Generate Molecular Weight Distribution Curve

step_measure_mw_distribution() creates a specification of a recipe step that generates molecular weight distribution curves from SEC/GPC data.

This step has been superseded by measure.sec::step_sec_mw_distribution(). For new code, we recommend using the measure.sec package which provides more complete SEC/GPC analysis functionality.

Usage

step_measure_mw_distribution(
  recipe,
  measures = NULL,
  type = c("differential", "cumulative", "both"),
  calibration = NULL,
  n_points = 100L,
  mw_range = NULL,
  normalize = TRUE,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_mw_distribution")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

type

Type of distribution to generate:

• "differential" (default): dW/d(log M) differential distribution

• "cumulative": Cumulative weight fraction distribution

• "both": Generate both distributions

calibration

Calibration method for converting x-axis to log(MW). See step_measure_mw_averages() for details.

n_points

Number of points in the output distribution. Default is 100. If NULL, uses the original data resolution.

mw_range

Optional numeric vector c(min, max) specifying the MW range for the output distribution. If NULL, uses the range from data.

normalize

Logical. Should the differential distribution be normalized to integrate to 1? Default is TRUE.

role

Role for generated columns. Default is "predictor".

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

This step transforms the raw chromatogram into standard MW distribution representations:

Differential Distribution (dW/d(log M)): The weight fraction per unit log(MW). This representation is preferred because the area under the curve represents the weight fraction in that MW range.

Cumulative Distribution: The cumulative weight fraction from low to high MW. Values range from 0 to 1.

The output replaces the .measures column with the distribution data, where location contains log10(MW) values and value contains the distribution values.

See also

Other measure-chromatography: step_measure_mw_averages(), step_measure_mw_fractions(), step_measure_peaks_deconvolve()

Examples

library(recipes)

# Generate differential MW distribution
# rec <- recipe(~., data = gpc_data) |>
#   step_measure_input_wide(starts_with("signal_")) |>
#   step_measure_baseline_als() |>
#   step_measure_mw_distribution(type = "differential") |>
#   prep()