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

Info Box Applies to Machine Learning Applies to Machine Learning Technical Notes

History

Version	Date	Notes
1.0.0	October 2021	First public release

Introduction

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. The classification process makes use of spectroscopy: the model consists of a neural network (NN) fed with measurement data produced by a spectrometer.

In the rest of the document, we use the expression baseline model to refer to the model retrieved from TBD and that was used as starting point.

Platform: Xilinx Zynq UltraScale+ MPSoC

Baseline model

The tests detailed in this section make use of the baseline model.

NN accelerator: DPU

This configuration

References

• [1] 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
• [2] N. Salamati, C. Fredembach, S. Susstrunk, Material Classification Using Color and NIR Images, 2009
• [3] Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, 2019