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Simplex-Centroid Design and Artificial Neural Network-Genetic Algorithm for the Optimization of Exoglucanase Production by Penicillium Roqueforti ATCC 10110 Through Solid-State Fermentation Using a Blend of Agroindustrial Wastes

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Simplex-centroid design along with artificial neural network coupled with genetic algorithm (ANN-GA) was applied to maximize exoglucanase production by Penicillium roqueforti ATCC 10110 under solid-state fermentation (SSF), using a blend of agroindustrial wastes as substrate. The first statistical treatment determined the ideal contents of green coconut shell, corn cob, and sugarcane bagasse in the substrate, which were 0.44, 2.06, and 2.50 g, respectively. The optimum conditions by the ANN-GA were obtained as follows: 24 h, 21 °C, and 8.1 and 81.0% for the time, temperature, pH, and moisture, respectively. Moreover, the predicted and the experimental values of exoglucanase activity were 267.94 and 268.58 IU/g, respectively. The optimization process increased the enzyme activity by up to 1263% compared with the preliminary analysis using individual substrates, demonstrating the high efficiency of the algorithms on predicting and optimizing enzyme production. Biochemical characterization demonstrated good thermostability, basic pH stability, halotolerance, and increased enzyme activity in the presence of metal ions (Co2+, Ca2+, Mg2+, and Fe2), solvents (ethanol and dichloromethane), and organic compounds (EDTA, Triton-X, and lactose,). These results indicate the algorithm efficiency for enzyme production purposes.

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References

  1. Díaz AB, Blandino A, Caro I (2018) Value added products from fermentation of sugars derived from agro-food residues. Trends Food Sci Tech 71:52–64. https://doi.org/10.1111/jam.13672

    Article  CAS  Google Scholar 

  2. Kocaman S, Karaman M, Gursoy M, Ahmetli G (2017) Chemical and plasma surface modification of lignocellulose coconut waste for the preparation of advanced biobased composite materials. Carbohyd Polym 159:48–57. https://doi.org/10.1016/j.carbpol.2016.12.016

    Article  CAS  Google Scholar 

  3. Nawaz H, Muzaffar S, Aslam M, Ahmad S (2018) Phytochemical composition: antioxidant potential and biological activities of corn. In: Amanullah K, Fahad S (eds) Corn-production and human health in changing climate. IntechOpen, London, pp 49–68

    Google Scholar 

  4. Hashemi SS, Karimi K, Karimi AM (2019) Ethanolic ammonia pretreatment for efficient biogas production from sugarcane bagasse. Fuel 248:196–204. https://doi.org/10.1016/j.fuel.2019.03.080

    Article  CAS  Google Scholar 

  5. Longaresi RH, de Menezes AJ, Pereira-da-Silva MA, Baron D, Mathias SL (2019) The maize stem as a potential source of cellulose nanocrystal: cellulose characterization from its phenological growth stage dependence. Ind Crop Prod 133:232–240. https://doi.org/10.1016/j.indcrop.2019.02.046

    Article  CAS  Google Scholar 

  6. Mioso R, Marante FJ, Toledo-Laguna IHB (2015) Penicillium roqueforti: a multifunctional cell factory of high value-added molecules. J Appl Microbiol 118:781–791. https://doi.org/10.1111/jam.12706

    Article  CAS  Google Scholar 

  7. Ferraz JLA, Souza LO, Fernandes AGA, Ferreira MLO, Oliveira JR, Franco M (2019) Optimization of the solid-state fermentation conditions and characterization of xylanase produced by Penicillium roqueforti ATCC 10110 using yellow mombin residue (Spondias mombin L.). Chem Eng Commun 206:1–12. https://doi.org/10.1080/00986445.2019.1572000

    Article  CAS  Google Scholar 

  8. Marques GL, Silva TP, Lessa OA, Brito AR, Reis NS, Fernandes AGA, Ferreira MLO, Oliveira JR, Franco M (2019) Production of xylanase and endoglucanase by solid-state fermentation of jackfruit residue. Rev Mex Ing Quim 18:673–680. https://doi.org/10.24275/uam/izt/dcbi/revmexingquim/2019v18n2/Marques

    Article  CAS  Google Scholar 

  9. Souza LO, Brito AR, Bonomo RCF, Santana NB, Ferraz JLA, Fernandes AGA, Ferreira MLO, Oliveira JR, Franco M (2018) Comparison of the biochemical properties between the xylanases of Thermomyces lanuginosus (Sigma®) and excreted by Penicillium roqueforti ATCC 10110 during the solid state fermentation of sugarcane bagasse. Biocatal Agric Biotechnol 16:277–284. https://doi.org/10.1016/j.bcab.2018.08.016

