化学成分,营养质量和产量在Lactuca aff. indica种类使用多变量分析技术
Ramon Ivo Soares Avelar1, Marcelo Henrique Avelar Mendes1, Betsy Carolina Muñoz de Páez2
1Departament of Agriculture, Federal University of Lavras, Lavras, Minas Gerais CEP 37203-202, Brazil.
ACS omega
|September 15, 2025
概括
多变量分析有效地根据产量和质量区分了树品种. 科霍宁 (Kohonen) 是一个古老的城市.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
背景情况:
- 像树 (Lactuca aff indica) 这样的非传统食物植物 (PANC) 提供潜在的营养益处.
- 根据产量,叶子质量和营养价值区分种类对于优化种植和利用至关重要.
- 多变量分析技术越来越多地应用于复杂的生物数据集,用于模式识别和分类.
研究的目的:
- 为了比较主要成分分析 (PCA),Kohonen的自我组织地图 (SOM) 和多因素分析在区分三种树品种的有效性.
- 评估树品种的物理化学,感觉,抗氧化,营养和矿物质特征.
- 确定与潜在农业和食品应用的产量和质量相关的种类特征.
主要方法:
- 物理化学分析:可定位的酸度,可溶性固体,pH值.
- 抗氧化性质评估:维生素C,总,自由基清除能力.
- 营养和矿物成分:百分位组成,宏观和微观元素.
- 多变量统计分析:PCA,SOM和多因素分析用于数据差异化.
主要成果:
- 叶子产量在所有品种的第一个切割中是最高的.
- 营养质量,包括Ca,Mg,S,Fe和Mn,在第三次切割时显著增加.
- 这种SG品种表现出优越的抗氧化特性,具有更高的 Askorbic 酸和总,以及更高的自由基清除活性.
- 与PCA相比,SOM提供了更有效的样本分组可视化,识别了七个不同的集群.
结论:
- 多变量方法,特别是SOM,是根据一套全面的质量参数来区分树品种的有效工具.
- 培养SG显示出作为抗氧化剂来源的巨大潜力.
- 切片中的营养含量逐渐增加,凸显了收获时间对于优化树质量的重要性.
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