Standard Addition Correction

step_measure_standard_addition() creates a specification of a recipe step that performs standard addition correction to compensate for matrix effects. This method creates a sample-specific calibration for each unknown to accurately quantify in the presence of matrix interference.

Usage

step_measure_standard_addition(
  recipe,
  ...,
  addition_col = "addition",
  sample_id_col,
  min_points = 3,
  output_suffix = "_corrected",
  diagnostics = TRUE,
  role = "outcome",
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_standard_addition")
)

Arguments

recipe: A recipe object.
...: One or more selector functions to choose response columns to correct using standard addition.
addition_col: Name of the column containing the amount of standard added (spike amount). Default is "addition".
sample_id_col: Name of the column identifying unique samples. Each sample gets its own standard addition curve.
min_points: Minimum number of addition points required per sample. Default is 3.
output_suffix: Suffix for output concentration columns. Default is "_corrected".
diagnostics: Include diagnostic information (R², slope, intercept)? Default is TRUE.
role: Recipe role for new columns. Default is "outcome".
trained: Logical indicating if the step has been trained.
skip: Logical. Should the step be skipped when baking?
id: Unique step identifier.

Value

An updated recipe with the new step added.

Details

Standard Addition Method

Standard addition works by:

Splitting each unknown sample into multiple aliquots
Adding increasing known amounts of analyte to each aliquot
Measuring response for all aliquots
Fitting regression: response = intercept + slope * addition
Calculating original concentration from the x-intercept

The x-intercept (where response = 0) is at -intercept / slope. Since intercept is positive (response from original sample) and slope is positive (response increases with addition), the original concentration is: concentration = intercept / slope

Data Format

The input data should have:

A sample identifier column (each unique sample)
An addition amount column (0 for unspiked, then increasing amounts)
Response column(s) to be corrected

When to Use

Use standard addition when:

Significant matrix effects are present
Matrix-matched calibrators are not available
Sample-to-sample matrix variation is expected

Limitations

Requires multiple measurements per sample
Assumes linear response over the addition range
Does not correct for non-specific interferences

Examples

library(recipes)

# Standard addition data for two samples
sa_data <- data.frame(
  sample_id = rep(c("Sample1", "Sample2"), each = 4),
  addition = rep(c(0, 10, 20, 30), 2),
  response = c(
    # Sample 1: original conc ~15
    150, 250, 350, 450,
    # Sample 2: original conc ~25
    250, 350, 450, 550
  )
)

rec <- recipe(~ ., data = sa_data) |>
  step_measure_standard_addition(
    response,
    addition_col = "addition",
    sample_id_col = "sample_id"
  ) |>
  prep()
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable

bake(rec, new_data = NULL)
#> # A tibble: 8 × 7
#>   sample_id addition response response_corrected response_sa_slope
#>   <fct>        <dbl>    <dbl>              <dbl>             <dbl>
#> 1 Sample1          0      150                 15                10
#> 2 Sample1         10      250                 15                10
#> 3 Sample1         20      350                 15                10
#> 4 Sample1         30      450                 15                10
#> 5 Sample2          0      250                 25                10
#> 6 Sample2         10      350                 25                10
#> 7 Sample2         20      450                 25                10
#> 8 Sample2         30      550                 25                10
#> # ℹ 2 more variables: response_sa_intercept <dbl>, response_sa_rsquared <dbl>