4.6 Article

Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae

期刊

OPTICS EXPRESS
卷 29, 期 23, 页码 37281-37301

出版社

Optica Publishing Group
DOI: 10.1364/OE.438253

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资金

  1. National Key Research and Development Program of China [2018YFC1407503]
  2. Key Research and Development Program of Zhejiang Province [2021C03178]
  3. Ningbo Science and Technology Project [2020Z077, 20211ZDYF020103]
  4. Ningbo Science and Technology Plan Project-Key Core Technology Emergency Tackling Plan Project [2020G012]

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The CHMI is a confocal hyperspectral microscopic imager capable of measuring transmission and fluorescent spectra of individual microalgae, allowing for precise identification and classification. The system has excellent spectral and spatial resolutions, and demonstrates the ability to analyze concentration, species, and distribution differences of symbiotic microalgae in symbionts.
We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 mu m, respectively (using a 10x microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

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