# Acoustic Emissions to Measure Drought-Induced Cavitation in Plants
## 3 Acoustic Emission (AE) Application to Measure Drought-Induced Cavitation: From Past to Present
### AE Feature Extraction  
A major challenge in using AEs as an indirect measure for cavitation is determination of the source of the detected signal [31]. The AE signal is shaped by the followed path from source to sensor, which makes signal interpretation not straightforward because of the wood's anisotropic characteristics [85]. These include differences in sound propagation in the three wood directions, wood density, xylem water content and wood anatomy [34]. Previous AE application in cavitation research mainly focused on the cumulative AE signal because of the assumption that the majority of the signals correspond to a loss in hydraulic conductivity [43; 46; 86; 87]. Given the many different AE sources during dehydration, this assumption is, however, not always valid [64; 87]. Extraction of the AE signals caused by cavitation is therefore necessary. A method that has already proven its success is extraction of the signals based on corresponding wave features. In industrial lumber drying, for example, the amplitude and energy of AE signals were used to pinpointing wood checking [88; 89; 90; 91; 92]. [PERSON] _et al._[66] measured AEs on juvenile and mature wood samples, taken from living _Picea abies_ trees, with a broadband sensor in the frequency range 100-1000 kHz. After extraction of the waveform features amplitude, rise time, rise angle and absolute energy (Figure 5), cumulative AE energy appeared to be a good measure for PLC and, thus, for VC development. This was confirmed in other studies on leaves of _P. ponderosa, P. nigra, C. chrysophyllula_ and _P. japonica_[93], and on _P. abies_ wood samples [94]. [PERSON] and [PERSON] [34] found a correlation between tracheid lumen area and mean AE energy in _P. abies_ wood samples using a 150 kHz resonant sensor, which responds most strongly to acoustic waves in the 50-200 kHz frequency range. A comparison between samples with different early-latewood distribution revealed that the mean energy of AEs during wood drying increased in earlywood but decreased in samples containing more latewood. This was attributed to the homogeneous structure of earlywood and its larger tracheid diameters, causing AE energy to increase with increasing xylem tension. More resistant latewood tracheids, which cavitate at a more negative xylem tension, emitted lower energy because of their smaller diameters and more heterogeneous structure [34; 64]. [PERSON] _et al._[23] found a similar relationship, confirming that AE energy is linked to mechanical elastic energy, which is released during cavitation in conifer tracheids. Whereas AE feature extraction to determine cavitation-related AE has been successfully applied in conifers, it remains a challenge for the more heterogeneous angiosperms [31; 34]. Figure 5: (**A**) Frequency features peak frequency (PF), weighted peak frequency (WPF), frequency centroid (FC) and four partial powers (PP) were used for the automated clustering algorithm; and (**B**) Waveform features rise time, amplitude and rise angle describe the shape of the AE signal [80]. [PERSON] _et al._[80] proposed clustering to determine cavitation-related AE in angiosperms. By using state-of-the-art techniques from material sciences, finite element modelling and an automated clustering algorithm [85; 95; 96], in combination with broadband point-contact sensors, they were able to extract the AE that originated from cavitation during dehydration of grapevine branches. The flat frequency response of these sensors in a wide frequency range (20-1000 kHz) allows differentiation between different AE sources. Instead of relating waveform features of AE signals with cavitation, [PERSON] _et al._[80] recommended to use the frequency features of AE signals. Following [PERSON] and [PERSON] [97], they extracted from the frequency spectrum, peak frequency (PF), frequency centroid (FC), weighted PF (WPF, geometric mean of PF and FC) and the partial powers of the following frequency ranges: 0-100 kHz, 100-200 kHz, 200-400 kHz and 400-800 kHz (Figure 5).