 (p = 0.011), respectively. These constituents are vital as they assist, particularly, the samples from Mbogoni (M) and Gakoigo (S), in boosting cadmium (Cd²) adsorption. On the contrary, calcium and potassium were seen to have less influence, evidenced by their F-statistics of 2.45 (p = 0.068) and 2.13 (p = 0.080), respectively. Physicochemical water quality analysis was done from 12 rivers sites within Nairobi County, Kenya. Turbidity ranged between 2.75–95.67 NTU, exceeding WHO’s 5 NTU guideline in urban rivers due to runoff and effluent discharges. Electrical conductivity (556–1123 µS/cm) surpassed WHO limits across all sites, confirming high ionic loading, while dissolved oxygen (0.51–3.06 mg/L) was critically low, pointing to severe organic pollution. Total suspended solids (12–247 mg/L) and TDS (362–736 mg/L) were elevated in urbanized sites, further degrading aquatic health. Cadmium concentrations (0.0105–0.0498 mg/L) consistently exceeded WHO (0.003 mg/L) and KEBS (0.01 mg/L) standards, with highest levels in industrially impacted rivers, highlighting risks of bioaccumulation and human exposure. Batch adsorption studies demonstrated that pH was a key determinant, with maximum efficiency at neutrality (pH 7, 99.81% removal by K1), while acidic conditions reduced removal due to proton competition. Contact time experiments showed rapid uptake within 20 minutes, with equilibrium achieved at 40 minutes (69.16% removal by K1). Adsorbent dosage showed optimum performance at 0.1 g (99.72% removal at 8 ppm), though higher dosages reduced efficiency due to particle aggregation. Desorption confirmed strong Cd binding, indicating chemisorption via ion exchange and surface complexation. Agitation improved uptake by minimizing mass transfer resistance, with peak efficiency (92.29% by K1) at 400 rpm. Temperature exerted a negative effect, with maximum adsorption at 25 °C (97.52% by K1), confirming exothermic behavior. Isotherm modeling revealed Langmuir’s model (Qmax = 2.06 mg/g, KL = 5.44 L/mg, R² = 0.77) better fit the data compared to Freundlich (R² = 0.67), suggesting monolayer adsorption on homogeneous sites. Kinetic modeling indicated pseudo-second order (R² = 0.8496) best described the process, implying chemisorption. Thermodynamic evaluation showed that cadmium adsorption on the clay adsorbent was endothermic, with a positive enthalpy change (ΔH = 17,936.42 J/mol) and a positive entropy change (ΔS = 64.18 J/mol·K). Gibbs free energy (ΔG) remained negative across all temperatures (ΔG = 1.21 to -1.27 kJ/mol), confirming spontaneous adsorption at higher temperatures.
A CONVOLUTIONAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES HYBRID MODEL FOR NUMBER PLATE RECOGNITION
(Chuka University, 2022-03) Kibaara, Peter
ABSTRACT Automatic Number Plate Recognition (ANPR) systems are applied in many fields such as automatic electronic toll collection, car park management and access control, logistics and vehicle tracking, traffic law enforcement and crime resolution amongst others. Motion blur, plate orientation, lighting changes and image noise severely lower the detection speed and recognition accuracy of these systems. The incorporation of machine learning algorithms in ANPRs has seen Convolutional Neural Network (CNN) being used to develop ANPR models with improved performance in license plate detection. CNNs are best suited for image data where the number of features is large such as license plate detection. This is attributed to their design architecture which enables them to perform feature extraction automatically. However, their speed of execution is slow as the model has to learn a lot of features. Support Vector Machine (SVM) is a supervised machine learning algorithm suitable for classification and regression problems with datasets that have a small number of features. It doesn’t scale up well for large datasets with many features. It has demonstrated high speed and accuracy when used for classification in small datasets such as character recognition. The final stage in ANPR is a character recognition phase and involves few features. These two algorithms have been deployed independently, however the concept of combining the two algorithms for ANPR models remains highly unexplored. The research therefore combines the two models (CNN and SVM) to come up with an efficient hybrid ANPR system with improved number plate recognition accuracy. The two models were developed using a deep cascade framework; a CNN with a SoftMax classifier and a hybrid CNN with a SVM classifier. The Universidade Federal do Paraná (UFPR-ALPR) dataset was used to train validate and test the models. Recognition accuracy, precision, recall and F1 score metrics were used to evaluate the model. The hybrid CNN-SVM model had a recognition accuracy of 91.25% against 89.07 % from the pure CNN model. The weighted average precision, recall, and F1-score of the hybrid CNN-SVM was 92%, 91% and 91% respectively, which was better compared to that of pure CNN. The hybrid model was tested for external validity using the Smart Sense Laboratory (SSIG) dataset. The hybrid CNN-SVM model had a recognition accuracy of 91% against 89 % from the pure CNN model. The weighted average precision, recall, and F1 score of the hybrid CNN-SVM was 91%, 91% and 91% respectively which was better compared to that of pure CNN, which had 90%, 89% and 89% respectively.
