Application of machine learning in 3D bioprinting of cultivated meat
Journal Title: International Journal of AI for Materials and Design - Year 2024, Vol 1, Issue 1
Abstract
Cultivated meat production, an innovative and sustainable alternative to conventional animal farming, has gained significant attention in recent years. As the demand for ethical and environmentally friendly protein sources continues to rise, the need for efficient and scalable production strategies becomes critical. Notably, the integration of advanced technology, such as machine learning (ML), can enhance the efficiency of the cultivated meat production process. The goal of this review paper is to highlight the advantages and limitations of various ML approaches and provide a balanced discussion on the integration of ML techniques for three-dimensional (3D)-bioprinted cultivated meat. This review paper explores the application of ML techniques in various facets of 3D-bioprinted cultivated meat and highlights the potential for ML to optimize various aspects of the process, from predicting printability and optimizing printing parameters to characterizing meat flavor and monitoring meat quality. ML plays a pivotal role in optimizing the material formulation to improve ink printability and identifying an optimal combination of printing parameters to achieve high printing resolution and accuracy. Furthermore, ML can aid in modeling sensory attributes, ensuring that the cultivated meat replicates the desired meat flavor. Finally, ML can be applied for meat quality control as it facilitates the automated detection of harmful pathogens, ensuring the safety and consistency of 3D-bioprinted cultivated meat
Authors and Affiliations
Wei Long Ng|Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Republic of Singapore, Jian Song Tan|Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Republic of Singapore
Keywords
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- EP ID EP767341
- DOI 10.36922/ijamd.2279
- Views 16
- Downloads 0
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