Classificação de genótipos de café arábica usando espectroscopia de infravermelho próximo
The environmental conditions in coffee cultivation, such as climate, soil type and altitude, associated with agronomic practices, are responsible for influence the final chemical composition of the bean. They directly influence the essential features of the beverage, increasing its aggregate pric...
| Autor principal: | Marquetti, Izabele |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2015
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/1133 |
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| Resumo: |
The environmental conditions in coffee cultivation, such as climate, soil type and
altitude, associated with agronomic practices, are responsible for influence the final
chemical composition of the bean. They directly influence the essential features of
the beverage, increasing its aggregate price. Proof of geographic and genotypic
origin of the coffee genotypes must be done using reliable methods. Thus, the near
infrared spectroscopy (NIRS), in the 1100 to 2498nm range, was used for analyze
different coffee genotypes that were cultivated in different cities (Brazil - Paraná
State). As first approach linear methods, principal components analysis (PCA) and
partial least squares with discriminant analysis (PLS-DA), were used for data
interpretation due to the high complexity and amount of information contained in the
spectra. The obtained PLS-DA models had an average sensitivity of 93.75% and a
specificity of 100% for the geographical classification. While for genopyte
classification, the PLS-DA performance was 93.75% for sensitivity and 97.13% for
specificity. In an attempt to improve the performance and reliability of the developed
classifiers, both the PCA scores and the PLS-DA latent variables were fed into two
artificial neural networks, the multilayer perceptron (MLP) and radial basis function
network (RBF), that are nonlinear models. The architecture parameters of these
networks were optimized using the sequential simplex method. The two-stage
models, linear with PLS-DA and nonlinear with RBF, were able to classify
geographically and genotypically with 100% of selectivity and specificity all the
training and test samples. The latent variables of the PLS-DA are determined by
taking into account the desired response, so it contains more information than the
scores of the PCA. While the RBF network, by having fewer free parameters and a
simpler architecture compared to the MLP, has a faster and covergente training. The
spectra analysis in near-infrared region showed better results than mid-infrared
spectra. These results indicate that NIRS spectra contain important information that,
combined with appropriate methods of pattern recognition, allow the classification of
green arabica coffee samples by genotype and growing region. Besides, the PLS-DA
loadings analysis allows associating which NIRS bands are specific of each class.
This information can be correlated with the samples chemical composition, providing
preliminary data to evaluate the effect of growing region and genotype in the selected
green coffee chemical composition. |
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