区分分析作为一种工具,用于根据近红外光谱对草原进行分类
Silvia Parrini1, Maria Chiara Fabbri1, Giovanni Argenti1
1Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy.
Animals : an open access journal from MDPI
|September 28, 2024
概括
对近红外光谱的差别分析有效地分类了植物群落,达到77%的准确性. 最近的草作物和永久草显示出高度的分类成功,与草蔬菜混合物不同.
科学领域:
- 农业科学 农业科学
- 植物生态学植物生态学
- 频谱学是一种光谱学.
背景情况:
- 准确的植物群落分类对于草原管理和生态研究至关重要.
- 近红外光谱学 (FT-NIRS) 提供了一种快速,非破坏性的方法来分析植物材料.
- 多变量统计方法可以从光谱数据中提取有意义的模式.
研究的目的:
- 使用FT-NIRS数据对四个不同的草原植物群落进行分类.
- 评估基于主要组件 (DAPC) 进行基于光谱的分类的差异分析的有效性.
- 为了比较不同草原类型和验证方法之间的分类成功率.
主要方法:
- 从四种草地类型收集了156个新鲜草本样本:最近的草作物 (CAA),最近的草蔬菜混合物 (GLM),老草的永久草地 (PMA) 和老混合物的永久草地 (PLM).
- 所有样品的近红外谱 (FT-NIRS) 获取.
- 基于主要组件 (DAPC) 的应用差别分析,用于多变量光谱分析.
- 利用交叉验证和半外部验证技术来评估模型性能.
主要成果:
- 前两个主要组成部分解释了98%的光谱变异性.
- DAPC模型实现了77%的总体分配成功率.
- 观察到高成功率 (>80%) 来分类最近的植物作物 (CAA) 和老植物 (PMA) 的永久草.
- 草混合物 (GLM) 和老混合物的永久草 (PLM) 的成功率较低 (~52%).
- 外部验证表明PLM和GLM组之间存在很高的相似性.
结论:
- 与DAPC相结合的FT-NIRS是一个有前途的工具,用于对某些草原植物群落进行分类.
- 植物和化学差异有助于成功分类不同的社区类型,如CAA和PMA.
- 进一步增强数据可能会提高对更相似的社区类型 (如GLM和PLM) 的差异化准确性.
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