Abstract

Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computationally intensive, and thus slow. Here, we demonstrate that machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers. The resulting stream of precise particle-resolved tracking and characterization data provides unparalleled insights into the composition and dynamics of colloidal dispersions and enables applications ranging from basic research to process control and quality assurance.

Highlights

  • Holograms of colloidal spheres obtained with holographic video microscopy [1, 2] can be interpreted with predictions of the Lorenz-Mie theory of light scattering [3] to track each particle in three dimensions, and to measure its size and refractive index [4]

  • Whereas nonlinear fitting typically requires more than a second on a 1 Gflop computer, a trained support vector machines (SVMs) can estimate the size, refractive index or axial position of a micrometer-scale sphere in under a millisecond on the same hardware

  • SVM-accelerated tracking can be used for real-time three-dimensional particletracking velocimetry [8]

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Summary

INTRODUCTION

Holograms of colloidal spheres obtained with holographic video microscopy [1, 2] can be interpreted with predictions of the Lorenz-Mie theory of light scattering [3] to track each particle in three dimensions, and to measure its size and refractive index [4]. State-of-the-art implementations [4,5,6,7] can locate a sphere and resolve its radius both to within a few nanometers, and can determine its refractive index to within a part per thousand [8,9,10]. The cost of this powerful technique is the computational burden of fitting each hologram pixel-by-pixel to theoretical predictions [4, 11]. Whereas nonlinear fitting typically requires more than a second on a 1 Gflop computer, a trained SVM can estimate the size, refractive index or axial position of a micrometer-scale sphere in under a millisecond on the same hardware

FAST HOLOGRAPHIC CHARACTERIZATION WITH MACHINE LEARNING
CHARACTERIZATION OF COLLOIDAL MIXTURES
TRACKING AND ASSESSMENT OF PRECISION
Findings
CONCLUSIONS

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