A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

Abstract

Rapid and reliable detection and identification of unknown chemical substances is critical to homeland security. It is challenging to identify chemical components from a wide range of explosives. There are two key steps involved. One is a nondestructive and informative spectroscopic technique for data acquisition. The other is an associated library of reference features along with a computational method for feature matching and meaningful detection within or beyond the library.Recently several experimental techniques based on Raman scattering have been developed to perform standoff detection and identification of explosives, and they prove to be successful under certain idealized conditions. However data analysis is limited to standard least squares method assuming the complete knowledge of the chemical components. In this paper, we develop a new iterative method to identify unknown substances from mixture samples of Raman spectroscopy. In the first step, a constrained least squares method decomposes the data into a sum of linear combination of the known components and a non-negative residual. In the second step, a sparse and convex blind source separation method extracts components geometrically from the residuals. Verification based on the library templates or expert knowledge helps to confirm these components. If necessary, the confirmed meaningful components are fed back into step one to refine the residual and then step two extracts possibly more hidden components. The two steps may be iterated until no more components can be identified. We illustrate the proposed method in processing a set of the so called swept wavelength optical resonant Raman spectroscopy experimental data by a satisfactory blind extraction of a priori unknown chemical explosives from mixture samples.

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“…36−38 As original ICA, SCA works better when the same mixture of substances has been assessed with different proportions 37 or with multidimensional spectroscopic modalities. 38 The lack of convexity (several likely solutions, intractable problems) of partitioning techniques 39 and their high sensitivity to chemical shifts errors 40 limit their use in tailored applications with minimum supervision. In this work, we preferred a reference-based substance identification involving one or several reference spectral libraries.…”

“…A similar approach, but based on more restrictive assumptions, has been suggested independently for Raman spectra. 39 The authors assume that R is not too large, that the upper bounds of their concentrations are known and that spectral lines are not subjected to any squeeze or shift. Conversely to the approach followed by authors, the constraint on the non-negativity of residues is not considered, because it would affect the normality of errors.…”

“…To keep a neutral identification (all substances are equiprobable), the sums of weights related to each substance u = 1, ..., R , (∑ i =1,..., T u w u , t ) must finally be forced to be equal. A similar approach, but based on more restrictive assumptions, has been suggested independently for Raman spectra . The authors assume that R is not too large, that the upper bounds of their concentrations are known and that spectral lines are not subjected to any squeeze or shift.…”

“…The lack of convexity (several likely solutions, intractable problems) of partitioning techniques and their high sensitivity to chemical shifts errors limit their use in tailored applications with minimum supervision. In this work, we preferred a reference-based substance identification involving one or several reference spectral libraries.…”