3. FOOD ChEMISTRy & bIOTEChNOLOGy 3.1. Lectures

Chem. Listy, 102, s265–s1311 (2008) Food Chemistry & Biotechnology
L08 ARTIFICIAL NEuRAL NETwORKS IN FOOD
ANALySIS
Ján MOCáK a,b , VIERA MRáZOVá a , DáŠA
KRUŽLICOVá b and FILIP KRAIC a
a Department of Chemistry, University of Ss. Cyril & Methodius,
Nám. J. Herdu 2, 917 01 Trnava, Slovakia,
b Institute of Analytical Chemistry, Slovak University of Technology,
Radlinskeho 9, 812 37 Bratislava, Slovakia, jan.
mocak@ucm.sk
Introduction
During the last twenty years the chemists have get
accustomed to the use of computers and consequently to the
exploitation of various complex mathematical and statistical
methods, by which they have been trying to explore multivariate
correlations between the output and input variables
more and more in detail. Input variables of different kind are
often continuous and represent usually the results of instrumental
measurements but also other observations, sometime
discrete or categorical, are important for characterizing the
investigated objects. Such kinds of input variables are for
example the results of sensorial assessment of foodstuffs or
simply some qualitative attributes like odour or colour (agreeable/disagreeable
or clear/yellowish/yellow). The output
variables are the targets of the corresponding study, which
again can be represented by some continous variable or a categorical
one with two or more levels. With the increasing complexity
of analytical measurements, and the analysed sample
itself, it becomes clear that all effects that are of interest cannot
be described by a simple univariate relation but they are
multivariate and often they are not linear. A set of methods,
which allow study of multivariate and non-linear correlations
that have recently found very intensive use among chemists
are the artificial neural networks (Anns for short) 1 .
The Anns are difficult to describe using a simple definition.
Perhaps the closest description would be a comparison
with a black box having multiple input and multiple output
which operates using a large number of mostly parallel connected
simple arithmetic units. The most important thing to
characterize about all Ann methods is that they work best
if they are dealing with non-linear dependence between
the inputs and outputs. They have been applied for various
purposes, e.g. optimisation 2,3 quantification of unresolved
peaks 4,5 , estimation of peak parameters, estimation of model
parameters in the equilibria studies, etc. Pattern recognition
and sample classification is also an important application area
for the Ann 6–8 , which is important and fully applicable in
food chemistry.
The most widespread application areas of implementing
the Anns for solution of foodstuff problems are: (i) wine
characterization and authentification, (ii) edible oil characterization
and authentification, (iii) classification of dairy products
and cheese, (iv) classification of soft drinks and fruit
products, (v) classification of strong drinks. In this paper two
examples are given, which exemplify the application of arti-
s556
ficial neural networks for authentification of varietal wines
and olive oils.
Theory
The theory of the Ann is well described in monographs
9–13 and scientific literature. Therefore only a short
description of the principles needed for understanding the
Ann application will be given here. The use of the Ann for
data and knowledge processing can be characterized by analogy
with biological neurons. The artificial neural network
itself consists of neurons connected into networks. The neurons
are sorted in an input layer, one or more hidden layer(s)
and an output layer. The input neurons accept the input data
characteristic for each observation, the output neurons provide
predicted value or pattern of the studied objects, and
the hidden neurons are interconnected with the neurons of
two adjacent layers but neither receive inputs directly nor
provide the output values directly. In most cases, the Ann
architecture consists of the input layer and two active layers
– one hidden and one output layer. The neurons of any two
adjacent layers are mutually connected and the importance of
each connection is expressed by weights.
The role of the Ann is to transform the input information
into the output one. During the training process the weights
are gradually corrected so as to produce the output values as
close as possible to the desired (or target) values, which are
known for all objects included into training set. The training
procedure requires a pair of vectors, x and d, which together
create a training set. The vector x is the actual input into the
network, and the corresponding target - the desired pre-specified
answer, is the vector d. The propagation of the signal through
the network is determined by the connections between
the neurons and by their associated weights, so these weights
represent the synaptic strengths of the biological neurons. The
goal of the training step is to correct the weights w ij so that
they will give a correct output vector y for the vector x from
the training set. In other words, the output vector y should be
as close as possible to the vector d. After the training process
has been completed successfully, it is hoped that the network,
functioning as a black box, will give correct predictions for
any new object x n , which is not included in the training set.
The hidden x i and the output y i neuron activities are defined
by the relations:
where j = 1, …, p concern neurons x j in the previous
layer which precede the given neuron i. ξ i is the net signal
– the sum of the weighted inputs from the previous layer, υ i is
the bias (offset), w ij is the weight and, finally, t(ξ i ) is transfer
function, expressed in various ways, usually as the threshold
(1)
(2)
(3)