Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA)
The present study aimed to optimize the production of L-asparaginase from <i>Aspergillus arenarioides</i> EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Fermentation |
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| Online Access: | https://www.mdpi.com/2311-5637/9/3/200 |
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| author | Shehab Abdulhabib Alzaeemi Efaq Ali Noman Muhanna Mohammed Al-shaibani Adel Al-Gheethi Radin Maya Saphira Radin Mohamed Reyad Almoheer Mubarak Seif Kim Gaik Tay Noraziah Mohamad Zin Hesham Ali El Enshasy |
| author_facet | Shehab Abdulhabib Alzaeemi Efaq Ali Noman Muhanna Mohammed Al-shaibani Adel Al-Gheethi Radin Maya Saphira Radin Mohamed Reyad Almoheer Mubarak Seif Kim Gaik Tay Noraziah Mohamad Zin Hesham Ali El Enshasy |
| author_sort | Shehab Abdulhabib Alzaeemi |
| collection | DOAJ |
| description | The present study aimed to optimize the production of L-asparaginase from <i>Aspergillus arenarioides</i> EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (<i>x</i><sub>1</sub>), pH (<i>x</i><sub>2</sub>), incubation time (<i>x</i><sub>3</sub>), and soybean concentration (<i>x</i><sub>4</sub>). The coefficient of the predicted model using the Box–Behnken design (BBD) was R<sup>2</sup> = 0.9079 (<i>p</i> < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL<sup>−1</sup> of the actual and predicted enzyme production was recorded at 34 °C, pH 8.5, after 7 days and with 10 g L<sup>−1</sup> of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data. |
| first_indexed | 2024-03-11T06:34:08Z |
| format | Article |
| id | doaj.art-a0d13c5ebd994338a271680947c002ac |
| institution | Directory Open Access Journal |
| issn | 2311-5637 |
| language | English |
| last_indexed | 2024-03-11T06:34:08Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fermentation |
| spelling | doaj.art-a0d13c5ebd994338a271680947c002ac2023-11-17T11:01:24ZengMDPI AGFermentation2311-56372023-02-019320010.3390/fermentation9030200Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA)Shehab Abdulhabib Alzaeemi0Efaq Ali Noman1Muhanna Mohammed Al-shaibani2Adel Al-Gheethi3Radin Maya Saphira Radin Mohamed4Reyad Almoheer5Mubarak Seif6Kim Gaik Tay7Noraziah Mohamad Zin8Hesham Ali El Enshasy9Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaMicropollutant Research Centre (MPRC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaMicropollutant Research Centre (MPRC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaMicropollutant Research Centre (MPRC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaMicropollutant Research Centre (MPRC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaFaculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus 21030, MalaysiaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaFaculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, MalaysiaCenter for Diagnostic, Therapeutics & Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, MalaysiaInstitute of Bioproduct Development (IBD), Universiti Teknologi Malaysia (UTM), Skudai 81310, MalaysiaThe present study aimed to optimize the production of L-asparaginase from <i>Aspergillus arenarioides</i> EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (<i>x</i><sub>1</sub>), pH (<i>x</i><sub>2</sub>), incubation time (<i>x</i><sub>3</sub>), and soybean concentration (<i>x</i><sub>4</sub>). The coefficient of the predicted model using the Box–Behnken design (BBD) was R<sup>2</sup> = 0.9079 (<i>p</i> < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL<sup>−1</sup> of the actual and predicted enzyme production was recorded at 34 °C, pH 8.5, after 7 days and with 10 g L<sup>−1</sup> of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data.https://www.mdpi.com/2311-5637/9/3/200L-asparaginase<i>Aspergillus arenarioides</i>submerged fermentationorganic soybean |
| spellingShingle | Shehab Abdulhabib Alzaeemi Efaq Ali Noman Muhanna Mohammed Al-shaibani Adel Al-Gheethi Radin Maya Saphira Radin Mohamed Reyad Almoheer Mubarak Seif Kim Gaik Tay Noraziah Mohamad Zin Hesham Ali El Enshasy Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) Fermentation L-asparaginase <i>Aspergillus arenarioides</i> submerged fermentation organic soybean |
| title | Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title_full | Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title_fullStr | Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title_full_unstemmed | Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title_short | Improvement of L-asparaginase, an Anticancer Agent of <i>Aspergillus arenarioides</i> EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title_sort | improvement of l asparaginase an anticancer agent of i aspergillus arenarioides i ean603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm rbfnn ga |
| topic | L-asparaginase <i>Aspergillus arenarioides</i> submerged fermentation organic soybean |
| url | https://www.mdpi.com/2311-5637/9/3/200 |
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