Please use this identifier to cite or link to this item:
Title: Genetic multivariate calibration methods for near infrared (NIR) spectroscopic determination of complex mixtures
Authors: Özdemir, Durmuş
Öztürk, Betül
Keywords: Genetic algorithms
Classical Least Squares
Genetic regression
Inverse Least Squares
Multivariate calibration
Near infrared spectroscopy
Publisher: TUBITAK
Source: Özdemir, D., and Öztürk, B. (2004). Genetic multivariate calibration methods for near infrared (NIR) spectroscopic determination of complex mixtures. Turkish Journal of Chemistry, 28(4), 497-514.
Abstract: The simultaneous determination of ternary mixtures of methylene chloride, ethyl acetate, and methanol using near infrared (NIR) spectroscopy and 4 different genetic algorithms based multivariate calibration methods was demonstrated. The 4 genetic multivariate calibration methods are genetic partial least squares (GPLS), genetic regression (GR), genetic classical least squares (GCLS) and genetic inverse least squares (GILS). The sample data set contains the NIR spectra of 63 ternary mixtures and covers the range from 900 to 2000 nm in 2 nm intervals. Of these 63 spectra, 42 were used as the calibration set, and 21 were reserved for the prediction purposes. Several calibration models were built with the 4 genetic algorithm based methods for each component that makes up the mixtures. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) were in the range of 0.22 to 2.5 (% by volume (v/v)) for all the 4 methods. A comparison of genetic algorithm selected wavelengths for each component and for each method was also included.
ISSN: 1300-0527
Appears in Collections:Chemistry / Kimya
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
4723.pdfMakale315.25 kBAdobe PDFThumbnail
Show full item record

CORE Recommender


checked on Jun 28, 2024


checked on Jun 29, 2024

Page view(s)

checked on Jul 1, 2024


checked on Jul 1, 2024

Google ScholarTM


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.