Machine learning methods for the in-vitro analysis of preimplantation embryo Raman micro-spectroscopy

A. V. Karmenyan; Y. V. Kistenev; E. V. Perevedentseva; A. S. Krivokharchenko; M. N. Sarmiento; E. L. Barus; C.-L. Cheng; D. A. Vrazhnov

doi:10.1117/12.2580485

17 November 2020 Machine learning methods for the in-vitro analysis of preimplantation embryo Raman micro-spectroscopy

A. V. Karmenyan, Y. V. Kistenev, E. V. Perevedentseva, A. S. Krivokharchenko, M. N. Sarmiento, E. L. Barus, C.-L. Cheng, D. A. Vrazhnov

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Proceedings Volume 11582, Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications; 115820W (2020) https://doi.org/10.1117/12.2580485
Event: Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications, 2020, Tomsk, Russian Federation

Abstract

The paper presents an analysis of the Raman spectra of mouse preimplantation embryos using machine learning for visualization, assessing the separability of classes, and highlighting informative areas of the spectrum. Separation of lipid reach areas and nucleus spectra was shown by principal component analysis coupled with a linear support vector machine.

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A. V. Karmenyan, Y. V. Kistenev, E. V. Perevedentseva, A. S. Krivokharchenko, M. N. Sarmiento, E. L. Barus, C.-L. Cheng, and D. A. Vrazhnov "Machine learning methods for the in-vitro analysis of preimplantation embryo Raman micro-spectroscopy", Proc. SPIE 11582, Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications, 115820W (17 November 2020); https://doi.org/10.1117/12.2580485

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KEYWORDS

Raman spectroscopy

Principal component analysis

Machine learning

In vitro testing

Visualization

Physics

Plasma

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