Segun Emmanuel Adebayo

The object of the research is an optimal wholesome breakfast cereal. The problem to be solved is to make a wholesome breakfast cereal by using local ingredients with complementary nutritional and health-promoting qualities rather than mono-grain products. This will lessen the global epidemic of malnutrition and diet-related diseases such as diabetes, obesity, overweight and heart diseases. In order to improve nutritional quality, the formulation substituted natural flavorings, artificial colors, sweeteners, synthetic minerals, and preservatives with natural ones. It also made use of experimental design and numerical optimization of ingredient proportions and processing parameters to achieve improved nutritional qualities. Five components, two processing parameters constrained D-optimal mixture-process experimental design was employed in the research. The design constraints were: malted finger millet flour (20‒60%), malted roasted Bambara groundnut flour (10‒40%), turmeric powder (5‒15%), moringa leaf powder (2‒5%), date fruit powder (5‒20%), salt (0.3%); screw speed (70‒150 rpm) and barrel temperature (90‒100°C). Numerical optimization, via desirability function technique, yielded an overall desirability index of 0.7 with optimized components and factors levels as: 42.7% malted finger millet, 40.0% malted roasted Bambara groundnut, 5% date fruit, 2% moringa leaf powder, 10% of turmeric powder, 0.3% salt, 110°C barrel temperature and 110 rpm screw speed. The optimal quality of the breakfast cereal were: 12.5% protein, 67.4% carbohydrate, 6.9% moisture content, 3.6% ash content, 2.4 crude fiber and 7.2% crude fat. The optimal formulation conditions obtained can serve as a data base for future production in food processing industries.

Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R² of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy–Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS.

Fruits and vegetables that suffer post-harvest losses are a severe bottleneck in food systems all over the world, and it has been estimated that 30-50% of perishable fruits and vegetables are lost between farm and consumer, especially in low-and-middle-income countries (LMICs) that do not have access to reliable cold chain infrastructure. Evaporative cooling (EC) has long been known as a low-cost, energy-efficient alternative to mechanical refrigeration, particularly in hot, dry environments. The conventional EC systems, however, have the disadvantage of being statically operated, climate-dependent, and inefficiently controlled, thereby restricting their utilisation and utility. A combination of the Internet of Things (IoT), machine learning (ML), and advanced control theory in recent history has led to the emergence of intelligent evaporative cooling systems (IECS)-adaptive, data-driven platforms through which real-time environmental regulation and predictive maintenance can be undertaken and autonomous optimisation achieved to obtain an improved preservation of the post-harvest of goods. The review indicates that the temperatures of the refrigerators can be reduced by 8 to 15°C, extend shelf life by 50-100%, and the energy used can be cut by 75-90% compared to traditional refrigeration. Economic analysis indicates payback periods as short as 1.2 years and total system costs of less than USD 100 for IoT components. There is a strong, consistent (R2) relationship of 0.98 or higher between machine learning models, especially Long Short-Term Memory (LSTM) networks and tree-based ensembles, and microclimate variables. Despite these improvements, critical research gaps that require attention include limited validation in tropical and high-humidity settings, the lack of standardised Ag-IoT protocols, the lack of life-cycle and Food-Energy-Water (FEW) nexus evaluations, and explainable AI (XAI) to promote farmer confidence. To address these issues, a new, combined 4-layer framework comprising the Physical, Sensing/Actuation, Data/Communication, and Intelligence/Control layers is proposed as the roadmap for next-generation IECS. Such an SLR concludes with a future-oriented research agenda that focuses on robustness, interoperability, sustainability, and human-centred design, and provides practical recommendations for researchers, engineers, policymakers, and agricultural practitioners who are determined to create climate-resilient and equitable food systems.