Full-Spectrum Analysis With Machine Learning for Quantitative Assessment of Lateral Flow Immunoassays: A Platform Approach.

Lateral flow immunoassays (LFIAs) provide rapid point-of-care results but lack quantitative capabilities. This study presents a platform technology integrating full-spectrum analysis (400-700 nm) with machine learning to enhance qualitative LFIAs with semi-quantitative assessment capabilities. We analyzed 241 clinical nasopharyngeal specimens using portable spectrometry to capture gold nanoparticle optical signatures from SARS-CoV-2 rapid tests as a validation model. Systematic evaluation of normalization strategies revealed T-C differential outperformed T/C ratio normalization. Signal processing through Savitzky-Golay filtering, standard normal variate transformation, and principal component analysis reduced dimensionality from 601 to 4 features while retaining 97.26% variance. Among five evaluated algorithms, random forest achieved optimal performance (R2 = 0.961, RMSE = 2.235 Ct) across clinically relevant ranges (PCR Ct 10.8-35.0). Bland-Altman analysis revealed measurement uncertainty of ±4.2 Ct, indicating suitability for population surveillance rather than precise individual quantification. Feature importance analysis identified 520-570 nm as the critical spectral region, consistent with gold nanoparticle surface plasmon resonance. This platform approach demonstrates that standard LFIAs contain extractable semi-quantitative information accessible through spectral-machine learning integration. While validated using COVID-19, the framework's modular design enables adaptation to diverse analytes including biomarkers, therapeutic drugs, and environmental contaminants without fundamental architectural changes. The methodology establishes a foundation for enhanced lateral flow diagnostics, particularly valuable in resource-limited settings where rapid semi-quantitative results provide greater utility than delayed laboratory measurements.