This paper studies the application of principal component analysis, multiple polynomial regression, and artificial neural network ANN techniques to the quantitative analysis of binary mixture of dye solution. The binary mixtures of three textile dyes including blue, red and yellow colors were analyzed by PCA-Multiple polynomial Regression and PCA-Artificial Neural network PCA-ANN methods. The obtained results indicate that the accurateness of PCA-ANN technique is higher than PCA-Multiple polynomial regression and normal methods. The PCA-ANN technique is applicable for dye concentration bicomponent solution with both overlapping and non-overlapping spectra. Also the developed method can be a practical solution to remove noise in absorbance spectra and quantitative analysis of binary mixture of dye solutions with overlapping.
Shams nateri, A. (2013). Evaluating Dye Concentration in Bicomponent Solution by PCA-MPR and PCA-ANN Techniques. Progress in Color, Colorants and Coatings, 6(2), 129-139. doi: 10.30509/pccc.2013.75814
MLA
A. Shams nateri. "Evaluating Dye Concentration in Bicomponent Solution by PCA-MPR and PCA-ANN Techniques", Progress in Color, Colorants and Coatings, 6, 2, 2013, 129-139. doi: 10.30509/pccc.2013.75814
HARVARD
Shams nateri, A. (2013). 'Evaluating Dye Concentration in Bicomponent Solution by PCA-MPR and PCA-ANN Techniques', Progress in Color, Colorants and Coatings, 6(2), pp. 129-139. doi: 10.30509/pccc.2013.75814
VANCOUVER
Shams nateri, A. Evaluating Dye Concentration in Bicomponent Solution by PCA-MPR and PCA-ANN Techniques. Progress in Color, Colorants and Coatings, 2013; 6(2): 129-139. doi: 10.30509/pccc.2013.75814
)
