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Latent Variable Regression for Laboratory Hyperspectral Images

In: Partial Least Squares Path Modeling

Author

Listed:
  • Paul Geladi

    (SBT, SLU)

  • Hans Grahn

    (Örebro University)

  • Kim H. Esbensen

    (KHE Consult)

Abstract

This chapter is about the application of latent variable-based regression methods on hyperspectral images. It is an applied chapter, and no new PLS algorithms are presented. The emphasis is on visual diagnostics and interpretation by showing how these work for the examples given. Section 16.1 of this chapter introduces the basic concepts of multivariate regression and of multivariate and hyperspectral images. In Sect. 16.2 the hyperspectral imaging technique used and the two examples (cheese and textile) are explained. Also some sampling issues are discussed here. Principal component analysis (PCA) is a powerful latent variable-based tool for cleaning images. Section 16.3 describes PLS quantitative model building and diagnostics, both numerical and visual for the cheese example, and finishes with PLSDA qualitative modeling for the textile example.

Suggested Citation

  • Paul Geladi & Hans Grahn & Kim H. Esbensen, 2017. "Latent Variable Regression for Laboratory Hyperspectral Images," Springer Books, in: Hengky Latan & Richard Noonan (ed.), Partial Least Squares Path Modeling, chapter 0, pages 339-365, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64069-3_16
    DOI: 10.1007/978-3-319-64069-3_16
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