Portable LIBS sensor communication bandwidth limitations favor local material classification for low power consumption. Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component Analysis (PCA) have been implementation via general purpose computers and are accepted for some Department of Defense applications. Prior publications address the creation of a low mass, low power, robust hardware spectra classifier for a limited set of predetermined materials in an atmospheric matrix. The incorporation of a PCA or a PLS-DA classifier into a predictorcorrector implementation on a TI6701 has been developed. The performance modeling of the control system with an emphasis on further optimization needs addressing. This paper characterizes, from a control system standpoint, the predictor-corrector architecture applied to LIBS data collection. In addition, the application of this as a material classifier is presented. Updates in the model implemented on a low power multi-core DSP will be presented as well. Performance comparisons to alternative control system structures will be considered.

Date Published: 29 May 2013
PDF: 6 pages
Proc. SPIE 8710, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIV, 87101P (29 May 2013); doi: 10.1117/12.2016193

Published in SPIE Proceedings Vol. 8710:
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIV
Augustus Way Fountain III, Editor(s)