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A calibration curve plot showing limit of detection (LOD), limit of quantification (LOQ), dynamic range, and limit of linearity (LOL).. In analytical chemistry, a calibration curve, also known as a standard curve, is a general method for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration. [1]
A calibration curve is obtained by measuring a series of standard solutions with known concentrations, which can be used to determine the concentration of an unknown sample using linear regression analysis. [12] For example, by comparing the absorbance values of a solution with an unknown concentration to a series of standard solutions with ...
The calibration curve can then be used to calculate the analyte concentration in an unknown sample. [ 1 ] Selecting an appropriate internal standard accounts for random and systematic sources of uncertainty that arise during sample preparation or instrument fluctuation.
[3] [4] The most common approach for accounting for matrix effects is to build a calibration curve using standard samples with known analyte concentration and which try to approximate the matrix of the sample as much as possible. [2] This is especially important for solid samples where there is a strong matrix influence. [5]
In comparison to the calibration curve method, the standard addition method has the advantage of the matrices of the unknown and standards being nearly identical. [1] This minimizes the potential bias arising from the matrix effect when determining the concentration.
A calibration curve plot showing limit of detection (LOD), limit of quantification (LOQ), dynamic range, and limit of linearity (LOL) A general method for analysis of concentration involves the creation of a calibration curve. This allows for the determination of the amount of a chemical in a material by comparing the results of an unknown ...
This standard curve is then used to determine the concentration of the unknown protein. The following elaborates on how one goes from the standard curve to the concentration of the unknown. First, add a line of best fit, or Linear regression and display the equation on the chart.
The response factor can be expressed on a molar, volume or mass [1] basis. Where the true amount of sample and standard are equal: = where A is the signal (e.g. peak area) and the subscript i indicates the sample and the subscript st indicates the standard. [2]