aceNon-Perturbative Bacterial Identification Directly From Solid Agar Plates Using Raman Micro-Spectroscopy and Machine Learning
Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions used during incubation. Our approach enabled sensitive identification using two Escherichia coli variants, differing only in green fluorescent protein (GFP)…
Machine learning combined with DFT-based material computation identified E. coli variants through unopened, inverted agar plates at over 97.7% accuracy, avoiding the preparation that has limited Raman use in microbiology workflows.
The demonstration used only two E. coli variants differing in GFP expression, and the monitoring of growth, biofilm formation and antimicrobial resistance is described as a promise rather than shown.
Evidence
- Peer-reviewedSmall Methods2026-10-14
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Truvace Impact Record TRV-2026-1926, v1: “Non-Perturbative Bacterial Identification Directly From Solid Agar Plates Using Raman Micro-Spectroscopy and Machine Learning.” Truvace, 2026-10-19. /record/TRV-2026-1926 (accessed at citation time). sha256 c0963344d995f088…
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