Hello,
I am implementing EPA Method 525.3 in our lab. One question I have is, is it acceptable to use Average Response Factors for evaluating the calibration curve? I know this was acceptable for 525.1 and 525.2 but 525.3 does not mention Average Response Factors at all in the EPA method. I have come across a few Agilent application notes, in which they are comparing 525.2 and 525.3 and they do use Average Response Factors, but I am unsure if this is just for comparison purposes or if this is an acceptable way to evaluate the curve following EPA method 525.3.
Is anyone performing 525.3? If so, how are you evaluating your curves? In your experience, are some analytes better suited as linear or quadratic calibration curves?
Any help is appreciated. Thank you in advance.
~Kevin
525.3 along with other newer versions of 500 series methods are moving away from a simple curve fit and to being able to prove the accuracy of the calibration. We have used average response factor for our calibrations, but the method says that you must quantify each calibration point against the curve that is is a part of and it much be +/- 50% for points at or below the MRL used and +/-30% at all other points along the curve. If average RF can not meet this requirement then you must use linear of quadratic. There are some analytes that work best with quadratic and some with linear. Also you may need to use a weighting factor for the lowest points to quant within the limits.
I have had calibrations with a 0.998 r2 value miss for the lowest points while one using weighting with a 0.98 r2 will quant within 5% at all levels, which is the curve type the method would require you to use. I have seen curves with a perfect 1.00 r2 value that the lowest standard quanted back at over 200% of the true value, so just meeting a straight line does not mean it is accurate at all points on the line.
I also found this;
https://www.epa.gov/sites/default/files ... ct2010.pdf
Which includes;
A.
Linear calibration through origin – In this method, the mean calibration factor (CF) (ratio of instrument detector response to target analyte amount or concentration) of an external calibration or mean response factor (RF) (ratio of detector response of analyte to its amount or concentration times the ratio of internal standard concentration to its detector response) of an internal calibration is determined through the analysis of one or more calibration standards and used to quantify the amount or concentration of target analyte in a sample based on the sample detector response. The method is used in cases where the relative standard deviation of the CFs or RFs is less than or equal to 20%, the detector response is directly proportional to the target analyte amount or concentration and the calibration passes through the origin. External linear calibration through the origin is typically used for ICP metals, in which case the calibration curve consists of a blank and a single standard prepared at an appropriate concentration so as to effectively outline the desired quantitation range. External and internal linear calibrations are also used for certain GC and HPLC methods.
B.
Linear least squares regression – A mathematical model invoked for calibration data that describes the relationship between expected and observed values via minimization of the sum of the squared residuals (deviations between observed and expected values) - The final outcome of the least squares regression is a linear calibration model of the form: y = m1x + m2x2 + m3x3 + … + mnxn +b (where y = detector response and x = target analyte amount or concentration). Least squares regression calibrations are typically derived from a minimum of three standards of varying concentration and are applicable to data sets in which the measurement uncertainty is relatively constant across the calibration range. Most SW-846 methods rely on first-order least squares regression models (y = mx + b) for calibration. However given the advent of new detection techniques, and the fact that many techniques cannot be optimized for all analytes to which they are applied, or over a sufficiently wide working range, second-order (y = m2x2 + m1x + b) or third-order (y = m3x3 + m2x2 + m1x + b) linear regression models are often invoked for calibration. In any of these cases, SW-846 methods allow forcing a linear least squares regression through the origin, provided that the resulting calibration meets the acceptance criteria and can be verified by acceptable quality control results. External least squares regressions are typically used for ICP/MS metals calibrations. Internal least squares regressions are generally used for calibration in GC/MS applications.
This would suggest that the average response factor curve is also a Linear Calibration just through origin and not least squares regression.
