Artificial intelligence and machine learning applications for cultured meat

被引:4
作者
Todhunter, Michael E. [1 ]
Jubair, Sheikh [2 ]
Verma, Ruchika [2 ]
Saqe, Rikard [4 ]
Shen, Kevin [3 ]
Duffy, Breanna [5 ]
机构
[1] Todhunter Sci, Minneapolis, MN USA
[2] Alberta Machine Intelligence Inst, Edmonton, AB, Canada
[3] Univ Waterloo, Dept Math, Waterloo, ON, Canada
[4] Univ Waterloo, Dept Biol, Waterloo, ON, Canada
[5] New Harvest, Sacramento, CA 95811 USA
来源
FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2024年 / 7卷
关键词
machine learning; artificial intelligence; cultured meat; cell culture; culture media design; microscopy; bioprocessing; food science; CELL RNA-SEQ; GENE-EXPRESSION; NEURAL-NETWORK; REGULATORY CHALLENGES; ENVIRONMENTAL IMPACTS; MAMMALIAN-CELLS; MODEL; DESIGN; FLAVOR; LINE;
D O I
10.3389/frai.2024.1424012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.
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页数:23
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