Simultaneous UV-Vis spectrophotometric quantification of ternary basic dye mixtures by partial least squares and artificial neural networks

One of the main difficulties in quantification of dyes in industrial wastewaters is the fact that dyes are usually in complex mixtures rather than being pure. Here we report the development of two rapid and powerful methods, partial least squares (PLS-1) and artificial neural network (ANN), for spectral resolution of a highly overlapping ternary dye system in the presence of interferences. To this end, Crystal Violet (CV), Malachite Green (MG) and Methylene Blue (MB) were selected as three model dyes whose UV-Vis absorption spectra highly overlap each other. After calibration, both prediction models were validated through testing with an independent spectra-concentration dataset, in which high correlation coefficients (R²) of 0.998, 0.999 and 0.999 were obtained by PLS-1 and 0.997, 0.999 and 0.999 were obtained by ANN for CV, MG and MB, respectively. Having shown a relative error of prediction of less than 3% for all the dyes tested, both PLS-1 and ANN models were found to be highly accurate in simultaneous determination of dyes in pure aqueous samples. Using net-analyte signal concept, the quantitative determination of dyes spiked in seawater samples was carried out successfully by PLS-1 with satisfactory recoveries (90–101%).