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

Abstract

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 (VCAE-E) better resembling the standard µCT VC (VCCT), both in time and in absolute number of embolized vessels. Interestingly, the unfiltered acoustic vulnerability curve (VCAE) also closely resembled VCCT, 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

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