通过深度学习辅助传感图书馆选策略设计多种癌症VOCs分析平台
Xu Gao1, Shuoyang Ma1, Weiwei Ni1
1State Key Laboratory of Natural Medicines, National R&D Center for Chinese Herbal Medicine Processing, College of Engineering, China Pharmaceutical University, Nanjing 211198, China.
Analytical chemistry
|April 11, 2025
概括
本研究引入了一种深度学习方法,以有效地选择用于检测挥发性有机化合物 (VOC) 的传感器元素. 这种方法快速创建了用于化学和癌症检测的高精度传感器阵列,并被证明适用于临床检测.
科学领域:
- 化学传感器是一种化学传感器.
- 材料科学中的人工智能
- 传感器阵列开发的发展.
背景情况:
- 传感器阵列性能取决于元素数量和质量.
- 组合设计加速了化学库的生成,但选最佳元素是困难的.
- 开发用于多分析差别的高性能传感器阵列需要高效的元素选择.
研究的目的:
- 开发一种深度学习辅助的两步选策略,以实现最佳的传感器元素选择.
- 为了创建高性能传感器阵列,用于挥发性有机化合物 (VOC) 的歧视.
- 展示深度学习的实用性,用于组装并行传感平台.
主要方法:
- 为了检测VOC,创建了一个由400种元素 (20种阴离子,20种阴离子染料) 组成的传感器库.
- 一个feedforward神经网络随机森林递归特征消除 (FRR) 算法被用于元素选.
- 使用基于智能手机的医疗点测试 (POCT) 平台与基于图像的深度学习.
主要成果:
- 该FRR算法有效地选传感元件.
- 8元和10元阵列在两个VOC模型中实现了100%的区分精度.
- 一个模拟的癌症VOC模型成功地使用POCT平台进行了歧视.
结论:
- 深度学习提供了一种合理和实用的方法,用于在并行传感平台上组装传感器元件.
- 开发的策略可以快速创建高精度传感器阵列.
- 这项技术在临床诊断和化学传感应用方面表现有前途.
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