Example (preset)
Choose an example to fill inputs and see results immediately.
- Paste standards (concentration and absorbance) or import a CSV/TSV file.
- Select assay (BCA/Bradford) and fit (auto/linear/quadratic).
- The standard curve and unknown concentrations appear (dilution factors supported).
Out-of-range estimates (extrapolation) can be inaccurate. Check warnings in the results. This is a calculation tool and does not prescribe experimental optimization.
Input (paste / CSV)
Weighting (optional)
Standards
Format: col 1 = concentration, col 2+ = absorbance (rep1, rep2, …). TSV/CSV supported.
Unknowns
Format: col 1 = sample name, col 2+ = absorbance (rep columns supported), optional last column: dilution_factor.
Advanced (point exclusion / residuals / CI)
Results
Plots
Table (standards)
| Concentration | Mean | SD | ŷ | Residual | Excluded |
|---|
Table (unknowns)
| Sample | Mean Abs | Conc (measured) | Dilution factor | Conc (stock) | In range |
|---|
Note: Out-of-range estimates (extrapolation) can be inaccurate.
Workflow
- Enter standards (concentration x and absorbance y). With replicates, the tool computes mean±SD.
- Optionally subtract blank (0 concentration) mean absorbance (applied consistently to standards and unknowns).
- Fit with linear (y=a+bx) or quadratic (y=a+bx+cx²) and show metrics (R²/RMSE/AICc).
- For unknowns, back-calculate concentration from absorbance and apply dilution factors to get stock concentrations.
FAQ
What is the difference between BCA and Bradford assays?
Both estimate protein concentration from absorbance. Curve shape can change by conditions and range, so this tool supports linear and quadratic fits.
What should I enter first?
Enter standards (known concentrations) and their absorbance. If you provide replicate columns, the tool computes mean±SD automatically.
Do I need blank subtraction?
Often, subtracting the 0-concentration (blank) absorbance improves stability. Apply the same blank subtraction to both standards and unknowns.
Should I use a linear or quadratic fit?
Quadratic may fit better over a wide range, while a narrowed range can be fine with a linear fit. Use auto comparison (AICc); if the difference is small, preferring the simpler linear model is often safer.
My unknown concentration is outside the standard range.
Out-of-range estimates are extrapolations and can be inaccurate. Consider adjusting dilution factors or the standard range.
How do I enter dilution factors?
You can enter a dilution factor per unknown row. Stock concentration is calculated as measured concentration × dilution factor.
Can I use these results in a paper or report?
Yes, but include key settings such as fit type (linear/quadratic), whether blank subtraction was used, and whether extrapolations were present, so the analysis is reproducible.