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Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Simon Crase1,2, Benjamin Hall2, Suresh N. Thennadil3,*

1 College of Engineering, IT & Environment, Charles Darwin University, Casuarina, NT 0810, Australia
2 Defence Science and Technology Group, Edinburgh, 5111, Australia
3 Energy and Resources Institute, Charles Darwin University, Casuarina, NT 0810, Australia

* Corresponding Author: Suresh N. Thennadil. Email: email

Computers, Materials & Continua 2021, 69(2), 1945-1965.


Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size. The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data. With this in mind, we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.


Cite This Article

S. Crase, B. Hall and S. N. Thennadil, "Cluster analysis for ir and nir spectroscopy: current practices to future perspectives," Computers, Materials & Continua, vol. 69, no.2, pp. 1945–1965, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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