DocumentCode :
185274
Title :
Artificial intelligence application built for ATS detection with a new portable hollow fiber IRAS spectrometer
Author :
Praisler, Mirela ; Ciochina, Silviu ; Stoica, Atanasia
Author_Institution :
Dept. of Chem., “Dunarea de Jos” Univ. of Galati, Galati, Romania
fYear :
2014
fDate :
17-19 Oct. 2014
Firstpage :
237
Lastpage :
242
Abstract :
We are presenting signal preprocessing and analysis methods that were successfully applied for improving the performance of a new portable hollow fiber infrared absorption spectrometer built for screening for amphetamine-type stimulants (ATS) in law enforcement operations. The challenge in developing the automated class identity assignment system was to model the similarity of the spectra of amphetamines and, in the same time, to discriminate them from non-amphetamines based only on the absorptions recorded in a very narrow spectral window (1550-1330 cm-1), as the radiation source of the portable spectrometer is a Quantum Cascade Laser. The presented signal selective amplifier has significantly enhanced the efficiency of the developed artificial intelligence application screening for the targeted drugs of abuse. The main variables contributing to the formation of distinct clusters (associated to stimulant and to hallucinogenic amphetamines) has been performed by Principal Component Analysis. The classification efficiency has been evaluated based on Hierarchical Cluster Analysis, i.e. agglomerative clustering.
Keywords :
artificial intelligence; drugs; infrared spectra; laser applications in medicine; medical signal processing; organic compounds; principal component analysis; quantum cascade lasers; signal classification; ATS detection; Artificial intelligence; amphetamine-type stimulants; automated class identity assignment system; distinct clusters; enforcement operations; hierarchical cluster analysis; portable hollow fiber IRAS spectrometer; portable hollow fiber infrared absorption spectrometer; principal component analysis; quantum cascade laser; radiation source; signal analysis methods; signal preprocessing methods; signal selective amplifier; spectral window; Absorption; Artificial intelligence; Compounds; Databases; Loading; Principal component analysis; Quantum cascade lasers; Principal Component Analysis; aglomerative clustering; amphetamines; selective amplifier;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, Control and Computing (ICSTCC), 2014 18th International Conference
Conference_Location :
Sinaia
Type :
conf
DOI :
10.1109/ICSTCC.2014.6982422
Filename :
6982422
Link To Document :
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