R Scripts for Full-Spectrum Analysis with Machine Learning Framework for Quantitative Assessment of SARS-CoV-2 Lateral Flow Immunoassays

This dataset contains R scripts for spectral data processing and machine learning model development used in the study "Integrated Full-Spectrum Analysis with Machine Learning Framework for Quantitative Assessment of Lateral Flow Immunoassays: Application to SARS-CoV-2 Detection." The scripts enable the processing of visible-range spectral data (400-700 nm) from lateral flow immunoassays and the development of machine learning models for quantitative analysis. The repository includes: 1. Spectral preprocessing scripts for noise reduction (Savitzky-Golay filtering), standard normal variate (SNV) transformation, and principal component analysis. 2. Machine learning model development scripts for training and evaluating five algorithms: polynomial regression, partial least squares regression, support vector regression, random forest, and gradient boosting. 3. Dual-channel normalization methods implementing both differential (T-C) and ratio (T/C) spectral analyses. These tools support the transformation of qualitative lateral flow tests into quantitative analytical tools by extracting meaningful patterns from spectral data of gold nanoparticle immunocomplexes. While the raw clinical data cannot be shared due to patient confidentiality restrictions, these scripts facilitate the reproducibility of the analytical methodology.