Universidade Federal de Santa Catarina, Florianopolis, Brazil.
Escola do Mar, Ciência e Tecnologia da Universidade do Vale do Itajaí, (UNIVALI), Itajaí, Brazil.
J Integr Bioinform. 2021 Jun 4;18(3):20190056. doi: 10.1515/jib-2019-0056.
Some species of cover crops produce phenolic compounds with allelopathic potential. The use of math, statistical and computational tools to analyze data obtained with spectrophotometry can assist in the chemical profile discrimination to choose which species and cultivation are the best for weed management purposes. The aim of this study was to perform exploratory and discriminant analysis using R package specmine on the phenolic profile of L., L. and L. shoots obtained by UV-vis scanning spectrophotometry. Plants were collected at 60, 80 and 100 days after sowing and at 15 and 30 days after rolling in experiment in Brazil. Exploratory and discriminant analysis, namely principal component analysis, hierarchical clustering analysis, -test, fold-change, analysis of variance and supervised machine learning analysis were performed. Results showed a stronger tendency to cluster phenolic profiles according to plant species rather than crop management system, period of sampling or plant phenologic stage. PCA analysis showed a strong distinction of L. and L. 30 days after rolling. Due to the fast analysis and friendly use, the R package specmine can be recommended as a supporting tool to exploratory and discriminatory analysis of multivariate data.
一些覆盖作物品种会产生具有化感潜力的酚类化合物。使用数学、统计和计算工具来分析分光光度法获得的数据，可以帮助进行化学特征分析，从而选择最适合杂草管理目的的物种和种植方式。本研究的目的是使用 R 包 specmine 对通过紫外可见扫描分光光度法获得的 L.、L. 和 L. 芽的酚类分布进行探索性和判别分析。在巴西的试验中，于播种后 60、80 和 100 天以及镇压后 15 和 30 天采集植物。进行了探索性和判别分析，即主成分分析、层次聚类分析、t 检验、倍数变化、方差分析和有监督的机器学习分析。结果表明，根据植物物种而不是作物管理系统、采样期或植物物候期聚类酚类分布的趋势更强。PCA 分析显示，镇压后 30 天 L. 和 L. 之间有很强的区别。由于分析速度快且易于使用，因此推荐 R 包 specmine 作为多元数据分析的探索性和判别性分析的支持工具。
