Academic Journal
Προβολή σε EDS

Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate.
Συγγραφείς: Lilaria K; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India., Tiwari S; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India. Electronic address: shubhratiwari77@gmail.com., Jadhav SK; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India.
Πηγή: International journal of biological macromolecules [Int J Biol Macromol] 2026 Jun; Vol. 368, pp. 152665. Date of Electronic Publication: 2026 May 23.
Τύπος έκδοσης: Journal Article; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 7909578 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0003 (Electronic) Linking ISSN: 01418130 NLM ISO Abbreviation: Int J Biol Macromol Subsets: MEDLINE
Imprint Name(s): Publication: Amsterdam : Elsevier
Original Publication: Guildford, Eng., IPC Science and Technology Press.
Ιατρικοί όροι (MeSH): Musa*/chemistry , Musa*/microbiology , Cellulase*/biosynthesis , Bacillaceae*/enzymology , Bacillaceae*/isolation & purification , Bacillaceae*/genetics , Machine Learning*, RNA, Ribosomal, 16S/genetics ; Neural Networks, Computer ; Support Vector Machine ; Hydrogen-Ion Concentration ; Temperature ; Bayes Theorem
Περίληψη: A key bottleneck in the sustainable valorization of lignocellulosic biomass remains the efficient, low-cost production of cellulase. Although widely used, Response Surface Methodology (RSM) is limited by its quadratic assumptions, which may not capture nonlinear interactions. Despite progress in using Machine Learning (ML) for cellulase prediction, integrated approaches that combine RSM with interpretable ML methods, along with regression diagnostics, permutation importance, and SHAP-inspired (Shapley Additive Explanations) methods, remain exceedingly scarce in cellulase studies, limiting both predictive performance and biological insight. In this work, a cellulolytic bacterium was isolated from the banana pseudostem and identified as Lysinibacillus capsici through 16S rRNA gene analysis. Box-Behnken Design (BBD) was adopted to optimize cellulase production, considering key process parameters such as pH, temperature, incubation time, substrate concentration, and inoculum volume. To improve predictive capability, a comparative study was conducted on four ML techniques: Artificial Neural Network (ANN), Bayesian Regularized Neural Network (BRNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). Among the evaluated models, BRNN and RBFNN showed slightly better performance; however, the differences were marginal, indicating comparable overall predictive behavior. Regression plots and statistical metrics validated model robustness, while SHAP-inspired analysis quantified the relative contributions of substrate concentration, pH, and temperature. The findings emphasize that predictive performance is influenced by both the modeling strategy and data availability; therefore, comparisons between RSM and machine learning models should be interpreted in this context. Overall, this integrated RSM-ML framework provides a structured and interpretable approach for optimizing enzyme production from lignocellulosic biomass.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
Contributed Indexing: Keywords: Banana Pseudostem; Cellulase; Lysinibacillus capsici; Machine learning; Response surface methodology; SHAP-inspired analysis; Support vector machine
Substance Nomenclature: EC 3.2.1.4 (Cellulase)
0 (RNA, Ribosomal, 16S)
Entry Date(s): Date Created: 20260523 Date Completed: 20260611 Latest Revision: 20260611
Update Code: 20260611
DOI: 10.1016/j.ijbiomac.2026.152665
PMID: 42176927
Βάση Δεδομένων: MEDLINE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.doi.org/10.1016/j.ijbiomac.2026.152665?
    Name: ScienceDirect (all content) (s7799221)
    Category: fullText
    Text: View record from ScienceDirect
    MouseOverText: View record from ScienceDirect
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=18790003&ISBN=&volume=368&issue=&date=20260601&spage=152665&pages=152665&title=International journal of biological macromolecules&atitle=Comparative%20RSM-ML%20analysis%20to%20predict%20cellulase%20production%20from%20Lysinibacillus%20capsici%20isolate%20using%20banana%20pseudostem%20as%20a%20sustainable%20substrate.&aulast=Lilaria%20K&id=DOI:10.1016/j.ijbiomac.2026.152665
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 42176927
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Lilaria+K%22">Lilaria K</searchLink>; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India.<br /><searchLink fieldCode="AU" term="%22Tiwari+S%22">Tiwari S</searchLink>; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India. Electronic address: shubhratiwari77@gmail.com.<br /><searchLink fieldCode="AU" term="%22Jadhav+SK%22">Jadhav SK</searchLink>; School of Studies in Biotechnology, Pt. Ravishankar Shukla University, Raipur, 492 010, Chhattisgarh, India.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%227909578%22">International journal of biological macromolecules</searchLink> [Int J Biol Macromol] 2026 Jun; Vol. 368, pp. 152665. <i>Date of Electronic Publication: </i>2026 May 23.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Comparative Study
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>7909578 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-0003 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2201418130%22">01418130 </searchLink><i>NLM ISO Abbreviation: </i>Int J Biol Macromol <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Publication</i>: Amsterdam : Elsevier<br /><i>Original Publication</i>: Guildford, Eng., IPC Science and Technology Press.
