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| Home > Publications database > Establishing Ground Rules for Sensor-like Application of NMR Relaxometry for theStudy of Water and Dry Matter Dynamics in Living Plants |
| Conference Presentation (After Call) | FZJ-2026-01204 |
;
2025
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Please use a persistent id in citations: doi:10.34734/FZJ-2026-01204
Abstract: Establishing Ground Rules for Sensor-like Application of NMR Relaxometry for theStudy of Water and Dry Matter Dynamics in Living PlantsThe largest challenge in measuring living plant organs by means of time domain NMR (TD-NMR) isthat they constitute complex systems characterized by strong natural variation and spatialheterogeneity in their physicochemical properties. In NMR relaxometry this is reflected by a speciesand organ specific continuous distribution of T1- and T2- relaxation constants. Advancedmultidimensional approaches can deal with this complexity and take advantage of the informationthat is available in the NMR data, but are time-consuming and difficult to execute and interpret.Routine characterization of intact plant organs requires dealing with this complexity in a way that isfast and suitable for non-expert operators. This might be achieved by a more sensor-like approach.A straight-forward way to quantify some of the most elementary parameters to evaluate plantgrowth and yield; fresh weight (FW), water weight (WW) and dry weight (DW) is to distinguishbetween proton pools of different mobilities based on T2 contrast. This can be done by comparinga measure of the total signal intensity representing both the liquid (PDliq) and solid (PDsol) protonfractions (=PDtot), to a T2-weighted estimate that correlates with more mobile liquid proton fractionsin the sample. In this study, we explore the basic ground rules for such a T2-weighted approach forthe study of water and dry matter dynamics in living plants.As a starting point of our analysis, we employ the previously defined Solid Liquid Content (SLC)method1 which is based on this general idea. To test its applicability, we analyzed the relaxometricdata of samples of organs of various species with widely varying water and dry matter contents anddegrees of lignification (intact leaves, wheat kernels and bean pods). Despite the simplicity of theapproach and the extreme heterogeneity of the physicochemical properties of plant tissue, the SLCmethod yields surprisingly good and linear correlations between PDtot and FW, and PDliq and WW,respectively. Yet, our results indicate that the SLC parameters are (slightly) susceptible to the samplespecific structural and/or physicochemical properties. This has two consequences. First, the methodis not generic for all species and samples, and thus needs frequent recalibration. Second, due toinherent variation in the structural and physicochemical composition of plant organs of similarorigin, the accuracy of the SLC method is limited. Depending on the sample type and biologicalprocess to be studied, the accuracy may suffice to resolve the key dynamics of interest. Indeed, ourresults indicate that the method works well for tracking the dynamics of moisture and solid contentin seeds and bean pods that exhibit large developmental changes. Contrary, the dynamics of watercontent in leaves of similar origin are in general small compared to the spread in the SLC results.To ensure proper application of the method, further insight into the physicochemical and microandmacro structural features that define the SLC parameters is required. The latter is especiallyrelevant in the context of long-term measurements during which plant organs are expected tochange these features due to e.g. senescence or as a response to environmental (stress) factors.Here, we present a systematic assessment of the relaxometric sample features probed by the SLCmethod in dependence of the choice of T2-weighting and explore if finetuning of the weighting canreduce deviations in the SLC parameters and make the method more generic.Reference: [1] C.W. Windt, M. Nabel, J. Kochs, S. Jahnke, U. Schurr, A mobile NMR sensor and relaxometric method tonon-destructively monitor water and dry matter content in plants, Frontiers in Plant Science,12 (2021).
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