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/9203/ http://eprints.uthm.edu.my/9203/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf |
| _version_ | 1848889611753881600 |
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| author | Alzaeemi, Shehab Abdulhabib Ali Noman, Efaq Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah El Enshasy, Hesham Ali |
| author_facet | Alzaeemi, Shehab Abdulhabib Ali Noman, Efaq Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah El Enshasy, Hesham Ali |
| author_sort | Alzaeemi, Shehab Abdulhabib |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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 | 2025-11-15T20:28:56Z |
| format | Article |
| id | uthm-9203 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:28:56Z |
| publishDate | 2023 |
| publisher | Mdpi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-92032023-07-17T07:40:01Z http://eprints.uthm.edu.my/9203/ 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) Alzaeemi, Shehab Abdulhabib Ali Noman, Efaq Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah El Enshasy, Hesham Ali 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/9203/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf Alzaeemi, Shehab Abdulhabib and Ali Noman, Efaq and Al-shaibani, Muhanna Mohammed and Al-Gheethi, Adel and Radin Mohamed, Radin Maya Saphira and Almoheer, Reyad and Seif, Mubarak and Kim Gaik Tay, Kim Gaik Tay and Mohamad Zin, Noraziah and El Enshasy, Hesham Ali (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) Alzaeemi, Shehab Abdulhabib Ali Noman, Efaq Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Kim Gaik Tay, Kim Gaik Tay Mohamad Zin, Noraziah El Enshasy, Hesham Ali 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/9203/ http://eprints.uthm.edu.my/9203/ http://eprints.uthm.edu.my/9203/1/J15954_e6d2f6d510e1cf566688dd574c4620cd.pdf |