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

PLoS One. 2021 Mar 4;16(3):e0236853. doi: 10.1371/journal.pone.0236853. eCollection 2021.


The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Dietary Fiber / metabolism
  • Genomics / methods*
  • Genomics / standards
  • Plant Breeding / methods*
  • Quantitative Trait, Heritable*
  • Saccharum / genetics*
  • Saccharum / metabolism
  • Sensitivity and Specificity
  • Spectroscopy, Near-Infrared / methods*
  • Spectroscopy, Near-Infrared / standards
  • Sugars / metabolism


  • Dietary Fiber
  • Sugars

Associated data

  • figshare/10.6084/m9.figshare.12635717

Grant support

MTVG received a masters degree scholarship (154611/2017-4) from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Grant Number 310503/2015-9 to LAP. We are also thankful for the Inter-University Network for the Development of Sugarcane Industry (RIDESA) for all the field experiment support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.