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Abstract We introduce a digital inline holography (DIH) method combined with deep learning (DL) for real-time detection and analysis of bacteria in liquid suspension. Specifically, we designed a prototype that integrates DIH with fluorescence imaging to efficiently capture holograms of bacteria flowing in a microfluidic channel, utilizing the fluorescent signal to manually identify ground truths for validation. We process holograms using a tailored DL framework that includes preprocessing, detection, and classification stages involving three specific DL models trained on an extensive dataset that included holograms of generic particles present in sterile liquid and five bacterial species featuring distinct morphologies, Gram stain attributes, and viability. Our approach, validated through experiments with synthetic data and sterile liquid spiked with different bacteria, accurately distinguishes between bacteria and particles, live and dead bacteria, and Gram-positive and negative bacteria of similar morphology, all while minimizing false positives. The study highlights the potential of combining DIH with DL as a transformative tool for rapid bacterial analysis in clinical and industrial settings, with potential extension to other applications including pharmaceutical screening, environmental monitoring, and disease diagnostics.
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The color of aerosol particles
Abstract Digital in-line holography (DIH) is an established method to image small particles in a manner where image reconstruction is performed computationally post-measurement. This ability renders it ideal for aerosol characterization, where particle collection or confinement is often difficult, if not impossible. Conventional DIH provides a gray-scale image akin to a particle’s silhouette, and while it gives the particle size and shape, there is little information about the particle material. Based on the recognition that the spectral reflectance of a surface is partly determined by the material, we demonstrate a method to image free-flowing particles with DIH in color with the eventual aim to differentiate materials based on the observed color. Holograms formed by the weak backscattered light from individual particles illuminated by red, green, and blue lasers are recorded by a color sensor. Images are reconstructed from the holograms and then layered to form a color image, the color content of which is quantified by chromaticity analysis to establish a representative signature. A variety of mineral dust aerosols are studied where the different signatures suggest the possibility to differentiate particle material. The ability of the method to resolve the inhomogeneous composition within a single particle in some cases is shown as well.
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- Award ID(s):
- 2107715
- PAR ID:
- 10416439
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Digital in-line holography is a versatile method to obtain lens-less images of small particles, such as aerosol particles, ranging from several to over one hundred microns in size. It has been shown theoretically, and verified by measurement, that a particle’s extinction cross section can also be obtained from a digital hologram. The process involves a straightforward integration, but if noise is present it fails to give accurate results. Here we present a method to reduce the noise in measured holograms of single particles for the purpose of rendering the cross-section estimation more effective. The method involves masking the complex-valued particle image-amplitude obtained from a noisy hologram followed by a Fresnel transformation to generate a new noise-reduced hologram. Examples are given at two wavelengths, 440 nm and 1040 nm, where the cross section is obtained for a micro-sphere particle and several non-spherical particles approximately 50 microns in size.more » « less
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The optical extinction caused by a small particle, such as an aerosol particle, is an important measurable quantity. Understanding the influence of atmospheric aerosols on the climate, assessing visibility in urban environments, and remote sensing applications such as lidar all need accurate measurements of particle extinction. While multiple methods are known to measure extinction, digital in-line holography (DIH) features the unique ability to provide contact-free images of particles simultaneously with estimates for the extinction cross section. This is achieved through an integration of a measured hologram followed by an extrapolation. By means of a supercontinuum laser, we investigate the measurement of the cross section via DIH for stationary particles across a broad spectrum, from 440 nm to 1040 nm. The particles considered include a 50 µm glass microsphere, a volcanic ash particle, and an iron(III) oxide particle. The results show the ability to estimate a particle’s cross section to within 10% error across portions of the spectrum and approximately 20% error otherwise. An examination of the accompanying hologram-derived particle images reveals details in the images that evolve with wavelength. The behavior suggests a basic means to resolve whether absorption or scattering dominates a particle’s extinction.more » « less
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Abstract Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real‐time, in situ analysis using machine learning (ML)‐assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML‐driven DIH approach as a sensitive and versatile diagnostic tool for real‐time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.more » « less
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This work applies digital holography to image stationary micro-particles in color. The approach involves a Michelson interferometer to mix reference light with the weak intensity light backscattered from a distribution of particles. To enable color images, three wavelengths are used, 430, 532, and 633 nm, as primary light sources. Three separate backscattered holograms are recorded simultaneously, one for each wavelength, which are resolved without spectral cross talk using a three-CMOS prism sensor. Fresnel diffraction theory is used to render monochrome images from each hologram. The images are then combined via additive color mixing with red, green, and blue as the primary colors. The result is a color image similar in appearance to that obtained with a conventional microscope in white-light epi-illumination mode. A variety of colored polyethylene micro-spheres and nonspherical dust particles demonstrate the feasibility of the approach and illustrate the effect of simple speckle-noise suppression and white balance methods. Finally, a chromaticity analysis is applied that is capable of differentiating particles of different colors in a quantitative and objective manner.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-023-28823-6
