3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
S t a t i s t i c s
Multivariate statistical methods, e.g., cluster analysis,
factor analysis, and canonical discriminant analysis, were
performed using the Unistat ® software.
Results
Fig. 1. shows the preliminary predisposition of cows’,
sheeps’ and goats’ cheese samples under the study to natural
grouping, obtained by hierarchical cluster analysis of determined
elemental markers. Clusters were constructed using
ni, Cu and Mg variables, as they were found to have the most
discriminating impact on cheeses distinction. As follows from
data presented, samples are grouped into two major clusters:
the first one correspond mostly to sheeps’ cheeses and the
second to cow and goat cheese samples, respectively.
Fig, 1. Cluster graph of sheeps’ (S), cows’ (C) and goats’ (G)
cheeses, constructed using the Ni, Cu and Mg content as variables.
Distance measure: block; Method: Median
Although there is a clear differentiation tendency, both
groups of clusters contain some incorrectly classified samples.
In order to achieve the better differentiation of examined
cheese species, factor analysis using the principal component
factoring with the varimax-rotation was performed to
describe the main variations between the Ba, Cr, Cu, Hg, Mg,
Mn, ni and V content.
Using this approach, mathematical model explaining
the mutual correlation between a large set of variables was
Fig. 2. Principal component factoring of sheeps’ (S), cows’ (C)
and goats’ (G) cheeses; Rotation: Varimax; Variables selected:
ba, Cr, Cu, hg, Mg, Mn, Ni and V
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constructed. As we found, first factor, related mainly to the
content of Hg, Mg and ni, explain 26 % of the total markers’
variation; the second one (18.5 %) is strongly influenced
by Cu and Mn variability, and the last factor (15.4 %) by
the variability of Ba and V. These three factors sufficiently
explain more than 60 % of overall elemental data variations.
Visualisation of data obtained (Fig. 2.) suggest, that the most
effective differentiation of cows’, sheeps’ and goats’ cheeses
can be achieved following the first factor axis.
Results of canonical discriminant analysis (Fig. 3.)
demonstrated very high potential to distinguish the differences
among the cows’, sheeps’ and goats’ cheeses.
Fig. 3. Canonical discriminant analysis of sheeps’ (S), cows’
(C) and goats’ (G) cheeses; Plot of discriminant score; Variables
selected: ba, Cr, Cu, hg, Mg, Mn, Ni and V
Discriminant functions correctly classified 92.6 % of all
samples according to the species’ origin. First discriminant
function reveals the highest canonical correlation (89.5 %)
and explains up to 83.6 % of the total variance. As follows
from the values of standardized coefficients, Mg, Mn and ni
markers most significantly influence the discrimination at
first function, whereas for the second discriminant function,
the presence of Cu and Hg is essential.
In addition, K th -neighbour discriminant analysis provided
100 % correctness of samples classification at k = 1; and
91.1 % at k = 2. Stepwise discriminant analysis, which sorts
the used markers according to their descending influence on
the discrimination, gives the following order: Mg, Cu, ni,
Mn, Hg, V, Ba and Cr.
Some of the markers used in this work, e.g. Cu, Mg,
and Mn, were previously successfully used for cheeses’ origin
authentification also by other authors. 5–7,13,14 Significant
discrimination of cheeses’ species presented in this work can
be effectively explained by the geochemical differences in
sheeps’, cows’ and goats’ pasture soils. Sheeps’ pastures are
located in the mid-mountain regions whereas cows’ and goats’
ones are to be found predominantly in lowland agricultural
areas. Thus, the content of minerals and other trace elements
in feeding diet vary significantly, subsequently influencing
their content in cattle milk and milk products.