4.7 Article

Integrated pretreatment of banana agrowastes: Structural characterization and enhancement of enzymatic hydrolysis of cellulose obtained from banana peduncle

Journal

INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
Volume 201, Issue -, Pages 298-307

Publisher

ELSEVIER
DOI: 10.1016/j.ijbiomac.2021.12.179

Keywords

Cellulose; Glucose production; Optimization; Modelling; Banana peduncle

Funding

  1. Department of Biotechnology (DBT) , Government of India [BT/PR16008/NER/95/47/2015]
  2. Department of Biotechnology and Ministry of Education, Government of India [STARS/APR2019/NS/527/FS]

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An integrated treatment method combining alkali, steam explosion, and ammonia/chlorine-free bleaching was used to isolate and characterize cellulose from banana agrowastes. The cellulose was then enzymatically hydrolyzed to glucose. The study investigated the yield, composition, microstructure, and morphology of cellulose from different banana agrowastes. The optimal conditions for acid hydrolysis and enzymatic conversion to glucose were determined, resulting in high cellulose and glucose yields.
An integrated treatment coupling alkali, steam explosion and ammonia/chlorine-free bleaching with sequential mild acid pretreatment were performed to isolate and characterize cellulose from banana agrowastes followed by optimized enzymatic hydrolysis to glucose. The cellulose yield, compositional, microstructural, and morphological analysis initially obtained from three post-harvest banana agrowastes (peel, pseudostem, and peduncle) were surveyed. Isolation parameters for banana peduncle agrowastes, the most efficient precursor, were reconfigured for acid hydrolysis by applying an orthogonal L9 array of Taguchi design. Effects of solution-to-pulp ratio, acid concentration, temperature, and reaction time on physicochemical parameters were assessed resulting in similar to 81% cellulose recovery. Subsequently, cellulase driven enzymatic conversion to glucose was modelled using response surface methodology (RSM), where the mutual influences of incubation time, enzyme concentration, substrate concentration, and surfactant concentration were investigated. Artificial Neural Network (ANN) modelling further improved upon RSM optimizations ensuing-97% optimized glucose yield, verified experimentally.

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