3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
P60 CATEGORIZATION OF OLIVE OILS
DáŠA KRUŽLICOVá a , Ján MOCáK a,b and ERnST
LAnKMAYR c
a Institute of Analytical Chemistry, Slovak University of Technology,
Radlinskeho 9, 812 37 Bratislava, Slovakia,
b Department of Chemistry, University of Ss. Cyril & Methodius,
Nám. J. Herdu 2, 917 01Trnava, Slovakia,
c Institute for Analytical Chemistry, University of Technology
Graz, A-8010 Graz, Austria,
dasa.kruzlicova@stuba.sk
Introduction
Olive oil is an important food component, which enjoys
special and increasing popularity in many countries not only
due to its delicate taste but also because of its nutrition value.
Depending on regional conditions a variety of oils are produced
in different quality.
Olive oil has several favourable health effects related to:
reducing the content of adversely acting blood LDL cholesterol
causing risk of cardiovascular diseases, decreasing the blood
pressure, glucose content in blood, and increasing absorption of
vitamins A, D, E, and K. The beneficial health effects of olive
oil are connected to high contents of mono-unsaturated fatty
acids and antioxidative substances.
Chemical analysis of olive oil is cumbersome since it
consists of a complex mixture of chemical compounds and
due to enormous matrix effect 1 . In this study, olive oil samples
of different oil types were characterized by absorbances
in their UV-VIS spectra and by sensorial assessment. Using
a new chemometrical approach 2 , based on the absorbances
at the most informative wavelengths, neither the standard
materials nor assignment of chemical compounds were
necessary so that it is cheaper, requires less laboratory work
but demands more computations.
Experimental
O l i v e O i l S a m p l e s a n d T h e i r
C h a r a c t e r i s t i c s
193 olive oil samples of Greek origin were studied
belonging to five different oil types coded by M (31 samples),
K (37 samples), E (13 samples), n (94 samples), and T (18
samples); these codes were used instead of commercial brand
on demand. The sensorial assessment of the oils was made in
a ten-point scale. Absorbances were measured at 2001 wavelengths
in the range 200–700 nm. Acidity, the peroxide value,
and traditional measurements K232 and K270 were used as
further characteristics of the oil quality.
I n s t r u m e n t a t i o n a n d
C h e m o m e t r i c a l T e c h n i q u e s
UV-Vis spectra of olive oil samples were measured by
spectrophotometer Varian, Cary 50 Conc (Varian, Vic., Australia)
and software Cary Win UV was used for data acquisition
and processing. Absorption spectra of diluted (1 : 300, v/v)
olive oil were measured in isooctane (spectroscopy grade);
s706
then they were digitized using a 0.25 nm step and saved to
the PC.
For classification purposes new categorical variables
Sens, Variety and Location were used denoting sensorial
quality, oil type and geographical origin of the sample, resp.
Linear discriminant analysis, quadratic discriminant analysis,
logistic regression, the K-th nearest neighbour method and
artificial neural networks were utilized as the classification
techniques. The classification performance was evaluated
for: (i) the training set used for computing the classification
model, (ii) the test set created by the individual samples
excluded from the training set by the “leave-one-out” principle
3 .
Results
C a t e g o r i e s o f O l i v e O i l s
According to sensorial characteristics, the collected
samples were categorized into three classes using categorical
variable Sens: the highest quality oils (6.5–9.0 points,
denoted as “best”), the medium quality samples (3.5–6.4,
“good”), and not acceptable quality (1.0–3.4, “worst”).
Two further categorization principles were applied: (i)
the type of olive oil using categorical variable Variety - five
categories M, K, E, n, and T, (ii) the geographic locality,
Peloponnese, Central Greece, and Crete, using the three-class
categorical variable Location.
C l a s s i f i c a t i o n o f O l i v e O i l s b y
D i f f e r e n t C r i t e r i a
Linear discriminant analysis (LDA) and other classification
techniques need an optimal reduction of the original
number of variables eliminating unimportant ones. For this
purpose the stepwise variable selection was used, by which
60 optimal wavelengths were selected in the case of the oil
type categorization, 37 wavelengths were selected when sensorial
quality was categorized, and 60 wavelengths when categorizing
the oils by geographical origin.
Fig. 1. 3D LDA plot of 5 different varieties of olive oils