# 8: Calibrating Data

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A calibration curve is one of the most important tools in analytical chemistry as it allows us to determine the concentration of an analyte in a sample by measuring the signal it generates when placed in an instrument, such as a spectrophotometer. To determine the analyte's concentration we must know the relationship between the signal we measure , $$S$$, and the analyte's concentration, $$C_A$$, which we can write as

$S = k_A C_A + S_{blank} \nonumber$

where $$k_A$$ is the calibration curve's sensitivity and $$S_{blank}$$ is the signal in the absence of analyte.

How do we find the best estimate for this relationship between the signal and the concentration of analyte? When a calibration curve is a straight-line, we represent it using the following mathematical model

$y = \beta_0 + \beta_1 x \nonumber$

where y is the analyte’s measured signal, S, and x is the analyte’s known concentration, $$C_A$$, in a series of standard solutions. The constants $$\beta_0$$ and $$\beta_1$$ are, respectively, the calibration curve’s expected y-intercept and its expected slope. Because of uncertainty in our measurements, the best we can do is to estimate values for $$\beta_0$$ and $$\beta_1$$, which we represent as b0 and b1. The goal of a linear regression analysis is to determine the best estimates for b0 and b1.

This page titled 8: Calibrating Data is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by David Harvey.