Authors : Osisami Olubukunola F.; Egwim C. Evans; Adeboye E. Seyi; Madaki F. M.; Busari M. B.; Salith H. Suleiman; Hassan S. Abu; Ononokpono G. E.

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/f75jvbf7

Scribd : https://tinyurl.com/52w2tpz6

DOI : https://doi.org/10.38124/ijisrt/26jan294

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Large volumes of byproducts are generated during the processing of citrus, plantain, and banana, much of which is discarded, contributing to environmental pollution and inefficient resource use. These agro-wastes, however, are rich sources of bioactive compounds with significant nutritional and therapeutic potential. In the context of increasing global interest in sustainable resource utilization and circular economy strategies, the valorization of fruit peels represents a promising pathway for developing high-value functional ingredients for food and pharmaceutical applications. This review synthesizes current knowledge on the major phytochemicals present in these wastes with particular emphasis on total phenolics, flavonoids, including quercetin and its derivatives, carotenoids, and sulfur-containing compounds. The reported biological activities of these compounds, such as antidiabetic, anti-obesity, anticancer, and antimicrobial effects, are discussed in relation to their relevance for pharmacological and biomedical applications. The review further examines recent advances in green extraction technologies, focusing on enzyme-assisted and microwave-assisted extraction methods, which offer improved efficiency and sustainability compared with conventional solvent-intensive and high-temperature techniques. Additionally, emerging applications of machine learning approaches for identifying, predicting, and prioritizing bioactive compounds from complex plant matrices are briefly considered.

Keywords : Bioactive Compounds, Phytochemicals, Total Phenolics, Agro-Wastes, Machine Learning.

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