Determination and removal of selected heavy metals in treated wastewater from Ruai sewage treatment plant for possible agricultural applications
(Chuka University, 2024) Kakuta Peace Kaviti
Fresh water has become scarce and many arid and semi-arid regions in the world suffers from water shortage. Wastewater reuse remains the only reliable and potential source of water. One of the major challenges in recycling of wastewater is the presence of toxic heavy metals which are persistent and non-biodegradable and are known to affect human health. This study sought to synthesize and characterize soda lime and borosilicate waste glass and a composite of Multi-walled Carbon Nanotubes (MWCNTs)/soda lime waste glass adsorbents and utilize them for removal of Pb2+ from wastewater. Glass wastes were collected within Chuka University and MWCNTs purchased from Hongwu International Group Ltd. The adsorbents were washed, dried, functionalized with nitric-sulfuric acid mixture and characterized using FTIR. Wastewater samples were collected in Ruai wastewater treatment plant, Kenya using grab method, transferred to 250 ml plastic bottles and were transported to Chuka university for analysis in a cooler box at 4oC. Standard laboratory procedures of determining the physicochemical parameters were employed. Batch adsorption experiments were conducted to study the effect of contact time, pH, temperature, shaking speed, initial adsorbate concentration and adsorbents dosage on removal of lead (II) ions. Residual Pb2+ concentration was determined using AAS. The findings were: pH 5.5-7.9, Temperature 22.70C-26.10C, Conductivity 526.7- 1209.7µS, turbidity 73- 1000 NTU, nitrates 6.66-25.1 mg/l, Phosphorus 1.16-10.30 mg/l, BOD5 ranged from 10-480 mg/L, COD 90-980 mg/L, TSS 14-422 mg/L, and TDS 244-967 mg/L. pH, temperature, NO3-, BOD5(wet season) results met the WHO and NEMA standards for wastewater reuse in irrigation while EC, turbidity, P, COD, BOD5(dry season), TDS and TSS did not. The results of heavy metals were Ni 0.02-0.22 mg/L, Zn 0.03-1.67 mg/L, Cu 0.01-0.23 mg/L, Cd 0.01-0.05 mg/L, Fe 0.05-7.24 mg/L, Mn 0.14-2.26 mg/L, and Pb 0.04-0.78 mg/L. The levels of Zn, Cu, and Ni were within WHO and NEMA standards while Cd, Mn, Pb and Fe did not meet the threshold at some sampling points. All the metals studied met the FAO guidelines for reuse of wastewater in irrigation. Characterization of the adsorbents was done using FTIR which displayed the dominant functional groups to be silanols, hydroxyls, carboxylic and carbonyl groups. Adsorption of lead (II) ions was conducted using a composite of soda lime waste glass and multiwalled carbon nanotubes, borosilicate and soda lime waste glass. The composite and borosilicate adsorbents reported 100% adsorption of lead (II) ions while soda lime was average. Adsorption of Pb (II) ions followed Freundlich isotherms for borosilicate and soda lime adsorbents with r2 of 0.8665 and 0.9257 while Composite had a better fit in Langmuir isotherm with r2 of 0.9446. Cd (II) and Ni (II) ions did not interfere with adsorption of lead (II), but a stiff competition for the adsorption sites was observed for the case of Mn (II) ions. Regeneration efficiencies of 99.61%, 97.45%, and 99.82% were observed for borosilicate, soda lime, and composite adsorbents. The findings of this study clearly showed that soda lime waste, borosilicate waste glass and composite of soda lime waste glass/MWCNTs are effective for the removal of lead (II) ions from waste water.
