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Deconvolve Overlapping Peaks

Source: R/peak-operations.R
step_measure_peaks_deconvolve.Rd

step_measure_peaks_deconvolve() creates a specification of a recipe step that resolves overlapping peaks using curve fitting. This step requires peaks to have been detected first using step_measure_peaks_detect().

Usage

step_measure_peaks_deconvolve(
  recipe,
  model = c("gaussian", "emg", "bigaussian"),
  max_iter = 100L,
  tol = 1e-06,
  peaks_col = ".peaks",
  measures_col = ".measures",
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_peaks_deconvolve")
)

Arguments

recipe

A recipe object.

model

Peak model to use: "gaussian" (symmetric), "emg" (exponentially modified Gaussian for tailing peaks), or "bigaussian" (asymmetric). Default is "gaussian".

max_iter

Maximum iterations for optimization. Default is 100.

tol

Convergence tolerance. Default is 1e-6.

peaks_col

Name of the peaks column. Default is ".peaks".

measures_col

Name of the measures column. Default is ".measures".

role

Not used.

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. The .peaks column will be updated with deconvolved peak parameters and fitted areas.

Details

Peak deconvolution fits mathematical models to overlapping peaks to determine their individual contributions. This is essential for quantitative analysis when peaks are not baseline-resolved.

Peak Models:

  • gaussian: Symmetric Gaussian peak (3 params: height, center, width)

  • emg: Exponentially Modified Gaussian (4 params, handles tailing)

  • bigaussian: Bi-Gaussian (5 params, flexible asymmetry)

The optimization uses initial estimates from detected peak positions and refines them to minimize the residual sum of squares.

See also

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

Examples

library(recipes)

# Deconvolve overlapping peaks
# rec <- recipe(~., data = chromatogram_data) |>
#   step_measure_input_long(signal, location = vars(time)) |>
#   step_measure_peaks_detect(method = "derivative") |>
#   step_measure_peaks_deconvolve(model = "gaussian") |>
#   prep()