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Single Wheat Kernel Color Classification by Using Near‐Infrared Reflectance Spectra
Author(s) -
Wang D.,
Dowell F. E.,
Lacey R. E.
Publication year - 1999
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.1999.76.1.30
Subject(s) - partial least squares regression , chemistry , second derivative , derivative (finance) , linear regression , analytical chemistry (journal) , kernel (algebra) , wavelength , infrared , principal component regression , spectral line , statistics , mathematics , optics , chromatography , physics , combinatorics , mathematical analysis , astronomy , financial economics , economics
An optical radiation measurement system, which measured reflectance spectra, log (1/ R ), from 400 to 2,000 nm, was used to quantify single wheat kernel color. Six classes of wheat samples were used for this study, including red wheat that appears white and white wheat that appears red. Partial least squares regression and multiple linear regression were used to develop classification models with three wavelength regions, 500–750, 500–1,700, and 750–1,900 nm, and three data pretreatments, log (1/ R ), first derivative, and second derivative. For partial least squares models, the highest classification accuracy was 98.5% with the wavelength region of 500–1,700 nm. The log (1/ R ) and the first derivative yielded higher classification accuracy than the second derivative. For multiple linear regression models, the highest classification accuracy was 98.1% obtained from log (1/ R ) spectra from the visible and near‐infrared wavelength regions.