Staff View: 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)

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 temp...

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Main Authors:	Alzaeemi, Shehab Abdulhabib, Noman, Efaq Ali, Al-shaibani, Muhanna Mohammed, Al-Gheethi, Adel, Radin Mohamed, Radin Maya Saphira, Almoheer, Reyad, Seif, Mubarak, Tay, Kim Gaik, Mohamad Zin, Noraziah, El Enshasy, Hesham Ali
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/107101/1/HeshamAliMetwally2023_ImprovementofLasparaginaseanAnticancerAgentofAspergillus.pdf http://eprints.utm.my/107101/ http://dx.doi.org/10.3390/fermentation9030200
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id	my.utm.107101
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spelling	my.utm.1071012024-08-21T07:25:14Z http://eprints.utm.my/107101/ 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 Noman, Efaq Ali Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Tay, Kim Gaik Mohamad Zin, Noraziah El Enshasy, Hesham Ali TP Chemical technology 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 application/pdf en http://eprints.utm.my/107101/1/HeshamAliMetwally2023_ImprovementofLasparaginaseanAnticancerAgentofAspergillus.pdf Alzaeemi, Shehab Abdulhabib and Noman, Efaq Ali and Al-shaibani, Muhanna Mohammed and Al-Gheethi, Adel and Radin Mohamed, Radin Maya Saphira and Almoheer, Reyad and Seif, Mubarak and Tay, Kim Gaik 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 (3). pp. 1-15. ISSN 2311-5637 http://dx.doi.org/10.3390/fermentation9030200 DOI : 10.3390/fermentation9030200
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Alzaeemi, Shehab Abdulhabib Noman, Efaq Ali Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Tay, Kim Gaik 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)
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.
format	Article
author	Alzaeemi, Shehab Abdulhabib Noman, Efaq Ali Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Tay, Kim Gaik Mohamad Zin, Noraziah El Enshasy, Hesham Ali
author_facet	Alzaeemi, Shehab Abdulhabib Noman, Efaq Ali Al-shaibani, Muhanna Mohammed Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Almoheer, Reyad Seif, Mubarak Tay, Kim Gaik Mohamad Zin, Noraziah El Enshasy, Hesham Ali
author_sort	Alzaeemi, Shehab Abdulhabib
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_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_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_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)
publisher	MDPI
publishDate	2023
url	http://eprints.utm.my/107101/1/HeshamAliMetwally2023_ImprovementofLasparaginaseanAnticancerAgentofAspergillus.pdf http://eprints.utm.my/107101/ http://dx.doi.org/10.3390/fermentation9030200
_version_	1809136626172952576
score	13.211869

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)

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