Identification of complex chemical mixtures using portable hand-held devices
Implementation of a Raman spectral decomposition technique that allows effective identification of complex mixtures. The computationally and memory efficient software enables new functionality to be added to portable hand-held devices.
- Software implemented on hand-held Raman spectrometers for use in Defence, Homeland Security, Life Sciences and Anti-counterfeiting.
- Early stage laboratory data.
- Software package.
Raman spectroscopy is an established method for identifying unknown materials across various sectors. Conventional analysis methods are based on comparing the measured spectrum with a reference spectral library of known chemicals to find the best match. While effective for identifying a single spectrum from a library, a sample composed of a mixture of different chemicals provides a greater challenge.
Edinburgh researchers have developed a Raman spectral decomposition technique based on a new fast sparse approximation method. Inputting a set of reference spectra and an unknown mixture yields the identity of mixture elements and their contribution percentages. It also has the capability of detecting cases where the mixture has a spectrum outside the reference library. The method is highly computationally and memory efficient, which means that it can run on a low power real-time platform. Implemented as a hardware independent C package, which can handle a given library and input spectrum, the technology enables use with hand-held devices. This provides a portable, non-invasive approach for identification of real-life mixtures of chemical substances.
A hardware independent C version of the mixture-matching algorithm has been prepared. Performance has been successfully demonstrated in the identification of real mixtures in different measurement scenarios, including where components are close to noise level.
- Can be implemented on portable hand-held devices
- Provides real-time results
- Effective identification of complex mixtures and component composition
- Efficient analysis of unknown hazardous material
- A Sparse Regularized Model for Raman Spectral Analis, Wu et al, Sensor Signal Processing for Defence, Edinburgh, 2014.