Which regression does this use?

It fits an ordinary least squares straight line, absorbance equals slope times concentration plus intercept, through your standard points. This linear model is the standard approach for the working range of both BCA and Bradford assays.

How are unknown concentrations calculated?

Once the line is fitted, each unknown concentration is found by rearranging the equation: concentration equals absorbance minus the intercept, divided by the slope. The tool flags any result that falls below the curve as out of range.

What does R-squared tell me?

R-squared measures how well the line fits your standards, where 1.0 is a perfect fit. A good protein standard curve usually reaches 0.99 or better. A lower value suggests pipetting error, a bad blank, or absorbances outside the linear range.

What if a sample reads above my top standard?

Do not extrapolate beyond the highest standard, because both assays curve at high absorbance. Dilute the sample, re-read it within the standard range, and multiply the result back by the dilution factor.

Does BCA or Bradford change the maths?

The fitting maths is identical. The chemistries differ in their useful range and linearity, so you choose the standards and read window appropriate to the kit, but the standard curve is fitted and inverted the same way for both.

What is the BCA / Bradford Protein Assay Standard Curve Calculator?

Accepts BSA standard absorbance values at known protein concentrations, fits an ordinary least squares linear regression with an R-squared quality check, and interpolates unknown sample concentrations. For biochemistry labs quantifying protein before SDS-PAGE or western blot. It runs free in your browser on Gera Tools, with nothing uploaded.

BCA / Bradford Protein Assay Standard Curve Calculator

Name: BCA / Bradford Protein Assay Standard Curve Calculator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

Both the BCA and Bradford assays measure protein indirectly through a colour change read as absorbance. To turn an absorbance into a concentration you build a standard curve from known amounts of a reference protein, usually bovine serum albumin, then read your unknowns off that line. This tool does the regression for you.

How it works

The standards are fitted with ordinary least squares to a straight line relating absorbance to concentration:

absorbance    = m × concentration + b
concentration = (absorbance - b) / m

The slope m and intercept b come from minimising the squared vertical distances between the points and the line. The tool also reports the coefficient of determination, R², so you can judge whether the fit is good enough to trust. Each unknown absorbance is then inverted through the equation to give its concentration in the same units you used for the standards.

BCA vs Bradford: what the chemistry difference means for the curve

The two assay chemistries are interchangeable for standard curve fitting purposes, but they have different properties that affect how you design your standards:

BCA (bicinchoninic acid) assay is generally more linear across a wider concentration range and less susceptible to interference from detergents, making it the preferred choice for membrane protein samples. The reaction is temperature-sensitive and typically runs at 37°C; the absorbance is read at 562 nm.

Bradford assay is faster (typically 5 minutes) and more sensitive at low concentrations. However, it has a narrower linear range and is more sensitive to detergent and basic buffer conditions. Absorbance is read at 595 nm. The curve often shows a slight non-linearity at higher concentrations, which is why the linear working range of the Bradford assay is typically kept tighter.

In both cases, the linear regression approach is appropriate within the working range. The key discipline is keeping your unknowns within the span of your standards.

Designing a good standard set

The quality of the fitted curve depends on how well you design your standard series. Common conventions:

Include a blank (zero BSA) as the first standard; this anchors the intercept to the actual reagent background rather than forcing it through the origin.
Use at least five non-zero standards. More points improve the quality of the fit and make outliers easier to identify.
Space the standards to cover the likely range of your unknowns, with the middle standards occupying the range you care about most.
Prepare standards from the same BSA stock on the same day as your samples to minimise day-to-day variation.

A typical BCA standard set might use BSA concentrations of 0, 25, 125, 250, 500, 750, 1000, and 1500 µg/mL; a Bradford set might use 0, 100, 200, 400, 600, 800, and 1000 µg/mL, depending on the kit.

Diagnosing a poor R-squared

An R² below 0.99 in a protein standard curve warrants investigation before you report your unknowns:

Check your blank. A contaminated zero-concentration standard will displace the intercept and distort every other point relative to the fitted line.

Look for outliers. One mis-pipetted standard can pull the regression significantly. If removing one point raises R² substantially, repeat that standard.

Check whether you are in the linear range. Both assays curve at high absorbances. If your top standards are showing less colour per unit concentration than your middle ones, you have moved outside the linear range — remove the highest standards until linearity is restored.

Check the instrument. A plate reader with inconsistent well-to-well performance, stray light at high absorbance, or a dirty light path will scatter your standards in ways that look like poor technique.

Tips and notes

Always include a blank (zero concentration) as one standard so the intercept reflects your reagent background. Keep unknown readings inside the span of your standards; a value above the top standard sits in the non-linear region and must be diluted and re-read rather than extrapolated. Aim for an R² of at least 0.99, and if it falls short, suspect a pipetting error, a contaminated blank, or absorbances drifting out of the linear range of the assay.