    Article  Google Scholar 

  10. Castro RJS, Sato HH (2015) Enzyme production by solid state fermentation: general aspects and an analysis of the physicochemical characteristics of substrates for agro-industrial wastes valorization. Waste Biomass Valori 6:1085–1093. https://doi.org/10.1007/s12649-015-9396-x

    Article  CAS  Google Scholar 

  11. Silva TP, Albuquerque FS, Santos CWV, Franco M, Caetano LC, Pereira HJV (2018) Production, purification, characterization and application of a new halotolerant and thermostable endoglucanase of Botrytis ricini URM 5627. Bioresour Technol 270:263–269. https://doi.org/10.1016/j.biortech.2018.09.022

    Article  CAS  Google Scholar 

  12. Mahmood RT, Asad MJ, Mehboob N, Mushtaq M, Gulfraz M, Asgher M, Minhas NM, Hadri SH (2013) Production, purification, and characterization of exoglucanase by Aspergillus fumigatus. Appl Biochem Biotech 170:895–908. https://doi.org/10.1007/s12010-013-0227-x

    Article  CAS  Google Scholar 

  13. Meleiro LP, Carli S, Fonseca-Maldonado R, Torricillas MS, Zimbardi ALRL, Ward RJ, Jorge JA, Furriel RPM (2018) Overexpression of a cellobiose-glucose-halotolerant endoglucanase from Scytalidium thermophilum. Appl Biochem Biotech 185:316–333. https://doi.org/10.1007/s12010-017-2660-8

    Article  CAS  Google Scholar 

  14. Xu J, He B, Wu B, Wang B, Wang C, Hu L (2014) An ionic liquid tolerant cellulase derived from chemically polluted microhabitats and its application in in situ saccharification of rice straw. Bioresour Technol 157:166–173. https://doi.org/10.1016/j.biortech.2014.01.102

    Article  CAS  Google Scholar 

  15. Hmad IB, Boudabbous M, Belghith H, Gargouri A (2017) A novel ionic liquid-stable halophilic endoglucanase from Stachybotrys microspore. Process Biochem 54:59–66. https://doi.org/10.1016/j.procbio.2017.01.007

    Article  CAS  Google Scholar 

  16. Yoon LW, Ngoh GC, Chua ASM, Patah MFA, Teoh WH (2019) Process intensification of cellulase and bioethanol production from sugarcane bagasse via an integrated saccharification and fermentation process. Chem Eng Process 142:107528. https://doi.org/10.1016/j.cep.2019.107528

    Article  CAS  Google Scholar 

  17. Tavares IMC, Umsza-Guez MA, Martin N, Tobal TM, Boscolo M, Gomes E, Da-Silva R, Lago-Vanzela ES (2020) The improvement of grape juice quality using Thermomucor Indicae-Seudaticae pectinase. J Food Sci Technol 57:1565–1575. https://doi.org/10.1007/s13197-019-04192-9

    Article  CAS  Google Scholar 

  18. Brito AR, Reis NS, Silva TP, Bonomo RCF, Uetanabaro APT, Assis SA, Santos EGP, Oliveira EA, Oliveira JR, Franco M (2017) Comparison between the univariate and multivariate analysis on the partial characterization of the endoglucanase produced in the solid state fermentation by Aspergillus oryzae ATCC 10124. Prep Biochem Biotech 47:977–985. https://doi.org/10.1080/10826068.2017.1365247

    Article  CAS  Google Scholar 

  19. Kiran EU, Trzcinski AP, Ng WJ, Liu Y (2014) Enzyme production from food wastes using a biorefinery concept. Waste Biomass Valori 5:903–917. https://doi.org/10.1007/s12649-014-9311-x

    Article  CAS  Google Scholar 

  20. Naik B, Goyal SK, Tripathi AD, Kumar V (2019) Screening of agro-industrial waste and physical factors for the optimum production of pullulanase in solid-state fermentation from endophytic Aspergillus sp. Biocatal Agric Biotechnol 22:101423. https://doi.org/10.1016/j.bcab.2019.101423

    Article  Google Scholar 

  21. Santos TCD, Filho GA, Brito ARD, Pires AJV, Bonomo RCF, Franco M (2016) Production and characterization of cellulolytic enzymes by Aspergillus niger and Rhizopus sp. by solid state fermentation of prickly pear. Rev Caatinga 29:222–233. https://doi.org/10.1590/1983-21252016v29n126rc

