Calibration Curve Calculator
ChemistryBuild a calibration curve with linear regression from standard data points. Calculate unknown concentration, R², slope, intercept, and LOD/LOQ.
Unknown Concentration
What is a Calibration Curve?
The Calibration Curve Calculator performs linear regression on five standard data points (concentration vs absorbance or signal) and uses the resulting equation (A = m × C + b) to determine the concentration of an unknown sample from its measured absorbance. Outputs include slope, intercept, R², and unknown concentration.
Calibration curves are the foundation of quantitative analytical chemistry — every UV-Vis spectrophotometry, AAS, HPLC, and immunoassay result relies on a validated calibration curve. The linear regression (least squares fit) minimises the sum of squared residuals between measured points and the fitted line. R² ≥ 0.999 is required for pharmaceutical analysis under ICH Q2(R1) guidelines (enforced by India's CDSCO), while R² ≥ 0.995 suffices for environmental and clinical applications under NABL ISO 17025 accreditation.
The Beer-Lambert law is the physical basis for linear A vs C relationships — the Beer-Lambert Law Calculator computes concentration directly when the molar absorptivity ε is known. For enzyme assays where absorbance is used to track substrate consumption, the Enzyme Activity Calculator converts substrate consumed to enzyme Units using calibration-validated concentration data.
How to use this Calibration Curve calculator
- Prepare 5 standard solutions at known concentrations spanning your expected sample range (e.g., 0, 2, 5, 10, 20 mg/L).
- Measure each standard's absorbance at the appropriate wavelength under the same conditions as samples.
- Enter the 5 (Concentration, Absorbance) pairs in the corresponding fields.
- Enter the Unknown Sample Absorbance.
- Read R² — if ≥ 0.9990, the curve is valid. If lower, re-check standard preparation, wavelength setting, and cuvette cleanliness.
- Read Unknown Concentration — the analyte concentration in your sample.
Formula & Methodology
Linear regression (least squares):A = m × C + b (Beer-Lambert linear model) m = (n·ΣCᵢAᵢ − ΣCᵢ·ΣAᵢ) / (n·ΣCᵢ² − (ΣCᵢ)²) b = (ΣAᵢ − m·ΣCᵢ) / n R² = 1 − SS_residual/SS_total = 1 − Σ(Aᵢ − Â_i)² / Σ(Aᵢ − Ā)² Unknown concentration: C_unknown = (A_unknown − b) / mWorked example — phosphate analysis in Indian wastewater: Five phosphate standards (mg/L) with molybdenum blue absorbance at 880 nm:C: 0, 1, 2, 5, 10 mg/L A: 0.000, 0.048, 0.096, 0.240, 0.481 Slope m = 0.0481; Intercept b = −0.0002; R² = 0.9999Unknown wastewater sample A = 0.155:C_unknown = (0.155 − (−0.0002)) / 0.0481 = 0.1552 / 0.0481 = 3.23 mg/LCPCB effluent discharge standards: phosphate ≤ 5 mg/L for streams (General Standard IS 2490-1 for inland surface water); 3.23 mg/L is below limit — discharge compliant. Indian wastewater treatment plants (ETPs, CETPs) run this analysis routinely. CPCB has established 48 monitoring parameters and 85 reference methods, all requiring calibration curves with documented linearity meeting ISO 17025 requirements from NABL-accredited labs across India's industrial clusters.
Frequently Asked Questions