Abstract

Imaging is a widely used technique in machine vision and has a long history in quality analysis and control. Classification of samples by visually identifying based on color, shape, size, and texture is the most common and simplest method used in the industry. The term hyperspectral in this context is used to document that the image obtained should visualize the distribution of different chemical components within a sample based on their characteristic spectral features. The reflected or transmitted light at multiple wavelengths is recorded by a spectrograph. Qualitative and quantitative information is extracted from the spectra by chemometric methods. In this contribution we will show two applications in which hyperspectral imaging gives an additional added value in comparison to classical imaging or spectroscopic measurements.

© 2016 Optical Society of America