A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Area of Science:

• Analytical Chemistry
• Biotechnology
• Microbiology

Background:

• Traditional biosensors face limitations in detecting multiple bacterial species simultaneously.
• Optical sensor arrays, including fluorescent and colorimetric types, are emerging as a promising alternative.
• These arrays leverage pattern recognition and machine learning for bacterial identification.

Purpose of the Study:

• To provide a comprehensive overview of fluorescent and colorimetric sensor arrays for bacterial determination.
• To elucidate the mechanisms of optical signal generation in these sensor arrays.
• To compare sensor arrays based on their performance in identifying various bacterial species.

Main Methods:

• Review of existing literature on optical sensor arrays for bacterial detection.
• Analysis of signal generation mechanisms in fluorescent and colorimetric sensor arrays.
• Comparative assessment of sensor array performance and identified bacterial species.

Main Results:

• Optical sensor arrays can simultaneously acquire multiple features for pattern formation.
• Fluorescent and colorimetric sensor arrays demonstrate potential for multiplexed bacterial detection.
• Performance varies across different sensor array designs and target bacteria.

Conclusions:

• Optical sensor arrays represent a significant advancement over single-target biosensors.
• Understanding signal generation mechanisms is crucial for optimizing sensor design.
• Further research is needed to address current limitations and enhance the capabilities of these arrays.