Mineração de dados para o reconhecimento da origem e do tipo de alimentos e outras substâncias com base em sua composição química
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2016-03-29
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Universidade Federal de Goiás
Resumo
A practical way to characterize consumable substances is through its chemical elements in
its composition and theirs concentrations. By using these elements as feature variables, it
is possible to arrange these substances samples in a data matrix in which data mining
and statistical techniques can be applied for predictive analysis. The classification of
consumable substances based on its chemical components is an interesting problem
and provides useful information for various purposes, as: recognition of geographical
origin of a substance; validation and authenticity; determination of the characteristics of
a product which can aid companies in the quality control and preservation; differentiation
of categories of a product, and others.
This study presents a methodology for predictive analysis of substances and food based
on its chemical components, using data mining concepts and techniques allied to ICPMS.
Four applications of the proposed methodology are described: recognition of the
geographical origin of Brazilian white rice produced in São Paulo and Goiás states; differentiation
of organic and conventional Brazilian grape juice; differentiation of organic
and conventional Brazilian chocolate, and analysis of its toxic and essential elements; recognition
of the source of ecstasy tablets apprehended in two cities from Sao Paulo state,
Ribeirão Preto and Campinas.
For all applications presented, the classification models obtained showed high predictive
performance (over 85%), which attest the efficiency of the proposed methodology, and
the variable selection techniques used helped us to identify the chemical elements which
are more important to the differentiation of the analyzed samples. For the purpose of
distinguishing food samples into organic and conventional, our approach is pioneer and
yielded good results.
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MAIONE, C. Mineração de dados para o reconhecimento da origem e do tipo de alimentos e outras substâncias com base em sua composição química. 2016. 82 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2016.