Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data

Summary Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen‐protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high‐throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full‐range reflectance spectra (visible (VIS)‐near‐infrared (NIR)‐short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC‐MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj‐R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo‐inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ‐aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri‐food industry. Significance Statement The study of the light reflected from plant surfaces can inform us about compositional traits. The development of this technique is critical for advances in field phenotyping and thereby plant breeding. This study aims to predict the metabolite profiles of wheat leaves and ear bracts from hyperspectral data recorded in the field. Key sugars, organic acids and amino acids in central metabolism were satisfactorily predicted, illustrating the great potential of this technique.