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.

中文翻译：

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X 射线显微断层扫描和线性判别分析能够检测与栓塞相关的声发射。

背景 自 1960 年代后期以来，声发射 (AE) 传感作为一种非侵入性的连续方法一直用于干旱引起的栓塞研究。它非常适合评估植物对脱水的脆弱性。在过去的几年里，由于声发射传感器、数据采集方法和分析系统的进步，声发射传感得到了进一步的改进。尽管最近取得了这些进展，但在脱水期间传感器记录的 AE 源中检测干旱引起的栓塞事件仍然具有挑战性，这有时会质疑 AE 传感的定量潜力。结果为了寻求一种将栓塞相关AE信号与其他脱水相关信号分离的方法，对一棵2岁的盆栽白蜡树进行了干旱实验。栓塞形成通过两个宽带点接触 AE 传感器进行声学测量，同时通过 X 射线计算机显微断层扫描 (µCT) 进行可视化。使用机器学习方法将 µCT 视觉检测到的栓塞形成与相应的 AE 信号联系起来。具体而言，对 100-200 kHz 范围内的六个 AE 波形参数幅度、计数、持续时间、信号强度、绝对能量和部分功率应用线性判别分析 (LDA)，得到与栓塞相关的声波易损性曲线 (VCAE-E)在时间和栓塞血管的绝对数量上更类似于标准 µCT VC (VCCT)。有趣的是，未经过滤的声波脆弱性曲线 (VCAE) 也非常类似于 VCCT，表明从所有已注册的 AE 信号构建的 VC 并未影响对该物种对干旱诱导的栓塞形成的脆弱性的定量解释。结论 尽管机器学习可以检测到与 µCT 相似数量的栓塞相关 AE，但仍然没有足够的基于模型的证据来最终将这些信号归因于栓塞事件。因此，未来的研究应侧重于对声波波形进行更深入分析的类似实验，并探索快速傅里叶变换 (FFT) 去除非栓塞相关 AE 信号的可能性。仍然没有足够的基于模型的证据来最终将这些信号归因于栓塞事件。因此，未来的研究应侧重于对声波波形进行更深入分析的类似实验，并探索快速傅里叶变换 (FFT) 去除非栓塞相关 AE 信号的可能性。仍然没有足够的基于模型的证据来最终将这些信号归因于栓塞事件。因此，未来的研究应侧重于对声波波形进行更深入分析的类似实验，并探索快速傅里叶变换 (FFT) 去除非栓塞相关 AE 信号的可能性。