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Related Experiment Video

Updated: Aug 12, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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In situ biological particle analyzer based on digital inline holography.

Delaney Sanborn1,2, Ruichen He1,2, Lei Feng2

  • 1Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota, USA.

Biotechnology and Bioengineering
|January 30, 2023
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Summary

This study introduces a new machine learning-assisted digital inline holography method for real-time analysis of biological microparticles. The approach offers accurate, fast detection and classification of plankton and yeast cells, improving diagnostics.

Keywords:
hologramsimagingmachine learningplanktonyeast

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Area of Science:

  • Biotechnology
  • Microscopy
  • Machine Learning

Background:

  • Accurate in situ measurements of biological microparticles are vital for scientific research and industrial applications.
  • Current diagnostic methods lack the timeliness, accuracy, and detailed information required for effective analysis.
  • Challenges exist in real-time monitoring of microparticles like plankton and yeast cells.

Purpose of the Study:

  • To develop a novel, real-time, in situ method for analyzing biological microparticles.
  • To enhance diagnostic capabilities for applications such as harmful algal bloom detection and fermentation monitoring.
  • To leverage machine learning with digital inline holography for improved particle analysis.

Main Methods:

  • Utilized digital inline holography (DIH) combined with machine learning (ML).
  • Developed a customized YOLOv5 architecture for detecting and classifying small biological particles.
  • Applied the ML-assisted DIH method to analyze plankton species and yeast cells with varying metabolic states and strains.

Main Results:

  • Achieved high accuracy in detecting and classifying 10 plankton species, with significantly reduced processing time.
  • Successfully differentiated yeast cells across four metabolic states and two strains.
  • Demonstrated accurate detection and differentiation of cellular and subcellular features linked to metabolic states and strains.

Conclusions:

  • The ML-driven DIH approach provides a sensitive and versatile tool for real-time, in situ analysis.
  • This method offers a significant improvement over existing techniques for biological microparticle diagnostics.
  • The technology is suitable for scalable, distributive deployment in scientific research and industrial manufacturing.