High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods.
Food Chem X
; 23: 101583, 2024 Oct 30.
Article
in En
| MEDLINE
| ID: mdl-39071925
ABSTRACT
Near-infrared spectroscopy (NIRS) provides a high-throughput phenotyping technique to assist breeding for improved faba bean seed quality. We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models. Protein was the most reliably predicted trait (R2 = 0.96-0.98) across field trials, followed by oil (R2 = 0.82-0.86) and oleic acid (R2 = 0.31-0.68). Samples for training the models were selected using K-means clustering. The optimal statistical approach for prediction was compound-specific PLS for protein (Root Mean Squared Error - RMSE = 0.46), BB for oil (RMSE = 0.067), and EN for oleic acid content (RMSE = 2.83). Reduced training set simulations revealed different effects on prediction accuracy depending on the model and compound. Several NIR regions were pinpointed as highly informative for the compounds, using the shrinkage and variable selection capabilities of EN and BB.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Food Chem X
Year:
2024
Document type:
Article
Affiliation country:
Países Bajos
Country of publication:
Países Bajos