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

• vignette("getting-started", package = "libscanR")
• vignette("preprocessing-workflow", package = "libscanR")
• vignette("calibration-quantification", package = "libscanR")
• vignette("tissue-classification", package = "libscanR")
• vignette("spatial-mapping", package = "libscanR")

Citation

citation("libscanR")

License

MIT © Raban Heller

Use of LLM tools

Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.

All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors’ own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.

Local software setup: vLLM with Python/PyTorch, plus R and RStudio.

Branding

Editable R sources and regeneration instructions are in tools/branding.