Fill in the missing piece on your journey to publication Enter the draw

All tags Immunoassays kits and reagents Calculating and evaluating ELISA data

Calculating and evaluating ELISA data

Calculation of results from ELISA data and recommended guidelines on statistical assay validation.

Print this protocol.

Calculation of results

Always run ELISA samples in duplicate or triplicate. This will provide enough data for statistical validation of the results. Many computer programs are now available to help process ELISA results in this way.

​1. Calculate the average absorbance values for each set of duplicate standards and duplicate samples. Duplicates should be within 20% of the mean.

2. Create the standard curve

Create a standard curve by plotting the mean absorbance for each standard concentration (x axis) against the target protein concentration (y axis). Draw a best fit curve through the points in the graph (we suggest that a suitable computer program be used for this).

We recommend including a standard on each ELISA plate to provide a standard curve for each plate used.

A representative standard curve is shown in the figure below, from human HIF1 alpha SimpleStep ELISATM kit (ab171577) (please note we recommend to generate a standard curve for each ELISA plate used and not to use the one below to analyze results).

Example with human HIF1 alpha SimpleStep ELISATM kit

A representative standard curve from human HIF1 alpha SimpleStep ELISATM kit (ab171577) was diluted in a serial two fold steps in assay buffer. Each point on the graph represents the mean of the three parallel titrations.

We recommend having a sample of known concentration to use as a positive control. A separate protein/peptide sample of known concentration can be used to create the standard curve. The concentration of the positive control sample should be within the linear section of the standard curve in order to obtain valid and accurate results.

3. Determine the concentration of target protein in the sample

To determine the concentration of target protein concentration in each sample:

  • First find the mean absorbance value of the sample. From the Y axis of the standard curve graph, extend a horizontal line from this absorbance value to the standard curve. E.g. if the absorbance reading is 1, extend the line from this absorbance point on the Y axis (a).

  • At the point of intersection, extend a vertical line to the X axis and read the corresponding concentration (b).
Calculating and evaluating ELISA

4. Samples that have an absorbance value falling out of the range of the standard curve.

When the standard curve has a plateau and samples are too concentrated an inaccurate quantification can occur. To obtain an accurate result, samples should be diluted before proceeding with the ELISA staining. For these samples, the concentration obtained from the standard curve when analyzing the results must be multiplied by the dilution factor.

Calculating the coefficient of variation

The coefficient variation (CV) is the ratio of the standard deviation σ to the mean µ:

Cv=  σ

This is expressed as a percentage of variance to the mean and therefore indicates any inconsistencies and inaccuracies in the results. The larger the variance, the more inconsistency and error there is.

For example, some laboratories will set guidelines for ELISA results so that the coefficient of variance is less than 10%. This will indicate that the results are not valid if they have a difference of more than 10% CV to the mean, as there is too much variance and the results are considered to be inaccurate. This sets a standard for the quality of the validated results. Computer programs can be used to calculate the CV values from ELISA results.

Fluctuations in CV can be caused by:

  • Inaccurate pipeting (ensure pipette tips are sealed to the pipette before use so they draw up the correct volume of liquid)
  • Splashing of reagents between wells
  • Bacterial of fungal contamination of either screen samples or reagents
  • Cross contamination between reagents
  • Temperature variations across the plate - ensure the plates are incubated in a stable temperature environment away from drafts
  • Some of the wells drying out - ensure the plates are always covered at incubation steps


Reproducibility of results can be assessed by calculating CV values to compare mean concentrations:

For the replicas of the same samples on different plates (intra-assay).

Intra-assay variation

Reproducibility of results can be assessed by calculating CV values to compare mean concentrations:

For the samples on different plates (intra-assay). For example, within an assay, several independent assays should be undertaken on different plates at the same time. The overall intra-assay coefficient of variation can then be calculated by working out the CV differences between the mean absorbencies for the same samples on each plate.

For example, six replicates of eight serum samples containing different concentrations of the target protein. Two standard curves can be run on each plate.

Inter-assay variation

Assay to assay reproducibility within one laboratory can be evaluated by conducting several experiments undertaken by several technicians. CV values are calculated to compare mean concentrations for the same samples on different plates tested by different technicians.

For example, six replicates of eight samples containing different concentrations of target protein can be tested. Two standard curves can be run on each plate. The mean concentration and the coefficient of variation can be calculated on 18 determinations of each sample from different plates and different technicians.

Spike recovery

A spike recovery sample determines the effect of the constituents of the sample on the results. This determines whether the antibody antigen detection is affected by the difference between the diluent used to prepare the standard curve and the biological sample matrix.

For example, serum tissue culture supernatant, which is often used in ELISA, contains many proteins. These may affect the binding of the antibody and affect the overall results. It can also affect the signal to noise ratio of the color detection. Due to these factors, the concentration can sometimes be underestimated.

In a spike recovery sample, a known concentration of standard protein/analyte is added (spiked) into the natural test sample matrix. Often, the spike analyte samples will be tested at several known concentrations. For example, if the samples to be tested in the ELISA are serum, then a serum sample should be used for the spike recovery test. The result from this can be compared to an identical spike, which should be done in standard diluent.

If the result, or 'recovery', for the spike in the sample matrix is identical to the result for the analyte prepared in standard diluent, then the sample matrix is considered to be valid for the assay procedure. If the recovery is different, then components in the sample matrix are interfering with the analyte detection.

What if a spike recovery experiment indicates that the sample matrix is affecting the results?

We would recommend diluting the standard in sample matrix (e.g. serum) in order to produce the standard curve. Many of our ELISA kits contain a standard serum diluent for this purpose, any effects on the results from the sample matrix will also be present in the standard, and therefore comparison between the standard curve and the samples is more accurate.

Another solution is to alter the sample matrix. For example, if neat biological sample is used, try diluting this in standard diluent. However, with this option, you will need to ensure that the dilution factor is taken into account when analyzing the results and that the concentration stays within the linear section of the standard curve (in order for it to remain accurate).