Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses
Résumé
As global warming and changing market demand reshape agricultural practices, optimising the quality and utility of crop products, particularly wheat, is becoming increasingly complex and critical. Wheat plays a central role in human and animal nutrition, with its quality influenced by multiple factors at different scales, from grain composition to end-product performance, usually evaluated through sensory evaluation. Understanding the relationship between wheat composition and technological quality is essential for improving product value in agri-food systems. This dataset represents a broad panel of wheat samples encompassing diverse genetic backgrounds grown under varying environmental conditions in France. It collects measurements of grain, flour, dough and bread characteristics, facilitating a comprehensive comparison of wheat quality at different stages of production. The dataset encompasses 35 classical technological tests, 31 detailed compositional analyses—including in-depth characterization of protein composition (glutenin and gliadin), pentosan content measurement, and fatty acid profile analysis—and 37 sensory evaluations from the French Bread baking test providing detailed assessments of flour quality and dough behavior across key bread-making stages. In addition, raw data sets from Alveograph® and Farinograph® tests are included to support the development of innovative quality assessment criteria. This dataset will be valuable not only for the crop industry in its efforts to optimize wheat quality, but also for researchers and data scientists exploring the complex relationships between composition, processing and final bread quality. The data are registered in the French Research Data Gouv public repository and also stored in the PO2 Evagrain database using the PO2/TransformON ontology. The SPO2Q web tool allows for online database consultation, with further access available through the PO2 Manager desktop application.
Domaines
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Cite 10.57745/9T6Q56 Jeu de données MUNCH, Mélanie; REZETTE, Laura; BUCHE, Patrice; CHAMBREY, Baptiste; DEBORDE, Catherine; DERVAUX, Stéphane; GEOFFROY, Sonia; KANSOU, Kamal; LE GALL, Sophie; LINOSSIER, Laurent; MELEARD, Benoit; MENUT, Luc; MOREL, Marie-Hélène; WEBER, Magalie; SAULNIER, Luc, 2025, "Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses: materials & methods", https://doi.org/10.57745/9T6Q56, Recherche Data Gouv, V2, UNF:6:RC+Cv5pqvY6xBek6EJuZnQ== [fileUNF]
Cite 10.57745/EBBGE8 Logiciel Buche, Patrice, 2025, "Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses: raw and calculated analytical data SPARQL queries", https://doi.org/10.57745/EBBGE8, Recherche Data Gouv, V2
Cite 10.57745/GQC1L9 Jeu de données Buche, Patrice, 2025, "Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses: farinograph and alveograph curves", https://doi.org/10.57745/GQC1L9, Recherche Data Gouv, V1
Cite 10.57745/SUPRIB Jeu de données MUNCH, Mélanie; REZETTE, Laura; BUCHE, Patrice; CHAMBREY, Baptiste; DEBORDE, Catherine; DERVAUX, Stéphane; GEOFFROY, Sonia; KANSOU, Kamal; LE GALL, Sophie; LINOSSIER, Laurent; MELEARD, Benoit; MENUT, Luc; MOREL, Marie-Hélène; WEBER, Magalie; SAULNIER, Luc, 2025, "Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses: raw and calculated analytical data", https://doi.org/10.57745/SUPRIB, Recherche Data Gouv, V2, UNF:6:5FO4eBKsNaxMyd2OZVdScA== [fileUNF]