ML-TN-004 — Machine Learning, spectroscopy, and materials classification

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NeuralNetwork.png Applies to Machine Learning


History[edit | edit source]

Version Date Notes
1.0.0 October 2021 First public release

Introduction[edit | edit source]

Classification of materials is another interesting use case where Machine Learning (ML for short) opens the doors for promising developments related to smart edge devices. In this Technical Note (TN), different implementations of ML-based material classification applications are compared in terms of performance and development workflow. Tests were run on embedded platforms such devices may be built upon.


References[edit | edit source]

  • Hadi Parastara, Geert van Kollenburgb, Yannick Weesepoelc, André van den Doelb, Lutgarde Buydensb, Jeroen Jansen, Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity, 2020
  • N. Salamati, C. Fredembach, S. Susstrunk, Material Classification Using Color and NIR Images, 2009
  • Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, 2019