Application of revised version of neural independent component analysis to classification problems of confiscated methamphetamine.
Recently, profiling the chemical substances in illegally distributed drugs has been needed in order to reveal the drug channels. However, this kind of profiling is often difficult because such drugs contain various kinds of impurities and the quantity of these impurities changes. Due to these circumstances, several methods, including a slightly revised ICA (Independent Component Analysis) by a Hebbian learning artificial neural network, were applied for profiling illegally distributed methamphetamine. Eventually, better classification results with the ICA than with other methods were obtained. These results show that ICA could make it easier to profile illegally distributed methamphetamine.
Takagi
Tatsuya
T
Graduate School of Pharmaceutical Sciences, Osaka University, Yamadaoka, Suita, Osaka, Japan. ttakagi@phs.osaka-u.ac.jp
Makino
Yukiko
Y
Tanaka
Yuko
Y
Okamoto
Kousuke
K
Yamashita
Noriyuki
N
Matsumoto
Tomoko
T
Yokota
Masahiko
M
Kurokawa
Ken
K
Yasunaga
Teruo
T
D000465:Algorithms; D000697:Central Nervous System Stimulants / Q000032:analysis*; D004340:Drug Contamination; D008401:Gas Chromatography-Mass Spectrometry; D013287:Illicit Drugs / Q000032:analysis*; D008694:Methamphetamine / Q000032:analysis*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
D000697:Central Nervous System Stimulants; D013287:Illicit Drugs; D008694:Methamphetamine