The conservation and efficient use of natural and especially strategic resources like oil and water have become global
issues, which increasingly initiate environmental and political activities for comprehensive recycling programs. To
effectively reutilize oil-based materials necessary in many industrial fields (e.g. chemical and pharmaceutical industry,
automotive, packaging), appropriate methods for a fast and highly reliable automated material identification are required.
One non-contacting, color- and shape-independent new technique that eliminates the shortcomings of existing methods is
to label materials like plastics with certain combinations of fluorescent markers ("optical codes", "optical fingerprints")
incorporated during manufacture. Since time-resolved measurements are complex (and expensive), fluorescent markers
must be designed that possess unique spectral signatures. The number of identifiable materials increases with the number
of fluorescent markers that can be reliably distinguished within the limited wavelength band available.
In this article we shall investigate the reliable detection and classification of fluorescent markers with specific
fluorescence emission spectra. These simulated spectra are modeled based on realistic fluorescence spectra acquired
from material samples using a modern VNIR spectral imaging system. In order to maximize the number of materials that
can be reliably identified, we evaluate the performance of 8 classification algorithms based on different spectral
similarity measures. The results help guide the design of appropriate fluorescent markers, optical sensors and the overall