Dataset for Characterizing viral samples using machine learning for Raman and absorption spectroscopy

We demonstrated the application of machine learning methods, such as convolutional neural networks and random forests, to predict the concentration of viral samples through Raman and absorption Spectroscopy. Our study demonstrates that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. In this dataset, we've included the raw and processed data files associated with the manuscript.本研究展示了机器学习方法（如卷积神经网络（convolutional neural networks）与随机森林（random forests））在利用拉曼光谱（Raman Spectroscopy）与吸收光谱（absorption Spectroscopy）预测病毒样本浓度中的应用。本研究表明，相较于单独使用拉曼光谱或吸收光谱，将两类光谱数据进行拼接可有效提升预测准确率。本数据集包含与本研究稿件相关的原始数据与经预处理后的数据文件。