用古典和先进分析进行普通小麦 (Triticum aestivum L.) 谷物和面粉特征的数据集
Mélanie Munch1, Laura Rezette2,3, Patrice Buche4
1INRAE, UMR 1253 STLO, Institut Agro Rennes, 35042 Rennes, France.
Data in brief
|March 4, 2025
概括
这项研究提供了一个全面的小麦质量数据集,将谷物成分与面包性能联系起来. 它有助于优化小麦以满足各种农业和工业需求.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 数据科学数据科学数据科学
背景情况:
- 由于气候变化和市场需求,优化小麦质量至关重要.
- 小麦质量影响营养和最终产品性能,通常通过感官评估进行评估.
- 了解小麦成分与技术的关系可以提高农业食品价值.
研究的目的:
- 提供关于小麦质量的综合数据集,将成分与技术性能联系起来.
- 为农作物行业和数据科学家促进对小麦质量优化的研究.
- 支持开发创新的小麦质量评估方法.
主要方法:
- 从一个多样化的小麦样本组收集数据,这些样本是在法国不同的环境条件下种植的.
- 进行了35项技术测试,31项组成分析 (蛋白质,托桑,脂肪酸) 和37项感官评估.
- 包括来自Alveograph®和Farinograph®测试的原始数据进行高级分析.
主要成果:
- 该数据集提供了对谷物,面粉,面团和面包特征的详细测量.
- 它可以全面比较不同生产阶段的小麦质量.
- 来自关键的风湿学试验的原始数据可用于制定新的质量标准.
结论:
- 这一数据集对于优化农作物行业的小麦质量非常有价值.
- 它支持研究人员和数据科学家探索构成-处理-质量联系.
- 数据可通过法国研究数据库Gouv和PO2 Evagrain数据库访问,并提供查询工具.
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