The fact that they are much lower than they should be could indicate that we are starting to overload our detector at higher calibration levels we are putting more mass of analyte into the detector than it can reliably detect. However, if we look at our two highest calibration points, we can see that they do not match the trend for the rest of the data the response values should be closer to 12. Let’s take a closer look at these curves:Ĭurve A: This represents a case where the curve perfectly matches the instrument data, meaning our calculated unknown values will be accurate across the entire calibration range.Ĭurve B: The r 2 value is good and visually the curve matches most of the data points pretty well. Figure 1: Representative Curves and r2 values Figure 1 shows a few representative curves, their associated data, and r 2 values (concentration and response units are arbitrary). Generally, r values ≥0.995 and r 2 values ≥ 0.990 are considered ‘good’. The closer the values are to 1.00, the more accurately our curve represents our detector response. The r or r 2 values that accompany our calibration curve are measurements of how closely our curve matches the data we have generated. back-calculated accuracy (reported as % error).correlation coefficient (r) or coefficient of determination (r 2).In order to be able to claim that our calibration curve accurately represents our instrument response, we have to take a look at a couple of quality indicators for our curve data: Just because we have run several standards across a range of concentrations and plotted a curve using the resulting data, it does not mean our curve accurately represents our instrument’s response across that concentration range. In the last installment of The Practical Chemist, I introduced instrument calibration and covered a few ways we can calibrate our instruments. Despite the title, this article is not about weight loss – it is about generating valid analytical data for quantitative analyses.