Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Chao-Tung Yang; Endah Kristiani; Yoong Kit Leong; Jo-Shu Chang

doi:10.1016/j.biortech.2023.128625

Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Bioresour Technol. 2023 Mar:372:128625. doi: 10.1016/j.biortech.2023.128625. Epub 2023 Jan 13.

Authors

Chao-Tung Yang¹, Endah Kristiani², Yoong Kit Leong³, Jo-Shu Chang⁴

Affiliations

¹ Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan.
² Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia.
³ Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan.
⁴ Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan. Electronic address: changjs@thu.edu.tw.

PMID: 36642201
DOI: 10.1016/j.biortech.2023.128625

Abstract

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.

Keywords: Artificial intelligence; Artificial neural networks; Bioprocessing; Hybrid models; Machine learning; Natural language processing.

MeSH terms

Algorithms
Artificial Intelligence*
Big Data*
Machine Learning
Natural Language Processing