Determination of micronutrients, heavy metals and Proximate analysis of selected indigenous vegetables in Kirinyaga East Sub-County, Kirinyaga County
(Chuka University, 2025) Maina Janet Njeri
Socio-economic changes that have taken place in Africa have influenced peoples eating habits in both rural and urban set-ups. Indigenous vegetables are important for food insecurity, malnutrition reduction and therapeutics in sub-Saharan African countries. In Kirinyaga County, indigenous vegetables are underutilized or neglected due to some nutritional content are known and others unknown. Limited data exist on the precise levels of key micronutrients (e.g. iron, zinc, magnesium) in specific indigenous vegetables cultivated in Kirinyaga East Sub-County. Variability in nutrient content due to farming practices, soil types, and climatic conditions in the region has not been comprehensively studied. Leaves are the most preferred parts of indigenous vegetables for consumption. Although they have nutritional benefits, there is a need to determine the safety levels due to toxic metals in vegetables contaminated with pesticides, heavy metals and toxins leading to failure of certain organs of the human body. The study aimed to determine levels of micronutrients (calcium, iron, magnesium and zinc), heavy metals (lead and cadmium) using AAS (atomic absorption spectrometer) using and proximate analysis (ash content, moisture content, crude fat, protein content, carbohydrates and crude fiber) of selected African indigenous vegetables (African nightshade, Spider plant, Vine spinach and Pumpkin leaves) in Kirinyaga east sub county. The results revealed that African indigenous vegetables are rich in essential micronutrients. Pumpkin leaves had the highest calcium levels (14,070.81 mg/kg), spider plant showed the highest iron concentration (233.53 mg/kg), African nightshade recorded the highest zinc content (483.33 mg/kg), while vine spinach was richest in magnesium (570.87 mg/kg). Proximate analysis indicated that spider plant and African nightshade contained higher protein levels (up to 17.9%), while moisture content ranged from 82–91%. Crude fat levels were consistently low, confirming African indigenous vegetables as nutrient-dense but energy-light vegetables Cadmium and lead concentrations varied across species and locations, with African nightshade and vine spinach showing relatively higher levels. However, all values remained within WHO/FAO permissible limits, indicating that the vegetables are safe for human consumption in terms of heavy metal contamination. African indigenous vegetables have high nutritional potential and can be promoted as affordable dietary sources of essential minerals and protein to combat malnutrition.
Determination of Physico-Chemical Parameters and Removal of Bis(2-Ethylhexyl) Phthalate from Wastewater Using Prosopis juliflora Biochar/Carbon Nanotubes Composite Adsorbent
(Chuka University, 2024) Mutua John Mutinda
Phthalates, such as BEHP, are endocrine-disrupting compounds commonly used as plasticizers. Their presence in wastewater, often from industrial and household effluents, poses health risks including congenital anomalies, cancer, and chronic toxicity. The high cancer prevalence in Meru County has been linked to toxicants in effluent released into Kathita River, used for domestic purposes and irrigation. This is attributed to the inefficiency of the lagoon wastewater treatment technology in removing chemicals like BEHP. This study evaluates the physico-chemical properties and BEHP levels in wastewater from Meru Sewage Treatment Plant and explores the adsorption of BEHP using a Prosopis juliflora biochar/carbon nanotubes composite adsorbent. The temperature, pH, conductivity, turbidity, TDS, TSS, BOD and COD of the wastewater were determined using the standard APHA methods for wastewater, the concentrations of heavy metals using AAS while those of BEHP using HPLC. The following mean values were reported after data analysis: BEHP 0.055mg/L, Cu ND, Pb 0.042mg/L, Cd 0.0019 mg/L, COD 65.99 mg/L, TSS 29.3mg/L, TDS 639.17mg/L, turbidity 117.9FTU, conductivity 1079.9µS, pH 7.3, temperature 26oC and BOD5 65.9 mg/L; for dry season. Only pH and COD exceeded WHO limits for wastewater discharge into environment. The wet season parameter mean values were: COD 359.7mg/L, TSS 198.3mg/L, TDS 2094.2mg/L, turbidity 105FTU, conductivity 1244µS, pH 8, temperature 26 , BEHP 0.0429mg/L, Cu 0.47mg/L, Pb 0.037mg/L, Cd 0.056mg/L and BOD5 71.2mg/L. The temperature, TDS, BOD, Cu and Pb met WHO limit while conductivity, turbidity, TSS, COD and Cd exceeded. The composite adsorbent was characterized using FTIR and powder XRD. The dominant functional groups of the composite were C=O, CO2, OH-, Si-OH, C=N, MgO, CaCO3, and SiO2. The composite adsorbent was very efficient in the adsorption of BEHP with up to 96% removal in the samples at determined optimum adsorption parameters of; pH 5, temperature 24 , 15 minutes contact time and an adsorbent dose of 200 mg. The isotherm studies showed that the adsorption process was in agreement with the Freundlich isotherms with R2 value of 0.90469 while the kinetic studies revealed that BEHP adsorption followed pseudo second order model with R2 of 0.994. It was concluded that the application of biochar/CNTs composite adsorbent for the removal of BEHP from the wastewater is relatively cheaper and eco-friendly and should be applied for treatment of wastewater for irrigation and domestic use to improve water quality and minimize health risks associated with BEHP.
