Spectral Normalization Recipe Step

step_spectral_norm() creates a specification of a recipe step that divides each spectrum by one of its norms, with normalize(): its total area, L1 norm, L2 (Euclidean) norm, or maximum absolute intensity.

Usage

step_spectral_norm(
  recipe,
  ...,
  method = "l1",
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("spectral_norm")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose the predictors (for example, the spectral channels). See recipes::selections().
method: The norm: "l1" (default), "area", "l2" or "max".
role: Not used by this step, since no new variables are created.
trained: A logical indicating whether the step has been trained.
columns: The names of the selected columns, stored once the step has been trained.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.
id: A character string that is unique to this step.

Value

An updated version of recipe with the new step added to the sequence of existing steps.

Details

The selected columns form one spectrum per row, and are replaced by the normalized values. Unlike recipes::step_normalize(), which scales each column (variable), this step scales each row (spectrum), to remove multiplicative variation between spectra such as shot-to-shot changes of the ablated mass. Nothing is estimated from the training data. Spectra whose norm is zero are set to NA, with a warning.

The methods are those of normalize(): "l1" (sum of absolute values, default), "area" (sum of values, equal to "l1" for non-negative spectra), "l2" (Euclidean norm, also called vector normalization) and "max" (largest absolute value). The method can be tuned with tune::tune() (dials parameter spectral_norm_method()). tidy() returns the terms, the method and id.

Author

Christian L. Goueguel

Examples

if (rlang::is_installed("recipes")) {
  data(forageLIBS)
  # potassium and the K I resonance lines
  wl <- suppressWarnings(as.numeric(names(forageLIBS)))
  dat <- forageLIBS[c(which(names(forageLIBS) == "K"), which(wl > 760 & wl < 780))]
  rec <- recipes::recipe(K ~ ., data = dat) |>
    step_baseline(recipes::all_predictors()) |>
    step_spectral_norm(recipes::all_predictors(), method = "l2") |>
    recipes::prep()
  baked <- recipes::bake(rec, new_data = NULL)
  spectra <- baked[setdiff(names(baked), "K")]
  rowSums(spectra[1:3, ]^2)   # 1
}
#> [1] 1 1 1