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Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach
The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields...
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Published in: | International journal of biological macromolecules 2024-12, Vol.282 (Pt 1), p.136708, Article 136708 |
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description | The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the fine-tuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.
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•Modeled and optimized chitin and chitosan yields from Fusarium verticillioides using many factors simultaneously.•Employed ALO and AGE-MOEA algorithms for single- and multi-objective optimization to maximize chitin and chitosan yields.•Key factors identified for chitin yield included arginine, NH4Cl, oligochitin, and K2HPO4.•Significant contributors to chitosan yield included arginine, NaCl, NH4Cl, oligochitin, FeSO4, K2HPO4, and CaCl2.•WSM, WPM, and NED were utilized as decision-making methods. |
doi_str_mv | 10.1016/j.ijbiomac.2024.136708 |
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[Display omitted]
•Modeled and optimized chitin and chitosan yields from Fusarium verticillioides using many factors simultaneously.•Employed ALO and AGE-MOEA algorithms for single- and multi-objective optimization to maximize chitin and chitosan yields.•Key factors identified for chitin yield included arginine, NH4Cl, oligochitin, and K2HPO4.•Significant contributors to chitosan yield included arginine, NaCl, NH4Cl, oligochitin, FeSO4, K2HPO4, and CaCl2.•WSM, WPM, and NED were utilized as decision-making methods.</description><identifier>ISSN: 0141-8130</identifier><identifier>ISSN: 1879-0003</identifier><identifier>EISSN: 1879-0003</identifier><identifier>DOI: 10.1016/j.ijbiomac.2024.136708</identifier><identifier>PMID: 39427799</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Algorithms ; arginine ; biopolymers ; Chemical engineering ; Chemical Sciences ; Chitin ; Chitin - chemistry ; Chitosan ; Chitosan - chemistry ; culture media ; Evolutionary algorithms ; Fusarium ; Fusarium verticillioides ; geometry ; regression analysis ; Spectroscopy, Fourier Transform Infrared</subject><ispartof>International journal of biological macromolecules, 2024-12, Vol.282 (Pt 1), p.136708, Article 136708</ispartof><rights>2024 Elsevier B.V.</rights><rights>Copyright © 2024 Elsevier B.V. All rights reserved.</rights><rights>Attribution - NonCommercial</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c312t-27db2e43368ce73e2f2d32717f48f37aff3a7222bf53d38388efb210de78cf243</cites><orcidid>0000-0003-2622-2384 ; 0000-0001-6510-9101</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,778,782,883,27911,27912</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39427799$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04772612$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Arbia, Wassila</creatorcontrib><creatorcontrib>Kouider Amar, Mohamed</creatorcontrib><creatorcontrib>Adour, Lydia</creatorcontrib><creatorcontrib>Amrane, Abdeltif</creatorcontrib><title>Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach</title><title>International journal of biological macromolecules</title><addtitle>Int J Biol Macromol</addtitle><description>The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the fine-tuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.
