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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]
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]
The calibration curve that does not use the internal standard method ignores the uncertainty between measurements. The coefficient of determination (R 2 ) for this plot is 0.9985. In the calibration curve that uses the internal standard, the y-axis is the ratio of the nickel signal to the yttrium signal.
For example, if narrow molar mass distribution standards are available for polystyrene, these can be used to construct a calibration curve (typically vs. retention volume ) in eg. toluene at 40 °C. This calibration can then be used to determine the "polystyrene equivalent" molecular weight of a polyethylene sample if the Mark-Houwink ...
Standard solutions are commonly used to determine the concentration of an analyte species via calibration curve. 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. [10]
Using the calibration curve method, the analyst can calibrate the spectrometer with a pure silver aqueous solutions, and use the calibration graph to determine the amount of silver present in the waste samples. This method, however, assumes the pure aqueous solution of silver and a photographic waste sample have the same matrix and therefore ...
Setting = and = , which corresponds to using a log–log graph, yields the equation = + where m = k is the slope of the line ( gradient ) and b = log a is the intercept on the (log y )-axis, meaning where log x = 0, so, reversing the logs, a is the y value corresponding to x = 1.
The Steinhart–Hart equation assumes is 1 ohm. The curve fit is much less accurate when it is assumed = and a different value of such as 1 kΩ is used. However, using the full set of coefficients avoids this problem as it simply results in shifted parameters.