Reference - Detail
| RRC ID | 75597 |
|---|---|
| Author | Kanno N, Kato S, Ohkuma M, Matsui M, Iwasaki W, Shigeto S. |
| Title | Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm. |
| Journal | STAR Protoc |
| Abstract |
Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we detail steps to discriminate prokaryotic species using single-cell Raman spectra acquisitions followed by data preprocessing and random forest model tuning. In addition, we describe the steps required to evaluate the model. This protocol requires minimal preprocessing of Raman spectral data, making it accessible to non-spectroscopists, yet allows intuitive visualization of feature importance. For complete details on the use and execution of this protocol, please refer to Kanno et al. (2021). |
| Volume | 3(4) |
| Pages | 101812 |
| Published | 2022-12-16 |
| DOI | 10.1016/j.xpro.2022.101812 |
| PII | S2666-1667(22)00692-X |
| PMID | 36386892 |
| PMC | PMC9641085 |
| MeSH | Algorithms Machine Learning* Serogroup Spectrum Analysis, Raman* / methods |
| Altmetric score |
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| The most frequently cited source | X(Twitter) |
| Total number of mentions | 7 |
| Altmetric score changes over past 6months | 0.0 |
| Resource | |
| General Microbes | JCM 20135 JCM 1465 JCM 10941 JCM 12380 JCM 8929 JCM 19564 |