Forensic Analysis of drugs

Drug abuse is a destructive force in our society. There are over 300 substances in use as recreational drugs. This includes the illicit use of various pharmaceutical products, such as opioid analgesics, psychotropics, stimulants, and hallucinogens.
It also includes various substances produced in thousands of clandestine labs, ranging from one-person operations to highly sophisticated chemical processing facilities operated by large crime syndicates.
In the US there exists a network of several hundred city, county, state, and national forensic labs; tasked with analysis of samples obtained by law enforcement personnel, and with presentation of evidential materials to US courts. The analytical laboratory tasks include identification of active ingredients, and of other species (adulterants, excipients, reaction byproducts). With the passing of time, the courts have imposed increasingly stringent standards for forensic scientists testifying as expert witnesses in trials involving illegal manufacture and use of drugs. Rigorous methods are required to provide unequivocal evidence in criminal proceedings. As of 2008 the Scientific Working Group for the Analysis of Seized Drugs (www.SWGDRUG.org/approved.htm) has emphasized including uncertainty principle in the selection of laboratory methods. The specificity of Infrared Spectroscopy is well suited to reduce uncertainty in the identification of isomers. Spectra Analysis provides an instrument designed to meet the work flow needs of forensic laboratories.

The DiscovIR GC is a “walk-up” instrument, capable of high sample volume automated processing. It provides requisite sensitivity for the analysis of drugs and illicit substances. Identification based on infrared spectra is highly discriminating, and fills a gap in identification by mass spectrometry. The DiscovIR is configured for the high workload demands of forensics, provides archived, retrievable data, and makes use of a spectral database search  engine to facilitate rapid unequivocal identification of sample unknowns.


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