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ICH Q2(R2) LOD/LOQ Calculator.

Validate analytical method linearity, calculate LOD and LOQ per ICH Q2(R2) guidelines. Assess precision (%RSD) and accuracy (% recovery). Data never leaves your browser.

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Validated2026-04-05
CitableMethods and citation included

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Load example ICH Q2 calibration data to see the full workflow

Calibration Data

Paste tab or comma-separated data. First column = concentration, second = response. Replicates at the same concentration are grouped automatically.

Accuracy / Recovery Data (Optional)

Paste expected and measured values (spike-and-recovery). First column = expected, second = measured.

When to use

  • Validate linearity of HPLC, GC, UV-Vis, or other analytical calibration curves
  • Calculate LOD and LOQ per ICH Q2(R2) from calibration regression residuals
  • Assess repeatability precision (%RSD) at each concentration level
  • Evaluate accuracy via % recovery at QC levels
  • Generate validation summary reports for regulatory submissions

Do not use for

  • Bioassay calibration (ELISA, cell-based) — use the ELISA Curve Fitter for 4PL/5PL nonlinear fits
  • Signal-to-noise LOD — requires raw chromatographic data, not supported here
  • Stability testing — ICH Q1A is a separate guideline

LOD/LOQ from regression can underestimate true limits

The calibration-based method assumes homoscedastic residuals. If your method shows heteroscedastic error (variance increases with concentration), consider weighted regression or use the blank-based method for more conservative LOD/LOQ estimates.

R² alone does not prove linearity

A high R2\text{R}^{2} can mask systematic curvature. Always inspect the residual plot for patterns. Random scatter around zero indicates good linearity; a U-shape or S-shape indicates nonlinearity even with R2\text{R}^{2} > 0.999.

Include zero concentration in calibration

Blank responses (0 concentration) anchor the intercept and improve LOD/LOQ estimates. Without blanks, the intercept is extrapolated, which increases uncertainty.

%RSD is unreliable near zero

At very low concentrations, the mean approaches zero and %RSD becomes artificially large. Evaluate precision at LOQ separately with wider acceptance criteria (%RSD \leq 10%).

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Method

OLS linear regression on calibration data (all replicates). LOD = 3.3σ/S, LOQ = 10σ/S where σ\sigma = residual standard deviation (ICH Q2 calibration method). Precision as %RSD, accuracy as % recovery.

2

Validated

Last validated 2026-04-05. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.

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How to cite

How to Cite

ConductScience ICH Q2(R2) LOD/LOQ Calculator (v1.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/ich-q2-loq-calculator

ICH Expert Working Group. Q2(R2) Validation of Analytical Procedures. 2023.

ICH Q2(R2) Method Validation Framework

ICH Q2(R2) defines the analytical parameters that must be validated for any quantitative method:

Specificity: Method measures only the intended analyte • Linearity: Response is proportional to concentration (R2\text{R}^{2} \geq 0.999) • Range: Validated from LOQ to the highest linear concentration • Accuracy: % recovery at multiple levels (spike-and-recovery) • Precision: Repeatability (%RSD) and intermediate precision • LOD/LOQ: Sensitivity limits based on calibration statistics

This tool automates linearity, LOD/LOQ, precision, and accuracy assessment from calibration and QC data.

LOD and LOQ Calculation Methods

Three approaches exist for LOD/LOQ determination:

1. Signal-to-Noise (S/N): LOD at S/N = 3, LOQ at S/N = 10. Requires access to chromatographic noise.
2. Based on Calibration: LOD = 3.3σ/S, LOQ = 10σ/S. σ\sigma = residual standard deviation of the regression. This is the method used here.
3. Based on Blank: LOD = mean_blank + 3.3 ×\times SD_blank. Requires replicate blank measurements.

The calibration-based method is most commonly used because it requires only the calibration data that you already have.

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