• DocumentCode
    3574019
  • Title

    NIR spectrometer used for material modeling with neural networks

  • Author

    Yee, Nigel

  • Author_Institution
    Electrotechnol. Dept., Unitec Inst. of Technol., Auckland, New Zealand
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    6
  • 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 preprocessing combined with a neural network full spectrum model for measurement of material property.
  • Keywords
    aberrations; computerised instrumentation; infrared imaging; infrared spectrometers; infrared spectroscopy; materials science computing; neural nets; nondestructive testing; NIR spectrometer; biological data set modeling; custom built imaging spectrometer; image spectra instrumentation; light nonlinearity; light scatter; material modeling; near infrared multispectral image analysis; near infrared multispectral quantitative image measurement; neural network full spectrum model; nondestructive biological material property determination; optical aberrations; optical component selection; orthogonal signal correction preprocessing; sample shape contour; sample shape curvature; spectral range determination; spectral resolution determination; Adaptive optics; Biological materials; Instruments; Moisture; Neural networks; Optical imaging; Neural network; biological material; near infrared spectrometer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Engineering (APWC on CSE), 2014 Asia-Pacific World Congress on
  • Print_ISBN
    978-1-4799-1955-0
  • Type

    conf

  • DOI
    10.1109/APWCCSE.2014.7053878
  • Filename
    7053878