Abstract
The basic principles of multivariate image analysis were presented in (1), together with a simple example that explained the concepts of principal component analysis and an introduction to regression on multivariate images. Figure 1 gives an overview of the methods of multivariate image analysis. This chapter contains two more advanced examples, that are closer to chemical practice. One example is from a visual and near-infrared camera digitization of powder mixtures in 13 wavelengths. It serves as an illustration of a chemical classification using spectroscopic information and feedback to the imaging process. It is shown that mixtures of different chemical composition and physical status can be classified using the spectral information in the multivariate image. The second example is from Magnetic Resonance Imaging (MRI) where different experimental pulse parameter combinations were used to create 12 differently weighted images for the same slice of an egg. Principal Component Analysis and Partial Least Squares (PLS) regression were applied to this multivariate image. The technique of MRI is very useful in medical applications, but industrial applications in product quality control can also be envisioned with increasing instrument capabilities and decreasing price/performance ratios.
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Geladi, P., Grahn, H., Lindgren, F. (1991). Chemical Multivariate Image Analysis: Some Case Studies. In: Devillers, J., Karcher, W. (eds) Applied Multivariate Analysis in SAR and Environmental Studies. Eurocourses: Chemical and Environmental Science, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3198-8_13
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DOI: https://doi.org/10.1007/978-94-011-3198-8_13
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