基于深度学习的拉曼光谱的超快速识别,其信号噪声比较低
Kunxiang Liu1,2, Fuyuan Chen1,2, Lindong Shang1,2
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China.
Journal of biophotonics
|August 31, 2023
概括
现在可以使用拉曼光谱来快速识别细胞系. 这项研究证明了高精度,显著减少了光谱采集时间,提高了实验效率.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 细胞生物学 细胞生物学
- 机器学习 机器学习
背景情况:
- 准确的细胞系认证对于可复制的研究和资源管理至关重要.
- 拉曼光谱提供细胞系识别功能,但通常需要长时间的数据采集 (10-30秒).
研究的目的:
- 显著减少用于细胞系识别的拉曼光谱采集时间.
- 通过使用短期获取时间的拉曼光谱和机器学习来评估细胞系识别的准确性.
- 增强光谱数据以进一步提高识别准确性.
主要方法:
- 从五个细胞系获得了拉曼光谱,整合时间为0.1s和8s.
- 利用长短期记忆 (LSTM) 神经网络进行光谱识别.
- 应用了实值非体积保存 (RV-NVP) 方法来增强0.1s频谱的数据.
主要成果:
- 使用0.1s光谱实现了细胞系鉴定95%的平均准确性.
- 使用8s光谱达到99.8%的平均精度.
- 改进的0.1s光谱提高了LSTM识别精度,达到96.2%.
结论:
- 将拉曼光谱获取时间缩短到原始持续时间的1/80.
- 通过优化拉曼光谱和LSTM网络证明了快速准确的细胞系识别的可行性.
- 显著提高了生物研究中细胞识别过程的效率.
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