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Musa%22">Musa*</searchLink>/<searchLink fieldCode="MM" term="%22Musa+chemistry%22">chemistry</searchLink> <br /><searchLink fieldCode="MM" term="%22Musa%22">Musa*</searchLink>/<searchLink fieldCode="MM" term="%22Musa+microbiology%22">microbiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Cellulase%22">Cellulase*</searchLink>/<searchLink fieldCode="MM" term="%22Cellulase+biosynthesis%22">biosynthesis</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacillaceae%22">Bacillaceae*</searchLink>/<searchLink fieldCode="MM" term="%22Bacillaceae+enzymology%22">enzymology</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacillaceae%22">Bacillaceae*</searchLink>/<searchLink fieldCode="MM" term="%22Bacillaceae+isolation+%26+purification%22">isolation & purification</searchLink> <br /><searchLink fieldCode="MM" term="%22Bacillaceae%22">Bacillaceae*</searchLink>/<searchLink fieldCode="MM" term="%22Bacillaceae+genetics%22">genetics</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22RNA%2C+Ribosomal%2C+16S%22">RNA, Ribosomal, 16S</searchLink>/<searchLink fieldCode="MH" term="%22RNA%2C+Ribosomal%2C+16S+genetics%22">genetics</searchLink> ; <searchLink fieldCode="MH" term="%22Neural+Networks%2C+Computer%22">Neural Networks, Computer</searchLink> ; <searchLink fieldCode="MH" term="%22Support+Vector+Machine%22">Support Vector Machine</searchLink> ; <searchLink fieldCode="MH" term="%22Hydrogen-Ion+Concentration%22">Hydrogen-Ion Concentration</searchLink> ; <searchLink fieldCode="MH" term="%22Temperature%22">Temperature</searchLink> ; <searchLink fieldCode="MH" term="%22Bayes+Theorem%22">Bayes Theorem</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A key bottleneck in the sustainable valorization of lignocellulosic biomass remains the efficient, low-cost production of cellulase. Although widely used, Response Surface Methodology (RSM) is limited by its quadratic assumptions, which may not capture nonlinear interactions. Despite progress in using Machine Learning (ML) for cellulase prediction, integrated approaches that combine RSM with interpretable ML methods, along with regression diagnostics, permutation importance, and SHAP-inspired (Shapley Additive Explanations) methods, remain exceedingly scarce in cellulase studies, limiting both predictive performance and biological insight. In this work, a cellulolytic bacterium was isolated from the banana pseudostem and identified as Lysinibacillus capsici through 16S rRNA gene analysis. Box-Behnken Design (BBD) was adopted to optimize cellulase production, considering key process parameters such as pH, temperature, incubation time, substrate concentration, and inoculum volume. To improve predictive capability, a comparative study was conducted on four ML techniques: Artificial Neural Network (ANN), Bayesian Regularized Neural Network (BRNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). Among the evaluated models, BRNN and RBFNN showed slightly better performance; however, the differences were marginal, indicating comparable overall predictive behavior. Regression plots and statistical metrics validated model robustness, while SHAP-inspired analysis quantified the relative contributions of substrate concentration, pH, and temperature. The findings emphasize that predictive performance is influenced by both the modeling strategy and data availability; therefore, comparisons between RSM and machine learning models should be interpreted in this context. Overall, this integrated RSM-ML framework provides a structured and interpretable approach for optimizing enzyme production from lignocellulosic biomass.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>Banana Pseudostem; Cellulase; Lysinibacillus capsici; Machine learning; Response surface methodology; SHAP-inspired analysis; Support vector machine
– Name: NumberCAS
  Label: Substance Nomenclature
  Group: ID
  Data: EC 3.2.1.4 (Cellulase)<br />0 (RNA, Ribosomal, 16S)
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20260523 <i>Date Completed: </i>20260611 <i>Latest Revision: </i>20260611
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260611
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1016/j.ijbiomac.2026.152665
– Name: AN
  Label: PMID
  Group: ID
  Data: 42176927
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42176927
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ijbiomac.2026.152665
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 152665
    Subjects:
      – SubjectFull: RNA, Ribosomal, 16S genetics
        Type: general
      – SubjectFull: Neural Networks, Computer
        Type: general
      – SubjectFull: Support Vector Machine
        Type: general
      – SubjectFull: Hydrogen-Ion Concentration
        Type: general
      – SubjectFull: Temperature
        Type: general
      – SubjectFull: Bayes Theorem
        Type: general
      – SubjectFull: Musa chemistry
        Type: general
      – SubjectFull: Musa microbiology
        Type: general
      – SubjectFull: Cellulase biosynthesis
        Type: general
      – SubjectFull: Bacillaceae enzymology
        Type: general
      – SubjectFull: Bacillaceae isolation & purification
        Type: general
      – SubjectFull: Bacillaceae genetics
        Type: general
      – SubjectFull: Machine Learning
        Type: general
    Titles:
      – TitleFull: Comparative RSM-ML analysis to predict cellulase production from Lysinibacillus capsici isolate using banana pseudostem as a sustainable substrate.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lilaria K
      – PersonEntity:
          Name:
            NameFull: Tiwari S
      – PersonEntity:
          Name:
            NameFull: Jadhav SK
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: 2026 Jun
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1879-0003
          Numbering:
            – Type: volume
              Value: 368
          Titles:
            – TitleFull: International journal of biological macromolecules
              Type: main
ResultId 1