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MATA, M. M. da; ROCHA, P. D.; FARIAS, I. K. T. de; SILVA, J. L. B. da; MEDEIROS, E. P.; SILVA, C. S.; SIMÕES, S. da S.. |
The use of vibrational spectroscopy, such as near infrared (NIR) and Raman, combined with multivariate analysis methods to analyze agricultural products are promising for investigating genetically modified organisms (GMO). In Brazil, cotton is grown under humid tropical conditions and is highly affected by pests and diseases, requiring the use of large amounts of phytosanitary chemicals. To avoid the use of those pesticides, genetic improvement can be carried out to produce species tolerant to herbicides, resistant to fungi and insects, or even to provide greater productivity and better quality. Even with these advantages, it is necessary to manage and limit the contact of transgenic species with native ones, avoiding possible contamination or even... |
Tipo: Artigo de periódico |
Palavras-chave: DNA; Algodão; Raio Infravermelho; Organismo Transgênico; Cotton; Infrared radiation; Transgenic plants. |
Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139823 |
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SANTANA, F. B. de; SOUZA, A. M. de; ALMEIDA, M. R.; BREITKREITZ, M. C.; FILGUEIRAS, P. R.; SENA, M. M.; POPPI, R. J.. |
A teaching experiment on supervised pattern recognition in chemometrics was proposed in this tutorial to introduce partial least squares discriminant analysis (PLS-DA). A new approach of the experiment published in the first tutorial of this series was revisited and employed to the classification of edible vegetable oils. The spectra of olive, canola, soybean and corn oils were obtained using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometer in the range of 600 to 4000 cm-1. The combination of ATR-FTIR and PLS-DA classification method was able to correctly classify 100% of the validation samples. The Matlab commands, routines and functions were presented, and a didactic explanation of the concepts and interpretation of the... |
Tipo: Artigo de periódico |
Palavras-chave: Experimento didático; Espectroscopia; Quimiometria; Raio Infravermelho; Infrared spectroscopy. |
Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123410 |
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CRUSIOL, L. G. T.; SUN, L.; SIBALDELLI, R. N. R.; FELIPE JUNIOR, V.; FURLANETI, W. X.; CHEN, R.; SUN, Z.; WUYUN, D.; CHEN, Z.; NANNI, M. R.; FURLANETTO, R. H.; CEZAR, E.; NEPOMUCENO, A. L.; FARIAS, J. R. B.. |
Tipo: Artigo de periódico |
Palavras-chave: Soja; Maquina; Raio Infravermelho; Soybeans; Yield mapping; Yield monitoring; Multispectral imagery; Spectral analysis. |
Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151089 |
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