在高通量实验中进行自动拉曼测量的设置
Christoph Lange1, Simon Seidel1, Madeline Altmann1
1Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany.
Biotechnology and bioengineering
|July 11, 2025
概括
这项研究引入了一种用于快速拉曼光谱测量的自动化系统,用于高通量生物技术,使得在细菌培养过程中更快地分析代谢物度.
科学领域:
- 生物技术和分析化学
- 工艺工程和自动化过程工程.
- 机器学习在科学研究中的应用
背景情况:
- 高通量 (HT) 实验加速了生物研究,但通常受到慢速分析方法的限制.
- 当前的分析技术很难跟上HT工作流中的快速样本生成.
- 需要自动化,高速的分析解决方案来充分利用HT的能力.
研究的目的:
- 开发和验证用于自动化,高通量拉曼光谱测量的综合系统.
- 为了加快分析生物样本中的代谢物度.
- 为了使大数据集的产生机器学习模型培训在生物技术.
主要方法:
- 开发一个集物理设备和软件用于拉曼光谱的自动化系统.
- 同时处理和测量八个并行的50μL样品.
- 实施机器学习模型,从拉曼光谱中预测代谢物度 (葡萄糖和酸盐).
主要成果:
- 该系统在45秒内完成了每个样本的测量,处理,清洁和度预测.
- 机器学习模型实现的平均绝对误差为0.27g L-1对于葡萄糖和0.06g L-1对于乙酸盐.
- 证明了用于发酵监测的一致的高通量光谱数据收集.
结论:
- 自动化的拉曼光谱测量系统显著加快了生物技术中的分析吞吐量.
- 综合机器学习方法可以准确预测代谢物度.
- 这项技术支持生成广泛的数据集,用于开发强大的机器学习模型,用于生物过程分析.
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