Abstract

Enzyme-linked immunosorbent assays (ELISA) are specific and highly sensitive procedures for identifying and quantifying analytes in samples. This assay is based on the binding of a target molecule (analyte/antigen) to antibodies which recognize the compound. The presence of an antigen-antibody complex is detected using a secondary enzyme-conjugated antibody. Detection is obtained by addition of a substrate which yields a measurable product. Enzyme-linked immunosorbent assays are routinely used in many areas of biological research. The determination of the analyte concentration relies upon construction of a calibration curve. The standard curve is prepared by performing a dilution series of a known concentration of the analyte across a range of concentrations near the expected unknown concentration. The calibration curves are then used to calculate the concentration of an unknown sample.

For most analyses a plot of response versus concentration will create a linear relationship, at least within a certain range of concentrations, and can be analyzed with linear regression. However, for those calibration plots which are sigmoid that is, a curve having an "S" shape performing a linear fit leads to errors in estimating sample values. These inaccuracies are most significant at the extremes of the standard curve, most often in the low end but sometimes in the high end as well. In this study we compare the results of using linear fit and 4-parameter analysis on ELISA data and report our findings.

For research use only, not for diagnostic or therapeutic use.