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Multi-Detector SEC Data

Source: R/data.R
sec_triple_detect.Rd

A synthetic dataset containing multi-detector Size Exclusion Chromatography (SEC) data for 12 polymer samples with realistic signal characteristics.

Usage

sec_triple_detect

Format

A tibble with 24,012 rows and 11 columns:

sample_id

Character. Unique sample identifier (e.g., "PS-10K", "PMMA-Low")

sample_type

Character. Either "standard" (narrow dispersity calibrants) or "sample"

polymer_type

Character. Polymer type: "polystyrene", "pmma", "peg", or "copolymer"

elution_time

Numeric. Elution time in minutes (5-25 min range)

ri_signal

Numeric. Refractive index detector signal (reference detector)

uv_signal

Numeric. UV detector signal at 280 nm

mals_signal

Numeric. Multi-angle light scattering detector signal

known_mw

Numeric. True weight-average molecular weight (Mw) in g/mol

known_dispersity

Numeric. True dispersity (Mw/Mn)

dn_dc

Numeric. Refractive index increment in mL/g

extinction_coef

Numeric. UV extinction coefficient in mL/(mg*cm)

Source

Synthetic data generated for package testing and examples.

Details

The dataset includes realistic features commonly encountered in SEC analysis:

Sample Composition:

  • 5 polystyrene standards (1K to 500K MW, narrow dispersity ~1.01-1.05)

  • 3 PMMA samples (25K to 200K MW, broader dispersity 1.8-2.2)

  • 2 PEG samples (5K and 20K MW, low dispersity ~1.1-1.15)

  • 2 copolymer samples (40K and 80K MW, intermediate dispersity 1.5-1.7)

Multi-Detector Features:

  • Inter-detector volume delays: UV is 0.05 mL before RI, MALS is 0.15 mL after RI

  • Different detector responses based on polymer chemistry

  • PEG has no UV response (extinction_coef = 0)

  • MALS signal scales with MW for absolute MW determination

Signal Characteristics:

  • Gaussian noise appropriate for each detector

  • Slight baseline drift

  • Log-normal peak shapes with tailing

Typical SEC Workflow: 1 . Convert to measure format with step_measure_input_long 2. Correct inter-detector delays with step_sec_detector_delay 3. Apply baseline correction with step_sec_baseline 4. Process detectors with step_sec_ri or step_sec_uv 5. Convert to concentration with step_sec_concentration 6. Calculate MW averages with step_sec_mw_averages

See also

Other sec-data: sec_branched, sec_calibration_standards, sec_copolymer, sec_pmma_standards, sec_protein, sec_ps_standards, sec_raw_multidetector, sec_raw_standards, sec_raw_unknowns, sec_system_suitability

Examples

if (FALSE) { # \dontrun{
library(recipes)
library(measure)
library(measure.sec)

# Load the dataset
data(sec_triple_detect)

# View sample distribution
table(sec_triple_detect$polymer_type)

# Plot RI chromatograms for polystyrene standards
library(ggplot2)
sec_triple_detect |>
  dplyr::filter(polymer_type == "polystyrene") |>
  ggplot(aes(elution_time, ri_signal, color = sample_id)) +
  geom_line() +
  labs(x = "Elution Time (min)", y = "RI Signal", color = "Sample")

# Process with SEC recipe
rec <- recipe(~ ., data = sec_triple_detect) |>
  step_measure_input_long(ri_signal, location = vars(elution_time), col_name = "ri") |>
  step_sec_baseline(measures = "ri") |>
  step_sec_ri(dn_dc_column = "dn_dc") |>
  prep()
} # }