What Limit of Detection and Limit of Quantitation actually mean, how the regression (calibration curve) method works, what Slope, Intercept, r, R² and σ represent, ICH Q2(R2) acceptance criteria, and the common mistakes that quietly distort the result.
Limit of Detection (LOD) is the lowest concentration of an analyte in a sample that can be reliably detected, but not necessarily quantified with precision. Limit of Quantitation (LOQ) is the lowest concentration that can be measured with acceptable precision and accuracy — it's always higher than the LOD. Both are required validation characteristics under ICH Q2(R2) for any method intended to detect or quantify trace-level analytes, such as impurity or related-substance methods.
This calculator uses the most widely accepted approach: build a calibration curve of peak area vs. concentration across a series of dilution levels around the expected LOD/LOQ range, then derive LOD and LOQ from the line's slope and the scatter of points around it.
Each level row uses its own set of area readings — however many reading columns your table currently has (start with 3, add more with ➕ Add Cell if your protocol calls for more replicates) — averaged, against its own calculated concentration. Never lock every row to a single copied-down value, which is a common spreadsheet mistake that quietly distorts the regression.
A common beginner mistake is computing σ as the plain standard deviation of the area readings. The correct σ for this formula is the standard error of the y-estimate (STEYX) — it measures how far the actual area readings scatter above and below the fitted regression line, not how spread out the raw readings are from their own average. A tight cluster of readings that still sits far from a straight line will have a small "simple" SD but a large STEYX — and it's the STEYX-based σ that correctly reflects the assay's real noise around the calibration curve.
For methods where baseline noise is clearly visible on the chromatogram (common for impurity/trace methods), Signal-to-Noise Ratio is an accepted alternative approach: LOD is typically the concentration giving an S/N of 3:1, and LOQ the concentration giving an S/N of 10:1. This calculator records the S/N Ratio you observe at each level alongside the regression data — useful for cross-checking that the regression-based LOD/LOQ lands in a sensible place relative to the S/N trend, and for the printed report.
Exact limits always come from your own validation protocol/SOP, but these are commonly seen in industry:
| Metric | Typical Limit |
|---|---|
| System Suitability %RSD | ≤ 2.0% (standard replicate injections) |
| Regression Linearity (R²) | ≥ 0.99 (ICH Q2(R2) benchmark) |
| Number of Concentration Levels | Minimum 3 (5–6 typical, spanning the expected LOD/LOQ range) |
The calculator itself opens with 5 starter levels labeled 80 / 90 / 100 / 110 / 120 (fully editable) — rename any label, add more levels with ➕ Add Level, or remove one with its ✕. Reading columns work the same way: ➕ Add Cell adds one reading column to every level at once, and the × on a column header removes it from every level at once — just like inserting or deleting a column in Excel.