Genetic Regression as a Calibration Technique for Solid-Phase Extraction of Dithizone-Metal Chelates

Abstract

The application of genetic regression (GR) to reflectance spectra of a solid-phase colorimetric extraction for the determination of Hg(II) is demonstrated. GR is a technique that combines wavelengths that optimize linear regression using a genetic algorithm. Solid polystyrene (PS) beads with a molecular weight distribution of 125,000–250,000 were impregnated with Hg(II)-dithizonate, filtered, allowed to dry, and packed in quartz cuvettes, and their reflectance spectra were collected. Quantitation with the use of normal calibration methods could not be achieved because of baseline fluctuations which originate in sample packing, presentation to the spectrometer, and particle size. Results indicate that GR can easily compensate for these baseline fluctuations and provide excellent calibrations.

Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration

“…Genetic inverse least squares (GILS) is the modified version of original ILS method in which a small set of wavelengths is selected from a full spectral data matrix and evolved to an optimum solution using a genetic algorithm (GA) applied to a number of wavelength selection problems (Ö zdemir and Dinç 2004;Ö zdemir and Ö ztürk 2004;Ö zdemir 2005). GAs are nonlocal search and optimization methods that are based upon the principles of natural selection (Hibbert 1993;Paradkar and Williams 1997;Pizarro et al 1998;Mosley and Williams 1998;Ö zdemir and Williams 1999).…”

Automatic estimation of varying continuum background emission in laser-induced breakdown spectroscopy

“…During the last two decades numerous algorithms have been studied and discussed in literatures, such as convolution [16], differentiation [17], Fourier transforms [18], neural networks [19], genetic regression [20], principal component analysis and partial least squares [21], Kalman filtering [22,23], and wavelet transforms [24], all of which have been reviewed particularly by van Veen and de Loos-Vollebregt [25].…”

Near Infrared Spectroscopic Determination of Diesel Fuel Parameters Using Genetic Multivariate Calibration

“…Genetic Inverse Least Squares (GILS) is modified versions of original ILS methods in which a small set of wavelengths are selected from a full spectral data matrix and evolved to an optimum solution using a genetic algorithm (GA), and has been applied to a number of wavelength selection problems (Özdemir and Dinç, 2004;Özdemir and Öztürk, 2004;Özdemir, 2005). GAs are non-local search and optimization methods that are based upon the principles of natural selection (Hibbert, 1993;Paradkar and Williams, 1997;Pizarro et al, 1998;Mosley and Williams, 1998;Özdemir and Williams, 1999).…”