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The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts

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Abstract

Background

Monitoring jar fermenter–cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of these microorganisms during monitoring. Oleaginous yeasts are used for their high productivity of single-cell oils but the relationship between lipid productivity and morphology has not been elucidated in these organisms.

Results

In this study, we investigated the relationship between the morphology of oleaginous yeasts (Lipomyces starkeyi and Rhodosporidium toruloides were used) and their cultivation state in a large-scale cultivation setting using a real-time monitoring system. We combined this with deep learning by feeding a large amount of high-definition cell images obtained from the monitoring system to a deep learning algorithm. Our results showed that the cell images could be grouped into 7 distinct groups and that a strong correlation existed between each group and its biochemical activity (growth and oil-productivity).

Conclusions

This is the first report describing the morphological variations of oleaginous yeasts in a large-scale cultivation, and describes a promising new avenue for improving productivity of microorganisms in large-scale cultivation through the use of a real-time monitoring system combined with deep learning.

Key points

A real-time monitoring system followed the morphological change of oleaginous yeasts.

Deep learning grouped them into 7 distinct groups based on their morphology.

A correlation between the cultivation state and the shape of the yeast was observed.

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Data availability

The data generated or analyzed during this study are included in this published article and its additional information files. Further data used and analyzed during this study are available from the corresponding author upon reasonable request.

References 

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Acknowledgements

We thank Mr. A. Tsurumune of Nikon Solution Corporation (Tokyo, Japan) for constructing the microscopy system and Mr. H. Ushiyama of ABLE Corporation (Tokyo, Japan) for developing the AFU.

Funding

This study was funded by the New Energy and Industrial Technology Development Organization (NEDO, Kanagawa prefecture, Japan) (grant number JPNP19001).

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Authors and Affiliations

Authors

Contributions

YK, YS, MO, RH, MI, TK, and WO conceived and designed the research. YK and AI conducted the experiments. YO and MM contributed analytical tools. YK, KO, and YT analyzed the data. YK wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Wataru Ogasawara.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare no competing interests.

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Kitahara, Y., Itani, A., Ohtomo, K. et al. The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts. Appl Microbiol Biotechnol 107, 915–929 (2023). https://doi.org/10.1007/s00253-022-12338-7

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  • DOI: https://doi.org/10.1007/s00253-022-12338-7

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