For example if you samples concentrations may range from 10 – 1000 ppb I’d suggest following solutions: 10, 25, 50, 75, 100, 250, 500, 7 ppb.Īfter analysing these solutions I would break up this calibration into two parts 1) 10-100 ppb and 2) 100-1000 ppb. So what to do if you are in the lab doing your actual analyses? I’d suggest you to prepare at least 5 point approximately equally spaced standard solutions for each order of magnitude your method needs to work in. On the other hand if a narrower calibration range – around one order of magnitude – would be used there is no significant difference in using or not using weighting.
This simulation only included random errors. Sample result is at the lower end of the calibration range.įor example if calibration points 2, 1000, 2000, 30 units were used for calibration and the sample with actual concentration of 2.0 units was measured unweighted regression yielded answer of 8.3 but weighted resulted in 1.95 units. Calibration points are distributed equally over the calibration range.Ĥ. Absolute repeatability standard deviation is not constant over given concentration range.ģ. Their simulations effectively demonstrate that advantages of weighting are observed only if all following four things happen simultaneously:ġ. Veronica Meyer 1 published in LC/GC a good simulation aiming to show how much results are influenced by either using or not using weighting in linear regression.
