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

Niels J F De BaerdemaekerMichiel StockJan Van den BulckeBernard De BaetsLuc Van HoorebekeKathy Steppe
Published in: Plant methods (2019)
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.
Keyphrases
  • machine learning
  • computed tomography
  • high resolution
  • dual energy
  • mass spectrometry
  • current status
  • deep learning
  • image quality
  • heavy metals
  • loop mediated isothermal amplification
  • quantum dots