specProc
Preprocessing Tools for Laser-Induced Breakdown Spectroscopy
Tools for exploring, preprocessing and modeling spectroscopic
data, developed for laser-induced breakdown spectroscopy (LIBS) and also
applicable to other techniques such as Raman, infrared, and inductively
coupled plasma optical emission spectroscopy. Covers the workflow from
raw spectra to calibrated results: peak finding and fitting, wavelength
calibration, baseline correction, normalization and scaling, orthogonal
signal correction including orthogonal partial least squares (OPLS;
Trygg and Wold (2002)
specProc 
specProc processes and analyzes emission spectra, streamlining the pipeline from raw spectra to calibrated concentrations. While specifically developed for laser-induced breakdown spectroscopy (LIBS), it seamlessly supports other plasma techniques such as ICP-OES. Select functions can also be used to process Raman and infrared spectroscopy data. The package structures data with one spectrum per row and wavelengths as column names, leveraging a C++ backend to power heavy computations.
Installation
# install.packages("remotes")
remotes::install_github("ChristianGoueguel/specProc", build_vignettes = TRUE)
A C++ compiler is needed to build the package from source (Rtools on Windows, Xcode Command Line Tools on macOS).
A LIBS workflow
The vignettes follow a typical LIBS analysis, on the forageLIBS
spectra of forage samples:
- Fitting emission lines (
vignette("peak-fitting")): line identification, wavelength calibration, line profiles and overlapping lines. - Preprocessing with recipe steps (
vignette("preprocessing")): baseline correction, normalization and otherstep_*()functions, in a tidymodels workflow. - Calibration curves and figures of merit
(
vignette("calibration")): univariate curves, limits of detection and quantification, and the net analyte signal of multivariate models. - Plasma diagnostics (
vignette("plasma-diagnostics")): electron density, temperature, LTE, self-absorption and calibration-free LIBS.
They are also available as articles on the package website.
Function overview
| Task | Functions |
|---|---|
| Line identification | libs_lines(), plot_lines(), line_finder() (Shiny app), wavelength_calibration(), apply_calibration() |
| Line fitting | peak_fit(), multipeak_fit(), plot_fit(), line_intensities(), voigt_profile(), pseudo_voigt_profile(), gaussian_profile(), lorentzian_profile(), voigt_fwhm() |
| Preprocessing | baseline_arpls(), baseline_als(), baseline_lsp(), snv(), msc(), emsc(), normalize(), savitzky_golay(), wavelet_features(), reject_shots(), average() |
| Recipe steps | step_baseline(), step_snv(), step_msc(), step_emsc(), step_spectral_norm(), step_line_ratio(), step_savgol(), step_wavelet(), step_line_intensities(), step_reject_shots(), step_opls(), step_o2pls(), step_epo(), step_glsw(), and more |
| Orthogonalization | osc(), direct_osc(), projected_osc(), opls(), o2pls(), epo(), glsw(), y_gradient_glsw() |
| Calibration | calibration_curve(), plot_calibration(), nas(), pds() |
| Plasma diagnostics | saturation_summary(), electron_density(), boltzmann_plot(), saha_boltzmann_plot(), plot_boltzmann(), mcwhirter_criterion(), self_absorption(), correct_self_absorption(), cf_libs(), nist_lines(), starkb_lines() |
| Outliers and robust PCA | robpca(), rospca(), macropca(), cellpca(), plot_outlier_map(), plot_cell_map(), q_residuals(), dmodx(), plot_influence(), hotelling_t2() |
| Robust statistics | summary_stats(), biweight_location(), biweight_scale(), rousseeuw_croux(), umad(), adjusted_boxplot(), robust_bcyj() |
| Visualization | plot_spectra(), plot_embedding(), plot_loadings(), plot_contributions() |
See the reference for the full list.
License
MIT © Christian L. Goueguel