[Display omitted]
•Modeled and optimized chitin and chitosan yields from Fusarium verticillioides using many factors simultaneously.•Employed ALO and AGE-MOEA algorithms for single- and multi-objective optimization to maximize chitin and chitosan yields.•Key factors identified for chitin yield included arginine, NH4Cl, oligochitin, and K2HPO4.•Significant contributors to chitosan yield included arginine, NaCl, NH4Cl, oligochitin, FeSO4, K2HPO4, and CaCl2.•WSM, WPM, and NED were utilized as decision-making methods.</description><subject>Algorithms</subject><subject>arginine</subject><subject>biopolymers</subject><subject>Chemical engineering</subject><subject>Chemical Sciences</subject><subject>Chitin</subject><subject>Chitin - chemistry</subject><subject>Chitosan</subject><subject>Chitosan - chemistry</subject><subject>culture media</subject><subject>Evolutionary algorithms</subject><subject>Fusarium</subject><subject>Fusarium verticillioides</subject><subject>geometry</subject><subject>regression analysis</subject><subject>Spectroscopy, Fourier Transform Infrared</subject><issn>0141-8130</issn><issn>1879-0003</issn><issn>1879-0003</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqNkUtv1DAQgC0EotvCX6h8hEMWP7Kxc6OqKK20iAucLcces7NK4sVOViy_Hi9pewX5YGv8zUPzEXLN2Zoz3nzYr3HfYRysWwsm6jWXjWL6BVlxrdqKMSZfkhXjNa80l-yCXOa8L9Fmw_VrciHbWijVtisyfbG_cMDfOP6gbocTjtSO_u8zZjvSBC4eIZ3oCaH3mYYUB3o3Z5twHmj5mdBh32NED5nO-VzH0sGOpypYN8WUKztVtppwAGoPhxSt270hr4LtM7x9vK_I97tP327vq-3Xzw-3N9vKSS6mSijfCailbLQDJUEE4aVQXIVaB6lsCNIqIUQXNtJLLbWG0AnOPCjtgqjlFXm_1N3Z3hwSDjadTLRo7m-25hxjtVKi4eLIC_tuYcuIP2fIkxkwO-h7O0Kcs5F8U4tG67b9D5RrLetyCtosqEsx5wTheQzOzFmk2ZsnkeYs0iwiS-L1Y4-5G8A_pz2ZK8DHBYCywCNCMtkhjA48FmeT8RH_1eMPdZ2y4A</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Arbia, Wassila</creator><creator>Kouider Amar, Mohamed</creator><creator>Adour, Lydia</creator><creator>Amrane, Abdeltif</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-2622-2384</orcidid><orcidid>https://orcid.org/0000-0001-6510-9101</orcidid></search><sort><creationdate>20241201</creationdate><title>Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach</title><author>Arbia, Wassila ; Kouider Amar, Mohamed ; Adour, Lydia ; Amrane, Abdeltif</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c312t-27db2e43368ce73e2f2d32717f48f37aff3a7222bf53d38388efb210de78cf243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>arginine</topic><topic>biopolymers</topic><topic>Chemical engineering</topic><topic>Chemical Sciences</topic><topic>Chitin</topic><topic>Chitin - chemistry</topic><topic>Chitosan</topic><topic>Chitosan - chemistry</topic><topic>culture media</topic><topic>Evolutionary algorithms</topic><topic>Fusarium</topic><topic>Fusarium verticillioides</topic><topic>geometry</topic><topic>regression analysis</topic><topic>Spectroscopy, Fourier Transform Infrared</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arbia, Wassila</creatorcontrib><creatorcontrib>Kouider Amar, Mohamed</creatorcontrib><creatorcontrib>Adour, Lydia</creatorcontrib><creatorcontrib>Amrane, Abdeltif</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>International journal of biological macromolecules</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arbia, Wassila</au><au>Kouider Amar, Mohamed</au><au>Adour, Lydia</au><au>Amrane, Abdeltif</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach</atitle><jtitle>International journal of biological macromolecules</jtitle><addtitle>Int J Biol Macromol</addtitle><date>2024-12-01</date><risdate>2024</risdate><volume>282</volume><issue>Pt 1</issue><spage>136708</spage><pages>136708-</pages><artnum>136708</artnum><issn>0141-8130</issn><issn>1879-0003</issn><eissn>1879-0003</eissn><abstract>The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the fine-tuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.
[Display omitted]
•Modeled and optimized chitin and chitosan yields from Fusarium verticillioides using many factors simultaneously.•Employed ALO and AGE-MOEA algorithms for single- and multi-objective optimization to maximize chitin and chitosan yields.•Key factors identified for chitin yield included arginine, NH4Cl, oligochitin, and K2HPO4.•Significant contributors to chitosan yield included arginine, NaCl, NH4Cl, oligochitin, FeSO4, K2HPO4, and CaCl2.•WSM, WPM, and NED were utilized as decision-making methods.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>39427799</pmid><doi>10.1016/j.ijbiomac.2024.136708</doi><orcidid>https://orcid.org/0000-0003-2622-2384</orcidid><orcidid>https://orcid.org/0000-0001-6510-9101</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms arginine biopolymers Chemical engineering Chemical Sciences Chitin Chitin - chemistry Chitosan Chitosan - chemistry culture media Evolutionary algorithms Fusarium Fusarium verticillioides geometry regression analysis Spectroscopy, Fourier Transform Infrared |
title | Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach |
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