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    NIR spectrometer used for material modeling with neural networks

    Yee, Nigel

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    Conference-paper.pdf (778.5Kb)
    Date
    2014
    Citation:
    Yee, N. (2014) NIR spectrometer used for material modeling with neural networks. IEEE Computer Science (Ed.), Proceedings of IEEE Asia-Pacific World Congress on Computer Science and Engineering 2014 (337-342).
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/3195
    Abstract
    Near infrared multi-spectral image analysis is a tool used for non-destructive determination of biological material properties. In this investigation a custom built imaging spectrometer is constructed and used for the image spectra instrumentation and tests are performed on this instrument to determine its spectral resolution and spectral range; a biological data set (moisture in potato crisps) is then captured using this instrument and this data set is modeled using near infrared multi-spectral image analysis. A common problem with near infrared multi-spectral quantitative image measurements is light scatter and light non-linearity resulting from sample shape contours/curvatures and optical aberrations from optical component selection/layout. In this paper we detail an imaging spectrometer and the use of orthogonal signal correction pre- processing combined with a neural network full spectrum model for measurement of material property
    Keywords:
    potato crisps, neural networks, standard normal variate, near infrared spectrometer, biological material, non-destructive testing
    ANZSRC Field of Research:
    030399 Macromolecular and Materials Chemistry not elsewhere classified, 080108 Neural, Evolutionary and Fuzzy Computation
    Copyright Holder:
    IEEE Computer Science

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    • Computing Journal Articles [51]

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