Abstract. The quick detection of specific substances in objects such as
produce items via non-destructive visual cues is vital to ensuring the
quality and safety of consumer products. At the same time, it is wellknown that the fluorescence excitation-emission characteristics of many
organic objects can serve as a kind of “fingerprint” for detecting the
presence of specific substances in classification tasks such as determining
if something is safe to consume. However, conventional capture of the
fluorescence excitation-emission matrix can take on the order of minutes
and can only be done for point measurements. In this paper, we propose a coded illumination approach whereby light spectra are learned
such that key visual fluorescent features can be easily seen for material
classification. We show that under a single coded illuminant, we can capture one RGB image and perform pixel-level classifications of materials
at high accuracy. This is demonstrated through effective classification of
different types of honey and alcohol using real images