Plot Multi-Detector SEC Overlay

Creates an overlay plot of multiple SEC detectors, optionally normalized and aligned.

Usage

plot_sec_multidetector(
  data,
  detectors,
  sample_id = NULL,
  samples = NULL,
  normalize = TRUE,
  x_label = "Elution Time (min)",
  facet = FALSE,
  ...
)

Arguments

data

A data frame containing SEC results with multiple detector measure columns.

detectors

Character vector of detector column names to include. Common values: c("ri", "uv", "mals", "visc").

sample_id

Column name containing sample identifiers. If NULL, plots all samples or auto-detects.

samples

Character vector of specific sample IDs to plot. If NULL, plots all samples.

normalize

Logical. Normalize each detector to 0-1 range for comparison? Default is TRUE.

x_label

Label for x-axis. Default is "Elution Time (min)".

facet

Logical. Create separate panel for each sample? Default is FALSE (overlay).

...

Additional arguments passed to ggplot2::geom_line().

Value

A ggplot2 object.

Details

Multi-detector overlay plots are essential for:

• Verifying detector alignment after delay correction

• Identifying composition drift in copolymers (UV/RI differences)

• Detecting aggregates (MALS response higher than expected from RI)

• Checking for baseline issues across detectors

When normalize = TRUE (default), each detector signal is scaled to 0-1 range, making it easy to compare peak shapes and positions across detectors with very different response magnitudes.

See also

Other sec-visualization: autoplot.sec_results(), plot_sec(), plot_sec_calibration(), plot_sec_chromatogram(), plot_sec_composition(), plot_sec_conformation(), plot_sec_mwd(), sec_results()

Examples

if (FALSE) { # \dontrun{
# Multi-detector overlay for single sample
plot_sec_multidetector(
  processed_data,
  detectors = c("ri", "uv", "mals"),
  samples = "PMMA-1"
)

# Faceted by sample
plot_sec_multidetector(
  processed_data,
  detectors = c("ri", "uv"),
  facet = TRUE
)
} # }