Determination of physicochemical parameters and estriol levels in nyeri waste water treatment plant and the adsorption of estriol using sugarcane bagassemultiwalled carbon nanotube composite
(Chuka University, 2024) Njue Jediel Mwenda
Persistent organic pollutants and endocrine disrupting substances have been found to be highly resistant to degradation. Exposure to these chemicals interferes with normal functioning of endocrine system by causing adverse effects such as cancer and impaired neurodevelopment. Waste water treatment plant mostly concentrate on removal of microorganisms leaving behind micro contaminants such as Estriol. The objective of the study was determination of physicochemical parameters and Estriol levels in Nyeri sewage treatment plant and the removal of Estriol using carbon nanotubes-sugarcane bagasse composite adsorbate. The composite was synthesized by oxidizing Multiwalled carbon nanotube in a ratio 3:1 nitric (V) acid to sulphuric (VI) acid mixture. Multiwalled carbon nanotube were then washed with deionized water and then added to a suspension of sugarcane bagasse powder in a ratio of 100:1 ,100:0.5 and 100:0.1. The characterization of the composites was done using Fourier transform infrared spectroscopy and X-ray diffraction techniques. The results showed that turbidity was higher in the dry season at 103.954 NTU compared to 46.5647 NTU in the wet season. Cd was recorded at 0.0168 mg/L during the wet season, which is significantly higher than the 0.0015 mg/L observed in dry season. Copper exhibited a concentration of 0.0833 mg/L in the wet season, which increased substantially to 1.0172 mg/L in the dry season. For Lead, the wet season value was 0.0159 mg/L, while the dry season value of 0.0050 mg/L. Dissolved Oxygen decreased markedly from 7.0617 mg/L in the wet season to 2.2396 mg/L in dry season. These differences in Pb, Cu, and DO levels were confirmed to be statistically significant p < 0.05 based on the Fisher’s LSD test with Bonferroni correction for p-value adjustment. Electrical Conductivity increased from 695.782 µS/cm in the wet season to 1056.104 µS/cm in the dry season. A t-test was conducted to compare Estriol concentrations between the wet and dry seasons at the Nyeri Water Treatment Plant. The mean Estriol concentration during the dry season was 1.444 ±0.671 Mg/L while in the wet season it was 0.982 ±0.870 Mg/L. The mean difference between the two seasons was 0.463, with a weighted standard deviation of 0.777. The optimum conditions obtained from the adsorption of Estriol were; equilibrium time 10 minutes, pH 4, maximum adsorbate concentration adsorbed at 0.1 g of composite 2 was 8 ppm. The composite that gave the best results was composite 2 (100:0.5) with 76.7% adsorption efficiency. The kinetic analysis of estriol best fitted the pseudo first order model with the rate constant for reaction as k1=−0.1−1, with an R2 value of 1.0. The analysis of adsorption isotherms for Estriol indicated that the Langmuir model provided a good model for fitting adsorption data than the Freundlich model, with a higher R² value of 0.5280 compared to 0.439 in Freundlich model. The enthalpy change (ΔH) for the process was calculated to be 26,165.44 J/mol, indicating that the adsorption is endothermic, meaning it absorbs heat from its surroundings. The entropy change (ΔS) was positive 68.85 J/mol-K suggesting an increase in disorder at the solid-liquid interface during adsorption. The Gibbs free energy change (ΔG) was negative at all the tested temperatures showing that the process naturally progresses without the need for external energy input.The findings demonstrated that sugarcane bagasse-carbon nanotube composite is a good low cost and environmentally friendly adsorbent for removal of Estriol from waste water.
