3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
logic function, sigmoid function or hyperbolic tangent function.
The most common sigmoid function is of the form
where k is a constant. The aim of the neural network
training is to minimize the error E by changing the weights
and offsets
where r is the number of the input-output vector pairs in
the training set, d i is the respective component of the required
output vector and y i is the response to the adequate component
x i of the input vector. The error E is minimized most often by
the steepest descent method or another gradient method. The
described theory is adequate mainly for the multilayer perceptron
algorithms like Back Propagation, Quick Propagation
and Quasi-newton, with some differences in details.
The Ann calculations can be effectively made using
several commercial software packages like Trajan 14 , Statistica
neural networks 15 , SAS JMP 16 and others 17,18 .
Experimental
D e s c r i p t i o n o f W i n e S a m p l e s a n d
I n s t r u m e n t a t i o n
72 wine samples of 6 varieties originated from Small
Carpathian region (Slovakia) and produced in West Slovakia
in 2003 were quantitatively analysed by gas chromatography
using headspace solid-phase microcolumn extraction.
The set of samples contained 11 samples of Frankovka Blue
(code FM), 12 samples of Chardonnay (Ch), 16 samples of
Műller Thurgau (MT), 9 samples of Welsch Riesling (RV),
7 samples of Sauvignon (Sv) and 17 samples of Gruener
Veltliner (VZ). Wine aroma compounds were extracted from
the headspace into a microcolumn; the microcolumn was
then transferred into a modified GC injection port for thermal
desorption and the released compounds were analysed.
Areas of chromatographic peaks of the same retention time
corresponding to the selected 65 volatile aroma compounds
were used in all samples. Wines were characterised by a set
of identified compounds with corresponding relative abundances.
Analyses were carried out on a GC 8000 Top Series,
CE Instruments (Rodano-Milan, Italy) equipped with a modified
split-splitless inlet and flame ionization detector. The
inlet was modified so that it was possible to insert a glass
microcolumn (1 mm i.d., packed with 5.0 mg of 60–80 mesh
Tenax TA). The fused silica capillary column Omegawax
250, 30 m × 0.25 mm × 0.25 μm film thickness (Supelco, Bellefonte,
Pennsylvania, USA) was used. The GC inlet and the
detector temperatures were 250 °C and the initial column
temperature was maintained at 25 °C. The thermal desorption
was performed at 10 kPa pressure for 5 min, then the
pressure was increased to 50 kPa and the column tempera-
(4)
(5)
s557
ture was programmed at a rate of 4 °C min –1 up to 210 °C and
maintained at 210 °C for 10 min. Helium was used as the carrier
gas. A computer program Class-VP 7.2, SP1 (Shimadzu,
Columbia, Maryland, USA) was used for data acquisition.
Analyses of each wine sample were repeated twice.
D e s c r i p t i o n o f D r i n k i n g W a t e r
S a m p l e s a n d I n s t r u m e n t a t i o n
93 water samples containing potable, spring and mineral
waters, originated from Croatia (54 samples), Slovenia (30),
the Czech Republic (6), and France (3) were studied. From
each brand 3 specimens were sampled so that the analyses
were finally made for 15 tap water samples, 51 spring water
samples, of which 12 samples were carbonated, and 27 mineral
water samples, of which 9 samples were carbonated.
Experiments were performed using a high-resolution inductively-coupled
plasma mass spectrometer (ICP MS) Element 2
(Thermo, Bremen, Germany) equipped by autosampler (ASX
510, Cetac Technologies, USA), the sample introduction kit
with a conical nebulizer (Thermo, Bremen, Germany) and a
Scott-type glass spray chamber (Thermo, Bremen, Germany)
for transporting the analytes into the plasma of the ICP MS
unit. The investigated water samples were analysed and
characterized by thirty one continuous variables – nuclide
concentrations determined by the ICP MS measurements:
Ag107, Ag109, Al27, As75, B11, Ba138, Be9, Bi209, Cd111,
Cd114, Co59, Cr52, Cu63, Fe56, Li7, Mn55, Mo95, Mo98,
ni60, Pb208, Sb121, Sb123, Se77, Sn118, Sn120, Sr86, Ti47,
Tl205, U238, V51, and Zn66. The standard solutions and the
blank solutions were prepared by adding of 1 % high purity
nitric acid (Fluka, Steinheim, Switzerland) and 1 % high
purity hydrochloric acid (Merck, Darmstadt, Germany).
Results
C l a s s i f i c a t i o n o f V a r i e t a l W i n e s
For quantitative analysis, based on the integrated peak
area, 65 chromatographic peaks were selected. A complete
assignment of the analysed compounds to the selected peaks
was not necessary in the applied approach, however, for 19
peaks the corresponding species were identified. It is very
important to note that the retention time order for all selected
compounds was the same for all 72 samples and the way of
chromatographic signal evaluation was identical. The obtained
final data matrix suitable for chemometrical processing
contained 72 rows (objects) and 65 columns (variables).
Since the number of variables was too large compared to the
number of objects, selection of the best variables, based on
the F-test, was performed by stepwise feature selection. In
this way, 30 best variables were chosen enabling best discrimination
among the six studied wine varieties. For comparison
purposes, the wine classification was performed not only
using the Anns but also several techniques of discriminant
analysis were implemented.
The classification model was calculated using the training
set of samples containing all samples but one when the
leave-one-out validation was used or without three samples in