ASTM D6512-07(2014)

5.1 Appropriate application of this practice should result in an IQE achievable by most laboratories properly using the test method studied. That is, most laboratories should be capable of measuring concentrations greater than IQE_{5.2 The IQE values may be used to compare the quantitation capability of different methods for analysis of the same analyte in the same matrix. The IQE is not an indicator of individual laboratory performance.}

5.3 The IQE procedure should be used to establish the interlaboratory quantitation capability for any application of a method where interlaboratory quantitation is important to data use. The intent of the IQE is not to set reporting limits.

1.1 This practice establishes a uniform standard for computing the interlaboratory quantitation estimate associated with Z % relative standard deviation (referred to herein as IQE_Z %), and provides guidance concerning the appropriate use and application. The calculations involved in this practice can be performed with DQCALC, Microsoft Excel-based software available from ASTM.²

1.2 IQE_Z % is computed to be the lowest concentration for which a single measurement from a laboratory selected from the population of qualified laboratories represented in an interlaboratory study will have an estimated Z % relative standard deviation (Z % RSD, based on interlaboratory standard deviation), where Z is typically an integer multiple of 10, such as 10, 20, or 30, but Z can be less than 10. The IQE_10 % is consistent with the quantitation approaches of Currie (1)³ and Oppenheimer, et al. 1.3 The fundamental assumption of the collaborative study is that the media tested, the concentrations tested, and the protocol followed in the study provide a representative and fair evaluation of the scope and applicability of the test method as written. Properly applied, the IQE procedure ensures that the IQE has the following properties:

1.4 The IQE applies to measurement methods for which calibration error is minor relative to other sources, such as when the dominant source of variation is one of the following:

1.5 Data Quality Objectives—Typically, one would compute the lowest % RSD possible for any given dataset for a particular method. Thus, if possible, IQE_10 % would be computed. If the data indicated that the method was too noisy, one might have to compute instead IQE_20 %, or possibly IQE