prep_derivative: Derivative constructor for spectral preprocessing

In proximetricsR: Spectral Preprocessing and Chemometric Calibration of NIR Sensors

Derivative constructor for spectral preprocessing

Description

\loadmathjax

Creates a preprocessing constructor for computing first or second order derivatives of spectral data. The constructor is intended to be passed to preprocess_recipe and executed via process.

Three algorithms are supported: Savitzky-Golay ("savitzky-golay"), Norris-Gap/Gap-Segment ("gap-segment"), and the derivative pre-treatment from BUCHI NIRWise PLUS software ("nwp").

Usage

prep_derivative(m, w, p, algorithm = c("savitzky-golay", "gap-segment", "nwp"))

Arguments

m	An integer indicating the derivative order. Must be 1 (first derivative) or 2 (second derivative).
w	A positive odd integer indicating the filter window size. For "gap-segment", w indicates the gap size (spacing between points over which the derivative is computed).
p	An integer. For "savitzky-golay", indicates the polynomial order and must satisfy p < w and p >= m. For "gap-segment" and "nwp", indicates the segment or smoothing window size and must be a positive odd integer.
algorithm	A character string specifying the algorithm. One of "savitzky-golay" (default), "gap-segment", or "nwp". See Details.

Details

Savitzky-Golay ("savitzky-golay"): fits a polynomial of order p within a moving window of size w and differentiates analytically. Implemented via savitzkyGolay.

Gap-Segment ("gap-segment"): computes the derivative over a gap of w points, with optional averaging over a segment of p points. When p = 1 this reduces to the standard Norris-Gap derivative. Implemented via gapDer.

NWP ("nwp"): reproduces the "DG" derivative pre-treatment from BUCHI NIRWise PLUS calibration software. A moving average of window p is applied first (pre-smoothing), followed by differentiation. For first order, a gap derivative with gap w is used. For second order, a centered second difference with spacing half_w is computed:

\mjdeqn

d^2x_i = \frac2x_i - (x_i+h + x_i-h)2hd2x_i = (2*x_i - (x_i+h + x_i-h)) / (2h)

where \mjeqnh = half_wh = half_w. Edge columns affected by the window are removed from the output.

For the "nwp" algorithm, the NIRWise PLUS half-window conventions are: \mjdeqnhalf_w = (w + 1) / 2half_w = (w + 1) / 2 \mjdeqnhalf_s = (p - 1) / 2half_s = (p - 1) / 2 These are stored internally for device file serialization and are not user-facing parameters.

Value

An object of class preprocessing to be used in preprocess_recipe and executed by process. The object is a list containing the method name, all parameters, and (for algorithm = "nwp") the NIRWise PLUS half-window values (half_w, half_s) required for device file serialization.

Author(s)

Leonardo Ramirez-Lopez and Claudio Orellano

See Also

preprocess_recipe, process

Examples

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

# Savitzky-Golay first derivative, window 11, polynomial order 3
sg <- prep_derivative(m = 1, w = 11, p = 3, algorithm = "savitzky-golay")

# Gap-Segment second derivative, gap 9, segment 3
gs <- prep_derivative(m = 2, w = 9, p = 3, algorithm = "gap-segment")

# NWP first derivative, window 5, pre-smoothing 11
nwp <- prep_derivative(m = 1, w = 5, p = 11, algorithm = "nwp")

# Apply via preprocess_recipe
recipe <- preprocess_recipe(sg, device = "unspecified")
X_der <- process(X, recipe)

proximetricsR documentation built on Sept. 4, 2026, 5:08 p.m.