Corrects gas-chromatography peak areas for the influence of ambient
air pressure on standard detectors open to the ambient atmosphere, such as
the flame ionization detector, whose pressure sensitivity was
characterised by Bocek, Novak and Janak (1969)
Atmospheric Pressure Peak Area Correction for Gas Chromatography with Standard Detectors
APPAC corrects gas-chromatography peak areas for the influence of ambient air pressure on standard detectors — those in open communication with the ambient atmosphere, whose response can therefore vary with barometric pressure. The flame ionization detector (FID) is the prime example, and the one demonstrated here: standard textbooks and current reviews treat the FID response as independent of ambient pressure, yet a real and correctable dependence exists. The thermal conductivity detector (TCD) shows the same effect, more weakly, and APPAC applies to it accordingly. Other open detectors (PID, FPD, PFPD) have not yet been investigated. Detectors sealed from ambient pressure, such as a mass spectrometer under vacuum, fall outside this class.
Per-cylinder peak areas are decomposed by principal components into a pressure-correlated component and per-peak drift; a single common pressure-sensitivity coefficient kappa is estimated with a heavy-tail-robust fit on a drift-reduced signal, and the slow drift plus a daily factor are removed. It returns the corrected areas with a chi-square goodness-of-fit diagnostic, and provides Bayesian change-point detection (via RBeast) for episode and breakpoint analysis.
APPAC is an a posteriori method: it corrects peak areas that have already been measured, using the ambient pressure recorded at the time of each injection, to remove a known pressure artifact. It has no forecasting ability — it does not predict areas, pressures, or future measurements, and infers nothing beyond the acquired data it is given.
From a local clone of the repository:
# install.packages("remotes")
remotes::install_local(".")
or, from the shell:
R CMD INSTALL .
The package depends only on CRAN packages; ggplot2 and patchwork (for the
plots) are optional.
APPAC ships an example dataset, PLOT_FID — FID injections from several
control cylinders, recorded on a real instrument with expert-annotated peak
integration.
library(appac)
# map your column names to the canonical roles (order does not matter)
acn <- list(
sample_col = "sample.name",
peak_col = "peak.name",
date_col = "injection.date",
pressure_col = "air.pressure",
area_col = "raw.area"
)
data <- check_cols(PLOT_FID, acn)
ap <- as.numeric(data[, "Air_Pressure"])
P_ref <- (max(ap, na.rm = TRUE) - min(ap, na.rm = TRUE)) / 2 + min(ap, na.rm = TRUE)
# two passes: estimate kappa + drift, de-bias the centres, refit
fit1 <- appac(data, P_ref = P_ref)
ct <- debias_ct(fit1, data = data, P_ref = P_ref, quiet = TRUE)
fit <- appac(data, ct = ct, P_ref = P_ref)
# fitted common pressure sensitivity
unlist(fit@correction@coefficients)
# per-peak goodness of fit (reduced chi-square ~ 1 = down to the noise floor)
goodness_of_fit(fit)[[1]]
On PLOT_FID the correction reduces the run-to-run scatter (RSD) of the control
peaks from roughly 0.7 % to about 0.14 %.
Four ggplot2 plots visualise a fitted object:
plot_area_pressure(fit, sample = 1, peak = "n.C4H10") # area vs pressure, raw + corrected
plot_area_date(fit, sample = 1, peak = "n.C4H10") # area vs date, with change-points
plot_residuals(fit, sample = 1, peak = "n.C4H10") # residual diagnostic panel
plot_area_pressure_fit(fit) # what actually enters the kappa fit
A full worked walkthrough — usage and the method decomposition — is in the package
vignette: vignette("appac").
Released under GPL-3. See the full license text.