Abstract
ABSTRACT In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits.
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Claims 6
Claim · model-written summary
Suitable preprocessing settings improved phenomic predictive ability.
- Outcome
- predictive abilitye0
Not reportedComparatorValue
Verbatim
e0“Our results showed that the predictive ability can be improved with the right settings.”
Claim · model-written summary
No preprocessing setting was optimal across all data sets.
Verbatim
e0“there was no global optimum that gave the best results for all data sets.”
Claim · model-written summary
Different traits within a data set required different settings for highest predictive ability.
Verbatim
e0“Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability.”
Claim · model-written summary
Some preprocessing combinations generally yielded very low predictive ability.
Not reportedComparatorValue
Verbatim
e0“some combinations generally result in a very low predictive ability”
Claim · model-written summary
Preprocessing produced no major change in the top-ranked 1%, 10%, or 20% of predicted individuals.
Not reportedComparatorValue
Verbatim
e0“preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals.”
Claim · model-written summary
The authors advise against preprocessing combinations that generally yield very low predictive ability.
- Taxon
- soybean
Glycine maxe1 - Outcome
- should not be used for preprocessinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“some combinations generally result in a very low predictive ability and should not be used for preprocessing.”
e1“three different data sets of soybean, triticale and maize”