Multiplicative Scatter Correction (MSC)

step_measure_msc() creates a specification of a recipe step that applies Multiplicative Scatter Correction to spectral data. MSC removes physical light scatter by accounting for additive and multiplicative effects.

Usage

step_measure_msc(
  recipe,
  measures = NULL,
  role = NA,
  trained = FALSE,
  ref_spectra = NULL,
  skip = FALSE,
  id = recipes::rand_id("measure_msc")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
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. Use this to limit processing to specific measure columns when working with multiple measurement types.
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.
ref_spectra: A named list of numeric vectors containing the reference spectra computed during training 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 by recipes::bake()? While all operations are baked when recipes::prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
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

Multiplicative Scatter Correction (MSC) is a normalization method that attempts to account for additive and multiplicative effects by aligning each spectrum to a reference spectrum. For a spectrum $x_i$ and reference $x_r$, the transformation is:

$$x_i = m_i \cdot x_r + a_i$$ $$MSC(x_i) = \frac{x_i - a_i}{m_i}$$

where $a_i$ and $m_i$ are the additive (intercept) and multiplicative (slope) terms from regressing $x_i$ on $x_r$.

The reference spectrum is computed as the mean of all training spectra during prep() and stored for use when applying the transformation to new data.

MSC is commonly used to remove physical light scatter effects in NIR spectroscopy caused by differences in particle size or path length.

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().

The measurement locations are preserved unchanged.

Tidying

When you tidy() this step, a tibble with columns terms (set to ".measures") and id is returned.

References

Geladi, P., MacDougall, D., and Martens, H. 1985. Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat. Applied Spectroscopy, 39(3):491-500.

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_msc() |>
  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