Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2167
Title: Genetic multivariate calibration for near infrared spectroscopic determination of protein, moisture, dry mass, hardness and other residues of wheat
Authors: Özdemir, Durmuş
Özdemir, Durmuş
Keywords: Genetic algorithms
Moisture
Multivariate calibration
Near infrared spectroscopy
Proteins
Wheat
Issue Date: Dec-2006
Publisher: John Wiley and Sons Inc.
Source: Özdemir, D. (2006). Genetic multivariate calibration for near infrared spectroscopic determination of protein, moisture, dry mass, hardness and other residues of wheat. International Journal of Food Science and Technology, 41(Issue SUPPL.), 12-21. doi:10.1111/j.1365-2621.2006.01243.x
Abstract: Determination of wheat flour quality parameters, such as protein, moisture, dry mass by wet chemistry analyses takes long time. Near infrared spectroscopy (NIR) coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. In this study, two different wheat data sets are investigated with the aim of establishing successful calibration models using NIR spectra of wheat samples. The first data set (material 1) was obtained from the ftp address (ftp://ftp.clarkson.edu/pub/hopkepk/Chemdata/) and contained 100 NIR spectra of wheat of which wet chemical analysis of protein and moisture content were done with reference methods. The second data set (material 2) contained 176 spectra and was downloaded from http://www.spectroscopynow.com/Spy/basehtml/SpyH/1,1181, 2-1-2-0-0-newsdetail-0-74,00.html. This wheat data set was given with the quality parameters, such as protein content, moisture content, other residues, dry mass, protein content in dry mass and hardness that were determined previously. Multivariate calibration models generated with genetic inverse least squares method demonstrated very good prediction results for the parameter mentioned here. Overall, the average per cent recoveries (APR) ranged between 99.23% and 100.34% with a standard deviation (SD) ranging from 0.34 to 3.15 for all the parameters investigated, except hardness. The APR value of hardness was 103.32 with the SD of 14.97.
URI: http://doi.org/10.1111/j.1365-2621.2006.01243.x
http://hdl.handle.net/11147/2167
ISSN: 0950-5423
0950-5423
Appears in Collections:Chemistry / Kimya
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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