Analysis and Visualization of Laser-Induced Breakdown Spectroscopy Data • libscanR

Vendor-agnostic analysis and visualization of Laser-Induced Breakdown Spectroscopy (LIBS) data in R, with a focus on biomedical tissue applications.

Features

Import: Generic CSV/TSV/TXT, SciAps Z-series, Applied Spectra J200/Aurora, auto-detection.
Preprocessing: SNIP/ALS baseline correction, five normalization modes, Savitzky-Golay / Gaussian / moving-average / median smoothing, shot averaging with outlier removal, wavelength cropping, gate delay optimization.
Peak analysis: SNR+prominence peak detection; identification against a curated NIST emission-line database (23 biomedically relevant elements).
Calibration: Univariate, internal-standard, PLS, and calibration-free LIBS (Saha-Boltzmann). LOD / LOQ with 3σ / 10σ criteria.
Chemometrics: PCA, PLS-DA, k-means / hierarchical clustering, SVM / Random-Forest classifiers.
Tissue analysis: Rule-based tissue classification using elemental ratios; discriminating-line analysis with FDR correction.
Spatial mapping: Single-element and multi-element 2-D maps from raster-scan datasets.
Visualization: Publication-ready ggplot2 plots; custom theme_libs() and wavelength color scale.
Shiny app: Six-tab interactive explorer (ls_run_app()).
Reproducible examples: ls_example_data() provides synthetic tissue, calibration, and spatial datasets — no instrument data required.

Installation

# install.packages("remotes")
remotes::install_github("cttir/libscanR")

Quick start

library(libscanR)

# Generate a synthetic spectrum
spec <- ls_simulate_spectrum(
  elements = c(Ca = 5000, Na = 1000, Fe = 200),
  seed = 1
)

# Preprocess: baseline, smooth, normalize
spec_proc <- spec |>
  ls_baseline(method = "snip") |>
  ls_smooth(method = "moving_avg", window = 5) |>
  ls_normalize(method = "total")

# Detect and identify peaks
peaks <- ls_find_peaks(spec_proc)
id <- ls_identify_peaks(peaks, elements = c("Ca", "Na", "Fe"))

# Plot with element annotation
ls_plot_spectrum(spec_proc, show_elements = c("Ca", "Na"))

# Launch the interactive app
ls_run_app(data = ls_example_data("tissue"))

Workflow vignettes

Citation

citation("libscanR")

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author’s own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.

libscanR