Wine samples analyzed by GC-MS and FT-IR instruments

Wine Samples

Red wines, 44 samples, produced from the same grape (100% Cabernet Sauvignon), harvested in different geographical areas, have been collected from local supermarkets in the area of Copenhagen, Denmark. Details on the geographical origins and number of wine samples analysed are given in Table 1.

Table 1. Geographical origin of the analysed red wines

OriginWine samples
South Africa11

The wine samples have been analyzed using head space GC-MS and FT-IR analytical instruments. The FT-IR was a commercial WineScan instrument provided by FOSS Analytical A/S.

GC-MS data

For each sample a mass spectrum scan (m/z: 5-204) measured at 2700 elution time-points was obtained providing a data cube of size 44×2700×200. In Figure 1 an example of a chromatogram for one red wine sample is shown.

Figure 1. Typical chromatogram showing the TIC (Total Ion Count) of one red wine sample.

In the figure the abundance at each scan is found by summing the contribution of all intensities of mass channels investigated (m/z: 5-204).

FT-IR data

For all wine samples 14 quality parameters were predicted from the IR spectra (Figure 2) using the FOSS WineScan build-in calibration models (Table 2).

Figure 2. Typical IR spectrum of one red wine sample. The water band regions around 1545-1710 cm-1 and 2968-3620 cm-1 should be excluded from the data analysis.

Table 2. Quality parameters measured on the WineScan instrument and used in MVP (units shown in brackets)

#Quality parameter
1Ethanol (vol. %)
2Total acid (g/L)
3Volatile acid (g/L)
4Malic acid (g/L)
6Lactic acid (g/L)
7Rest Sugar (Glucose + Fructose) (g/L)
8Citric acid (mg/L)
9CO2 (g/L)
10Density (g/mL)
11Total polyphenol index
12Glycerol (g/L)
13Methanol (vol. %)
14Tartaric acid (g/L)

 Get the data

The data are available in zipped MATLAB 6.x format. Download the data and write load Wine_v6 in MATLAB.


If you use the data we would appreciate that you report the results to us as a courtesy of the work involved in producing and preparing the data. Also you may want to refer to the data by referring to:

T. Skov, D. Balabio, R. Bro (2008). Multiblock Variance Partitioning. A new approach for comparing variation in multiple data blocks. Analytica Chimica Acta, 615 (1): 18-29

Zip-file information

Aroma_compounds                            Peak areas of aroma compounds44×57
Class                                      Classes of wines (see Table 1)44×1
Data_GC                                    Three-way data44×2700×200
Elution_profiles                           Summed mass dimension – see Figure 144×2700
IR_spectra                                 IR spectra without waterband44×842
IR_spectra_with_waterband                  IR spectra with waterband – see Figure 244×1056
Label_Aroma_comp                           Label aroma compounds1×57
Label_Elution_time                          Label elution time in minutes1×2700
Label_Mass_channels                         Label m/z1×200
Label_Pred_values_IR                       Label quality parameters1×14
Label_Wine_samples                         Label wine samples
ARG: Argentina
AUS: Australia
CHI: Chile
SOU: South Africa
Mass_profiles                              Summed elution time dimension44×200
Pred_values_IR                             Quality parameters (see Table 2)44×14
axis_spectra_wavenumber                     Axis for spectra in cm-11×842
axis_spectra_with_waterband_wavenumberAxis for spectra with waterband in cm-11×1056

Published by Rasmus Bro

Chemometrics really - AI/ML if you insist, but chemo- is the magic ingredient

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