高精度的溶剂识别使用动态蒸发反射光谱从反向珀传感器与深度学习模型相结合
Jin Huang1, Soumia Cheddah1, Yinjie Ma1
1School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China. wangyan11@sjtu.edu.cn.
Nanoscale
|October 19, 2023
概括
一种新的深度学习方法使用来自简单传感器的动态蒸发反射光谱 (DERS) 准确识别溶剂. 这种方法提供了快速的,自动化的溶剂检测,优于传统方法.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 准确的溶剂识别是至关重要的,特别是对于结构相似的化合物.
- 现有的光谱方法往往需要复杂的传感器材料.
- 动态蒸发反射光谱 (DERS) 为溶剂分析提供了潜力.
研究的目的:
- 开发一种低成本,快速和准确的方法,用于使用DERS检测溶剂.
- 应用深度学习来直接分析DERS数据.
- 评估用于溶剂分类的深度学习模型的性能.
主要方法:
- 使用联合组装和牺牲模板方法制造一个简单的反向 (IO) 传感器.
- 获取来自14种不同溶剂的985个DERS数据集.
- 使用经典机器学习和深度学习算法的DERS分类,包括ResNet18-CBAM.
主要成果:
- 在5倍交叉验证中,ResNet18-CBAM深度学习模型实现了97.7 ± 0.9%的准确性.
- 该模型在独立测试组中显示出100%的准确性.
- 开发的方法在区分各种溶剂方面证明是有效的.
结论:
- 通过深度学习方法,可以自动,快速和准确地从DERS中识别溶剂.
- 提出的方法为溶剂检测提供了一个简单,高效和可重复的解决方案.
- 这一策略在溶剂分析中的实际应用方面具有显著的潜力.
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