Pre-process Vis-NIR or MIR spectra — preprocess

Applies a chosen pre-processing pipeline to a numeric matrix of soil spectra. Rows are samples (typically horizons) and columns are wavelengths. Returns a numeric matrix; SG-based methods shorten the spectrum by w - 1 columns at the edges (default w = 5 so two columns are dropped from each side).

Usage

preprocess_spectra(X, method = c("snv+sg1", "snv", "sg1"), w = 5L, p = 2L)

Arguments

X: Numeric matrix or data.frame of spectra (rows = samples, columns = wavelengths). Wavelengths should be evenly spaced.
method: One of "snv", "sg1", "snv+sg1". Default "snv+sg1".
w: Window size for the SG filter. Must be odd; default 5.
p: Polynomial order for the SG filter. Default 2.

Value

A numeric matrix. Column names (wavelengths) are preserved where possible; SG trimming drops (w - 1) / 2 columns from each edge.

Details

Supported method values:

"snv": Standard Normal Variate. Each row is centered on its own mean and divided by its own standard deviation.
"sg1": Savitzky-Golay 1st derivative with a window of five wavelengths and a quadratic polynomial.
"snv+sg1": SNV followed by SG1 (default; the standard pipeline used by OSSL pretrained models for Vis-NIR).

If prospectr is available, we use prospectr::standardNormalVariate and prospectr::savitzkyGolay (Rcpp implementation, faster and supports arbitrary window/polynomial). The native fallback uses the classical 5-point first-derivative coefficients (-2, -1, 0, 1, 2) / 10, which is the closed-form Savitzky-Golay solution for window 5 / polynomial 2 / derivative 1.

References

Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.

Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard Normal Variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777.

Stevens, A., & Ramirez-Lopez, L. (2024). prospectr: Misc. functions for processing and sample selection of spectroscopic data. R package version 0.2.7.

Examples

set.seed(1)
X <- matrix(runif(5 * 2151, 0, 1), nrow = 5)
colnames(X) <- 350:2500
Xp <- preprocess_spectra(X, method = "snv+sg1")
dim(Xp)  # 5 x 2147 (4 columns dropped by SG window 5)
#> [1]    5 2147