Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica

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@Article{ALANZI:2024:jer,

• author = "Hamdan Alanzi and Hamoud Alenezi and Oladayo Adeyi and Abiola J. Adeyi and Emmanuel Olusola and Chee-Yuen Gan and Olusegun Abayomi Olalere",
• title = "Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica",
• journal = "Journal of Engineering Research",
• year = "2024",
• ISSN = "2307-1877",
• DOI = "doi:10.1016/j.jer.2024.02.020",
• URL = "https://www.sciencedirect.com/science/article/pii/S2307187724000476",
• keywords = "genetic algorithms, genetic programming, leaf, bioactive extract, Heat-assisted technology, multi gene genetic programming, reliability assessment",
• abstract = "This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100percent) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50degreeC), solid-to-liquid ratio (1:20-1:60g/mL), and extraction time (45-180min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50percent ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565percent), TPC (67.116mg GAE/g), and AA (3.68583uM AAE/g) were determined as OT of 41.61degreeC, S:L of 1:60g/mL, and ET of 180min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985percent certainty for TPC, 97.569percent for EY, and 98.661percent for AA values",

}

Genetic Programming entries for Hamdan Alanzi Hamoud Alenezi Oladayo Adeyi Abiola John Adeyi Emmanuel Olusola Chee-Yuen Gan Olusegun Abayomi Olalere

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