Build and execute spectral preprocessing recipes

The preprocess_recipe function assembles an ordered sequence of preprocessing steps into a recipe, while process executes the recipe on a spectral data matrix.

Usage

preprocess_recipe(..., device)

process(X, recipe, device)

Arguments

...

one or more objects of class preprocessing as returned by any of the following constructor functions:

• prep_resample

• prep_smooth

• prep_snv

• prep_derivative

• prep_detrend

• prep_transform

• prep_wav_trim

The order in which the objects are provided defines the order of execution. If no arguments are provided, an empty recipe is returned and process will return the input data unchanged.

device

a character string specifying the target device: "unspecified" (no validation), "proximate", or "proxiscout". When "proximate" or "proxiscout" is specified, preprocess_recipe validates that all steps are compatible with that device and raises an informative error if not. Pass "unspecified" to skip validation explicitly.

device is required whenever the recipe contains any preprocessing step, with one exception: a recipe containing only a single prep_snv step does not require device, because SNV is device-agnostic (identical behaviour for both "proximate" and "proxiscout"). In that case device defaults to "unspecified".

X

a numeric matrix of spectral data to be preprocessed (samples in rows, wavelengths in columns).

recipe

an object of class preprocess_recipe as returned by preprocess_recipe. A single object of class preprocessing is also accepted and treated as a one-step recipe.

Value

For preprocess_recipe, an object of class preprocess_recipe with three components: steps (the ordered list of preprocessing step objects), device (the target device string), and preprocessing_order (a simplified string summarising the sequence of applied transformations).

For process, a numeric matrix of preprocessed spectral data. The applied recipe is stored as the attribute "preprocess_recipe" on the returned matrix and can be retrieved with attr(result, "preprocess_recipe").

See also

prep_smooth, prep_snv, prep_derivative, prep_resample, prep_detrend, prep_transform, prep_wav_trim

Author

Leonardo Ramirez-Lopez

Examples

data("NIRcannabis")
X <- NIRcannabis$spc

# SNV alone — no device needed (SNV is device-agnostic)
recipe_snv <- preprocess_recipe(prep_snv())
X_snv <- process(X, recipe_snv)

# Any other combination requires device
recipe <- preprocess_recipe(
  prep_smooth(w = 7, p = 1, algorithm = "savitzky-golay"),
  prep_snv(),
  prep_derivative(m = 1, w = 5, p = 2, algorithm = "savitzky-golay"),
  device = "proxiscout"
)

X_proc <- process(X, recipe)
attr(X_proc, "preprocess_recipe")
#> Spectral preprocessing recipe (device: "proxiscout"): 
#>  - Step 1: prep_smooth
#>     w: 7; p: 1; algorithm: 'savitzky-golay'
#>  - Step 2: prep_snv
#>  - Step 3: prep_derivative
#>     m: 1; w: 5; p: 2; algorithm: 'savitzky-golay'