Subtract Blank Measurement

step_measure_subtract_blank() creates a specification of a recipe step that subtracts or divides by a blank measurement. The blank can be provided externally or learned from training data.

Usage

step_measure_subtract_blank(
  recipe,
  blank = NULL,
  blank_col = NULL,
  blank_value = NULL,
  method = "subtract",
  measures = NULL,
  role = NA,
  trained = FALSE,
  learned_blank = NULL,
  skip = FALSE,
  id = recipes::rand_id("measure_subtract_blank")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

blank

An optional external blank to use. Can be:

• A measure_tbl object with location and value columns

• A numeric vector (must match the number of locations in data)

• A data.frame with location and value columns (will be interpolated) If NULL, the blank is learned from training data using blank_col and blank_value.

blank_col

An optional column name (unquoted) that identifies sample types. Used with blank_value to identify blank samples in training data.

blank_value

The value in blank_col that identifies blank samples. When the step is prepped, the mean of all blank samples is computed and stored for use during baking.

method

The correction method to apply:

• "subtract" (default): Subtract the blank from each spectrum

• "divide": Divide each spectrum by the blank

measures

An optional character vector of measure column names to process. If NULL (the default), all measure columns (columns with class measure_list) will be processed.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

learned_blank

A named list containing the learned blank values for each measure column. This is NULL until the step is trained.

skip

A logical. Should the step be skipped when the recipe is baked?

id

A character string that is unique to this step to identify it.

Value

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

Details

Blank subtraction is a fundamental preprocessing step in analytical chemistry. It removes background signal that is present in all measurements but is not related to the analyte of interest.

Two modes of operation:

1. External blank: You provide a blank spectrum directly via the blank argument. This is useful when you have a known reference blank.

2. Learned blank: You specify which samples are blanks in your training data using blank_col and blank_value. During prep(), the mean of all blank samples is computed and stored. This approach is useful for batch-specific blank correction.

Common use cases:

• UV-Vis: Remove solvent absorbance

• IR: Remove atmospheric CO2/H2O interference

• Fluorescence: Remove buffer background and Raman scatter

• Chromatography: Remove ghost peaks and solvent artifacts

No selectors should be supplied to this step function. The data should be in the internal format produced by step_measure_input_wide() or step_measure_input_long().

Tidying

When you tidy() this step, a tibble with columns terms, method, blank_source, and id is returned.

See also

step_measure_subtract_reference() for simpler external reference

Other measure-preprocessing: step_measure_absorbance(), step_measure_calibrate_x(), step_measure_calibrate_y(), step_measure_derivative(), step_measure_derivative_gap(), step_measure_emsc(), step_measure_kubelka_munk(), step_measure_log(), step_measure_map(), step_measure_msc(), step_measure_normalize_istd(), step_measure_osc(), step_measure_ratio_reference(), step_measure_snv(), step_measure_subtract_reference(), step_measure_transmittance()

Examples

library(recipes)

# Example with external blank (numeric vector)
blank_spectrum <- rep(0.1, 100)

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_subtract_blank(blank = blank_spectrum)

# Example learning blank from training data
# (assuming sample_type column with "blank" values)
# rec <- recipe(outcome ~ ., data = my_data) |>
#   step_measure_input_long(...) |>
#   step_measure_subtract_blank(blank_col = sample_type, blank_value = "blank")