X-ray microtomography and linear discriminant analysis enable detection of embolism-related acoustic emissions

Niels J F De Baerdemaeker; Michiel Stock; Jan Van den Bulcke; Bernard De Baets; Luc Van Hoorebeke; Kathy Steppe

doi:10.1186/s13007-019-0543-4

X-ray microtomography and linear discriminant analysis enable detection of embolism-related acoustic emissions

Plant Methods. 2019 Dec 17:15:153. doi: 10.1186/s13007-019-0543-4. eCollection 2019.

Authors

Niels J F De Baerdemaeker¹, Michiel Stock², Jan Van den Bulcke^{3

4}, Bernard De Baets², Luc Van Hoorebeke^{4

5}, Kathy Steppe¹

Affiliations

¹ 1Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
² 2KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
³ 3UGent-Woodlab-Laboratory of Wood Technology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
⁴ 4Ghent University Centre for X-Ray Tomography (UGCT), Proeftuinstraat 86, 9000 Ghent, Belgium.
⁵ 5Radiation Physics Group, Department of Physics and Astronomy, Faculty of Sciences, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium.

Abstract

Background: Acoustic emission (AE) sensing is in use since the late 1960s in drought-induced embolism research as a non-invasive and continuous method. It is very well suited to assess a plant's vulnerability to dehydration. Over the last couple of years, AE sensing has further improved due to progress in AE sensors, data acquisition methods and analysis systems. Despite these recent advances, it is still challenging to detect drought-induced embolism events in the AE sources registered by the sensors during dehydration, which sometimes questions the quantitative potential of AE sensing.

Results: In quest of a method to separate embolism-related AE signals from other dehydration-related signals, a 2-year-old potted Fraxinus excelsior L. tree was subjected to a drought experiment. Embolism formation was acoustically measured with two broadband point-contact AE sensors while simultaneously being visualized by X-ray computed microtomography (µCT). A machine learning method was used to link visually detected embolism formation by µCT with corresponding AE signals. Specifically, applying linear discriminant analysis (LDA) on the six AE waveform parameters amplitude, counts, duration, signal strength, absolute energy and partial power in the range 100-200 kHz resulted in an embolism-related acoustic vulnerability curve (VC_AE-E) better resembling the standard µCT VC (VC_CT), both in time and in absolute number of embolized vessels. Interestingly, the unfiltered acoustic vulnerability curve (VC_AE) also closely resembled VC_CT, indicating that VCs constructed from all registered AE signals did not compromise the quantitative interpretation of the species' vulnerability to drought-induced embolism formation.

Conclusion: Although machine learning could detect similar numbers of embolism-related AE as µCT, there still is insufficient model-based evidence to conclusively attribute these signals to embolism events. Future research should therefore focus on similar experiments with more in-depth analysis of acoustic waveforms, as well as explore the possibility of Fast Fourier transformation (FFT) to remove non-embolism-related AE signals.

Keywords: Acoustic emissions; Drought-induced embolism formation; Fraxinus excelsior L.; Linear discriminant analysis; Machine learning; X-ray computed microtomography.