Application of orthogonal optimization and feedforward backpropagation model in the microwave extraction of natural antioxidants from tropical white pepper
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Open
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/23775/1/Application%20of%20orthogonal%20optimization%20and%20feedforward%20backpropagation%20model.pdf http://umpir.ump.edu.my/id/eprint/23775/ https://doi.org/10.1186/s40543-018-0157-x https://doi.org/10.1186/s40543-018-0157-x |
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| Summary: | 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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