Calculate Molecular Weight Averages for SEC/GPC

step_measure_mw_averages() creates a specification of a recipe step that calculates molecular weight averages from size exclusion chromatography data.

This step has been superseded by measure.sec::step_sec_mw_averages().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

Usage

step_measure_mw_averages(
  recipe,
  measures = NULL,
  calibration = NULL,
  integration_range = NULL,
  output_cols = c("mn", "mw", "mz", "mp", "dispersity"),
  prefix = "mw_",
  role = "predictor",
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_mw_averages")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

calibration

Calibration method for converting x-axis to log(MW). Can be:

• NULL (default): Assumes x-axis is already log10(MW)

• A numeric vector of length 2: Linear calibration c(slope, intercept) where log10(MW) = slope * x + intercept

• "auto": Estimate from data range (assumes typical polymer range)

integration_range

Optional numeric vector c(min, max) specifying the x-axis range for integration. If NULL, uses full range.

output_cols

Character vector of metrics to calculate. Default includes all: c("mn", "mw", "mz", "mp", "dispersity").

prefix

Prefix for output column names. Default is "mw_".

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 calculates standard molecular weight averages from SEC/GPC data:

Metric	Formula	Description
Mn	Σwᵢ / Σ(wᵢ/Mᵢ)	Number-average molecular weight
Mw	Σ(wᵢMᵢ) / Σwᵢ	Weight-average molecular weight
Mz	Σ(wᵢMᵢ²) / Σ(wᵢMᵢ)	Z-average molecular weight
Mp	M at peak maximum	Peak molecular weight
Đ	Mw/Mn	Dispersity (polydispersity index)

The detector signal is assumed to be proportional to weight concentration. For RI detection, this is typically valid. For UV detection, response factors may need to be applied first using step_measure_calibrate_y().

Prerequisites:

• Data should be baseline corrected

• X-axis should represent retention time/volume or log(MW)

• Integration limits should exclude solvent peaks

See also

Other measure-chromatography: step_measure_mw_distribution(), step_measure_mw_fractions()

Examples

library(recipes)

# Assuming x-axis is already calibrated to log10(MW)
# rec <- recipe(~., data = gpc_data) |>
#   step_measure_input_wide(starts_with("signal_")) |>
#   step_measure_baseline_als() |>
#   step_measure_mw_averages() |>
#   prep()