Application of orthogonal optimization and feedforward backpropagation model in the microwave extraction of natural antioxidants from tropical white pepper

被引:0
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作者
Olusegun Abayomi Olalere
Nour Hamid Abdurahman
Zulkafli Hassan
Oluwaseun Ruth Alara
Norlin Pauzi
机构
[1] University Malaysia Pahang,Centre of Excellence for Advanced Research in Fluid Flow
[2] University Malaysia Pahang,Faculty of Chemical and Natural Resources Engineering
来源
Journal of Analytical Science and Technology | / 9卷
关键词
Antioxidants; Artificial neural network (ANN); Free radicals; Microwave extraction; Taguchi design; White pepper;
D O I
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中图分类号
学科分类号
摘要
The tropical white peppercorns are common commodity crops which have been traditionally used for the treatment of many free radical-related diseases. These medicinal properties are due to the presence of natural antioxidants. This study investigated the combination of microwave extraction parameters for the recovery of natural antioxidants from the white pepper matrix. Microwave-assisted technique was used for the extraction of bioactive oleoresin from white pepper. Taguchi experimental design was employed to investigate the combination of independent extraction parameters for optimal recovery of natural antioxidants. The feed backpropagation artificial neural network model was thereafter applied to optimally predict the result for the different combination of operating parameters. This was achieved by evaluating different algorithms, transfer functions, and neurons. The result obtained from the orthogonal parametric study gave an optimal antioxidant activity of 91.02% at irradiation time of 120 min, microwave power level of 350 W, particle size of 0.300 mm, and liquid-to-solid ratio of 6 mL/g. The gradient descent (GD) algorithm, tansigmoid transfer function, and 4-x-3 topology were used to model the experimental data. A better prediction was then obtained with an overall coefficient (R) and mean square error (MSE) of 0.9595 and 1.4381, respectively. In this study, the feedforward backpropagation neural network was successfully applied to optimally evaluate the complex relationship between the input extraction parameters and the response.
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