3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
P74 MuLTIVARIATE METhODS IN FOOD
ANALySIS
VIERA MRáZOVá a , Ján MOCáK a , KATJA ŠnUDERL b
and ERnST LAnKMAYR c
a Department of Chemistry, University of Ss. Cyril & Methodius,
Nám. J. Herdu 2, 917 01 Trnava, Slovak Republic,
b Faculty of Chemistry and Chemical Engineering, Smetanova
17, SI-2000 Maribor, Slovenia,
c Institute for Analytical Chemistry, University of Technology
Graz, A-8010 Graz, Austria,
viera.mrazova@ucm.sk
Introduction
Chemometrical processing of the results of instrumental
analytical measurements takes advantage of modern statistical
methods and advanced software and creates new possibilities
for solution of problems in various practical fields of
application, e.g. in assessing quality of raw and processed
food 1,2 , or in advancement of diagnosis in laboratory medicine,
which we have studied in recent years 3,4 .
Wine belongs to the commodities, which are very
frequent objects of falsification 1 . Therefore it is necessary to
develop procedures which make possible wine classification
and authentication 5,6 , i.e. verification of the selected sample
with regard to the wine variety, producer/locality as well as
the year of production.
Pumpkin seed oils enjoy special and increasing popularity
mainly due to their characteristic taste. The oil is contained
in the seeds, consists of approx. 70 % unsaturated fatty
acids and contain a number of important compounds like triterpenoides,
carotenoides, tocopheroles and phytosteroles 2 .
The oil quality also depends on the geographical origin, seasonal
variations and climatic influences.
This study was focused on the classification of white
varietal wines based on the results of chemical analyses.
Another goal was spectral characterization of different sorts
of pumpkin seed oils accompanied by the sensory evaluation,
which after chemometrical data processing facilitated detecting
the properties most informative about the oil quality.
Experimental
W i n e S a m p l e s
Altogether 46 samples of Slovak varietal wines, Welsch
Riesling, Grüner Veltliner and Chardonnay, were analyzed
during two years using 18 traditionally analysed variables
like SO 2 , total acids, citric acid, malic acid, tartaric acid, lactic
acid, sugars by Shoorle, glucose, fructose, polyphenols,
density, etc. The wines were produced by two producers in
Bratislava and Hlohovec. Sensorial analysis of all examined
wine samples was also provided.
P u m p k i n O i l S a m p l e s
36 commercially available pumpkin oil samples of
Steyrian origin were studied. The samples were examined by
s736
sensorial analysis and spectroscopically using 38 variables
representing the maximum fluorescence signal.
S e n s o r i a l A n a l y s i s
Sensorial analysis of wines was made by a group of
experts who assessed in a twenty-point scale colour, bouquet,
taste, and the total points. Two or three wine categories
were made by sensorial quality according to the median or
the lower and upper terciles of total points. Smell, taste and
visual character of oils were concerned when rating the sensory
quality of oils. The collected oil were categorized into
two basic classes: fully satisfactory (“good”) vs. not fully
satisfactory (“bad”).
S t a t i s t i c a l A n a l y s i s
Statistical treatment of the obtained data was performed
using program packages SPSS (SPSS Inc., Chicago, U.S.A.)
and STATGRAPHICS Plus 5.0 (Manugistics, Inc., Rockville,
U.S.A.).
Results
W i n e
Principal Component Analysis (PCA)
Fig. 1. depicts the PCA representation of the samples
of three wine varieties and two vintages where some natural
grouping of the studied wines is visible. It is worth to note
that the observed natural wine clusters are not created by the
wine varieties but correspond mainly to the vintage categories:
the 1999 samples are below –1.0 on the PC1 axis, the
2,000 samples are above + 1.0.
Fig. 1. PCA scatterplot of the studied wine samples
Discriminant Analysis (LDA)
LDA is a supervised learning method, in which the classification
model is constructed using the the training data set.
Then the developed model is used to classify the test samples
data set. Three ways of classification were used: by variety,
year of vintage, total sensorial quality. In addition, partial
sensorial characteristics were used: colour, taste and bouquet
of wine. The LDA results using different criteria are summarized
in the last two columns of Table I. Success of classification
for the set of 46 wine samples was close to 100 %. Fig. 2.
exemplifies the successful classification by variety.