論文 - 詳細
| RRC ID | 66263 |
|---|---|
| 著者 | Lu W, Chen X, Wang L, Li H, Fu YV. |
| タイトル | Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification. |
| ジャーナル | Anal Chem |
| Abstract |
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. |
| 巻・号 | 92(9) |
| ページ | 6288-6296 |
| 公開日 | 2020-5-5 |
| DOI | 10.1021/acs.analchem.9b04946 |
| PMID | 32281780 |
| MeSH | Artificial Intelligence* Bacteria / chemistry Bacteria / classification Bacteria / genetics Discriminant Analysis Models, Biological Optical Tweezers Principal Component Analysis Single-Cell Analysis Spectrum Analysis, Raman / methods* |
| オルトメトリクス指標 |
オルトメトリクス指標項目
|
| 最多言及媒体 | News |
| 各媒体での言及数の合計 | 4 |
| 過去6か月間でのオルトメトリクス指標の変動値 | 0.0 |
| リソース情報 | |
| 一般微生物 | JCM15769 |