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Hyperspectral NIR image analysis

data exploration, correction, and regression

Burger, James (2006). Hyperspectral NIR image analysis. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae, 1652-6880 ; 2006:60
ISBN 91-576-7109-5
[Doctoral thesis]

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Hyperspectral images add a new dimension to the field of spectroscopy, specifically spatial resolution. In addition to the identification and quantification of bulk constituents provided by integrating type spectrometers, hyperspectral images provide a means of accurately quantifying and locating constituent variation within the field of view of the camera. Hyperspectral images provide a massive quantity of data, and as with NIR spectroscopy, multivariate chemometrics tools must be utilized to appropriately extract accurate information. This thesis looked at techniques to clean and modify or condition the raw spectral data to improve the prediction results of regression techniques such as PLS. It was found that extra diagnostic tools for regression models could be based on image data. A new metric based on a combination of prediction bias and variance was proposed for determining the number of latent variables. Data set conditioning was based on several approaches. Sets of standard reference materials were used to improve conversion of data counts into percent reflectance units and to provide instrument standardization. A multi-step approach to outlier detection was formulated that incorporated thresholding tests for excessive data values, combined with tests based on Euclidean distance measurements and angle cosines between spectra. Finally, various spectral pretreatments or filters were considered to complete the spectral cleaning and modification process. Results from the application of multivariate analysis techniques to this optimally conditioned data were presented. Data visualization tools included histograms and spatial mapping of constituent concentration predictions, colorization of score plots, and false color image presentations of combinations of score images or prediction maps. The use of these data exploration, correction, and regression tools was demonstrated by the systematic analysis of increasingly complex data samples. Carefully designed laboratory samples were used to examine the theoretical limitations of prediction of chemical content and correction for physical properties including the dependencies of diffuse light scattering effects on particle size. Sample sets of cheese and wood pellets were used to demonstrate the overall utility of proper data conditioning in the application of hyperspectral NIR imaging to more difficult real-world problems.

Authors/Creators:Burger, James
Title:Hyperspectral NIR image analysis
Subtitle:data exploration, correction, and regression
Series Name/Journal:Acta Universitatis Agriculturae Sueciae
Year of publishing :2006
Number of Pages:80
ALLI. Burger, J. & Geladi, P. 2005. Hyperspectral NIR image regression part I: Calibration and correction. Journal of Chemometrics 19, 355. II. Burger, J. & Geladi, P. 2006a. Hyperspectral NIR image regression part II: Preprocessing diagnostics. Journal of Chemometrics 2006. (in print) III. Burger, J. & Geladi, P. 2006b. Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst, 2006. (in print) IV. Burger, J. & Geladi, P. 2006c. Spectral pre-treatments of hyperspectral NIR images: analysis of diffuse reflectance scattering. Journal of Near Infrared Spectroscopy (submitted)
Place of Publication:Umeå
ISBN for printed version:91-576-7109-5
Publication Type:Doctoral thesis
Full Text Status:Public
Agrovoc terms:image analysis, image processing, standardizing, infrared spectrophotometry, statistical methods
Keywords:image standardization, reflectance transformations, spectral preprocessing, scatter correction, multivariate image regression, outlier detection and correction
Permanent URL:
ID Code:1200
Department:(NL, NJ) > Unit of Biomass Technology and Chemistry (t.o.m. 121231)
Deposited By: James Burger
Deposited On:12 Sep 2006 00:00
Metadata Last Modified:02 Dec 2014 10:10

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