Antioxidant Activity, Theaflavin, Total Polyphenol, and Catechin Composition of Camellia sinensis Processing Effluents from Various Factories in Kenya

Author:

Mangenya Thaddeus,Kariuki Daniel,Kinyua Johnson,Obanda Martin,Ochanda Simon,Moriasi Gervason

Abstract

Research into the antioxidant efficacy and sustainability implications of tea waste, a by-product of the rapidly growing global beverage industry, is increasingly necessary. This study scrutinized samples taken from various factories to explore their capacity for free radical scavenging and potential public health and environmental benefits. Analysis of the samples and reference antioxidant (BHT) revealed a notable dose- dependent rise in free-radical-scavenging action, implying a positive concentration-dependent antioxidant efficacy. Variations in antioxidant activity occurred from tea wastes sourced at various factories, alluding to geography having a pivotal effect on the biological contents and antioxidative capacity. Of particular interest were Boito's and Chelal's cyclone fluff samples that evinced relatively lower potency than those from other factories. Tombe factory's cyclone sample demonstrated a superior capacity for scavenging free radicals at different concentrations, suggesting that particular production or cultivation processes augmented its effectiveness. Notably, some tea waste samples even equalled the potency of BHT (a synthetic antioxidant) when augured to its highest level. Furthermore, this study revealed disparities in Theaflavin and Thearubigin content due to factors like maturation period and processing methods guiding these concentrations. We identified tea waste samples possessing remarkable polyphenol concentration, thereby providing valuable insights for consumers and industries. Further investigations on the chemical composition of tea waste phytocompounds are paramount due to their acclaimed health-promoting properties and the factors influencing their concentrations and antioxidant efficacy.

Publisher

International Journal of Innovative Science and Research Technology

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1. Development of Random Forest Model for Stroke Prediction;International Journal of Innovative Science and Research Technology (IJISRT);2024-05-16

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