AOAC Guidance on FA Immunoassay Validation (August 2023)

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2.1.2.2. 2.1.2.3.

Plot S i versus observed mean concentration.

Perform linear regression (ordinary least square estimate or weighted least Calculate the LOD according to the following formula (where ̅ (0) = calculated mean result from blank samples, S i(0) = intermediate precision standard deviation of blank samples, and slope is the slope from the linear regression above): square analysis)

2.1.2.4.

( 0 ) +3.3× ( 0 ) ) (1 − 1.65× )

= ( ̅

Values used as estimates of ̅ (0) and S i(0) cannot be negative. If the S i(0) from the linear regression (i.e., the intercept value) is negative, use the observed S i(0) from blank matrix samples. If the observed S i(0) also seems unacceptable, use S i from the

2.1.2.5.

lowest concentration test material.

2.1.3. Multiple Matrices:

2.1.3.1.

Plot S i versus observed mean concentration for all matrices. It is generally expected that the relationship between S i and concentration will be sufficiently similar across matrices to conduct a single regression analysis for the combined data Values used as estimates of ̅ (0) and S i(0) cannot be negative. If the S i(0) from the linear regression (i.e., the intercept value) is negative, use the observed S i(0) from blank matrix samples. If the observed S i(0) also seems unacceptable, use S i from the lowest concentration test material. For estimation of ̅ (0) , calculate the mean observed concentration across all blank matrices. If the relationship between standard deviation and concentration appears to be substantially different between matrices, consult with the AOAC Statistics from all matrices.

2.1.3.1.1.

2.1.3.1.2.

2.1.3.2.

Committee and Expert Review Panel.

2.1.4. Example Data and Calculations

Table 1:EXAMPLE DATA TABLE

0 ppm

0.5 ppm 1.0 ppm 2.5 ppm

Overall mean

x

0.04

0.612

0.882

2.395

Intermediate precision standard deviation

S i

0.108

0.211

0.22

0.305

Intermediate precision relative standard deviation

%RSD i

273.438

34.456

24.888

12.721