3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
Fig. 2. LDA plot of three wine varieties
The stepwise discriminate analysis was applied to the
complete set of variables in order to select the variables most
important regarding the classification criterion. The classification
performance was evaluated for the best group of variables,
the number of which is given in brackets.
When the leave-one-out validation technique was
applied for classification of wine by sensorial quality (“good”
or “bad”) a 78 % and 87 % success were obtained for all and
five best variables, resp.
Table I
Criteria for wine classification and success in classification
when all or best variables were used
Criterion number Classification success in %
of classes All * Best *
Variety 3 100.00 95.65 (3)
Vintage 2 100.00 100.00 (2)
Quality 2 93.48 93.48 (5)
Quality 3 86.96 78.26 (9)
Colour 2 91.30 89.13 (7)
Colour 3 76.09 78.26 (6)
Bouquet 2 97.83 93.48 (8)
Bouquet 3 93.48 84.78 (10)
Taste 2 91.30 91.30 (10)
Taste 3 86.96 91.30 (7)
Producer 2 100.00 100.00 (4)
* “All” refers to 18 originally used variables; “Best” refers to
the optimally selected variables with their number in brackets
P u m p k i n O i l s
Principal Component Analysis
The data set of pumpkin oils characterized by 38 variables
(maximal intensity of fluorescence using exitation wavelengths
280–650 nm) was used for this study. The inspection
of the PCA scatterplot (not shown) has revealed that two oil
samples as outliers. The remaining oils are located in two
natural clusters at negative values of PC1 and positive values
of PC2, resp. In the loadings plot (Fig. 3.), all excitation wavelengths
are divided into three groups. A reasonable assignment
of these groups is a task of our current study.
s737
Fig. 3. PCA loadings plot showing the interposition of the used
wavelengths for fluorescence measurements.
Discriminant Analysis
Fig. 4. represents the LDA graphical output, which
shows that very good quality oils samples are located in a
cluster at positive values of the first discriminant function
(DF1) whilst the lower quality oils form a cluster at negative
DF1 values. The separation of two sorts of oils differing by
the sensorial quality is remarkable. The classification performance
was 100 % for cross-validation using the leave-oneout
procedure.
Fig. 4. LDA plot of the oil sample number vs. the sole discriminant
function DF1
Conclusions
White varietal wines were succesfully classified according
to several classification criteria: by variety, vintage,
producer as well as partial sensorial descriptors. A very good
quantitative separation of the wine samples according to all
chosen criteria was obtained by discriminant analysis.
Stepwise variable selection enabled to find an optimal
reduced set of variables. The established and validated discriminant
models are fully applicable for the category prediction
of the unclassified wine samples.
Fluorescence analysis can be successfully applied for
classification of commercial pumpkin oils according to their
sensorial quality. The investigation of the species causing the
most important fluorescent signals reflecting the oil quality is
the object of our further study.