cc_by-ncNagel‐Held, JohannesEl Hassouni, KhaoulaLongin, FriedrichHitzmann, Bernd2024-08-192024-08-192023https://hohpublica.uni-hohenheim.de/handle/123456789/16086https://doi.org/10.1002/cche.10732Background and Objectives: Quality assessment of bread wheat is time-consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near-infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods. Findings: Successful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences. Conclusions: The proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations. Significance and Novelty With up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro-doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.engWheat quality assessmentNear-infrared spectroscopy (NIRS)Protein content predictionRaman and fluorescence spectroscopyLoaf volume predictionModel robustnessAgronomic traitsDough rheological parameters630Article