A 6 min segment (RT 19.00-25.00 min) of a GC/MS chromatogram recorded from the SPME-headspace of beer. Data were acquired on a Thermo ISQ single-quad coupled to a 30 m x 0.25 mm x 0.25 um RXi-5Sil MS column using a DVB/Carboxen/PDMS (50/30 um) fibre. Many volatiles co-elute in this interval, including by a prominent, triangular, "shark-fin" octanoic-acid peak whose apex lies at RT 21.50 min. For details see Schreurs et al. Nature Communications 2024.
Format
A numeric matrix with 1764 rows (time slices) and 518 columns (m/z values).
- Rows
Individual full-scan acquisitions (time scans). Row names give the scan mid-point in minutes (19.00 <= RT <= 25.00).
- Columns
Unit-mass channels, m/z 33:550. Column names are the integer m/z values.
Additional attribute:
attr(GCMS_data2, "alkanes_TIC"): named numeric vector with
the total ion chromatogram of a C7 to C22 alkane ladder standard run
in the same sample batch; names give the retention time in minutes;
the total ion trace for the chromatogram starting from a RT of
6 mins is included.
Source
Schreurs, M. et al. (2024) "Predicting and improving complex beer flavor through machine learning." *Nature Communications* **15**, 2368. doi:10.1038/s41467-024-46346-0
Author
Tom Wenseleers tom.wenseleers@kuleuven.be \ Laboratory of Socioecology and Social Evolution, KU Leuven \ https://bio.kuleuven.be/eeb/tw/research \ ORCID: 0000-0002-1434-861X
Examples
data(GCMS_data2)
plotmat(GCMS_data2,
xlab = "Elution time (min)",
ylab = "Mass (m/z)",
main = "Section of beer headspace GC/MS profile")
matplot(x = as.numeric(rownames(GCMS_data2)),
y = GCMS_data2^0.2, type = "l", lty = 1, col = rgb(0, 0, 0, 0.1),
xlab = "Elution time (min)",
ylab = "Intensity ^ 0.2",
main = "Section of beer headspace GC/MS profile")
plot(x=as.numeric(names(attr(GCMS_data2, "alkanes_TIC"))),
y=attr(GCMS_data2, "alkanes_TIC"),
xlab="Retention time (min)",
ylab="Total ion intensity",
main="C7 to C22 alkane ladder standard",
type="l")