通过机器学习分析同时识别和检测腺酸,用于表面增强的拉曼散射光谱数据
Ryosuke Nishitsuji1, Tomoharu Nakashima2, Hideaki Hisamoto3
1Department of Information Networking, Graduate School of Information Science and Technology, Osaka University, 2-8 Yamadaoka, Suita 565-0871, Osaka, Japan.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一种使用表面增强拉曼散射 (SERS) 和机器学习检测腺酸盐 (AMP,ADP,ATP) 的新方法. 多层感知子模型实现了高精度,从有限的光谱数据中实现精确的分析.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 腺酸 (AMP,ADP,ATP) 对于生物能量储存和信号传导至关重要.
- 同时检测腺酸盐对于理解细胞能量动态至关重要.
- 由于腺酸盐的结构相似性,现有的方法面临挑战.
研究的目的:
- 开发一种用于同时识别和检测腺酸盐的新型测量系统.
- 克服当前技术的局限性,需要广泛的培训数据.
- 用有限的光谱数据提高腺酸检测的准确性.
主要方法:
- 利用表面增强的拉曼散射 (SERS) 与制造的黄金纳米结构进行光谱测量.
- 采用特征选择和数据增强来预处理有限的腺酸光谱数据.
- 训练并比较各种机器学习模型,包括多层感知.
主要成果:
- 一个多层感知子模型在增强的光谱数据上成功训练.
- 该模型在检测AMP,ADP和ATP时达到0.914的高精度.
- 从有限的SERS数据中证明有效地识别和检测腺酸.
结论:
- 建立了一种用于准确检测腺酸的新型测量系统.
- 展示了将SERS与机器学习结合用于复杂分子分析的潜力.
- 克服了在光谱分析方面的有限培训数据的挑战.
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