The role of OD value in ELISA

OD value (optical density value) is an absorbance indicator measured by an enzyme-linked immunosorbent assay (ELISA) reader, which directly reflects the content of the target substance in the sample. Its core functions include:

Quantitative analysis basis: OD value is positively correlated with the concentration of the target substance, and the higher the concentration, the greater the OD value.

Result interpretation basis: Determine the yin-yang nature by comparing the OD value of the sample with the critical value (such as S/CO value).

Standard curve calculation: The standard curve converts OD values to specific concentrations and requires fitting through models such as four parameter logistic regression.

Key steps for optimizing OD value readings

Optimization of experimental operations

Washing technique: Avoid background interference during washing, especially paying attention to the washing effect of low concentration standard products.

Sample addition accuracy: Use a calibrated pipette to prevent sample addition errors or contamination of adjacent wells.

Dual wavelength calibration: It is recommended to use the OD450-OD570/630 calibration method to reduce system errors.

Data Quality Control

Blank hole correction: If a negative OD value occurs, it is necessary to check whether the blank hole is contaminated or operated improperly.

Multiple well detection: Set 2-3 multiple wells for each sample and take the average to improve reliability.

Quality control serum: Monitor the stability of daily experiments through RCVK (routine quality control serum variation).

Sample and reagent optimization

Sample processing: Ensure rapid processing after sample collection to avoid degradation affecting OD values.

Reagent adjustment: Optimize antibody and substrate concentrations to avoid overly strong or weak signals.

Equipment calibration: Regularly maintain the enzyme-linked immunosorbent assay (ELISA) reader to ensure detection accuracy.

Standard curve optimization

Concentration range: The standard concentration should cover the sample to be tested, and linear intervals should be selected as much as possible when presenting an S-shaped curve.

Fitting model: Prioritize the four parameter logistic model to improve curve fitting.