Image analysis for the integrated robotic system was performed with a custom Python-based tool developed in-house. This tool identified the test and control lines, measured nitrocellulose background intensity, and reported line strength as the height of the strip-width-averaged, background-subtracted, peak pixel intensity in the red image channel. Faulty LFAs were identified by weak control lines and removed as outliers, however outlier removal was rare, occurring in fewer than 2% of all LFAs tested. Antibody pair rankings were determined by comparing mean test line strength from tests run with analyte-positive samples (signal, S) versus from tests run with analyte-negative samples (non-specific binding or noise, N). Two comparisons, or metrics, were used, signal divided by noise (S/N) and signal subtracted by noise (S-N). Both metrics were used to ensure the best pairs had both high positive control and low negative control signals.