Identification of mineral components from near-infrared spectra by a neural network.

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  • 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
  • ニューラル ネットワーク ニ ヨル キンセキガイ スペクトル カラ ノ コウブ

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Abstract

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

    The Japan Society for Analytical Chemistry

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