Spectral unmixing of fluorescence fingerprint imagery for visualization of constituents in pie pastry

In this work, we present a new method that combines fluorescence fingerprint (FF) imaging and spectral unmixing to visualize microstructures in food. The method is applied to visualization of three constituents, gluten, starch, and butter, in two types of pie pastry. It is challenging to discriminate between starch and butter because both of them can be represented by similar FFs of low intensities. Two optimization approaches of FF unmixing that consider qualitative knowledge are presented and validated by comparison to the conventional staining method. Although starch and butter were represented by very similar FFs, a constrained-least-squares method with abundance quantization successfully visualized the distributions of constituents in pie pastry.