JIP-test荧光数据及其它生理生态数据主成分综合分析...-3
通过PCA技术分析发现，所选的JIP-test参数形成了三个很好分离的簇。其中两个位于PC1(Cluster 1&2)上，一个(Cluster 3)位于PC2上。每一组参数描述了不同的生理过程：光能捕获和光化学阶段(Cluster 1)、耗散(Cluster 2)和热阶段(Cluster 3)。基于PCA分析，该研究认为样品的整体光合性能可以用PITOT来表示，或者用Fv/Fm和ΔVIP共同来表示。
图3. 所选JIP-test参数的主成分分析
在大多数情况下，植物的光合性能可用Fv/Fm和ΔVIP来描述。经过验证，Fv/Fm和ΔVIP能够有效地代表各种调查(野外和实验室)、气候和时间跨度、植物物种和功能群(针叶树和阔叶树物种、草本植物)中样本的变异性。因此，可用于探索性调查，以筛选大样本植物的光合性能，以及它们对不同生态条件的适应性。
在林业或生态学调查方面，以JIP-test为代表的植被叶绿素荧光特性的大规模野外调查对验证无人机或卫星遥感观测结果具有重要意义，遥感观测数据和野外实地调查数据之间的衔接将是今后生态学研究的一个重要领域(Bussotti F, 2020)！
总述
以上实例说明，PCA分析与JIP-test结合应用越来越广泛，大大提高了数据分析效率，能够快速判断实验处理后的主要变化，并分析主要影响因素，从而对实验材料进行预判。近年来，PCA在植物科学研究中的应用呈上升趋势，相信科研工作者们会开发出更多更好的应用方向。
如何实现对叶绿素a荧光数据(JIP-test参数)、其它生理参数和基因、蛋白等分子数据组成的大数据库进行PCA分析？
通常我们可以使用学术界常用的商用数据分析软件进行PCA分析，如SPSS Statistics(IBM Corp)、Statistica(StatSoft Inc. 2011)和SAS(SAS Enterprise Miner; SAS Institute, Cary, NC)等。
在全球互联网化的大趋势下，也涌现出一批使用体验更佳、分析更加智能化的在线数据分析工具，如SPSSAU(QingSi Technology Ltd 2016-2020)、ClustVis(Metsalu, Tauno et al. 2015)等。
此外以R语言和Python为代表的计算机程序设计语言可以实现对大数据的快速智能处理、计算和制图，使用R语言和Python对JIP-test荧光数据进行PCA数据分析也已有非常成熟的语言包进行应用。
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