Generate and analyze ELISA standard curves with multiple curve fitting models including 4PL, linear, and polynomial regression for accurate concentration determination.
R² (Goodness of Fit)
--
Curve Equation
--
Average CV%
--
Limit of Detection
--
Samples Analyzed
--
The 4PL (Four Parameter Logistic) model is most commonly used for ELISA assays as it accounts for the sigmoidal shape of typical dose-response curves. Linear regression works well for narrow concentration ranges, while polynomial models can handle complex curves.
An R² value of 0.98 or higher is generally considered acceptable for ELISA standard curves. Values below 0.95 may indicate issues with the assay or curve fitting model selection.
CV% (Coefficient of Variation) measures the reproducibility of your replicates. CV values below 10% are excellent, 10-15% are acceptable, and above 20% may indicate pipetting errors or assay variability issues.
Try a different curve fitting model, check for outlier data points, ensure proper serial dilutions, and verify that your concentration range covers the samples' expected values. Consider using more standard points for better curve definition.
Typically 6-8 standard concentrations provide adequate curve definition. More points (up to 11) can improve accuracy but require more reagents and time. Ensure standards span the expected sample concentration range.
LOD represents the lowest concentration that can be reliably detected above background noise. It's typically calculated as the blank OD plus 2-3 standard deviations of blank replicates, then back-calculated using the standard curve.
Yes, this calculator works for any immunoassay that generates optical density or similar quantitative readouts, including CLIA, RIA, and other plate-based assays with standard curves.
Samples above the highest standard should be diluted and re-analyzed. Samples below the lowest standard may be below the limit of quantification and should be reported as such or the assay sensitivity improved.