Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning
Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning
复制标题
使用深度学习快速准确地解码拉曼光谱编码的悬架阵列
DOI：
10.1039/c9an00913b
复制
发表时间：
2019-07-21
期刊：
影响因子：
4.2
通讯作者：
Ji, Yanhong
中科院分区：
文献类型：
--
作者：
Chen, Xuejing;Xie, Luyuan;Ji, Yanhong
使用称为“残余神经网络”（ResNet）的深度学习网络来解码拉曼光谱编码的悬浮阵列（SA）。拉曼光谱具有窄带宽和稳定信号的特点，具有理想的编码特性。不同的拉曼报告分子组装的微石英片（MQPs）接枝有各种生物分子探针，这使得能够同时检测单个样品中的许多目标分析物。通过拉曼光谱对多种类型的混合多量子点进行测量，然后通过ResNet进行解码，以获得分析物的类型信息。通过t分布随机邻居嵌入（t-SNE）图验证了ResNet的良好分类性能。与其他机器学习模型相比，这些实验表明，ResNet在分类稳定性和训练收敛性方面明显上级不同的数据集。该方法简化了解码过程，分类准确率达到100%。
A deep learning network called "residual neural network" (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs). With narrow bandwidths and stable signals, Raman spectra have ideal encoding properties. The different Raman reporter molecules assembled micro-quartz pieces (MQPs) were grafted with various biomolecule probes, which enabled simultaneous detection of numerous target analytes in a single sample. Multiple types of mixed MQPs were measured by Raman spectroscopy and then decoded by ResNet to acquire the type information of analytes. The good classification performance of ResNet was verified by a t-distributed stochastic neighbor embedding (t-SNE) diagram. Compared with other machine learning models, these experiments showed that ResNet was obviously superior in terms of classification stability and training convergence to different datasets. This method simplified the decoding process and the classification accuracy reached 100%.