Skip to contents

SNIP Baseline Correction

Source: R/baseline-extended.R
step_measure_baseline_snip.Rd

step_measure_baseline_snip() creates a specification of a recipe step that applies SNIP (Statistics-sensitive Non-linear Iterative Peak-clipping) baseline correction.

Usage

step_measure_baseline_snip(
  recipe,
  measures = NULL,
  iterations = 40L,
  decreasing = TRUE,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_baseline_snip")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

iterations

Number of clipping iterations. More iterations produce lower baselines. Default is 40.

decreasing

Logical. If TRUE (default), iterations decrease from iterations to 1. If FALSE, uses fixed window size.

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.

Details

SNIP is a robust baseline estimation algorithm originally developed for gamma-ray spectroscopy. It works by iteratively replacing each point with the minimum of itself and the average of its neighbors at increasing distances.

The algorithm is particularly effective for:

  • Spectra with sharp peaks on slowly varying baseline

  • X-ray fluorescence and diffraction

  • Mass spectrometry

References

Ryan, C.G., et al. (1988). SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research B, 34, 396-402.

See also

Other measure-baseline: step_measure_baseline_airpls(), step_measure_baseline_als(), step_measure_baseline_arpls(), step_measure_baseline_auto(), step_measure_baseline_custom(), step_measure_baseline_gpc(), step_measure_baseline_minima(), step_measure_baseline_morph(), step_measure_baseline_poly(), step_measure_baseline_py(), step_measure_baseline_rf(), step_measure_baseline_rolling(), step_measure_baseline_tophat(), step_measure_detrend()

Examples

library(recipes)

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_baseline_snip(iterations = 30) |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows