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Hyperspectral image analysis. A tutorial.

José Manuel Amigo1, Hamid Babamoradi1, Saioa Elcoroaristizabal2

  • 1Spectroscopy and Chemometrics Group, Department of Food Sciences, Faculty of Science, University of Copenhagen, Rolighedsvej 30, Frederiksberg C DK-1958, Denmark.

Analytica Chimica Acta
|October 21, 2015
PubMed
Summary
This summary is machine-generated.

This tutorial provides practical tools for hyperspectral image analysis, focusing on classifying plastics using Near Infrared hyperspectral imaging and Partial Least Squares-Discriminant Analysis. It guides users through developing a step-wise methodology adaptable to various applications.

Keywords:
ChemometricsClassificationFlame retardantHyperspectralNear infraredPlastic sorting

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Area of Science:

  • Multivariate data analysis
  • Spectroscopy
  • Image processing

Background:

  • Hyperspectral imaging offers rich spectral information for material analysis.
  • Developing robust classification methodologies is crucial for industrial applications.
  • Existing methods require clear, step-wise guidance for practical implementation.

Purpose of the Study:

  • To provide guidelines and practical tools for hyperspectral image analysis.
  • To detail a step-wise methodology for classifying plastics using Near Infrared hyperspectral imaging.
  • To adapt strategies for diverse user applications.

Main Methods:

  • Hyperspectral image acquisition and pre-processing.
  • Multivariate exploratory analysis.
  • Classification using Near Infrared hyperspectral imaging and Partial Least Squares-Discriminant Analysis (PLS-DA).

Main Results:

  • A comprehensive tutorial on hyperspectral image analysis is presented.
  • A specific methodology for plastic classification using NIR hyperspectral imaging and PLS-DA is demonstrated.
  • The approach is designed to be adaptable to various industrial chemical frameworks.

Conclusions:

  • The tutorial offers a practical framework for hyperspectral image analysis.
  • The presented methodology enables effective plastic classification.
  • Users are empowered to adapt these strategies for their specific needs.