Determination of physicochemical parameters and remediation of Pb (ii) using a composite of moringa oleifera seeds and kaolin clay in borehole water within Nakuru east sub county, kenya
(Chuka University, 2024) Kiprono Geofry
The contamination of borehole water in Nakuru East Sub County, primarily due to heavy metals and non-metals from natural origins and anthropogenic activities, poses significant health risks. Exposure to these contaminants, beyond the World Health Organization (WHO) and Kenya Bureau of Standards (KEBS) limits, can lead to various diseases and even death. Activities such as industrial processes, agriculture and waste disposal in the area contribute to the infiltration and leaching of toxic elements into borehole water. These toxic elements can enter the human body through ingestion, inhalation or dermal absorption. The Nakuru Water and Sanitation Service Company (NAWASSCO) operate 40 boreholes in the region; however, only 19 were functional during the sample collection, which took place in both dry and wet seasons. These boreholes supply water to Nakuru City and surrounding areas. This study aimed to determine the levels of physicochemical parameters and remediation of Pb (II) from these boreholes. Water samples were collected from six water fields: Kiundo (1 borehole), Kabatini (6 boreholes), Nairobi Road (6 boreholes), Baharini (4 boreholes), Madaraka (1 borehole) and Olbanita (1 borehole) during both seasons. The samples were analyzed for various parameters including Temperature, pH, Turbidity, Electrical Conductivity, Dissolved Oxygen, Fluoride, Lead (Pb), Cadmium (Cd) and Arsenic (As), with heavy metal concentrations measured using Flame Atomic Absorption Spectroscopy (FAAS). Results indicated that the temperature of the borehole water ranged from 23.8 to 26.4 ±0.2°C. Dissolved Oxygen levels were found to be below WHO acceptable limits in all samples. Turbidity levels ranged from 1.54 to 4.32 ±0.01 in the dry season and 0.36 to 9.30 ±0.02 in the wet season, with 36.8% of samples exceeding WHO limits in the dry season and all samples exceeding limits in the wet season. pH values were between 6.4 and 7.6 ±0.1 in the dry season and 7.0 to 7.7 ±0.2 in the wet season, mostly within WHO guidelines, except for a few boreholes that were slightly acidic. Electrical conductivity ranged from 392.0 to 823.1 ±0.1 mS/cm in the dry season and 186.7 to 350.6 ±0.2 mS/cm in the wet season; with all dry season samples above the WHO threshold of 400.0 mS/cm. Flouride had the same mean concentrations of 1.23mg/l for both the two seasons. Significant differences (P≤0.05) were noted between the physicochemical parameters and WHO standards. Correlations among parameters were observed, ranging from negative to positive (p < 0.001). Cd concentrations were below detection limits in all samples for both seasons, while Pb (II) levels exceeded the WHO limit of 0.01 mg/L in 31.6% of boreholes during the dry season and 42.1% in the wet season. Arsenic concentrations were above the WHO limit of 0.01 mg/L in both seasons. To address the contamination, a composite of Moringa oleifera seed powder obtained from Tharaka Nithi County and pure kaolin clay were used as an adsorbent for Pb (II). Characterization of the composite was conducted using Powder X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). The adsorption study focused on Pb, revealing that the base-activated composite effectively adsorbed Pb at a dosage of 0.5 g. The adsorption data fit the Langmuir isotherm, Temkin, and Pseudo-Second-Order models. Thermodynamic studies yielded values of 25,340.57 J/mol for ΔH, 58.32 J/mol/K for ΔS and a decrease from 7.95 kJ/mol at 25°C to -3.71 kJ/mol at 225°C for ΔG. The adsorbate was effective at 83.77% in sample NR7.The results obtained indicated the urgent need for water treatment due to elevated levels of As (III) and Pb (II). The composite showed a positive effectiveness on the remediation of Pb (II), however further research should be carried out to investigate its effectives on other heavy metals remediation and outside the boreholes of the study area.
