Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.
University of the Chinese Academy of Sciences, Beijing 100049, P. R. China.
Anal Chem. 2021 Aug 17;93(32):11089-11098. doi: 10.1021/acs.analchem.1c00431. Epub 2021 Aug 2.
The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism , including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.
鉴于当前的全球大流行，高效、准确地识别海产品和环境中的病原体变得愈发紧迫。传统方法不仅耗时，而且还会导致样本浪费。在这里，我们提出了两种新方法，它们涉及拉曼光谱结合长短期记忆（LSTM）神经网络，并将其与使用常规卷积神经网络（CNN）的方法进行了比较。我们使用从海洋生物中分离出的 8 株菌株，包括 4 种病原体。在配置和训练模型后，我们提出的 LSTM 方法的平均隔离水平准确率超过 94%，不仅满足了识别要求，而且表明所提出的方法比常规 CNN 模型更快、更准确。最后，通过计算方法，我们设计了一个损失函数来探索拉曼数据所反映的机制，找到了最有可能表现出核酸特征的拉曼片段。这些新颖的实验结果为开发额外的深度学习方法来准确分析复杂的拉曼数据提供了思路。
