A standard curve (aka calibration curve) for the protein of interest is constructed by
plotting the mean absorbance (y-axis) against the protein concentration (x-axis) and
choosing the best fit curve for the data points. Based on the standard curve, we interpolate
the sample absorbances to compute the sample concentrations. We will be discussing the steps
in more detail below.
The Standard Curve
After obtaining raw data from the ELISA reader, the ELISA results are ready for
analysis. We suggest using an ELISA data analysis software for the analysis. Our lab
with CurveExpert 1.4, but many other curve fitting software and tools are available,
GraphPad Prism. Microsoft Excel can also be used to analyze ELISA results, but it may
offer as many options or flexibility as other programs for scientists.
For this standard curve example, we will be using CurveExpert 1.4 to explain the process.
1. Enter ELISA data into software
Categorize the ELISA raw data into three sections:
- Absorbance of the blank well
- Absorbance of standards with known concentrations
- Absorbance of samples with unknown concentrations
It is important to run the blank well with sample diluent to determine the background
absorbance. Even without the presence of the protein, the buffer will still have an OD
value. The absorbance of the blank wells should be subtracted from all standard and
absorbances for accurate OD readings.
Open “CurveExpert 1.4” to see the interface below:
Enter the standard concentration in the x-axis column and the corresponding OD values in
y-axis column. The data plot will be presented in the bottom right corner.
2. Select the best fitting curve
Click the [Run] button in the top menu bar to allow the software to
the data and choose the best possible curve fit. The window below will show up:
Click [All On] to include all model families for calculation. However, if you prefer not
include all of them, specific model families can also be selected for calculation. If
“Polynomials” is checked, you will be asked to input the polynomial constraint, which we
recommend setting as “4”.
Press [OK] to run the calculation.
The resulting curve fits will be ranked based on the standard error and correlation
coefficient. Double click on each model to see the corresponding curve.
Choose the curve that meets the following criteria:
- The equation with the higher R value
- The curve should rise smoothly and closely resemble a straight line
Right click and select “Copy” to paste the graph into an excel sheet or word document.
Our lab and most companies generally recommend using a 4-parameter algorithm for the best
standard curve fit. [Why?]
In this example, we have chosen the quadratic fit curve. Apart from the polynomial fit,
quadratic curve has the highest R value and closely resembles a straight line that rises
smoothly. [Why aren’t we using the polynomial fit curve?]
3. Calculate target protein concentration
The calculation can be performed in the software or with Excel. If the samples were
before the ELISA, make sure to multiply the computed sample concentrations by the sample
Using software (CurveExpert 1.4) to find the sample
Using software will enable the user to easily find the x- and y-values, differentiate,
integrate the curve fit. Right click on the chosen curve fit graph for the graphing
menu and choose “Analyze”. For ELISA analysis, we would navigate to the “Find x=f(y)”
and enter the sample OD value (y value) in the “At Y =” field. Click [Calculate] to
the x value (the target protein concentration) at the specified y value.
Using MS Excel to find the sample concentrations
Click the [Info] button in the top left corner of the graph. This will
the model information for the curve fit along with other statistical information for the
The “Coefficients” tab displays the model function and the values of the coefficients a,
c, etc. Press [Copy] and paste the function and coefficient values in an Excel sheet.
In the Excel sheet, input the corresponding coefficient values and y values (OD value)
the formula to calculate the sample concentrations.
Are you a PicoKineTM ELISA kit user?
Feel free to contact us if you encounter issues with your ELISA data analysis. We will be happy to assist you!
Why is the 4-parameter algorithm recommended as the best standard curve fit?
Curve fitting software will provide different model options for data plotting, including
linear plots, semi-log plots, log/log plots, and 4- or 5-parameter logistic (4PL or 5PL)
curves. Although linear plots with R2 values greater than 0.99 indicate good fitting,
data points on the lower end of the range are compressed, which will reduce resolution.
Semi-log and log/log plots resolve this issue. Data points are spread out more evenly
with semi-log plots and log/log plots offer good linearity for the low to medium ranges
of the curves. The 4- or 5-parameter logistic curves (4PL or 5PL) are more complex
calculations that take into consideration additional parameters such as the maximum and
minimum. The main difference between the 4PL and 5PL curves is that the 4PL curve is
symmetrical around an inflection point, but the 5PL curve is asymmetrical.
If the data points suggest asymmetry near the plateaus, the 5PL curve would be useful.
However, more data points need to be collected to determine if asymmetry exists. As a
result, it is generally recommended to use the simpler 4-PL for the best standard curve
Why aren’t we using the polynomial fit curve?
Although the polynomial fit curve is ranked 1 in the list and the curve has the highest R
value for the example above, we should avoid using the polynomial fit as the standard
curve. One thing to keep in mind with polynomials is that data points may sometimes
result in a fitted curve that reaches maximum OD and then goes down again. This will
result in having two concentration values for the same OD value. For example, the 2
polynomial curves shown below are unsuitable to be used as the standard curves for your