Search results for "Dykstra's projection algorithm"
showing 3 items of 3 documents
Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry
2009
Abstract A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800–1500 cm− 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample prepa…
Quality based classification of gasoline samples by ATR-FTIR spectrometry using spectral feature selection with quadratic discriminant analysis
2013
Abstract A chemometric approach has been developed for characterization of gasoline samples regarding their quality. Attenuated total reflectance – infrared spectrometric data were processed by genetic algorithm (GA) and successive projection algorithm (SPA) feature selection techniques, being employed as an initial step prior to apply a discriminative tool. It was aimed to classify the fuel samples according to their quality passed/failed data. Chemometric predictive procedures were developed using quadratic discriminant analysis (QDA) combined with GA and SPA as a feature subset and feature selection strategy. Results showed 93.3% and 95.6% accuracy for SPA-QDA and GA-QDA models respectiv…
Feature selection strategies for quality screening of diesel samples by infrared spectrometry and linear discriminant analysis.
2012
Abstract A rapid approach has been developed for the characterization of diesel quality, based on attenuated total reflectance – Fourier transform infrared (ATR-FTIR) spectrometry, which could be useful for diagnosing the sample quality condition. As a supervised technique, linear discriminant analysis (LDA) was employed to process the spectrometric data. The role of variable selection methods was also evaluated. Successive projection algorithm (SPA) and genetic algorithm (GA) feature selection techniques were applied prior to the discriminative procedure. It was aimed to compare the effect of feature selection procedures on classification capability of IR spectrometry for the diesel sample…