ArticlePloS one2021
Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Review
- High-throughput phenotyping of sweetness and sourness components in tomato fruits by near-infrared spectroscopy and chemometrics methods.Current research in food science · 2025Article
- High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods.Food chemistry: X · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Insights and protocols for discrimination of sugarcane clones by dissimilarity measures on RGB and NIR data.PloS one · 2023Article
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
- The performance of phenomic selection depends on the genetic architecture of the target trait.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population ofFrontiers in plant science · 2022Article
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Authors and funding
6 authors.
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Abstract
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.
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