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Process‐centric and data‐centric strategies for enhanced production of l ‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger
Author(s) -
Vala Anjana K.,
Dudhagara Dushyant R.,
Dave Bharti P.
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3024
Subject(s) - aspergillus niger , response surface methodology , artificial neural network , mean squared error , production (economics) , biological system , mean absolute error , fungus , mathematics , chemistry , computer science , food science , biochemical engineering , statistics , biology , artificial intelligence , botany , engineering , economics , macroeconomics
The objective of the study was to achieve enhanced production of l ‐asparaginase (LA), an anticancer enzyme, by a marine‐derived Aspergillus niger isolate. To improve LA production, optimization of pH, incubation time, and inoculum size was performed using process‐centric (response surface methodology [RSM]) and data‐centric (artificial neural network [ANN]) approaches. The optimized conditions led to a 108.62% rise in LA production. Upon comparison of 2 models for enhanced LA production, based on the R 2 , mean absolute percentage error, root mean square error, and mean absolute deviation values, the ANN model was observed to be superior over the RSM model. To the authors' best knowledge, the present finding is the first ever report revealing detailed analyses for RSM and ANN models for LA production using a marine‐derived fungus.