Artificial neural network systems were built for detecting amino acids, sugars, and other solid organic matter by pattern recognition of their polarized light scattering signatures in the form of a Mueller matrix. Backward-error propagation and adaptive gradient descent methods perform network training. The product of the training is a weight matrix that, when applied as a filter, discerns the presence of the analytes on the basis of their cued susceptive Mueller matrix difference elements. This filter function can be implemented as a software or a hardware module to a future differential absorption Mueller matrix spectrometer.
© 1999 Optical Society of America
Original Manuscript: August 23, 1998
Revised Manuscript: February 12, 1999
Published: June 10, 1999
Arthur H. Carrieri, "Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy," Appl. Opt. 38, 3759-3766 (1999)