Preprocessing Workflow

library(libscanR)

LIBS spectra typically contain a continuum background, electronic noise, and shot-to-shot intensity variation. libscanR provides chainable preprocessing primitives: baseline correction, normalization, smoothing, cropping, and shot averaging.

Baseline correction

Four methods are supported: snip, als, rolling_ball, linear, polynomial. SNIP and ALS are the workhorses for LIBS continuum removal.

spec <- ls_simulate_spectrum(line_data = ls_demo_peaks(),
  elements = c(Ca = 5000, Na = 1000, K = 800, Fe = 200),
  n_channels = 1024, seed = 1
)
snip <- ls_baseline(spec, method = "snip", iterations = 60)
als  <- ls_baseline(spec, method = "als", iterations = 10, lambda = 1e5)

df <- rbind(
  data.frame(w = spec$wavelength,
             y = libscanR:::.mean_intensity(spec),
             method = "raw"),
  data.frame(w = spec$wavelength,
             y = libscanR:::.mean_intensity(snip),
             method = "SNIP"),
  data.frame(w = spec$wavelength,
             y = libscanR:::.mean_intensity(als),
             method = "ALS")
)
ggplot2::ggplot(df, ggplot2::aes(w, y, colour = method)) +
  ggplot2::geom_line() + theme_libs() +
  ggplot2::labs(x = "Wavelength (nm)", y = "Intensity (a.u.)")

Three curves compare the raw fictional spectrum with SNIP and ALS baseline-corrected intensities on the same wavelength axis.

Normalization

Five options:

norm_total <- ls_normalize(snip, method = "total")
cat("sum after total normalization:",
    round(sum(norm_total$intensity[1, ]), 2), "\n")
#> sum after total normalization: 1

norm_is <- ls_normalize(snip, method = "internal_std", ref_wavelength = 589)

Smoothing

sm <- ls_smooth(snip, method = "moving_avg", window = 7)
ls_plot_region(sm, 380, 410)

The 380 to 410 nm region of the fictional spectrum after SNIP baseline correction and seven-channel moving-average smoothing.

Averaging replicate shots with outlier removal

multi <- ls_simulate_spectrum(line_data = ls_demo_peaks(), seed = 1, n_channels = 512, n_shots = 20,
                              noise_level = 0.05)
avg <- ls_average_shots(multi, remove_outliers = TRUE)
avg$n_shots
#> [1] 1

Cropping

ca_region <- ls_crop(snip, 380, 450)
ls_plot_spectrum(ca_region)

SNIP-corrected fictional spectrum cropped to 380 through 450 nm, showing the simulated peak near the Ca-labelled teaching window.