3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
case of using the leave-three-out validation. Thus, the remaining
one sample or three samples were not included in the
training procedure but were used for inspecting the quality
of prediction whether the predicted variety of wine matches
the real wine variety. The results achieved by several classification
techniques and different software are summarized
in Table I. The presented Ann results were achieved after
optimising the neural network; the lowest error was obtained
when using a three layer perceptron with 30 input neurons
(areas of the selected best peaks), 3 hidden neurons and
one six-level output neuron (representing the predicted wine
variety).
All wine samples (100 %) were correctly classified into
six classes by variety when the calculated multidimensional
model is considered – no one sample was allocated to a wrong
class. Considering the validation results, the performance in
leave-one-out validation depend on the applied multivariate
technique. The leave-three-out manual technique can be used
also in the case when the automatic leave-one-out validation
is not enabled for the given method and software. All validation
results shown in Table I are above 90 %, which justifies
very good ways of wine variety prediction enabling to confirm
or reject wine authenticity.
C l a s s i f i c a t i o n o f D r i n k i n g W a t e r
Three classification criteria were used for the classification
of drinking waters: (1) by three types of water – potable,
mineral and spring water, (2) by five types of water – potable,
mineral, mineral carbonated, spring and spring carbonated
water, (3) by three countries of origin – Slovenia, Croatia
and Czechia (the category of French waters was not used due
to a very low number of samples). Before the calculations
new categorical variables were created, which correspond to
the first, second and third classification criterion: WType 3,
WType 5 and Country, resp. In addition, a special categorical
variable Carbon was created in order to mark whether the
sample is carbonated or not (c/n). When using the Anns it is
possible to utilize this variable at the input to provide some
Table I
Success in prediction of wine variety using 72 varietal wines,
30 optimally selected chromatographic peaks, 5 classification
and 2 validation techniques
Method Classif. Leave-1- Leave-3- Software
success out out
LDA
Correct/all
%
69/72
95.8
69/72
95.8
QDA
correct/all
%
66/72
91.7
65/72
90.3
Knn
correct/all
%
72/72
100.0
67/72
93.1
LR
correct/all
%
–
–
69/72
95.8
Ann
correct/all
%
–
–
68/72
94.4
SAS
SAS
SAS
SPSS
JMP
s558
additional information about the sample. It is worth noting
that the discriminant analysis techniques, except logistic
regression, do not permit the use of non continuous input
variables. The mentioned additional information cannot be
of course used when the classification by the second criterion
is used.
Table II shows the classification results for five cases
using categorization by water type into 3 and 5 classes, the
same categorization but with the help of additional categorical
variable Carbon, and finally categorization by 3 countries
of origin. Intelligent Problem Solver is an extremely useful
module of Trajan software facilitating the selection of the
optimal neural network. For the sake of place, Table II exhibits
only five best networks, automatically selected by this
module, but a good possibility is to make a choice among
a larger nuber of networks. Moreover, the networks belonging
to different Ann variants can be examined in this way
(e.g. Radial Base Ann). In Table II, the ordinal number of
the network is marked by no, the number of neurons in individual
layers is marked by I (input), H (hidden), and O (output);
Err. indicates the sum of squares error obtained both
for the training and test sets. The most important results are
Table II
Ann – selection of the best network for 5 different criteria of
water classification using Intelligent Problem Solver of software
Trajan 6.0
MLP networks
Categor
neurons
variables
no I H O
Train.
set
Err.
Test
set
Err.
Success
[%]
1 31 6 1(3) 0.009 3.759 77.3
2 31 6 1(3) 0.023 4.706 81.8
Country 3 31 5 1(3) 0.023 2.801 77.3
4 31 6 1(3) 0.017 3.464 81.8
5 31 6 1(3) 0.0044 5.239 86.4
1 32 5 1(3) 0.0079 3.215 86.4
Country
c/n
2 32
3 32
4 32
6
6
6
1(3)
1(3)
1(3)
0.001
0.100
0.0032
3.921
2.242
1.829
81.8
86.4
86.4
5 32 6 1(3) 0.144 0.480 90.9
6 31 6 1(3) 0.0054 0.0026 100.0
7 31 6 1(3) 0.0008 1.602 86.4
WType3 8 31 6 1(3) 0.0002 0.649 95.5
9 31 6 1(3) 0.0010 0.204 95.5
10 31 6 1(3) 0.0000 0.935 86.4
1 32 6 1(3) 1.4 × 10
WType3
–5 2. × 10 –5 100.0
c/n
2 32 6 1(3) 1.1 × 10 –6 5.8 × 10 –6 3 32 5 1(3)
100.0
5.9 × 10 –6 4 32 6 1(3)
0.0029 100.0
1.2 × 10 –9 8.2 × 10 –6 100.0
5 32 6 1(3) 1.5 × 10 –8 9.1 × 10 –3 100.0
1 31 6 1(5) 0.2946 0.744 90.9
2 31 6 1(5) 0.215 0.599 86.4
WType5 3 31 6 1(5) 0.424 1.154 81.8
4 31 6 1(5) 0.197 0.4498 59.1
5 31 6 1(5) 0.480 0.934 90.9