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In Biotechnology and bioengineering

The rapid, accurate and non-invasive 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. Firstly, the self-made laboratory system was established to obtain images. Then, the 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. This article is protected by copyright. All rights reserved.

Zhu Xiaofeng, Mohisn Ali, Zaman Waqas Qamar, Liu Zebo, Wang Zejian, Yu Zhihong, Tian Xiwei, Zhuang Yingping, Guo Meijin, Chu Ju


\nSiraitia grosvenorii, Non-invasive quantitative method, biomass, browning degree, computer-aided vision technology