Abstract
In this work, our proof-of-concept study can be used to predict the number of cells within printed droplets based on droplet velocity at two different points along the nozzle-substrate distance using machine learning approaches. A novel high-throughput contactless method that combines the use of an optical system and machine learning algorithms was utilized for various applications such as cell detection within single droplets (presence/absence of cells) and prediction of the total number of printed cells within multiple droplets by measuring the droplet deceleration between two positions along the nozzle-substrate distance. The proposed method in this work has demonstrated good accuracy in cell prediction within single droplet (presence/absence of cells) and low prediction error in determining number of cells within multiple droplets by reducing the error by a factor of \(\sqrt{N}\) for N droplets measured in a batch. The performance of five different machine learning algorithms such as linear regression, support vector regression, decision tree regressor, random forest regression, and extra tree regression were compared to determine the best algorithm for each type of application. The random forest regressor algorithm demonstrated the highest accuracy at 80% in cell prediction (presence/absence of cells) within single droplets, while the extra tree regressor demonstrated the lowest mean error of 12% in predicting the number of printed cells within multiple droplets (e.g., 20 droplets on same spot). By incorporating these models in a droplet monitoring system, live assessment of the number of printed cells during an inkjet-based bioprinting process can be achieved.
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Data Availability
The raw data for the training and the validation of the models, and the trained models for validation is available at https://github.com/huangxi90/Cell-prediction-by-velocity-raw-data/.
Change history
26 July 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10845-023-02184-3
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Acknowledgements
This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab. We would also like to acknowledge and thank the D300e HP team for supplying the C8 cell-dispensing cassettes for the experiments. Wei Long Ng would like to acknowledge support from NTU Presidential Postdoctoral Fellowship.
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Xi Huang: Methodology, Investigation, Writing. Wei Long Ng: Conceptualization, Methodology, Investigation, Writing, Review & Editing Wai Yee Yeong: Funding acquisition, Review.
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Xi Huang and Wei Long Ng have contributed equally towards this paper.
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Huang, X., Ng, W. & Yeong, W. Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02167-4
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DOI: https://doi.org/10.1007/s10845-023-02167-4