Calibration data from DIN 32645 — din32645

Dintest. Plugin for MS Excel for evaluations of calibration data. Written by Georg Schmitt, University of Heidelberg. Formerly available from the Website of the University of Heidelberg.

Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including +

Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations 1995). Analytica Chimica Acta 391, 105 - 126.

Examples

data(din32645) m <- lm(y ~ x, data = din32645) -calplot(m)

+calplot(m)

## Prediction of x with confidence interval -(prediction <- inverse.predict(m, 3500, alpha = 0.01))

#> $Prediction +(prediction <- inverse.predict(m, 3500, alpha = 0.01))

#> $Prediction #> [1] 0.1054792 #> #> $`Standard Error` @@ -114,7 +123,7 @@ # was collected from Procontrol 3.1 (isomehr GmbH) in this case round(prediction$Confidence,5)

#> [1] 0.07434

## Critical value: -(crit <- lod(m, alpha = 0.01, beta = 0.5))

#> $x +(crit <- lod(m, alpha = 0.01, beta = 0.5))

#> $x #> [1] 0.0698127 #> #> $y @@ -129,12 +138,12 @@ # In German, the smallest detectable value is the "Erfassungsgrenze", and we # should get 0.14 according to DIN, which we achieve by using the method # described in it: -lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din") +lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din") round(lod.din$x, 2)

#> [1] 0.14

## Limit of quantification # This accords to the test data coming with the test data from Dintest again, # except for the last digits of the value cited for Procontrol 3.1 (0.2121) -(loq <- loq(m, alpha = 0.01))

#> $x +(loq <- loq(m, alpha = 0.01))

#> $x #> [1] 0.2119575 #> #> $y @@ -143,7 +152,7 @@ #>

round(loq$x,4)

#> [1] 0.212

# A similar value is obtained using the approximation # LQ = 3.04 * LC (Currie 1999, p. 120) -3.04 * lod(m,alpha = 0.01, beta = 0.5)$x

#> [1] 0.2122306

Format

References

Examples

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