Determination of water quality of Nguue spring and river Mutonga in Tharaka-Nithi, Kenya and evaluation of corn-cob derived carbon powder in remediation of contaminated water
(Chuka University, 2015) Mwenda Bertha Kinya
Water quality is a pressing global concern, since water pollution negatively impacts water bodies and poses severe threats to both human and aquatic life.The aim of this study was to, determine physico-chemical and bacteriological parameters of Nguue spring and river Mutonga, and compare to the limits set by WHO and KEBS. The performance of low-cost adsorbent in removal of zinc and iron from contaminated water was also evaluated. Sampling was done each at six points along the river and the spring approximately 500 metres apart, in the dry and wet season in the months of September and November 2024. The physical parameters of water such as temperature, pH, DO, TDS and EC were measured using multiparameter. Total hardness was estimated by titrimetry. Anions of NO3 - and PO4 3- were determined by UV spectrophotometric method. The heavy metals of Zn2+ and Fe3+ in water samples were determined by Atomic Absorption Spectrophotometer. In the dry season, River Mutonga recorded a temperature between 25.9-26.6 °C, pH of 7.1-7.4 , electrical conductivity was 172.8-202 µS/cm, total dissolved solids 0.838-0.886 mg/L , dissolved oxygen, 3.19-3.25 mg/l, however, total hardness 0.838-0.886 mg/L, nitrate concentrations 16.57-19.35 mg/L, while phosphate was 1.95-2.12 mg/L. Nguue Spring during the dry season exhibited a temperature of 25.9-26.5 °C, pH was 5.21-6.58 indicating slight acidity, electrical conductivity of 147.6-193.6 µS/cm and total dissolved solids 0.552-1.2 mg/L, total hardness wa 58-160 mg/L, well Similar to Mutonga River, nitrate levels 16.83-19.47 mg/L. In the wet season, River Mutonga recorded a lower temperature of 21 °C compared to the dry.4 and 6.5-8.5 respectively, indicating increased acidity in sampling point M1. Electrical conductivity 57.2-60.4 µS/cm and total dissolved solids 31.3-36.6 mg/L were markedly lower than in the dry season, reflecting dilution effects of rainfall. Dissolved oxygen was 8.72-8.92 mg/L increased substantially and was well above the WHO minimum standard 4.0 mg/l. Total hardness decreased to 87.3-122 mg/L but remained within permissible limits. Nitrate levels dropped to 7.25-12.35 mg/L, now falling within KEBS standards which is 10 mg/l, while phosphates was 2.01-2.29 mg/L remained above WHO limits 0.5 mg/L. Nguue Spring in the wet season showed a temperature of 20.5-21 °C, pH was4.210-5.724 was consistently below both WHO and KEBS ranges, suggesting acidic conditions. Electrical conductivity was 37.8-55.6 µS/cm and total dissolved solids 27.7-59.5 mg/L decreased compared to the dry season, reflecting rainwater dilution. Dissolved oxygen was 8.58-8.86 mg/L was significantly elevated and met WHO standards, total hardness was 22.6-84 mg/L remained within permissible limits. Nitrate concentration was 7.8-14.65 mg/Lwas marginally above the KEBS guideline, while phosphate was 1.95-2.23 mg/L exceeded WHO standards but was acceptable under KEBS. Batch adsorption was carried out by varying the parameters of temperature, pH, intial concentration, contact time and adsorbent dosage. The total coliforms count in Mutonga were 11-460 and 36-1100 MPN per 100 ml in dry and wet season repectively and exceeded KEBS and WHO limits. In Nguue spring the toal coliform counts were 36-1100 and 43-˃1100 MPN per 100 ml in dry and wet season respectively exceeding KEBS and WHO limits. Adsorption of zinc and iron on CCAC and CCC was successfully represented by Freundlich and Lngmuir isotherm models. Adsorption of zinc on CCAC and CCC was best described by Freundlich with KF=2.35 mg/g, n=1.387, R2=0.9359 and KF=116.84mg/g, n=0.1634, R2= 0.9497 respectively indicating a favourable multilayer adsorption on heterogenous surface. Adsorption of iron on CCAC and CCC also Freundlich gave a reasonable fit with KF=1.1498mg/g, n=0.5826, R2=0.90703 and KF=1.8034mg/g, n=0.9063, R2=0.97692 respectively
Determination of water quality status, remediation and synthesis of multiwalled carbon nanotube/hydrochar composite in river Kathita, Meru, Kenya.
