Paper
20 January 2023 Identification of individual red blood cells by single-cell Raman spectroscopy combined with machine learning
Author Affiliations +
Proceedings Volume 12560, AOPC 2022: Biomedical Optics; 1256005 (2023) https://doi.org/10.1117/12.2646675
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China
Abstract
Raman spectroscopy, a “fingerprint” spectrum of substances, can be used to characterize various biological and chemical samples. To allow for blood classification using single-cell Raman spectroscopy, several machine learning algorithms were implemented and compared. A single-cell laser optical tweezer Raman spectroscopy system was established to obtain the Raman spectra of red blood cells. The Boruta algorithm extracted the spectral feature frequency shift, reduced the spectral dimension, and determined the essential features that affect classification. Next, seven machine learning classification models and deep learning model without dimensionality reduction are analyzed and compared based on the classification accuracy, precision, and recall indicators. The results show that support vector machines and convolutional neural network are the two most appropriate machine learning algorithms for single-cell Raman spectrum blood classification, and the findings provide essential guidance for future research studies.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yiming Liu and Zhehai Zhou "Identification of individual red blood cells by single-cell Raman spectroscopy combined with machine learning", Proc. SPIE 12560, AOPC 2022: Biomedical Optics, 1256005 (20 January 2023); https://doi.org/10.1117/12.2646675
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KEYWORDS
Raman spectroscopy

Blood

Machine learning

Data modeling

Animal model studies

Biological research

Data analysis

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