Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.

Area of Science:

• Microbiology
• Spectroscopy
• Machine Learning

Background:

• Current microbiota determination methods are often laboratory-bound, expensive, and time-consuming.
• There is a growing need for rapid, in situ analysis of bacterial composition for timely insights.
• Advancements in machine learning and fluorescence techniques present new opportunities for bacterial identification.

Purpose of the Study:

• To summarize machine learning algorithms for bacteria identification and microbiota determination.
• To review fluorescence spectroscopic methods for analyzing bacteria and their mixtures.
• To present fluorescence microscopic imaging techniques for bacterial identification.

Main Methods:

• Machine learning algorithms applied to spectroscopic and microscopic imaging data.
• Fluorescence spectroscopic methods including fluorescence lifetime spectroscopy, FRET, and SF spectroscopy.
• Fluorescence microscopy techniques such as epi-fluorescence, confocal, two-photon, and super-resolution imaging.

Main Results:

• High-dimensional imaging data can be used to identify bacterial makeup and its implications.
• Machine learning facilitates the classification of various microbiome states (e.g., healthy vs. non-healthy skin).
• Fluorescence-based methods offer potential for rapid and cost-effective bacterial analysis.

Conclusions:

• Fluorescence identification coupled with machine learning is emerging as a viable approach for microbiota determination.
• These methods hold promise for applications in human health and environmental science.
• Future research should focus on addressing challenges and exploring new opportunities in this field.