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Unlocking almond breeding for nutritional composition with hyperspectral imaging.

Jorge Mas-Gómez1, Manuel Rubio1, Federico Dicenta1

  • 1Fruit Breeding Group, Department of Plant Breeding, Centro de Edafología y Biología Aplicada del Segura- Spanish National Research Council (CEBAS-CSIC)., Campus Universitario Espinardo, Murcia, E-30100, Spain.

Plant Phenomics (Washington, D.C.)
|July 2, 2026
PubMed
Summary

A new high-throughput phenotyping platform uses hyperspectral imaging to rapidly assess nutritional components in almond breeding populations. This advanced method enables faster breeding and generates extensive data for improved almond varieties.

Keywords:
AlmondBreedingGenomicsHyperspectral imagingNutritionalPhenomics

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Area of Science:

  • Plant Breeding
  • Agricultural Science
  • Nutritional Genomics

Background:

  • Traditional phenotyping methods for plant nutritional components are slow and costly.
  • High-throughput phenotyping (HTP) offers a solution to accelerate plant breeding.
  • Developing efficient HTP platforms is crucial for modern crop improvement.

Purpose of the Study:

  • To develop and validate an HTP platform for phenotyping nutritional components in almond ( *Prunus dulcis* ) breeding populations.
  • To address the bottleneck in conventional phenotyping for traits like fats, protein, and fatty acids.
  • To generate a large-scale dataset for nutritional traits in almonds.

Main Methods:

  • Integration of hyperspectral imaging (HSI) with a Python workflow for HTP.
  • Analysis of kernel and powder samples from 112 almond genotypes using SWIR range HSI.
  • Development of Partial Least Squares (PLS) models to predict nutritional components (fats, protein, fiber, sucrose, fatty acids, phytosterols).
  • Estimation of narrow-sense heritability using linear mixed models with pedigree and genomic data (60K Almond SNP array).

Main Results:

  • PLS models achieved good predictive accuracy for protein (R2CV=0.82), fats (R2CV=0.86), β-sitosterol (R2CV=0.66), and oleic acid (R2CV=0.57).
  • The platform successfully predicted nutritional components in 528 genotypes and six F1 populations.
  • High narrow-sense heritability (ℎ 2 >0.5) was observed for predicted nutritional traits, indicating significant additive genetic effects.
  • Generated the largest phenotypic dataset for nutritional components in almonds to date.

Conclusions:

  • The developed HTP platform significantly advances nutritional almond breeding by enabling rapid and large-scale phenotyping.
  • This approach allows for the assessment of six times more individuals compared to previous studies.
  • The findings provide a foundation for selecting and breeding almonds with enhanced nutritional profiles.