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Applied Optics

Applied Optics


  • Vol. 38, Iss. 17 — Jun. 10, 1999
  • pp: 3759–3766

Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy

Arthur H. Carrieri  »View Author Affiliations

Applied Optics, Vol. 38, Issue 17, pp. 3759-3766 (1999)

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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

OCIS Codes
(200.4260) Optics in computing : Neural networks
(280.3420) Remote sensing and sensors : Laser sensors
(300.1030) Spectroscopy : Absorption
(300.6340) Spectroscopy : Spectroscopy, infrared

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)

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