Literature DB >> 30954799

Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques.

Nathália de A Porto1, Jussara V Roque1, Cleiton A Wartha2, Wilson Cardoso1, Luiz A Peternelli3, Márcio H P Barbosa2, Reinaldo F Teófilo4.   

Abstract

The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Genetic breeding; PLS-DA; Spectroscopies; Sugarcane borer

Mesh:

Year:  2019        PMID: 30954799     DOI: 10.1016/j.saa.2019.03.114

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  1 in total

1.  Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits.

Authors:  Mateus Teles Vital Gonçalves; Gota Morota; Paulo Mafra de Almeida Costa; Pedro Marcus Pereira Vidigal; Marcio Henrique Pereira Barbosa; Luiz Alexandre Peternelli
Journal:  PLoS One       Date:  2021-03-04       Impact factor: 3.240

  1 in total

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