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.

中文翻译：

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通过高光谱数据评估田间硬质小麦的耳朵和叶片代谢组。

目前，高光谱技术用于检索有关植物生物物理性状的信息，主要针对色素，水和氮蛋白含量，结构元素和叶面积指数。即便如此，可以通过推进高通量田间表型分析来更广泛地利用高光谱数据来克服全球气候变化所面临的育种挑战。在这项研究中，我们探索了现场光谱法预测硬粒小麦旗叶和耳ear片中代谢物谱的潜力。旗叶，穗和冠层的全范围反射光谱（可见光（VIS）-近红外（NIR）-短波红外（SWIR））被记录在一组在不同水分状况下于四种环境中生长的对比基因型中。在旗叶中分析了GC-MS代谢物谱，耳片，颖片和引理。对于每个植物器官中至少15种代谢物，回归模型的结果超过了所解释变异的50％（验证集中的adj-R2），而其误差却非常低。叶片中的苹果酸（82％），甘油酸和丝氨酸（63％）获得了最好的回归。外中的肌醇（81％）; 胶状乙醇酸（80％）；叶片和颖花中的蔗糖（68％）；叶片和颖片中的γ-氨基丁酸（GABA）（分别为61％和71％）；脯氨酸和葡萄糖在外mas（分别为74％和71％）和颖间（分别为72％和69％）中。讨论了模型中波段的选择以及基于冠层和VIS器官光谱以及产量预测的模型性能。我们认为，由于该技术在生态生理学研究中具有广泛的适用性，因此可能会引起人们的兴趣，