Novel filter design algorithm for multivariate optical computing

Olusola O. Soyemi; Paul J. Gemperline; Lixia Zhang; DeLyle Eastwood; Hong Li; Michael L. Myrick

doi:10.1117/12.417462

26 February 2001 Novel filter design algorithm for multivariate optical computing

Olusola O. Soyemi, Paul J. Gemperline, Lixia Zhang, DeLyle Eastwood, Hong Li, Michael L. Myrick

Proceedings Volume 4205, Advanced Environmental and Chemical Sensing Technology; (2001) https://doi.org/10.1117/12.417462
Event: Environmental and Industrial Sensing, 2000, Boston, MA, United States

Abstract

A new algorithm for the design of optical computing filters for chemical analysis otherwise known as Multivariate Optical Elements (MOEs), is described. The approach is based on the nonlinear correlation of the MOE layer thicknesses to the standard error in sample prediction for the chemical species of interest using a modified version ofthe Gauss-Newton nonlinear optimization algorithm. The design algorithm can either be initialized by random layer thicknesses or by a pre-existing design. The algorithm has been successfully tested by using it to design a MOE for the determination of copper uroporphynn in a quaternary mixture of uroporphyrin (freebase), nickel uroporphyrin, copper uroporphynn, and tin uroporphyrin.

Citation Download Citation

Olusola O. Soyemi, Paul J. Gemperline, Lixia Zhang, DeLyle Eastwood, Hong Li, and Michael L. Myrick "Novel filter design algorithm for multivariate optical computing", Proc. SPIE 4205, Advanced Environmental and Chemical Sensing Technology, (26 February 2001); https://doi.org/10.1117/12.417462

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