# Portable Raspberry Pi Platform for Automated Interpretation of Lateral Flow Strip Tests

## TL;DR

A low-cost Raspberry Pi system automates the accurate reading of paper-based tests for SARS-CoV-2, improving reliability over visual interpretation.

## Contribution

First Raspberry Pi-driven image processing system for quantifying nucleic acid lateral flow assays.

## Key findings

- The system achieved recoveries between 84 and 108% for DNA concentration analysis.
- Grayscale intensity comparison corrected for illumination and background variability.
- The platform enables low-cost automation for diverse diagnostic applications.

## Abstract

Paper-based rapid tests are widely used in point-of-care diagnostics due to their simplicity and low cost. However, their application in quantitative analysis remains limited. In this work, a nucleic acid lateral flow assay (NALFA) was integrated with an automated image acquisition system built on a Raspberry Pi platform for the quantitative detection of SARS-CoV-2 virus, increasing the accuracy of the test compared to subjective visual interpretation. The assay employed blue polystyrene microspheres as reporters, while automated image capturing, image processing and quantification were performed using custom Python software (version 3.12). Signal quantification was achieved by comparing the grayscale intensity of the test line with that of a simultaneously captured negative control strip, allowing correction for illumination and background variability. Calibration curves were used for the training of the algorithm. The system was applied for the analysis of a series of samples with varying DNA concentrations, yielding recoveries between 84 and 108%. The proposed approach integrates a simple biosensor with an accessible computational platform to achieve full low-cost automation. This work introduces the first Raspberry Pi-driven image processing approach for accurate quantification of NALFAs and establishes a foundation for future low-cost, portable diagnostic systems targeting diverse nucleic acids, proteins, and biomarkers.