ニューラルネットワークによる近赤外スペクトルからの鉱物成分の同定  [in Japanese] Identification of mineral components from near-infrared spectra by a neural network.  [in Japanese]

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Author(s)

Abstract

ニューラルネットワーク技術を応用して近赤外反射スペクトルから鉱物成分を同定するシステムを検討した.近赤外波長領域1300~2400nmにおける240点での反射スペクトルデータを3層構造のニューラルネットワークに入力し,エラーバックプロパゲーション法で学習を行った.各種の純品試料及び混合物試料のスペクトルを学習し,テスト用の試料に含有されている成分を同定して本法の妥当性を検証した.その結果,6種の鉱物成分をほぼ100%近い確度で同定するニューラルネットワークを構築することができ,鉱物成分を迅速に同定するシステムを開発できるめどがついた.

A system to identify mineral components from near-infrared spectra by applying a neural network technique was examined. Reflective spectral data at 240 wavelength points for the wavelength range between 1300 and 2400 nm were entered into the input layer of a three-layered neural network trained by the error-back-propagation method. Spectra of various kinds of pure and mixed samples were used for the training, and the mineral components contained in the test samples were examined. As a result, a neural network to identify six kinds of mineral components with a probability of nearly 100% was constructed, and the possibility to develop a system to identify mineral components rapidly is demonstrated.

Journal

  • BUNSEKI KAGAKU

    BUNSEKI KAGAKU 43(10), 765-769, 1994-10-05

    The Japan Society for Analytical Chemistry

References:  2

  • <no title>

    ZUPAN J.

    Neural Networks for Chemists, 1993

    Cited by (12)

  • <no title>

    TANABE K.

    Appl.Spectrosc. 46, 807, 1992

    Cited by (7)

Cited by:  9

Codes

  • NII Article ID (NAID)
    110002906643
  • NII NACSIS-CAT ID (NCID)
    AN00222633
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    05251931
  • NDL Article ID
    3899893
  • NDL Source Classification
    M055;PA24;ME51
  • NDL Source Classification
    ZP4(科学技術--化学・化学工業--分析化学)
  • NDL Call No.
    Z17-9
  • Data Source
    CJP  CJPref  NDL  NII-ELS  J-STAGE  NDL-Digital 
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