artificial neural networks applied in quantitative chemical analysis

Artificial Neural Networks Applied in Quantitative
Chemical Analysis
Luiza H.S. Nunes
Computer Science, FATEC – Faculdade de Tecnologia de São José dos Campos
São José dos Campos, São Paulo, 12247-004 Brazil
Matheus R.C. Teixeira
Computer Science, FATEC – Faculdade de Tecnologia de São José dos Campos
São José dos Campos, São Paulo, 12247-004 Brazil
Lucia Codognoto
Chemistry, UNICASTELO - Universidade Camilo Castelo Branco
São José dos Campos, São Paulo, 12247-004 Brazil
Rogério Marinke
Computer Science, FATEC – Faculdade de Tecnologia de São José dos Campos
São José dos Campos, São Paulo, 12247-004 Brazil
1.
ABSTRACT
The quantitative analysis of samples, aiming the separation of
compounds, can determine, classify and identify a compound
and it’s concentration in a mixture. However these methods are
expensive and laborious and one of the main problems in
quantitative analysis of samples using instrumental methods is
the interelemental effects, like the matrix which interferes in the
accurate determination of the analytes and requires a
complicated pretreatment of the sample. With technological
advances, new methods of analysis have emerged in order to
minimize these problems, for example, the appearance of
techniques using artificial intelligence. Thereafter, by
developing an Artificial Neural Network, the analytical
processes become automated and the data’s processing
generated is intended to b more efficient and sophisticated.
Keywords: Quantitative analysis, samples, separation of
compounds, new methods of analysis, Artificial Neural
Networks.
2.
INTRODUCTION
The chemical separation of compounds in analytical chemical,
aim the data identification, classification and quantification.To
perform the characterization and quantification of analytes of
interest in this area, one of the most used techniques that can be
cited is the chromatographic.
However, these methods are expensive and laborious and not
always easily accessible to the organs of control. Problems like
the matrix effect, which interferes in the accurate determination
of the analytes and requires a complex pre-treatment of the
sample, are common in this kind of techniques.
Considering the necessity of organizing the vast amount of
information about chemical compounds that arise every year, it
becomes necessary to use analytical tools and also the
knowledge for the formulation of new techniques of quantitative
and qualitative determination of chemical elements
contained in samples.
The demand for new methods that could help in the
optimization of the separation and identification of
chemical compounds, requires the development of a new
instrumental analytical technique that with the
technological advances, brings technical approaches aimed
at concepts of Artificial Intelligence such as Neural
Networks Artificial (ANN’s) [1].
The determination of certain analytes in complex matrices
requires the use of more sophisticated separation
techniques, such as chromatographic, which can make the
analysis more laborious and increase costs.
Thus, the use of techniques of artificial intelligence,
specifically artificial neural networks, can be a viable
alternative for the analysis of complex samples, reducing
the time and cost analysis.
3.
ARTIFICIAL NEURAL NETWORKS
Among the most important and efficient mathematical
methods in forecasting results without chemical
interferences and currently existing in the area of drug
analysis, are artificial neural networks, which bring
innovations in the models already in use and are
fundamental in this area’s development, besides the
analytical procedures becoming automated
and the
processing of generated data more efficient and
sophisticated. The ANN's suppress the problems previously
presented by instrumental techniques such as modeling of
nonlinear systems, and can be used in conjunction with
instrumental analytical techniques in order to become a
complementary tool [1],[6].
Conducted studies in the analytical chemistry’s area had
successful application of artificial neural networks. The
Kohonen neural networks were used in the prediction of the
chemical elements in the periodic table. The network trained by
Backpropagation algorithm was able to map and classify the
elements of the periodic table, as was done by Mendeleev in
1869 and this training was conducted using 5000 epochs [3].
4.
EXPECTATIONS AND GOALS
The aim of this work is the development of an artificial neural
network to assist in analytical separation and quantification of
compounds of environmental interest and pharmaceutical in
different matrices in order to facilitate the process of sample
treatment, making the analysis of these samples more accessible
agencies control. To do it, a Multi-layer Perceptron network
will be implemented, which uses the Error Backpropagation
algorithm to train the network.
The formulation of the correct architecture of a network is
crucial for the correct prediction of the data that will be
analyzed. The algorithm of Error Backpropagation is widely
used in applications in the applications of ANN’s in the
chemistry field for data modeling and pattern recognition
[4],[5]. The function used to activate the network will be the
sigmoid function, because this function allows the activation
function to take negative values, a fact that improves the
convergence of the algorithm training and for having the nearest
exit of a biological neuron [7],[8],[12],[13].
The results of eletroanalytical determination of nitrosamines
compouds
(N-nitrosodimethylamine
(NDMA),
Nnitrosodiethylamine (NDEA), N- nitrosopiperidine (NPIP) and
N-nitrosopyrrolidine (NPYR)) will be used in training and
validation Artificial Neural Network. N-nitrosamines are Nnitroso compounds which are considered powerful
carcinogenic, teratogenic and mutagenic effect in laboratory
animals[14]. These compounds can be found in several
matrices, like as, foods, soil, waters, air, pesticides, cigarette,
rubber products and cosmetics[15],[16],[17],[18],[19],[20],[21].
Electrochemical identification of these compounds in the
mixture is not possible without a prior separation, since they
present values of peak potential very close (NDMA 1.91 V vs
Ag/AgCl, NDEA 1.79 V vs Ag/AgCl, NPIP 1.81 V vs Ag/AgCl
and NPYR 1.84 V vs Ag/AgCl in Na2HPO4 0,10 mol L-1, pH
4,0). These results can be observed in figure 1.
I / A
80
60
Thus, the use of artificial neural networks can be a viable
alternative for the analytical separation of these compounds
in a mixture.[9],[10],[11].
As output, the data that refers the quantification of the
same compounds in a mixture will be presented, in other
words, each compound has a different concentration, which
must be determined in the mixture. The presentation of the
estimated output is justified because it is a supervised
learning ANN. The expectation is that the network can be
capable to learn from the data related to the compounds
separately and can identify and quantify them in the
presented mixture. The result of this work will be shown in
future articles.
5.
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E / V vs Ag/AgCl
Figure 1: Square wave voltammograms for the mixture of Nnitrosamines in the concentration of 11.7 10-5 mol L-1 and
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