Ultra-Fast Gradient Elution HPLC as a High Throughput, High Information Content Screening Tool for Drugs of Abuse
Peter W. Carr, University of Minnesota
We propose the development of Ultra-Fast Gradient Elution Reversed-Phase HPLC (UFGELC) with Diode-Array Detection (DAD) as a very high throughput tool for screening biological samples for the presence of regulated intoxicants. We will target an order-of-magnitude improvement in the speed of a gradient elution screening method over current methods, by optimizing the operational parameters of both the HPLC column and the HPLC instrument without compromising the reproducibility of HPLC retention times. Most importantly, a novel instrument configuration will be used to significantly reduce the time needed to re-equilibrate the HPLC column between gradient runs, thereby reducing the total time for each complete gradient elution analysis. Finally, upon development of the UFGELC method, we will compare the performance of the methodology to other established and emerging techniques in the forensic laboratory, including immunoassay-based techniques and capillary electrophoresis, using a set of 21 opiates and amphetamines as target analytes. Our goal is a gradient HPLC cycle time of < 2 minutes (>30 samples/hour) with a sensitivity and selectivity equal to or surpassing the current screening technologies.
Detection of Substituted PAH Residues by SPME in Arson Debris Analysis
Charles R. Cornett, University of Wisconsin-Platteville and Joseph Wermeling, Wisconsin State Crime Laboratory-Madison
This project addresses the need for method development in the recovery of ignitable liquid residues from a variety of matrices outlined in Forensic Sciences: Review of Status and Needs (1) by assessing novel screening applications of SPME in the analysis of polyaromatic compounds (ie. naphthalene) from gasoline residues. Project objectives include:
- development of optimal SPME protocols for assessing the aromatic analogs of interest,
- comparison of SPME methodologies with activated carbon strip (ACS), anda partnership with Wisconsin State Crime Laboratory â€“ Madison to assess the effectiveness of SPME in detecting gasoline residue in complex arson debris matrices.
Trace Metal Analysis of Ecstasy by Microwave-assisted Digestion and Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES)
Philip W. Crawford, Southeast Missouri State University, James McGill, Southeast Missouri State University, and Pam Johnson, Southeast Missouri Regional Crime Laboratory
Trace elements in ecstasy samples obtained from law enforcement agencies will be determined by inductively coupled plasma-optical emission spectrometry (ICP-OES) using microwave assisted digestion. Elemental patterns or signatures will be determined for the purpose of comparing and differentiating between ecstasy samples. The effect of changing the experimental parameters used during the microwave assisted digestion on the ICP data will be investigated. The emission data will be analyzed using statistical multivariate methods in order to determine if the results allow for discrimination between different categories of samples. In addition, ICP-OES of ecstasy samples synthesized in the lab will be performed to determine if samples from the same preparative batch can be positively linked together and distinguished from separate batches based upon their trace metal profiles. The applications of this project to forensic analysis will be studied.
Identifying Co-ops and Farmers as Illicit Sources of Anhydrous Ammonia for Meth Makers
John Verkade and George Kraus, Iowa State University
The main goal of this proposal is to identify employees of specific co-ops and farmers who are providing anhydrous ammonia to meth makers by lacing the anhydrous loaded into the co-opâ€™s main storage tank with a salt that decomposes to an identifiable compound only during the illicit synthesis of meth.
An Artificial Neural Network for Wavelet Steganalysis
Clifford Bergman and Jennifer Davidson, Iowa State University
Hiding messages in image data, called steganography, is used by criminals and noncriminals alike to send information over the Internet. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics. Automated detection of hidden messages is a requirement, since the shear amount of image data available online makes it impossible for a person to investigate each image separately. The proposed research is to develop a prototype software system that automatically classifies an image as having hidden information or not, using a powerful classifier called an artificial neural network (ANN). The novelty of this ANN will be its ability to detect messages hidden with wavelet embedding algorithms, in addition to other transforms, and the wide range of file types it can use, including jpeg2000, which will be more widely used in future image compression and hence on the Internet.