Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification
Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification
复制标题
人工智能方法与激光镊拉曼光谱相结合进行微生物鉴定
DOI：
10.1021/acs.analchem.9b04946
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发表时间：
2020-05-05
影响因子：
7.4
通讯作者：
Fu, Yu Vincent
中科院分区：
文献类型：
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作者：
Lu, Weilai;Chen, Xiuqiang;Fu, Yu Vincent
拉曼光谱是一种非破坏性、无标记、高度特异性的方法，可提供材料的化学信息。因此，它适合用作表征生物样品的有效分析工具。在这里，我们介绍了一种新的方法，使用人工智能来分析生物拉曼光谱，并在单细胞水平上识别微生物。卷积神经网络（ConvNet）和拉曼光谱的框架的组合允许提取单个微生物细胞的拉曼光谱特征，然后根据其光谱特征对细胞进行分类。作为概念的证明，我们在单细胞水平上测量了14种微生物的拉曼光谱，并使用拉曼数据构建了最佳ConvNet模型。ConvNet的平均分类准确率为95.64 +/-5.46%。同时，提出了一种基于遮挡的拉曼光谱特征提取方法，实现了拉曼光谱特征权值的可视化，以区分不同的物种。
Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 +/- 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.