(Chuka University, 2025-10) Gitonga, Glory Gatwiri
Freshwater is required for life as well as several other activities such as human consumption, agricultural processes and industrial processes. Heavy metals are absorbed into water bodies through various pathways have adverse effect on the liver, kidneys, lungs, brain, and bones. This study sought to determine the physiochemical parameters, bacteriological, heavy metals and synthesize and characterize a composite of multi-walled carbon nanotubes (MWCNTs)/hydrochar of tea waste and utilize it for removal of Cu2+ ions from river Kathita. Tea wastes were collected from Meru tea factories and MWCNTs purchased from reputable suppliers. The tea waste samples were washed, dried and the MWCNTs, functionalized with sulfuric-nitric acid. The MWCNTs/hydrochar composite was characterized using FTIR and XRD. The water samples and sediments were collected in two seasons from River Kathita using grab method, transferred to 500 ml plastic bottles and transported to Chuka University Laboratory for analysis in a cooler box at 4oC. Standard methods for determining physicochemical and bacteriological parameters were employed and batch adsorption experiments were conducted to study the effect of pH, temperature, contact time, speed, initial metal concentration and dosage on adsorption of Cu 2+ ions. The remaining copper (II) ions concentration was determined using AAS. The water turbidity, electrical conductivity, dissolved oxygen, nitrogen and phosphorous content, TDS and TSS were found to be high during the wet season compared to dry season. pH registered small changes to more neutral-alkaline during the wet season. The pH, temperature, conductivity, TDS, phosphorus and nitrates were within the guidelines by WHO in both seasons. Turbidity and TDO exceeded the WHO and KEBS guidelines in both seasons which indicated high amounts of organic matter. The total coliform counts and faecal coliforms during wet season were beyond proposed standards of safe recreational or agricultural use. This was due to extensive runoff as well as sewer discharge into the water during the rainy season. (MWCNTs) and hydrochar from tea waste composite had a big removal efficiency of 96.5% under optimum conditions. From the kinetic modeling, the adsorption process obeyed pseudo-second order reaction (R² = 0.96683) trend showing that the process is chemisorption driven. There was greater fit of the adsorption equilibrium data on Langmuir isotherm model (R² = 0.99529) indicating monolayer surface coverage of Cu²⁺ ions by homogenous surface. There was a high regeneration potential of the absorbed Cu²⁺ ions being efficiently desorbed. The MWCNTs/hydrochar composite is highly effective and sustainable since it can be recycled multiple times without the loss of functionality on a significant scale. The addition of Pb²⁺ ions did not drastically influence the removal of Cu²⁺ while addition of Cd²⁺ ions and the binary solution of Pb²⁺ and Cd²⁺ caused a significant decrease in the efficiency of copper adsorption. The MWCNTs/hydrochar composite is generally a sustainable option for enhancing the quality of the polluted rivers' water.
Effect of pigeon pea and sorghum flour supplementation on the physico-chemical, protein digestibility, sensory properties and shelf life of millet flour
(Chuka University, 2024) Mwangangi Catherine Nzisa
Malnutrition is a major health problem in many parts of the world. Protein-energy malnutrition (PEM) remains a significant health issue among children in many parts of Kenya. The aim of this study was to utilize locally available sorghum, pearl millet, and pigeon peas in the formulation of a protein-rich complementary flour to contribute to reduced PEM among children under 5 years in Tharaka Nithi, Kenya. This study adopted a nested design with two factors; pigeon pea level and sorghum level nested in pigeon pea level. For shelf life study an additional factor was introduced which was storage time in days. Preparation of the blends included 5 ratios of Pigeon peas (0, 15, 30, 45, 60% of the total flour blend), and 3 levels of sorghum (0, 20, 40% of the milletsorghum flour mix). These blends were then analysed for physicochemical properties, protein digestibility, sensory properties and shelf life. The sensory evaluation of flour blends was carried out with informed consent of the respondents who voluntarily participated in sample assessment. Data on physico-chemical properties, protein quality, titratable acidity, pH and sensory evaluation was subjected to analysis of variance (ANOVA) and significant means separated via Tukey’s Honest Significant Difference (HSD). Data on the willingness to buy the different blends was analysed using the Kruskal Wallis test. Microbial data was log transformed and subjected to ANOVA. All data was analysed using Minitab software at P < 0.05. The addition of pigeon peas to the millet-sorghum-pigeon peas composite blend significantly (P < 0.001) increased all functional properties except bulk density while sorghum significantly affected swelling capacity (P = 0.019), water holding capacity (P=0.001), gelling temperature (P < 0.001), and tapped density (P = 0.002). Addition of pigeon peas significantly (P < 0.001) increased the protein content from 11.845% to 16.016% and digestibility of the flour blends from 34.95% to 46.34%. Sensory evaluation revealed that the addition of pigeon peas significantly (P < 0.001) lowered the taste (3.862 to 3.420), flavour (3.908 to 3.299), viscosity (4.000 to 3.270) and texture (4.035 to 3.420) rating and general acceptability of porridge (3.690 to 3.339) prepared from the composite blends. Sorghum significantly (P < 0.001) increased colour (2.897 to 4.224), taste, flavour and overall acceptability of the flour blends. Sensory evaluation revealed that the samples containing 34% sorghum and 15% pigeon peas performed best in all sensory attributes as well as in overall acceptability. The average relative humidity and temperature during storage was 55.494% and 25.225 0C, respectively. The addition of pigeon peas to the blends significantly (P < 0.001) increased the Titratable acidity (0.1862 to 0.245g/l) of the composite blends which indicated a gradual deterioration of the flour over the storage period. The initial mean yeasts and mold counts were 2.252 Log CFU/g respectively which were below the East African Community recommended safety limit for yeast and Mold counts in millet flour (4 Log CFU/g). However. This number rose within the first 16 days to 2.628 log cfu/g, then began dropping to (0.492 log cfu/g. This can be explained by the increasing acidity of flour, change in the pH, reduced relative humidity and oxygen supply in the storage container. It is therefore possible to produce pigeon pea containing flour product that is safe for human consumption and that can last for two months and above. These outcomes demonstrate the potential of pigeon peas, sorghum, and millet in creating protein-rich porridge flour and hence improving protein intake for children under 5 years. This study encourages the utilization of pigeon peas to improve the protein content of energy dense cereal grains occasionally used in complementary diets.
