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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
Author(s) -
Zhu Xiaofeng,
Mohsin Ali,
Zaman Waqas Qamar,
Liu Zebo,
Wang Zejian,
Yu Zhihong,
Tian Xiwei,
Zhuang Yingping,
Guo Meijin,
Chu Ju
Publication year - 2021
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.27886
Subject(s) - browning , artificial intelligence , biological system , biomass (ecology) , computer aided , computer science , cell culture , biochemical engineering , computer vision , process engineering , pattern recognition (psychology) , chemistry , engineering , biology , food science , agronomy , programming language , genetics
The rapid, accurate and noninvasive detection of biomass and plant cell browning can provide timely feedback on cell growth in plant cell culture. In this study, Siraitia grosvenorii suspension cells were taken as an example, a phenotype analysis platform was successfully developed to predict the biomass and the degree of cell browning based on the color changes of cells in computer‐aided vision technology. First, a self‐made laboratory system was established to obtain images. Then, matrices were prepared from digital images by a self‐developed high‐throughput image processing tool. Finally, classification models were used to judge different cell types, and then a semi‐supervised classification to predict different degrees of cell browning. Meanwhile, regression models were developed to predict the plant cell mass. All models were verified with a good agreement by biological experiments. Therefore, this method can be applied for low‐cost biomass estimation and browning degree quantification in plant cell culture.