ACTIVE ANALYSIS OF CHEMICAL MIXTURES WITH MULTI-MODAL SPARSE NON-NEGATIVE LEAST SQUARES Conference Paper uri icon

abstract

  • New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is selected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near-optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MM-NNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability. © 2013 IEEE.

author list (cited authors)

  • Huang, J., & Gutierrez-Osuna, R.

citation count

  • 3

publication date

  • May 2013

publisher