期刊
BIOSENSORS-BASEL
卷 12, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/bios12040250
关键词
Raman spectroscopy; PCA; machine learning; non-invasive imaging; fast Raman imaging; cancer cells
资金
- Chevron Corporation's OU-MCEE Funding Program
This study introduces a rapid method using machine-learning-assisted Raman spectroscopic imaging to distinguish cancer cells from non-cancer cells, efficiently retrieving biomolecular information for cell line differentiation.
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据