STATISTICAL METHODS FOR IMPROVING AUTHENTICATION …...Maintenance of databank Wine data Arbitration...
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Climatic and geographical dependence of H, C and O stable isotope ratios of Italian wine N. Dordevic, F. Camin, R. Wehrens, M. Neteler, G. J. Postma, L. M. C. Buydens, Nikola Dordevic
Transcript of STATISTICAL METHODS FOR IMPROVING AUTHENTICATION …...Maintenance of databank Wine data Arbitration...
Climatic and geographical dependence of H, C and O stable
isotope ratios of Italian wine
N. Dordevic, F. Camin, R. Wehrens, M. Neteler, G. J. Postma, L. M. C. Buydens,
Nikola Dordevic
Stable isotope ratios of wine
(D/H)1
(D/H)2
δ13C
δ18O
France
400
Germany
200
Italy
400
Spain
200 U. Kingdom
4
Austria
50
Greece
50
Luxembourg
4
Portugal
50
Wine data Maintenance of databank
Arbitration of disputes
Analysis of samples
Development and
validation of methods
Validation of data
Training
Malta
4
Slovakia
15
Slovenia
20
Hungary
50 Czech Rep.
20
Cyprus
10
Bulgaria
30
Romania
70
Wine Databank EC Reg. Nº 555/2008
Aim of the study
1. To evaluate relationships between wine (D/H)1,
(D/H)2, δ13C and δ18O and climatic and
geographic parameters of provenance areas.
2. To build a model able to explain relationships
between wine isotope ratios and climatic and
geographic parameters of provenance areas.
Materials and methods Variables Data
type Resolution
Source
Date of harvest static day Wine DB
Latitude static point www.findlatitudeandlogitude.com
Longitude static point www.findlatitudeandlogitude.com
Elevation static 20m Italian elevation model
Distance from the sea
static 250m Derived from elevation map in GIS
Amount of precipitation [mm/day]
dynamic 25km ECA&D, http://www.ecad.eu
Maximum daily temperature
dynamic 25km ECA&D
Minimum daily temperature
dynamic 25km ECA&D
Mean daily temperature
dynamic 25km ECA&D
δ18O of precipitation
static 37km Bowen et al. (2005)
δ2H of precipitation
static 37km Bowen et al. (2005)
• Official samples from the
Italian Wine Databank
from 2000 to 2010 are
considered.
• Explorative analyses and
linear modelling
Results (D/H)1
99.33
99.74
100.18
100.52
100.76
101.09
101.3
101.43
101.62
101.67
101.83
102.06
102.07
102.45
102.91
103.12
103.3
103.42
104.12
Results : explorative analyses
(D/H)1
(D/H)2
C13
O18
harvest date
latitude
longitude
elevation
distance
O18 MW
D/H MW
mean T
min T
max T
precipitation
(D/H
)1
(D/H
)2C13
O18
harvest
date
latitude
long
itude
elev
ation
distanc
e
O18
MW
D/H
MW
mea
n T
min T
max
T
prec
ipita
tion
-1.0
-0.5
0.0
0.5
1.0
Results : explorative analyses
-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06
-0.0
6-0
.04
-0.0
20
.00
0.0
20
.04
0.0
6
PC1 (40.1%)
PC
2 (
18
.6%
)
-40 -20 0 20 40
-40
-20
02
04
0
(D/H)1(D/H)2C13
O18
harvest date
latitude
longitude elevation
distance
O18 MW(D/H) MW
meanT
min T
max T
precipitation
Results: linear modelling
Results: linear modelling Regression coefficients for the selected variables
Coefficient_size
Date of harvest
Distance to the sea
Elevation
Latitude
Longitude
Precipitation
Temperature
-0.4 -0.2 0.0 0.2
C13 O18
Date of harvest
Distance to the sea
Elevation
Latitude
Longitude
Precipitation
Temperature
DH1
-0.4 -0.2 0.0 0.2
DH2R2 = 0.42
R2 = 0.71 R2 = 0.24
R2 = 0.30
Conclusions
1. δ18O and (D/H)1 have the strongest relationship with
climate and location.
2. The dominant variables are latitude, δ18O and δ2H
of MW and temperature.
3. Models may be used in wine authenticity
assessments.
• F. Camin, N. Dordevic, R. Wehrens, M. Neteler, L. Delucchi, G.
Postma, L. Buydens (2014) Climatic and geographical
dependence of the H, C and O stable isotope ratios of Italian wine. Analytica Chimica Acta (accepted, in press).
Acknowledgment
Anti-Fraud Department of Italian Ministry of
Agricultural, Food and Forestry Policy, owner of the
Italian wine databank
