Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides 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 Aspergillus arenarioides 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 tem...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Mdpi
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11473/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf |
| _version_ | 1825638551163764736 |
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| author | Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi Efaq Ali Noman, Efaq Ali Noman Muhanna Mohammed Al-shaibani, Muhanna Mohammed Al-shaibani Adel Al-Gheethi, Adel Al-Gheethi Radin Mohamed, Radin Maya Saphira Reyad Almoheer, Reyad Almoheer Mubarak Seif, Mubarak Seif Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah Hesham Ali El Enshasy, Hesham Ali El Enshasy |
| author_facet | Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi Efaq Ali Noman, Efaq Ali Noman Muhanna Mohammed Al-shaibani, Muhanna Mohammed Al-shaibani Adel Al-Gheethi, Adel Al-Gheethi Radin Mohamed, Radin Maya Saphira Reyad Almoheer, Reyad Almoheer Mubarak Seif, Mubarak Seif Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah Hesham Ali El Enshasy, Hesham Ali El Enshasy |
| author_sort | Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi |
| collection | UTHM |
| description | The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides 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 (x1), pH (x2 ), incubation time (x3), and soybean concentration(x4). The coefficient of the predicted model using the Box–Behnken design (BBD) was R2 = 0.9079(p < 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−1 of the actual and predicted enzyme production was recorded at 34 ◦C, pH 8.5, after 7 days and with 10 g L−1 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 | 2024-09-24T00:10:47Z |
| format | Article |
| id | uthm.eprints-11473 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-09-24T00:10:47Z |
| publishDate | 2023 |
| publisher | Mdpi |
| record_format | dspace |
| spelling | uthm.eprints-114732024-08-01T02:59:35Z http://eprints.uthm.edu.my/11473/ Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi Efaq Ali Noman, Efaq Ali Noman Muhanna Mohammed Al-shaibani, Muhanna Mohammed Al-shaibani Adel Al-Gheethi, Adel Al-Gheethi Radin Mohamed, Radin Maya Saphira Reyad Almoheer, Reyad Almoheer Mubarak Seif, Mubarak Seif Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah Hesham Ali El Enshasy, Hesham Ali El Enshasy T Technology (General) The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides 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 (x1), pH (x2 ), incubation time (x3), and soybean concentration(x4). The coefficient of the predicted model using the Box–Behnken design (BBD) was R2 = 0.9079(p < 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−1 of the actual and predicted enzyme production was recorded at 34 ◦C, pH 8.5, after 7 days and with 10 g L−1 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. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11473/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi and Efaq Ali Noman, Efaq Ali Noman and Muhanna Mohammed Al-shaibani, Muhanna Mohammed Al-shaibani and Adel Al-Gheethi, Adel Al-Gheethi and Radin Mohamed, Radin Maya Saphira and Reyad Almoheer, Reyad Almoheer and Mubarak Seif, Mubarak Seif and Kim Gaik Tay, Kim Gaik Tay and Mohamad Zin, Noraziah and Hesham Ali El Enshasy, Hesham Ali El Enshasy (2023) Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA). Fermentation, 9 (200). pp. 1-15. https://doi.org/10.3390/fermentation9030200 |
| spellingShingle | T Technology (General) Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi Efaq Ali Noman, Efaq Ali Noman Muhanna Mohammed Al-shaibani, Muhanna Mohammed Al-shaibani Adel Al-Gheethi, Adel Al-Gheethi Radin Mohamed, Radin Maya Saphira Reyad Almoheer, Reyad Almoheer Mubarak Seif, Mubarak Seif Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah Hesham Ali El Enshasy, Hesham Ali El Enshasy Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA) |
| title | Improvement of L-asparaginase, an Anticancer Agent of
Aspergillus arenarioides 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
Aspergillus arenarioides 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
Aspergillus arenarioides 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
Aspergillus arenarioides 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
Aspergillus arenarioides 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 aspergillus arenarioides ean603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm rbfnn ga |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/11473/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf |
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