Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer‐aided vision technology and machine learning-Reference-Cited by-同舟云学术

Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer‐aided vision technology and machine learning

Published:2021-07-28 Issue:10 Volume:118 Page:4092-4104
ISSN:0006-3592
Container-title:Biotechnology and Bioengineering
language:en
Short-container-title:Biotechnol Bioeng

Author:

Zhu Xiaofeng¹²,Mohsin Ali¹²,Zaman Waqas Qamar³,Liu Zebo¹²,Wang Zejian¹²,Yu Zhihong⁴,Tian Xiwei¹²,Zhuang Yingping¹²,Guo Meijin¹²^ORCID,Chu Ju¹²^ORCID

Affiliation:

1. State Key Laboratory of Bioreactor Engineering East China University of Science and Technology Shanghai China

2. School of Biotechnology East China University of Science and Technology Shanghai China

3. Institute of Environmental Sciences and Engineering, School of Civil and Environmental Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan

4. School of Art Design and Media East China University of Science and Technology Shanghai China

Funder

Higher Education Discipline Innovation Project

Publisher

Wiley

Subject

Applied Microbiology and Biotechnology,Bioengineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bit.27886

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1. A non-destructive digital imaging method to predict immobilized yeast-biomass

2. Water activity and biomass estimation using digital image processing in solid-state fermentation