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. Issue 10 (28th July 2021)

• Record Type: Journal Article
• Authors: Zhu, Xiaofeng; Mohsin, Ali; Zaman, Waqas Qamar; Liu, Zebo; Wang, Zejian; Yu, Zhihong; Tian, Xiwei; Zhuang, Yingping; Guo, Meijin; Chu, Ju
• Is Part Of: Biotechnology and bioengineering. Volume 118:Issue 10(2021)
• Page Start: 4092
• Page End: 4104
• Publication Date: 2021-07-28
• Subjects: biomass -- browning degree -- computer‐aided vision technology -- noninvasive quantitative method -- Siraitia grosvenorii; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.27886
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: 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. Abstract : In this work, a simple and non‐invasive detection method for measuring biomass of plant cell was developed. It is a self‐made laboratory system for high throughput image analysis using an integrated computer‐aided vision technology and machine learning for quantitative analysis of biomass growth and browning degree in plant cell culture.