Effects of co-trimoxazole and amoxicillin therapy on gut microbiota population, physiological, biochemical and pathological parameters in Swiss mice
(Chuka University, 2024) Kiptoo Kamngoror Cosmas
Antibiotics have been utilized in treatment of bacterial infections since their discovery. Despite being beneficial in managing infections, antibiotics have significant implications on health by disrupting the gut microbiota. Gut microbiota comprises of wide range of microorganisms that inhabit the gut, including fungus, archaea, bacteria, and viruses. The gut microbiota plays pivotal role in health by influencing metabolic processes, immunological and neurobehavioral functions. This study investigated the impact of amoxicillin and co-trimoxazole on the gut bacterial population of mice, using three-week-old Swiss mice models simulating six-month-old human babies. The experiment aimed to assess physiological, biochemical, immuno-pathological changes, and the induction of oxidative stress. Male swiss mice were randomly assigned to five groups: normal control, amoxicillin group, septrin group, amoxicillin+septrin group, and amoxicillin+co-trimoxazole+probiotics. Over 63 days, mice were monitored, weighed after each antibiotic dosage. Euthanasia was performed using isoflurane, and blood samples was collected via cardiac puncture for hematological analysis. The liver, spleen, kidney, lungs and heart were harvested and weighed for determination relative organ weight (ROW), liver, brain and kidneys were harvested for histo-pathological examination. Serum obtained from whole blood underwent further analysis for various markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), creatinine, urea and cytokines. Tissue glutathione (GSH) and malondialdehyde (MDA) levels, along with serum nitric oxide (NO), were determined to gauge oxidative stress. Numeric data underwent analysis using one-way ANOVA followed by Tukeys' post hoc test, with significance reported at p<0.05. Results, in form of graphs and images, revealed amoxicillin and septrin administered singly or in combination resulted in reduced gut microbiota population resulting in gut microbiota dysbiosis. Probiotics administration ameliorated the gut microbiota dysbiosis. There were no significant changes in body weight as well as relative organ weight (ROW) of the selected organs. Hematological exams revealed significant drop in the red blood cells (RBCs) count, hematocrit level and hemoglobin especially in amoxicillin+septrin treated group. White blood cells count (WBCs) was significantly elevated in septrin group compared to control, amoxicillin group, amoxicillin+septrin treated group and amoxicillin+septrin+probiotics treated group. Liver function test markers aspartate aminotransferase (AST), alanine aminotransferase (ALT), AST:ALT ratio and alkaline phosphate (ALP), were significantly (p<0.05) elevated indicating liver damage. Kidney function markers showed elevated levels of creatinine, urea, uric acid and significant drop in the levels of albumin indicating kidney damage. Gut microbiota dysbiosis results in electrolyte imbalances noted by a drop in the levels of serum electrolytes; sodium, chloride and potassium. There were significant (p<0.05) elevated levels of interferon gamma (IFN-γ), tumor necrotic factor alpha (TNF-α) indicating active inflammation, histological exams revealed tissue damage in the liver and kidneys, and oxidative stress indicated by elevated malondialdehyde (MDA) and glutathione (GSH) levels in target organs. There were significant (p<0.05) elevated nitric oxide (NO) levels in the serum indicating active inflammation or damage to organ functions. Probiotics administration alongside antibiotics showed promising outcomes, by restoring gut microbiota population and consequently protecting the body from induction of immunological responses and inflammation, protection from oxidative stress and organ damage suggesting a potential avenue for ameliorating complications associated with antibiotic-induced dysbiosis. This comprehensive study highlights the intricate effects of antibiotics on gut microbiota and associated health parameters, emphasizing the need for cautious antibiotic use to mitigate potential adverse outcomes.

Browse

Browsing Faculty of Science, Engineering and Technology (FSET) by Title