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Author(s): BAQUETA, M. R.; RUTLEDGE, D. N.; ALVES, E. A.; MANDRONE, M.; POLI, F.; COQUEIRO, A.; SANTOS, A. C. C.; REBELLATO, A. P.; LUZ, G. M.; GOULART, B. H. F.; PILAU, E. J.; PALLONE, J. A. L.; VALDERRAMA, P. The objective of this research was to apply the multi-block data analysis method Path-ComDim to evaluate the relationships between the multiple data blocks acquired on conilon Capixaba and indigenous... ... |
Author(s): BAQUETA, M. R.; VALDERRAMA, P.; MANDRONE, M.; POLI, F.; COQUEIRO, A.; COSTA-SANTOS, A. C.; REBELLATO. A. A.; LUZ, G. M.; ROCHA, R. B.; PALLONE, J. A. L.; MARINI, F. Different analytical techniques, mixing single and multi-block chemometric analyses in supervised and unsu- pervised approaches, and the selection of variables in the coffee discrimination domain have... ... |
Author(s): SANTOS, C. M.; MARTINS, N. F.; HORBERG, H. M.; ALMEIDA, E. R. de; COELHO, M. C.; ROGAWA, R. C.; SILVA, F. R. da; MILLER, R. N.; SOUZA JUNIOR, M. T. In order to discover genes expressed in leaves of Musa acuminata ssp. burmannicoides var. Calcutta 4 (AA), from plants submitted to temperature stress, we produced and characterized two full-length en... ... |
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Author(s): HIDALGO CHÁVEZ, D. W.; SILVA, F. L. C. DA; PINTO, R. V.; CARVALHO, C. W. P. de; FREITAS-SILVA, O. This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extru... ... |
Author(s): PEREIRA, M. A.; ALMEIDA, R. G. de; GOTARDO, N. L.
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Author(s): OLIVEIRA, G. F.; MIRANDA, T. L. R.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; CAIXETA, E. T.; SILVA, L. de F.; ALKIMIM, E. R.; SILVA, F. L. da Coffee growing is one of the most important agricultural activities in the world market. Among the commercially relevant species, there is Coffea canephora,which can be divided into the varietal group... ... |
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Author(s): FASIABEN, M. do C. R.; OLIVEIRA, A. de; MARIM, F.; MAIA, A.; ALMEIDA, M.; OLIVEIRA, O. de This paper addresses the classification and characterization of the sugarcane producing municipalities in Brazil, using techniques of multivariate statistical analysis (factor and cluster analysis). |
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