Portable Dual-Mode Biosensor for Quantitative Determination of Salmonella in Lateral Flow Assays Using Machine...

Area of Science:

• * Biomedical Engineering
• * Food Safety
• * Biosensor Technology

Background:

• * Foodborne pathogens pose significant global health risks.
• * Conventional detection methods are accurate but slow for on-site applications.
• * Need for rapid, accessible, and sensitive pathogen determination technologies.

Purpose of the Study:

• * To develop a portable, smartphone-assisted dual-mode biosensor.
• * To enhance sensitivity in lateral flow assays (LFAs) using colorimetric and photothermal imaging.
• * To establish a low-cost platform for quantitative pathogen detection.

Main Methods:

• * A prototype biosensor using a Raspberry Pi for illumination, image acquisition, and machine learning analysis.
• * Dual-mode detection combining colorimetric (RGB intensities) and photothermal speckle imaging.
• * Multivariate linear regression for Salmonella concentration prediction.

Main Results:

• * The fused model achieved high performance (R² = 0.91) for Salmonella detection.
• * Achieved a limit of detection (LOD) of 10^4 CFU/mL, a significant improvement over existing methods.
• * Demonstrated robustness in blind testing, though challenges remain at very low concentrations.

Conclusions:

• * The developed biosensor is a low-cost, field-deployable platform for quantitative pathogen detection.
• * Smartphone-assisted, machine learning-enabled diagnostics show promise for broader monitoring.
• * This technology provides a foundation for future advancements in food safety diagnostics.