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

Origin

Wine samples

Argentina

6

Chile

15

Australia

12

South Africa

11

Total

44

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

1

Ethanol (vol. %)

2

Total acid (g/L)

3

Volatile acid (g/L)

4

Malic acid (g/L)

5

pH

6

Lactic acid (g/L)

7

Rest Sugar (Glucose + Fructose) (g/L)

8

Citric acid (mg/L)

9

CO2 (g/L)

10

Density (g/mL)

11

Total polyphenol index

12

Glycerol (g/L)

13

Methanol (vol. %)

14

Tartaric 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.

 

DOWNLOAD DATA

 

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

Variable

Description

Dimensions

Aroma_compounds                            

Peak areas of aroma compounds

44×57

Class                                      

Classes of wines (see Table 1)

44×1

Data_GC                                    

Three-way data

44×2700×200

Elution_profiles                           

Summed mass dimension – see Figure 1

44×2700

IR_spectra                                 

IR spectra without waterband

44×842

IR_spectra_with_waterband                  

IR spectra with waterband – see Figure 2

44×1056

Label_Aroma_comp                           

Label aroma compounds

1×57

Label_Elution_time                          

Label elution time in minutes

1×2700

Label_Mass_channels                         

Label m/z

1×200

Label_Pred_values_IR                       

Label quality parameters

1×14

Label_Wine_samples                         

Label wine samples
ARG: Argentina
AUS: Australia
CHI: Chile
SOU: South Africa

44×1

Mass_profiles                              

Summed elution time dimension

44×200

Pred_values_IR                             

Quality parameters (see Table 2)

44×14

axis_spectra_wavenumber                     

Axis for spectra in cm-1

1×842

axis_spectra_with_waterband_wavenumber

Axis for spectra with waterband in cm-1

1×1056