Functional regression analysis of fluorescence curves

Fluorescence curves are useful for monitoring changes in photosynthesis activity. Various summary measures have been used to quantify differences among fluorescence curves corresponding to different treatments, but these approaches may forfeit valuable information. As each individual fluorescence curve is a functional observation, it is natural to consider a functional regression model. The proposed model consists of a nonparametric component capturing the general form of the curves and a semiparametric component describing the differences among treatments and allowing comparisons of treatments. Several graphical model-checking approaches are introduced. Both approximate, asymptotic confidence intervals as well as simulation-based confidence intervals are available. Analysis of data from a crop experiment using the proposed model shows that the salient features in the fluorescence curves are captured adequately. The proposed functional regression model is useful for analysis of high throughput fluorescence curve data from regular monitoring or screening of plant growth.