School of Information Science and Technology, Fudan University, Shanghai 200438, China.
School of Information Science and Technology, Fudan University, Shanghai 200438, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Nov 5;280:121560. doi: 10.1016/j.saa.2022.121560. Epub 2022 Jun 25.
Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Raman spectroscopy can be analyzed more efficiently and thus provide more accurate molecular information. However, no general neural network is designed for one-dimensional Raman spectral data so far. Furthermore, different combinations of hyperparameters of neural networks lead to results with significant differences, so the optimization of hyperparameters is a crucial issue in deep learning modeling. In this work, we propose a deep learning model designed for Raman spectral data and a hyperparameter optimization method to achieve its best performance, i.e., a method based on the simulated annealing algorithm to optimize the hyperparameters of the model. The proposed model and optimization method have been fully validated in a glioma Raman spectroscopy dataset. Compared with other published methods including linear regression, support vector regression, long short-term memory, VGG and ResNet, the mean squared error is reduced by 0.1557 while the coefficient determination is increased by 0.1195 on average.
拉曼光谱是一种光谱技术，通常用于确定分子的振动模式，在化学中常用于提供分子识别的结构指纹。借助深度学习，可以更有效地分析拉曼光谱，从而提供更准确的分子信息。然而，到目前为止，还没有针对一维拉曼光谱数据的通用神经网络。此外，神经网络的超参数组合会导致结果有很大的差异，因此超参数的优化是深度学习建模中的一个关键问题。在这项工作中，我们提出了一种专为拉曼光谱数据设计的深度学习模型和一种超参数优化方法，以达到最佳性能，即基于模拟退火算法优化模型超参数的方法。所提出的模型和优化方法已在神经胶质瘤拉曼光谱数据集上进行了全面验证。与包括线性回归、支持向量回归、长短期记忆、VGG 和 ResNet 在内的其他已发表方法相比，平均而言，均方误差降低了 0.1557，决定系数提高了 0.1195。