    Article  Google Scholar 

  22. Cruz-Quiroz RD, Roussos S, Hernandez-Castillo D, Rodríguez-Herrera R, López-López LI, Castillo F, Aguilar CN (2017) Solid-state fermentation in a bag bioreactor: effect of corn cob mixed with phytopathogen biomass on spore and cellulase production by Trichoderma asperellum. In: Jozala A (eds) Fermentation processes. London, pp. 43–56

  23. Dias LM, dos Santos BV, Albuquerque CJB, Baeta BEL, Pasquini D, Baffi MA (2018) Biomass sorghum as a novel substrate in solid-state fermentation for the production of hemicellulases and cellulases by Aspergillus niger and A. fumigatus. J Appl Microbiol 124:708–718. https://doi.org/10.1111/jam.13672

    Article  CAS  Google Scholar 

  24. Sathish T, Prakasham RS (2010) Enrichment of glutaminase production by Bacillus subtilis RSP-GLU in submerged cultivation based on neural network-genetic algorithm approach. J Chem Technol Biotechnol 85:50–58. https://doi.org/10.1002/jctb.2267

    Article  CAS  Google Scholar 

  25. Dias FFG, de Castro RJS, Ohara A, Nishide TG, Bagagli MP, Sato HH (2015) Simplex centroid mixture design to improve L-asparaginase production in solid-state fermentation using agroindustrial wastes. Biocatal Agric Biotechnol 4:528–534. https://doi.org/10.1016/j.bcab.2015.09.011

    Article  Google Scholar 

  26. Ohara A, Santos JG, Angelotti JAF, Barbosa PPM, Dias FFG, Bagagli MP, Sato HH, Castro RJS (2018) A multicomponent system based on a blend of agroindustrial wastes for the simultaneous production of industrially applicable enzymes by solid-state fermentation. Food Sci Technol 38:131–137. https://doi.org/10.1590/1678-457x.17017

    Article  Google Scholar 

  27. Sood A, Gupta M (2015) Extraction process optimization for bioactive compounds in pomegranate peel. Food Biosci 12:100–106. https://doi.org/10.1016/j.fbio.2015.09.004

    Article  CAS  Google Scholar 

  28. Oliveira PC, Brito AR, Pimentel AB, Soares GA, Pacheco CSV, Santana NB, da Silva EGP, Fernandes AG, Ferreira MLO, Oliveira JR, Franco M (2019) Cáscara de cacao para la producción de endoglucanasa por Penicillium roqueforti ATCC 10110 en fermentación de estado sólido y propiedades bioquímicas. Rev Mex Ing Quim 18:777–787. https://doi.org/10.24275/uam/izt/dcbi/revmexingquim/2019v18n3/Oliveira

    Article  CAS  Google Scholar 

  29. Santos TCD, Diniz GA, Brito ARD, Pires AJV, Franco M (2015) Effect of solid state fermentation on nutritional content and evaluation of degradability in cactus pear. Rev Caatinga 28:248–254. https://doi.org/10.1590/1983-21252015v28n328rc

    Article  Google Scholar 

  30. Chang C, Xu G, Yang J, Wang D (2011) Optimization of cellulase production using agricultural wastes by artificial neural network and genetic algorithm. Chem Prod Process Model 6. https://doi.org/10.2202/1934-2659.1553

  31. Muthusamy S, Manickam LP, Murugesan V, Muthukumaran C, Pugazhendhi A (2019) Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach. Int J Biol Macromol 124:750–758. https://doi.org/10.1016/j.ijbiomac.2018.11.036

    Article  CAS  Google Scholar 

  32. Khajeh M, Kaykhaii M, Sharafi A (2013) Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples. J Ind Eng Chem 19:1624–1630 10refe.1016/j.jiec.2013.01.033

    Article  CAS  Google Scholar 

  33. Haykin S (2008) Neural networks and learning machines, third edn. Pearson, Ontario

    Google Scholar 

  34. Khayet M, Cojocaru C (2018) Artificial neural network modeling and optimization of desalination by air gap membrane distillation. Sep Purif Technol 86:171–182. https://doi.org/10.1016/j.seppur.2011.11.001

    Article  CAS  Google Scholar 

  35. Simon D (2013) Evolutionary optimization algorithms: biologically-inspired and population-based approaches to computer intelligence, first edn. John Wiley & Sonsּּ, New Jersey

