# Portable Dual-Mode Biosensor for Quantitative Determination of Salmonella in Lateral Flow Assays Using Machine Learning and Smartphone-Assisted Operation

## TL;DR

A low-cost, smartphone-assisted biosensor improves Salmonella detection in food samples using machine learning and dual-mode imaging.

## Contribution

A portable biosensor using machine learning and smartphone integration for quantitative Salmonella detection in lateral flow assays.

## Key findings

- The fused model achieved an R2 of 0.91 for Salmonella concentration prediction.
- The biosensor detected Salmonella down to 104 CFU/mL, an improvement over previous methods.
- Blind testing showed robustness but difficulty distinguishing between negative and low-concentration samples.

## Abstract

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R2 = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 104 CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 103 CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications.