Deep learning-based component identification for the Raman spectra of mixtures
Deep learning-based component identification for the Raman spectra of mixtures
复制标题
基于深度学习的混合物拉曼光谱成分识别
DOI：
10.1039/c8an02212g
复制
发表时间：
2019-03-07
期刊：
影响因子：
4.2
通讯作者：
Lu, Hongmei
中科院分区：
文献类型：
--
作者：
Fan, Xiaqiong;Ming, Wen;Lu, Hongmei
拉曼光谱被广泛用作分子鉴定的指纹技术。然而，拉曼光谱包含来自多个组分的分子信息以及来自噪声和仪器的干扰。因此，使用拉曼光谱的组分鉴定仍然是具有挑战性的，特别是对于混合物。在这项研究中，提出了一种名为基于深度学习的组件识别（DeepCID）的新方法来解决这个问题。建立了卷积神经网络（CNN）模型来预测混合物中组分的存在。对比研究表明，DeepCID可以学习光谱特征并识别混合物的模拟和真实的拉曼光谱数据集中的组分，具有更高的准确性和显著更低的假阳性率。此外，DeepCID与L1正则化逻辑回归（LR），k-最近邻（kNN），随机森林（RF）和反向传播人工神经网络（BP-ANN）模型相比，对三元混合光谱数据集表现出更好的灵敏度。总之，DeepCID是解决混合物的拉曼光谱中的组分识别问题的一种有前途的方法。
Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.