    Google Scholar 

  36. Ruiz HÁ, Rodríguez-Jasso RM, Rodríguez R, Contreras-Esquivel JC, Aguilar CN (2012) Pectinase production from lemon peel pomace as support and carbon source in solid-state fermentation column-tray bioreactor. Biochem Eng J 65:90–95. https://doi.org/10.1016/j.bej.2012.03.007

    Article  CAS  Google Scholar 

  37. Reis NS, Santana NB, Tavares IMC, Lessa OA, Santos LR, Pereira NE, Soares GA, Oliveira RA, Oliveira JR, Franco M (2020) Enzyme extraction by lab-scale hydrodistillation of ginger essential oil (Zingiber officinale Roscoe): chromatographic and micromorphological analyses. Ind Crop Prod 146:112210. https://doi.org/10.1016/j.indcrop.2020.112210

    Article  CAS  Google Scholar 

  38. Rodrigues PO, Gurgel LVA, Psquini D, Badotti F, Góes-Neto A, Baffi MA (2020) Lignocellulose-degrading enzymes production by solid-state fermentation through fungal consortium among Ascomycetes and Basidiomycetes. Renew Energy 145:2683–2693. https://doi.org/10.1016/j.renene.2019.08.041

    Article  CAS  Google Scholar 

  39. Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426–428. https://doi.org/10.1021/ac60147a030

    Article  CAS  Google Scholar 

  40. Peng W, Zhong J, Yang J, Ren Y, Xu T, Xiao S, Zhou J, Tan H (2014) The artificial neural network approach based on uniform design to optimize the fed-batch fermentation condition: application to the production of iturin A. Microb Cell Factories 13:2–10. https://doi.org/10.1186/1475-2859-13-54

    Article  CAS  Google Scholar 

  41. Fu X, Li P, Zhang L, Li S (2018) RNA-Seq-based transcriptomic analysis of Saccharomyces cerevisiae during solid-state fermentation of crushed sweet sorghum stalks. Process Biochem 68:53–63. https://doi.org/10.1016/j.procbio.2018.02.024

    Article  CAS  Google Scholar 

  42. Zheng ZY, Guo XN, Zhu KX, Peng W, Zhou HM (2017) Artificial neural network–genetic algorithm to optimize wheat germ fermentation condition: application to the production of two anti-tumor benzoquinones. Food Chem 227:64–270. https://doi.org/10.1016/j.foodchem.2017.01.077

    Article  CAS  Google Scholar 

  43. Selvaraj S, Vytla RM, Vijay GS, Natarajan K (2019) Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network. 3 Biotech 9:2–12. https://doi.org/10.1007/s13205-019-1763-z

    Article  Google Scholar 

  44. Chiranjeevi PV, Pandian MR, Sathish T (2014) Integration of artificial neural network modeling and genetic algorithm approach for enrichment of laccase production in solid state fermentation by Pleurotus ostreatus. BioResources 9:2459–2470. https://doi.org/10.15376/biores.9.2.2459-2470

    Article  Google Scholar 

  45. Gurunathan B, Sahadevan R (2011) Design of experiments and artificial neural network linked genetic algorithm for modeling and optimization of L-asparaginase production by Aspergillus terreus MTCC 1782. Biotechnol Bioproc E 16:50–58. https://doi.org/10.1007/s12257-010-0119-7

    Article  CAS  Google Scholar 

  46. Patel AK, Singhania RR, Sim SJ, Pandey A (2019) Thermostable cellulases: review and perspectives. Bioresour Technol 279:385–392. https://doi.org/10.1016/j.biortech.2019.01.049

    Article  CAS  Google Scholar 

  47. Obeng EM, Ongkudon CM, Budiman C, Maas R, Jose J (2018) An optimal blend of single autodisplayed cellulases for cellulose saccharification-a proof of concept. J Chem Technol Biotechnol 93:719–2728. https://doi.org/10.1002/jctb.5628

    Article  CAS  Google Scholar 

  48. Kluska K, Adamczyk J, Krężel A (2018) Metal binding properties, stability and reactivity of zinc fingers. Coordin Chem Rev 367:18–64. https://doi.org/10.1016/j.ccr.2018.04.009

    Article  CAS  Google Scholar 

  49. Ghori MI, Ahmed S, Malana MA, Jamil A (2012) Kinetics of exoglucanase and endoglucanase produced by Aspergillus niger NRRL 567. Afr J Biotechnol 11:7227–7231. https://doi.org/10.5897/AJB12.329

