TY - JOUR
T1 - Big data and machine learning driven bioprocessing – Recent trends and critical analysis
AU - Yang, Chao Tung
AU - Kristiani, Endah
AU - Leong, Yoong Kit
AU - Chang, Jo Shu
N1 - Funding Information:
The authors thankfully acknowledge the financial support received from Taiwan’s MOST under grant number 109-3116-F-006-016-CC1, 109-2218-E-006-015, 107-2221-E-006-112-MY3, 109-2621-M-029 -002, NSTC 111-2811-E-029-001, 109-2221-E-029-020, 111-3116-F-006 -004, 111-2621-M-029-002, 111-2221-E-029-001-MY3, 110-2221-E-029 -004 -MY3, and 110-2622-E-110 -016.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/3
Y1 - 2023/3
N2 - Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
AB - Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
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UR - http://www.scopus.com/inward/citedby.url?scp=85146907602&partnerID=8YFLogxK
U2 - 10.1016/j.biortech.2023.128625
DO - 10.1016/j.biortech.2023.128625
M3 - Article
C2 - 36642201
AN - SCOPUS:85146907602
SN - 0960-8524
VL - 372
JO - Bioresource technology
JF - Bioresource technology
M1 - 128625
ER -
