Development of Fingerprint Verification Type Self-Organized Map Applied to Profiling Seized Methamphetamine

DOI Web Site 1 Citations 6 References
  • Nishikiori Rika
    Faculty of Pharmacy, Osaka Ohtani University
  • Makino Yukiko
    Narcotics Control Department, Kanto-Shin'etsu Bureau of Health and Welfare, Ministry of Health, Labor and Welfare
  • Ochi Yukino
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Yamashita Noriyuki
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Okamoto Kousuke
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Kawashita Norihito
    Research Collaboration Center on Emerging and Re-emerging Infections
  • Takahara Junichi
    Graduate School of Pharmaceutical Sciences, Osaka University
  • Yasunaga Teruo
    Research Collaboration Center on Emerging and Re-emerging Infections Genome Information Research Center, Research Institute for Microbial Diseases
  • Takagi Tatsuya
    Graduate School of Pharmaceutical Sciences, Osaka University Research Collaboration Center on Emerging and Re-emerging Infections Genome Information Research Center, Research Institute for Microbial Diseases
  • Kawase Masaya
    Faculty of Pharmacy, Osaka Ohtani University Graduate School of Pharmaceutical Sciences, Osaka University

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Other Title
  • 指紋照合型SOMの開発と押収メタアンフェタミンのプロファイリングへの応用

Description

In a previous study {Takagi, T. et al., Chem. Pharm. Bull., 52(12), 1427-1432 (2004)}, we applied a slightly revised neural Independent Component Analysis (ICA) for profiling illegally distributed methamphetamine. Using ICA and an hourglass type Hierarchical Neural Network (HNN), we obtained better classification results than by using Principal Component Analysis (PCA), CATegorical PCA (CATPCA) and the MultiDimensional Scaling method (MDS). The HNN is a nonlinear machine learning method, and the ICA applied in that study exhibited nonlinear characteristics. The results indicated that nonlinear analysis is more efficient than linear analysis for profiling confiscated methamphetamine. Consequently, in this study, we applied Self-Organizing Maps (SOMs) to impurity profiling of methamphetamine.<BR> While SOM is currently a frequently employed nonlinear classification method, the ordinary SOM uses only that information contained by the winner neuron for classification and the information of other grid points is neglected. We therefore attempted to simultaneously utilize the information of loser neurons in order to avoid information loss. First, we visualized the resultant reference vectors using a contour map of each sample. Although considerable information can be visually compared using the SOM contour maps, metric comparisons are difficult. We therefore used MDS to construct a similarity matrix using the data of the resultant reference vectors to visualize metric data. To assess the results, we assumed that there are four synthetic routes (Nagai, Leuckart, Emde and reductive amination methods), and that each of these can be identified by comparing route-specific impurities.

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