    Article  CAS  Google Scholar 

  50. Olajuyigbe FM (2017) Bioconversion of cellulose and simultaneous production of thermoactive exo- and endoglucanases by Fusarium oxysporum. Cellulose 24:4325–4336. https://doi.org/10.1007/s10570-017-1417-4

    Article  CAS  Google Scholar 

  51. Kaurin A, Cernilogar Z, Lestan D (2018) Revitalisation of metal-contaminated, EDTA-washed soil by addition of unpolluted soil, compost and biochar: effects on soil enzyme activity, microbial community composition and abundance. Chemosphere 193:726–736. https://doi.org/10.1016/j.chemosphere.2017.11.082

    Article  CAS  Google Scholar 

  52. Verma SK, Ghosh KK, Verma R, Verma S, Girish HN, Zhao X (2015) Activity of α-chymotrypsin in cationic and nonionic micellar media: ultraviolet and fluorescence spectroscopic approach. Int J Chem Kinet 48:79–87. https://doi.org/10.1002/kin.20972

    Article  CAS  Google Scholar 

  53. Chen YA, Zhou Y, Qin Y, Liu D, Zhao X (2018) Evaluation of the action of Tween 20 non-ionic surfactant during enzymatic hydrolysis of lignocellulose: pretreatment, hydrolysis conditions and lignin structure. Bioresour Technol 269:329–338. https://doi.org/10.1016/j.biortech.2018.08.119

    Article  CAS  Google Scholar 

  54. Dhillon GS, Kaur S, Brar SK, Verma M (2012) Potential of apple pomace as a solid substrate for fungal cellulase and hemicellulase bioproduction through solid-state fermentation. Ind Crop Prod 38:6–13. https://doi.org/10.1016/j.indcrop.2011.12.036

    Article  CAS  Google Scholar 

  55. Hwang EJ, Lee YS, Choi YL (2018) Cloning, purification, and characterization of the organic solvent tolerant β-glucosidase, OaBGL84, from Olleya aquimaris DAU311. Appl Biol Chem 61:325–336. https://doi.org/10.1007/s13765-018-0361-9

    Article  CAS  Google Scholar 

  56. Laane C, Boeren S, Vos K, Veeger C (1987) Rules for optimization of biocatalysis in organic solvents. Biotechnol Bioeng 30:81–87. https://doi.org/10.1002/bit.260300112

    Article  CAS  Google Scholar 

  57. Gunde-Cimerman N, Plemenitaš A, Oren A (2018) Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbial Rev 42:353–375. https://doi.org/10.1093/femsre/fuy009

    Article  CAS  Google Scholar 

  58. Madern D, Ebel C, Zaccai G (2000) Halophilic adaptation of enzymes. Extremophiles 4:91–98. https://doi.org/10.1007/s007920050142

    Article  CAS  Google Scholar 

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Acknowledgments

The authors would like to thank the philosopher, Mrs. Alessandra Honorato Benfica Franco, for corrections in the manuscript.

Funding

This study received financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), the National Council for Scientific and Technological Development (Bolsas de Produtividade em Pesquisa 302259/2018-0, CNPq, Brazil), and the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB, Brazil).

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  1. Departament of Exact Sciences and Technology, State University of Santa Cruz (UESC), Ilhéus, Brazil

    Nájila da Silva Nunes, Lucas Lima Carneiro, Luiz Henrique Sales de Menezes, Marise Silva de Carvalho, Adriana Bispo Pimentel, Tatielle Pereira Silva, Thiago Pereira das Chagas, Erik Galvão Paranhos da Silva, Julieta Rangel de Oliveira & Marcelo Franco

  2. Departament of Analytical Chemistry, Federal University of Bahia (UFBA), Salvador, Brazil

    Clissiane Soares Viana Pacheco

  3. Departament of Exact Sciences and Technology, State University of Southweast Bahia (UESB), Itapetinga, Brazil

    Iasnaia Maria de Carvalho Tavares

  4. Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil

    Pedro Henrique Santos

  5. School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an, China

    Muhammad Bilal

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  1. Nájila da Silva Nunes
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da Silva Nunes, N., Carneiro, L.L., de Menezes, L.H.S. et al. Simplex-Centroid Design and Artificial Neural Network-Genetic Algorithm for the Optimization of Exoglucanase Production by Penicillium Roqueforti ATCC 10110 Through Solid-State Fermentation Using a Blend of Agroindustrial Wastes. Bioenerg. Res. 13, 1130–1143 (2020). https://doi.org/10.1007/s12155-020-10157-0

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