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Related Experiment Video

Updated: Jan 29, 2026

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds

Published on: November 13, 2021

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

Natalia Nakou¹, Panagiotis K Tsikas¹, Despina P Kalogianni¹

¹Department of Chemistry, University of Patras, GR26504 Patras, Greece.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This study presents a Raspberry Pi-powered system for accurate, automated quantification of nucleic acid lateral flow assays (NALFAs). This low-cost approach enhances diagnostic accuracy for detecting viruses like SARS-CoV-2.

Keywords:

DNA OpenCV Python SARS-CoV-2 beads image analysis image processing lateral flow assay polystyrene microparticles strip

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Area of Science:

Biomedical Engineering
Point-of-Care Diagnostics
Biosensor Technology

Background:

Paper-based rapid tests are simple and affordable for point-of-care diagnostics but lack quantitative accuracy.
Existing methods for nucleic acid lateral flow assays (NALFAs) often rely on subjective visual interpretation, limiting their precision.

Purpose of the Study:

To develop an automated, quantitative detection system for NALFAs using a Raspberry Pi platform.
To improve the accuracy of NALFA-based diagnostics, specifically for SARS-CoV-2 detection, by replacing subjective interpretation with objective image analysis.

Main Methods:

Integration of a NALFA with an automated image acquisition system utilizing a Raspberry Pi and custom Python software (version 3.12).
Use of blue polystyrene microspheres as reporters and a negative control strip for signal quantification, correcting for illumination and background variations.
Development of calibration curves for algorithm training and validation with samples of varying DNA concentrations.

Main Results:

The automated system achieved quantitative detection of SARS-CoV-2, surpassing the accuracy of visual interpretation.
Recoveries between 84% and 108% were obtained when analyzing samples with varying DNA concentrations.
Demonstrated the first Raspberry Pi-driven image processing approach for accurate NALFA quantification.

Conclusions:

The proposed system offers a fully automated, low-cost solution for quantitative NALFA analysis by integrating a simple biosensor with an accessible computational platform.
This work lays the groundwork for future development of portable, cost-effective diagnostic systems for a wide range of biomarkers.