Abstract
Raman spectroscopy has been widely proposed as a technique to nondestructively and noninvasively interrogate the contents of glass and plastic bottles. In this work, Raman spectroscopy is used in a concealed threat scenario where hazardous liquids have been intentionally mixed with common consumer products to mask its appearance or spectra. The hazardous liquids under consideration included the chemical warfare agent (CWA) simulant triethyl phosphate (TEP), hydrogen peroxide, and acetone as representative of toxic industrial compounds (TICs). Fiber optic coupled Raman spectroscopy (FOCRS) and partial least squares (PLS) algorithm analysis were used to quantify hydrogen peroxide in whiskey, acetone in perfume, and TEP in colored beverages. Spectral data was used to evaluate if the hazardous liquids can be successfully concealed in consumer products. Results demonstrated that FOC-RS systems were able to discriminate between nonhazardous consumer products and mixtures with hazardous materials at concentrations lower than 5%.
Highlights
In August 2006, a terrorist plot to destroy aircrafts on transatlantic flights was discovered and timely stopped in London
The results suggested that Fiber optic coupled Raman spectroscopy (FOCRS) can be used to discriminate and quantify the hazardous liquid concealed in the commercial products
In this work, concealed liquids scenarios were studied by FOCRS
Summary
Raman spectroscopy has been widely proposed as a technique to nondestructively and noninvasively interrogate the contents of glass and plastic bottles. Raman spectroscopy is used in a concealed threat scenario where hazardous liquids have been intentionally mixed with common consumer products to mask its appearance or spectra. The hazardous liquids under consideration included the chemical warfare agent (CWA) simulant triethyl phosphate (TEP), hydrogen peroxide, and acetone as representative of toxic industrial compounds (TICs). Fiber optic coupled Raman spectroscopy (FOCRS) and partial least squares (PLS) algorithm analysis were used to
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