| RRC ID |
66263
|
| Author |
Lu W, Chen X, Wang L, Li H, Fu YV.
|
| Title |
Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification.
|
| Journal |
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.
|
| Volume |
92(9)
|
| Pages |
6288-6296
|
| Published |
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*
|
| Resource |
| General Microbes |
JCM15769 |
