Search results

Inadequacies in several micronutrients in complementary foods, notably iron, zinc, calcium, vitamin A, vitamin B6 and riboflavin have been reported. Moringa oleifera leaf powder (MLP), prepared from dried moringa leaves is nutrient-rich and has been explored for the treatment of micronutrient deficiencies among children in developing countries. This increasing interest in the use of moringa oleifera leaves to improve complementary foods notwithstanding, the unique sensory characteristics of the leaf powder potentially holds implications for the acceptability of local diets that are fortified with it. The purpose of this paper is to investigate the levels of MLP fortification that are most acceptable for feeding infants and young children.

The authors performed a review of the literature, with the aim of investigating the sensory attributes and acceptable levels of fortification of complementary food blends fortified with different levels of MLP.

The minimum amount of MLP to be added to a complementary food blend to observe significant improvements in its nutritional value was estimated to be about 10 per cent. However, at this 10 per cent fortification level also, sensory attributes of the products begin to become less desirable.

For the success of nutrition interventions that involve the use of MLP to improve the nutritional quality of complementary foods, there is a need to consider the acceptability of the sensory attributes of the formulated blends in the target group. Safety of MLP as an ingredient in infant foods must also be investigated.

The authors of this paper make recommendations for the use of MLP to fortify complementary foods to ensure its success as a food fortificant in nutrition interventions. The researchers are not aware of any published study that focuses on this subject.

The purpose of this study is to investigate the relationship between dietary diversity and haemoglobin concentration of women of reproductive age (WRA), 15−49 years in the Binduri District, of the Upper East Region (UER) of Ghana.

This cross-sectional study was conducted among 153 non-pregnant, non-lactating WRA from three randomly selected communities in the Binduri District of the UER of Ghana. Socio-demographic information, 24-h dietary recall and minimum dietary diversity scores (DDS) of the women were obtained using an interviewer-administered questionnaire. Haemoglobin concentration of the participants was measured by finger prick using Hemocue 301 Analyzer and classified according to WHO standards. Linear regression analysis was used to assess the relationship between DDS and haemoglobin concentrations of the participants.

Total study participants were 153 with mean age of 32.8 ± 8.7 years. Anaemia prevalence of 66.7% and a mean DDS of 4.0 ± 1.2 was recorded. DDS was found to be a strong predictor of haemoglobin concentration (p = 0.00, R² = 0.48).

Interventions that increase DDS may be effective in improving haemoglobin levels of WRA.

The results showed that DDS is a strong predictor of haemoglobin concentration of the WRA in this study. As the dietary diversity of the women increased, their mean haemoglobin concentration also improved. To the best of the authors’ knowledge, this is the first study of its kind to be conducted in the study area.

This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms.

The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model’s reliability and validity. Machine learning algorithms were used to determine the relationships among the constructs in the proposed research model.

The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students’ continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS.

The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context.