总的来说通过PCA我们可以分类植物对各种环境因素的不同反应：
(i)找到特定处理下植物样品OJIP曲线发生的特异性变化
(ii)筛选出发生显著变化的JIP-test荧光参数及其变化特征，可更好对植物样品光合机构发生的变化（伤害）进行定位分析，如PSⅡ供体侧/受体测或PSⅡ活性中心等。
(iii)我们还可以将JIP-test荧光数据与其他环境数据或生理参数进行聚类结合(Goltsev et al. 2012)。
(iv)此外Tyystjärvi等人应用PCA等人工智能方法分析不同类型光照(低光强、饱和脉冲、远红色等)激发的JIP-test荧光数据，可识别植物物种(Tyystjärvi et al. 1999; Keränen et al. 2003; Codrea et al. 2003;Kirova et al. 2009)。
(v)Kalaji等人利用JIP-test、主成分分析(PCA)和一种新的机器学习方法建立了一种无创检测和监测大田条件下油菜籽微量和大量营养素缺乏的方法(Kalaji et al. 2017)。
鉴于篇幅限制，我们将在下期文章中筛选数篇应用PCA方法分析JIP-test荧光数据具有代表性的文章进行详细介绍，期待您的关注，谢谢！
4.引用文献
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本文内PCA介绍部分内容及图4a/b/c源自CSDN博主「qian99」原创文章
原文链接：https://blog.csdn.net/qian99/java/article/details/105180110
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