The article introduces a novel low-glycemic-index ice cream targeted at improving the lipid profile and managing oxidative stress. Conventional dairy components were replaced with non-dairy natural sources, i.e., coconut (Cocos nucifera L.) and tiger nut (Cyperus esculentus L.). The research focused on the effect of this substitution on the sensory, glycemic, and hypocholesterolemic properties of the functional ice cream. The experimental formulations combined dairy components (whipping cream, skimmed milk), coconut milk, tiger nut milk, and orange (Citrus × sinensis (L.) Osbeck) peel powder. The samples were evaluated for their glycemic index, total phenolics, total flavonoids, amylose, and amylopectin contents. The developed formulations were fed to high-fat diet-fed rats for 21 days to assess their lipid profile, as well as the levels of malondialdehyde and reactive oxygen species. The rats fed the ice cream formulated with coconut milk, tiger nut milk, and orange peel powder demonstrated lower total cholesterol, triglycerides, and low-density lipoprotein cholesterol levels while the high-density lipoprotein cholesterol level was increased. Higher concentrations of coconut milk, tiger nut milk, and orange peel powder correlated with reduced malondialdehyde and reactive oxygen species levels in the rat plasma. They were also associated with a lower glycemic index whereas the phenolic content and amylose-to-amylopectin ratio were significantly higher. The partial or total replacement of dairy components with coconut milk and tiger nut milk combined with orange peel powder produced ice creams with acceptable sensory properties, a reduced glycemic index, and improved hypocholesterolemic properties.
The susceptibility of African yam bean (AYB) (Sphenostylis stenocarpa), a nutritionally valuable but underutilized legume, to the cowpea beetle (Callosobruchus maculatus) was investigated under controlled temperature (28°C) and relative humidity (55 ± 15%). Five AYB accessions (TSs 23, TSs 57, TSs 96, TSs 111A, TSs 125) were evaluated using cowpea and adzuki bean as reference hosts. Seed morphometrics varied significantly (p < 0.001), with cowpea and adzuki bean generally having larger seed volumes than AYB. After 8 days, fecundity on AYB ranged from 55 to 109 eggs per 10 g seed, with highest oviposition on TSs 111A, while hatchability remained high (86–93%) and did not differ significantly among seeds. Adult emergence was delayed on AYB, beginning at 34–36 days post-oviposition set up, compared with day 22 on cowpea. Survivorship was lower on AYB (28.7–60.8%) than cowpea (92.2%), resulting in reduced seed damage (32.4–64.1%) and weight loss (6.0–11.5%) compared with cowpea (80.7% and 31.0%, respectively). Susceptibility indices confirmed lower vulnerability of AYB (6.27–9.27) than cowpea (19.90). TSs 96 and TSs 111A showed trends consistent with moderate resistance to C. maculatus, with reduced emergence observed in both accessions, alongside lower damage in TSs 96 and prolonged development in TSs 111A. Correlation analysis linked seed traits, particularly surface area and number of seeds/10 g, with key beetle life-history parameters. Overall, AYB exhibited moderate resistance to C. maculatus, supporting its potential as a more resilient storage legume under smallholder conditions.
Commercial cold storage systems are energy-intensive thermal processes where fluctuating ambient temperatures often drive internal heat gains, leading to compressor short-cycling and reduced efficiency. To address these challenges, this study develops a Multiple Model Predictive Control (MMPC) strategy based on a data-driven state-space architecture. Utilizing a modified high-fidelity benchmark model from Aalborg University, subspace system identification was employed to characterize the system dynamics across a 14-day tropical temperature profile (8.9 to 32.8°C). The model incorporates ambient temperature and heat transfer rates as exogenous inputs, alongside suction pressure, expansion valve and compressor capacity, to predict air and goods temperatures based on expert knowledge. The optimal 7th-order model achieved a goodness-of-fit of 98.7% and 90.4% for the respective outputs, with a minimal MSE of 56.6e-05. Bayesian optimization was implemented to tune the MPC prediction and control horizons across varying operating conditions. Simulation results demonstrate that the MMPC framework performed better than both a single MPC and the benchmark PI-deadband controller. The MMPC reduced compressor switching by 38.5% (72 vs. 117 cycles) and achieved energy savings of 25.4% over the benchmark and 65.3% over the single MPC. Furthermore, the MMPC maintained food temperatures strictly below 5°C, ensuring food safety for perishable goods, whereas the single MPC failed to maintain regulation (5.9°C). All simulations were performed using the software package MATLAB R2025b and Simulink R2025b.
The growing plastic crisis and the necessity to shift the use of fossil resources have placed bioplastics as central alternatives to other materials in the global environment. These materials, which include polyhydroxyalkanoates (PHA), polylactic acid (PLA), and starch-based polymers, are derived as such that one can reduce carbon footprints as well as provide a viable end-of-life option due to biodegradability. However, without a conscious decision to be circular, the bioplastics will be sent to linear disposal models where, in the end, poor management will result in either contamination of recycling streams or inefficient degradation in an inappropriate environment. This lack of connection highlights the pressing need to incorporate bioplastics into strong Circular economy (CE) models, which would make a major focus on the constant retention of materials within utilisation cycles. This review argues that microorganisms and their enzymatic machineries are the biological catalysts that are needed to make this circular vision of bioplastics a reality. The systematic analysis of the microbial strategies is introduced, which begins with the enzymatic degradation of major classes of biopolymers and characterizes the specificity of depolymerases, cutinases, and hydrolases. The discussion then progresses into the field of microbial upcycling which represents a paradigm shift of treating waste to recovering resources, as bioplastic waste is used to generate value-added products in the form of platform chemicals, solvents, biosurfactants, and even next-generation biopolymers. Additionally, the state-of-the-art improvement with metabolic engineering and synthetic microbial consortia is investigated, which is to be efficient and resilient. The review critically evaluates how these biological processes may be incorporated into scalable CE systems, examines technological readiness, life-cycle, and the necessary synergy between policy, infrastructure and innovation. Finally, it hypothesizes that through adoption of microbial processes, bioplastic waste can be converted into a renewable feedstock, rather than a liability to the environment and thus creating an essential route toward a regenerative, circular bioeconomy.
