Forensics/Raman

Small Molecule/Forensics Research.

Research goals:

We were interested in:

• using Raman spectroscopy with multivariate analysis (chemometrics and machine-learning) to develop automated methods for the identification, classification, and quantification of  illicit narcotics. 
• Using Raman spectroscopy toc haracterise novel molecules and materials. 

Benefits of Raman Spectroscopy:  The Raman effect probes the vibrational energy levels of molecules and can provide immediate and useful information on the structure and identity of substances in solid, liquid and gaseous phases. The technique is non-contact, non-destructive and requires minimal or no sample preparation. Raman spectroscopy has been a well-established technique for the characterisation of materials such as thin hard films, polymers, proteins, tissue, DNA, and semiconductor devices. Raman spectroscopy has a number of distinct advantages over IR absorption spectroscopy. Sample preparation in many cases is not required, permitting analysis of bulk or microscopic materials in-situ. Unlike IR, water has a very weak Raman signal and so Raman spectra can be easily collected from aqueous solutions or moist materials. Micro-Raman spectroscopy can achieve a spatial resolution approaching 1 micron as opposed to 10-20 micron for IR absorption spectroscopy. This high spatial resolution has allowed the Raman analysis of discrete micron-sized particles, including the determination of drug levels in individual cells and the analysis of specific defect and/or cell adhesion regions of medical devices to determine the nature of any surface degradation. Using Raman spectroscopy, measurable parameters include conformational and structural changes in bio-films, chemical nature of microscopic inclusion defects in fabricated devices, and micro-stresses in ceramic materials. For polymer coatings and polymer fabricated medical devices, the rapid measurement of critical parameters such as crystallinity, film orientation, composition and degree of polymer cross-linking are possible.

Analysis of illicit narcotics:

Below are some Raman spectra of some common narcotics that we have collected on the LabRam Infinity using 785 nm excitation. As you can see the sharp well defined bands in the spectra make identification and discrimination of the narcotics relatively easy in simple mixtures. Some typical Ramam spectra of narcotics are shown below, they were taken using 785 nm excitation.

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 The spectrum above is a mixture, some of the Cocaine bands are marked by (*) while the band marked (^) is due to the caffeine dilutant. The glucose bands are very weak and not as well defined as the more crystalline cocaine and caffeine.

We use multivariate analysis to generate quantitative models of narcotic concentrations in solid mixtures, the plot below shows the result of one such model in which cocaine was mixed with glucose and caffeine. In this case we could predict the cocaine concentration to an accuracy of +/- ~8% (twice the RMSEP value) from a single Raman spectrum. We are continuing to develop new approaches to quantifying narcotic concentrations.

Past Research covered a wide variety of topics including:  2009-10: automated analysis of home made explosives as part of an undergraduate research project. This project made use of various spectroscopic methods to probe these materials, coupled with chemometric analysis for quantitative analysis. 2003-07: Analyze-IQ, development of software tools for materials analysis.

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Publications and Conference Presentations

1. Qualitative Analysis using Raman Spectroscopy and Chemometrics:  a comprehensive model system for narcotics analysis.  M.-L. O’Connell, A.G. Ryder, M.N. Leger, and T. Howley.  Applied Spectroscopy, 64(10), 1109-1121, (2010).   Online at:  http://www.opticsinfobase.org/abstract.cfm?URI="as-64-10-1109 .
2. Comparison of derivative pre-processing and automated polynomial baseline correction method for classification and quantification of narcotics in solid mixtures.  M.N. Leger and A.G. Ryder,  Applied Spectroscopy, 60(2), 182-193, (2006) . DOI: 10.1366/000370206776023304
   
3. Surface Enhanced Raman Scattering (SERS) for Narcotic detection and applications to Chemical Biology. A.G. Ryder, Current Opinion in Chemical Biology, 9(5), 489-493, (2005).
4. The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data.  T. Howley, M.G. Madden, M.-L. O’Connell, and A.G. Ryder, Knowledge-Based Systems ,  19(5), 363-370, (2006).   DOI: 10.1016/j.knosys.2005.11.014
5. Qualitative and quantitative analysis of chlorinated solvents using Raman spectroscopy and machine learning. J. Conroy, A.G. Ryder, M.N. Leger, K. Hennessey, and M.G. Madden.   Proc SPIE Int. Soc. Opt. Eng  .,  5826, 131-142, (2005). [ full paper]
6. Classification of a target analyte in solid mixtures using principal component analysis, support vector machines and Raman spectroscopy, M. O'Connell, A.G. Ryder, M.N. Leger, T. Howley, and M.G. Madden.   Proc SPIE  Int. Soc. Opt. Eng.,  5826, 340-350, (2005). [ full paper]
7. An improved genetic programming technique for the classification of Raman spectra. K. Hennessy, M. G. Madden, J. Conroy, and A.G. Ryder.  BCS Conference Series, Applications and Innovations in Intelligent Systems XII, Proceedings, 181-192, (2005).  Springer, ISBN 1-85233-908-X.
8. Combining Genetic Algorithms, Neural Networks and Wavelet Transforms for Analysis of Raman Spectra. K. Hennessy, M.G. Madden, and A.G. Ryder,  Proceedings of the 15th Irish Conference on Artificial Intelligence & Cognitive Science, Sept. 2004. [ full paper]
9. Machine Learning Methods for Quantitative Analysis of Raman Spectroscopy Data, M.G. Madden and A. G. Ryder, Proc SPIE vol. 4876, 1130-1139, (2003). [ paper availble on ARAN]
10. Classification of narcotics in solid mixtures using Principal Component Analysis and Raman spectroscopy. A.G. Ryder, Journal of Forensic Sciences, 47(2), 275-284, (2002). Abstract & paper from ASTM site 
11. Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods. A.G. Ryder, G.M. O'Connor, and T.J. Glynn, Journal of Raman Spectroscopy, 31(3), 221-227, (2000).
    DOI:  10.1002/(SICI)1097-4555(200003)31:3<221::AID-JRS518>3.0.CO;2-5
12. Identifications and Quantitative Measurement of Narcotics in Solid Mixtures Using Near-IR Raman Spectroscopy and Multivariate Analysis. A.G. Ryder, G.M. O'Connor, and T.J. Glynn, Journal of Forensic Sciences, 44(5), 1013-1019, (1999). Abstract & paper from ASTM site.   DOI: 10.1520/JFS12031J   Full text, available here (author version).

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