Dataset for common wheat (Triticum aestivum L.) grain and flour characterization using classical and advanced analyses

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2025-04-01 Epub Date: 2025-02-07 DOI:10.1016/j.dib.2025.111375
Mélanie Munch , Laura Rezette , Patrice Buche , Baptiste Chambrey , Catherine Deborde , Stéphane Dervaux , Sonia Geoffroy , Kamal Kansou , Sophie Le Gall , Laurent Linossier , Benoit Meleard , Luc Menut , Marie-Hélène Morel , Magalie Weber , Luc Saulnier
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Abstract

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.
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使用经典和高级分析对普通小麦(Triticum aestivum L.)谷物和面粉特征进行数据集
随着全球变暖和不断变化的市场需求重塑农业实践,优化作物产品(尤其是小麦)的质量和效用正变得越来越复杂和关键。小麦在人类和动物营养中发挥着核心作用,其品质受到多种因素在不同尺度上的影响,从颗粒组成到最终产品性能,通常通过感官评价来评价。了解小麦成分与技术质量之间的关系对于提高农业食品系统中的产品价值至关重要。该数据集代表了法国在不同环境条件下生长的多种遗传背景的小麦样本的广泛面板。它收集谷物、面粉、面团和面包特性的测量数据,便于对不同生产阶段的小麦质量进行全面比较。该数据集包括35个经典的技术测试,31个详细的成分分析-包括深入表征的蛋白质组成(谷蛋白和麦胶蛋白),戊聚糖含量测量和脂肪酸谱分析-和37感官评估从法国面包烘焙测试提供面粉质量和面团行为的详细评估在关键的面包制作阶段。此外,还包括Alveograph®和Farinograph®测试的原始数据集,以支持创新质量评估标准的开发。该数据集不仅对作物行业优化小麦质量的努力有价值,而且对研究人员和数据科学家探索成分、加工和最终面包质量之间的复杂关系也有价值。数据在French Research data Gouv公共存储库中注册,也使用PO2/TransformON本体存储在PO2 Evagrain数据库中。SPO2Q web工具允许在线数据库咨询,并可通过PO2 Manager桌面应用程序进一步访问。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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