3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
Display of the olive oils discrimination by the oil type
(Variety) in 3D space of first three discriminant functions is
shown in Fig. 1. Analogical 2D displays in Fig. 2. and Fig. 3.
depict the oils classification by sensorial quality and geographic
origin. A very good separation of the oil classes is evident
in all three studied cases.
Fig. 2. 2D LDA plot of three olive oil classes differing by the
sensorial quality
Fig. 3. 2D LDA plot of three olive oil classes differing by geographical
origin
Quadratic discriminant analysis (QDA), logistic regression
(LR) and K-th nearest neighbour method (Knn) are
further frequently used classification techniques 4 . They were
used together with LDA and artificial neural networks (Ann) 5
for a complex classification of olive oils. A very good separation
of the olive oil samples according to two selected criteria
is shown in Table I and Table II.
s707
Table I
Results of classification of 187 olive oils by variety
Method Classification Model Leave-1-out Software
success
LDA
number of correct
%
186
99.5
177
94.7
QDA
number of correct
%
187
100.0
131
70.1
Knn
number of correct
%
186
99.5
185
98.7
LR
number of correct
%
187
100.0
–
–
SPSS
SAS
SAS
SPSS
The effectivity of classification using different classification
techniques and software is here expressed by the ratio
of the successfully categorized objects to all objects in per
cents. The most important are the results of cross-validation
achieved here by leave-one-out method. They show the effectivity
of prediction new objects (olive oils) not involved
in the training procedure; the best techniques are close to
100 %.
Table II
Results of classification of 93 olive oils by sensorial quality
Method Classification Model Leave-1-out Software
success
LDA
number of correct
%
87
95.6
81
89.0
QDA
number of correct
%
91
100.0
60
65.9
Knn
number of correct
%
85
93.4
80
87.9
LR
number of correct
%
91
100.0
–
–
Ann
number of correct
%
91
100.0
–
–
SPSS
SAS
SAS
SPSS
JMP
Classification of the oil samples by geographic origin
was performed only by the LDA technique, which categorized
98.4 % of samples by leave-one-out technique in a correct
way (consistent with Fig. 3.).
Conclusions
Classification of olive oils by variety, sensorial quality
and geographic location was successfully performed by several
chemometrical techniques using modern commercial software.
The classification effectivity was in best cases close to
100 %. A speciality and novelty of the applied approach is
processing of spectral data without the need to utilize standards
representing the most important components of olive
oils. Therefore a very complex quantitative multicomponent
analysis is not necessary and the expenses for it and for the
standard materials are saved.